U.S. patent application number 17/087374 was filed with the patent office on 2021-02-18 for methods and apparatus to determine weights for panelists in large scale problems.
The applicant listed for this patent is The Nielsen Company (US), LLC. Invention is credited to Paul Donato, Peter Lipa, Matt Reid, Michael Sheppard, Jonathan Sullivan, Alejandro Terrazas.
Application Number | 20210049618 17/087374 |
Document ID | / |
Family ID | 1000005191050 |
Filed Date | 2021-02-18 |
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United States Patent
Application |
20210049618 |
Kind Code |
A1 |
Sheppard; Michael ; et
al. |
February 18, 2021 |
METHODS AND APPARATUS TO DETERMINE WEIGHTS FOR PANELISTS IN LARGE
SCALE PROBLEMS
Abstract
Methods and apparatus to solve for a minimum variance solution
for a constrained weight problem in a scalable and memory efficient
manner are disclosed. An example instructions cause a machine to at
least store a constraint matrix and a population matrix into a
memory, the constraint matrix representing demographics associated
with a plurality of panelists in a population, the population
matrix representing second demographics of the population;
determine a linear system based on a set of constraints including
the constraint matrix and the population matrix; determine a
solution to the linear system by determining an inverse of a first
matrix of the linear system; and output a resultant set of weights
based on the solution, the resultant set of weights satisfying the
set of constraints.
Inventors: |
Sheppard; Michael; (Holland,
MI) ; Sullivan; Jonathan; (Hurricane, UT) ;
Lipa; Peter; (Tuscon, AZ) ; Terrazas; Alejandro;
(Santa Cruz, CA) ; Reid; Matt; (Alameda, CA)
; Donato; Paul; (New York, NY) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
The Nielsen Company (US), LLC |
New York |
NY |
US |
|
|
Family ID: |
1000005191050 |
Appl. No.: |
17/087374 |
Filed: |
November 2, 2020 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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16459261 |
Jul 1, 2019 |
10825030 |
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17087374 |
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14865639 |
Sep 25, 2015 |
10339543 |
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16459261 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06Q 10/00 20130101;
G06Q 30/02 20130101 |
International
Class: |
G06Q 30/02 20060101
G06Q030/02; G06Q 10/00 20060101 G06Q010/00 |
Claims
1. A non-transitory computer readable storage medium comprising
instructions which, when executed, cause a machine to at least:
store a constraint matrix and a population matrix into a memory,
the constraint matrix representing demographics associated with a
plurality of panelists in a population, the population matrix
representing second demographics of the population; determine a
linear system based on a set of constraints including the
constraint matrix and the population matrix; determine a solution
to the linear system by determining an inverse of a first matrix of
the linear system, the determination of the inverse of the first
matrix including: separating components of the first matrix into a
first set of blocks; inverting the first matrix using blockwise
inversion to generate a second matrix; separating a first element
of the second matrix into a second set of blocks; inverting the
second matrix using blockwise inversion to generate a third matrix,
a second element of the third matrix having dimensions larger than
the constraint matrix; reducing the dimensions of the second
element to a size less than or equal to the dimensions of the
constraint matrix, the reduced second element being a third
element; replacing the second element with the third element in the
second matrix; and replacing the first element with the second
matrix; and output a resultant set of weights based on the
solution, the resultant set of weights satisfying the set of
constraints.
2. The computer readable storage medium of claim 1, wherein the
linear system is a predictor linear system, the instructions to
cause the machine to: determine a first solution to the predictor
linear system; determine a corrector linear system based on the
first solution; solve the corrector linear system based on the set
of constraints to determine an adjusted solution; and adjust an
initial set of weights based on an inverse of a fourth matrix of
the corrector linear system, wherein the resultant set of weights
is further based on the adjusted initial set of weights.
3. The computer readable storage medium of claim 2, wherein the
instructions cause the machine to calculate a second solution to
the corrector linear system multiple times until the adjusted
initial set of weight converges to the resultant set of weights,
the multiple calculations based on prior solutions to the corrector
linear system.
4. The computer readable storage medium of claim 1, wherein the
instructions cause the machine to apply the resultant set of
weights to data associated with the panelists.
5. The computer readable storage medium of claim 4, wherein the
instructions cause the machine to apply the resultant set of
weights by: receiving a media exposure event from a panelist from
the plurality of panelists with a weight from the set of weights;
and crediting demographics of the panelist for the media exposure
event based on the weight.
6. The computer readable storage medium of claim 1, wherein the
resultant set of weights is an optimal solution to an equality
constraint, the equality constraint being an equation including the
constraint matrix, the population matrix, and the set of
weights.
7. The computer readable storage medium of claim 6, wherein: the
set of constraints further includes at least one of a non-negative
constraint, a bound constraint, and the equality constraint; and
the optimal solution is a non-negative solution with minimum
variance.
8. The computer readable storage medium of claim 1, wherein the
instructions cause the machine to reduce the dimensions of the
second element using Sherman-Morrison formula.
9. The computer readable storage medium of claim 8, wherein the
instructions cause the machine to conserve the memory by
eliminating a need to store the linear system.
10. An apparatus for determining a plurality of weights for a
plurality of panelists representative of a population, the
apparatus comprising: memory to store a constraint matrix and a
population matrix into a memory, the constraint matrix representing
demographics associated with a plurality of panelists in a
population, the population matrix representing second demographics
of the population; a weight determiner to: determine a linear
system based on a set of constraints including the constraint
matrix and the population matrix; determine a solution to the
linear system by determining an inverse of a first matrix of the
linear system, the weight determiner to determine the inverse of
the first matrix by: separating components of the first matrix into
a first set of blocks; inverting the first matrix using blockwise
inversion to generate a second matrix; separating a first element
of the second matrix into a second set of blocks; inverting the
second matrix using blockwise inversion to generate a third matrix,
a second element of the third matrix having dimensions larger than
the constraint matrix; reducing the dimensions of the second
element to a size less than or equal to the dimensions of the
constraint matrix, the reduced second element being a third
element; replacing, in the third matrix, the second element with
the third element in the second matrix; and replacing the first
element of the second matrix with the second matrix; and output a
resultant set of weights based on the solution, the resultant set
of weights satisfying the set of constraints.
11. The apparatus of claim 10, wherein the linear system is a
predictor linear system, the weight determiner to: determine a
first solution to the predictor linear system; determine a
corrector linear system based on the first solution; solve the
corrector linear system based on the set of constraints to
determine an adjusted solution; and adjust an initial set of
weights based on an inverse of a fourth matrix of the corrector
linear system, wherein the resultant set of weights is further
based on the adjusted initial set of weights.
12. The apparatus of claim 11, wherein a second solution to the
corrector linear system is calculated multiple times until the
adjusted initial set of weight converges to the resultant set of
weights, the multiple calculations based on prior solutions to the
corrector linear system.
13. The apparatus of claim 10, wherein the weight determiner is to
apply the resultant set of weights to data associated with the
panelists.
14. The apparatus of claim 13, wherein the weight determiner is to
apply the resultant set of weights by: receiving a media exposure
event from a panelist from the plurality of panelists with a weight
from the set of weights; and crediting demographics of the panelist
for the media exposure event based on the weight.
15. The apparatus of claim 10, wherein the resultant set of weights
is an optimal solution to an equality constraint, the equality
constraint being an equation including the constraint matrix, the
population matrix, and the set of weights.
16. The apparatus of claim 15, wherein: the set of constraints
further includes at least one of a non-negative constraint, a bound
constraint, and the equality constraint; and the optimal solution
is a non-negative solution with minimum variance.
17. The apparatus of claim 10, wherein the weight determiner is to
reduce the dimensions of the second element using Sherman-Morrison
formula.
18. A method for determining a plurality of weights for a plurality
of panelists representative of a population, the method comprising:
storing a constraint matrix and a population matrix into a memory,
the constraint matrix representing demographics associated with a
plurality of panelists in a population, the population matrix
representing second demographics of the population; determining, by
executing an instruction with a processor, a linear system based on
a set of constraints including the constraint matrix and the
population matrix; determining, by executing an instruction with
the processor, a solution to the linear system by determining an
inverse of a first matrix of the linear system, the determination
of the inverse of the first matrix including: separating components
of the first matrix into a first set of blocks; inverting the first
matrix using blockwise inversion to generate a second matrix;
separating a first element of the second matrix into a second set
of blocks; inverting the second matrix using blockwise inversion to
generate a third matrix, a second element of the third matrix
having dimensions larger than the constraint matrix; reducing the
dimensions of the second element to a size less than or equal to
the dimensions of the constraint matrix, the reduced second element
being a third element; replacing the second element with the third
element in the second matrix; and replacing the first element with
the second matrix; and outputting a resultant set of weights based
on the solution, the resultant set of weights satisfying the set of
constraints.
19. The method of claim 18, wherein the linear system is a
predictor linear system, further including: determining a first
solution to the predictor linear system; determining a corrector
linear system based on the first solution; solving the corrector
linear system based on the set of constraints to determine an
adjusted solution; and adjusting an initial set of weights based on
an inverse of a fourth matrix of the corrector linear system,
wherein the resultant set of weights is further based on the
adjusted initial set of weights.
20. The method of claim 19, further including calculating a second
solution to the corrector linear system multiple times until the
adjusted initial set of weight converges to the resultant set of
weights, the multiple calculations based on prior solutions to the
corrector linear system.
Description
RELATED APPLICATION
[0001] This patent arises from a continuation of U.S. patent
application Ser. No. 16/459,261, which was filed on Jul. 1, 2019,
which is a continuation of U.S. patent application Ser. No.
14/865,639, which was filed on Sep. 25, 2015. U.S. patent
application Ser. No. 16/459,261 and U.S. patent application Ser.
No. 14/865,639 are hereby incorporated herein by reference in their
entirety. Priority to U.S. patent application Ser. No. 16/459,261
and U.S. patent application Ser. No. 14/865,639 are hereby
claimed.
FIELD OF THE DISCLOSURE
[0002] This disclosure related generally to audience measurement
and, more particularly, to methods and apparatus to determine
weights for panelists in large scale problems.
BACKGROUND
[0003] Content providers and advertisers hire panelist to yield a
representative sample of a desired demographic group of a
population of users. Calibrating the representative sample is a
technique used to improve estimates and reduce cost of having to
construct a larger sample to achieve the same accuracy.
Conventional calibration involves finding weights for the
representative sample that match the population constraints while
attempting to minimize variance (e.g., increasing the precision) of
the sample. However, as the number of constraints increases,
conventional calibration techniques require more memory than is
available on a computer. Therefore, conventional calibration
techniques are not scalable for medium-to-large scale problems.
BRIEF DESCRIPTION OF THE DRAWINGS
[0004] FIG. 1 is a block diagram of an example environment in which
an example audience measurement entity obtains panelist data and
population data from an example population.
[0005] FIG. 2 is a block diagram of an example implementation of
the sample calibrator of FIG. 1.
[0006] FIG. 3A is example panelist data to be calibrated by the
sample calibrate of FIG. 1 and/or FIG. 2.
[0007] FIG. 3B is example population data used to calibrate the
panelist data of FIG. 3A.
[0008] FIG. 4A is example constraint matrix based on the panelist
data of FIG. 3A.
[0009] FIG. 4B is example constrained population matrix based on
the population data of FIG. 3B.
[0010] FIGS. 5-7B are flowcharts representative of example machine
readable instructions that may be executed to implement the sample
calibrator of FIG. 1 and/or FIG. 2.
[0011] FIG. 8 is a block diagram of an example processor platform
that may be utilized to execute the example instructions of FIGS.
4-6 to implement the sample calibrator of FIG. 1 and/or FIG. 2.
[0012] The figures are not to scale. Wherever possible, the same
reference numbers will be used throughout the drawing(s) and
accompanying written description to refer to the same or like
parts
DETAILED DESCRIPTION
[0013] Determining the size and specific demographics of a media
viewing audience helps media content providers and distributors
schedule media programming and determine a price for advertising
during the programming. In addition, accurate estimates of media
viewing demographics enable advertisers to target certain types and
sizes of audiences. To collect these demographics, an audience
measurement company enlists a plurality of media viewers (e.g.,
panelists) to cooperate in an audience measurement study (e.g., a
panel) for a predefined length of time. The viewing habits and
demographic data associated with the panelists are collected and
used to statistically represent the total media viewing audience
(e.g., a population). In some examples, weights are applied to each
panelist such that the demographics of the weighted panelists match
the demographics of the total population. However, there are many
different solutions to match panelist data to population data. An
optimal solution may be any solution that optimizes any selected
criteria to determine a unique solution. In some examples, an
optimal solution may be a unique solution that minimizes the sum of
squares, sum of values, etc. In some examples a unique solution may
not exist. Examples disclosed herein determine a unique solution
that optimizes minimum variance (e.g. maximizing effective sampling
size) based on a set of auxiliary constraints (e.g., weights
satisfying the population, weights being non-negative, weights
satisfying preset boundaries, etc.).
[0014] Data determined for a sample population of panelists may
have demographic statistics corresponding to demographic statistics
of a target population to which the data is to be applied. For
example some rating campaigns (e.g., such as audience measurement
campaigns, market research campaigns, etc.) divide a sample
population into several different demographic categories (e.g.,
such as categories based on gender, age, income, location, mobile
phone usage, etc.) and an individual in the population may belong
to multiple of theses demographic categories. A given ratings
campaign may then involve determining viewing data (e.g., media
exposure viewing data and/or other audience measurement data,
market research ratings data, etc.) for individual demographic
categories or combination of such demographic categories for a
sample population, and applying the viewing data to different
(e.g., larger) target population. However, in order to accurately
represent the target population weights are determined and applied
to each individual in the sample population to satisfy all
auxiliary constraints.
[0015] Some conventional techniques to determine unique solution
(e.g., unique set of weights) with minimum variance involve
creating a linear system based on the constraints and solving the
linear system using conventional linear algebra calculations. Such
convention techniques include a predictor-corrector method. In such
examples, a set of weights is arbitrarily selected and two linear
systems (e.g., the predictor and the corrector linear system) are
created to a) predict an adjustment to the selected set of weights
and b) adjust the prediction to abide by constraints. During an
iteration the selected weights are predicted by solving the
predictor linear system and then corrected by inputting the
predictor solutions into the corrector linear system. Once a set of
weights applied to the linear systems, the selected weights are
adjusted based on a solution from the corrector linear system. In
such examples, the process is repeated such that each time the
resultant set of weights are applied to the linear systems they are
adjust to a set of weights closer to an optimal solution. The set
of weights converges to a unique solution (e.g., the optimal
solution). However, a number of elements in the linear systems
dramatically increases as the number of unknowns (e.g., demographic
categories and/or number of panelists) increases; thereby,
increasing the amount of memory needed to solve the linear system.
In fact, modern computer systems do not have enough available
memory to determine a unique solution for some medium-to-large
scale problems. Thus, conventional techniques of solving linear
equations based on medium sized problems are not scalable or memory
efficient.
[0016] Unlike such conventional techniques, example methods and
apparatus disclosed herein implement technical solutions to address
technical problems associated with finding a minimum variance
solution for a constrained weight problem in a scalable and memory
efficient matter. More specifically, example technical solutions
disclosed herein eliminate the need to store the potentially large
linear systems associated with the unique solution by solving the
linear systems analytically such that the amount of computer memory
required to solve the linear system is dramatically reduced.
Technical solutions allow for acquiring optimal weights (e.g.,
weights that satisfy specific conditions and minimize variance)
that could previously not be acquired via a computer. In some
examples, the size reduction of a matrix using example methods and
apparatus disclosed herein is on the order of 4,500 times less in
size than the conventional techniques. This magnitude not only
allows for larger systems to be calculated via a computer than
conventional techniques, but also faster calculation of the optimal
weights.
[0017] An example medium-scaled problem may include 10,000 sample
panelists with 100 constraints (e.g., different demographics) from
a total population. In order to determine a unique set of weights
for each of the 10,000 panelists that represent the total
population such that each weight is non negative and variance is
minimized, linear systems must be stored into memory including a
matrix with over 404 million entries. A large-scaled problem may
include 100,000 sample panelists from the total population with 100
constraints. In such examples, a linear system must be stored into
memory including a matrix with over 4.times.10.sup.10 entries, far
exceeding the memory available by modern computer systems. Example
methods and apparatus disclosed herein solve medium-scaled to
large-scaled problems that cannot be solved using conventional
techniques. Such methods and apparatus determine a set of weights
that represent the total population wherein the number of entries
for a matrix that needs to be stored into memory is no larger than
the number of inputs. For example, the number of entries needed to
be stored into memory for the medium-scaled problem (e.g., 10,000
panelists with 100 constraints) is 1 million (e.g., about 1/404 the
number of entries using conventional methods) and the number of
entries needed to be stored into memory for the large-scaled
problem (e.g., 100,000 panelist with 100 constraints) is 10 million
(e.g., about 1/4000 the number of entries using conventional
methods).
[0018] Variance is a statistical measurement of a data set that is
indirectly correlated to effective sample size (e.g., as variance
is decreased, the effective sample size is increased). As the
effective sample size increases, the precision of the sample
increases. Increased precision leads to less expensive and more
accurate estimations. Thus, samples with small variance are useful
for making accurate estimations. Given some data `x` and some
frequency `f`, the variance is computed using the following
formula:
Var ( X ) = 1 N 2 i = 1 n j = 1 n 1 2 f i f j ( x i - x j ) 2 ,
where N = i = 1 n f i ##EQU00001##
[0019] For some matrix H where:
H = diag ( p ) - pp T , where p i = f i i = 1 n f i
##EQU00002##
[0020] Then,
x T Hx = x T ( diag ( p ) - pp T ) x = x T ( diag ( p ) x - pp T x
) = ( x T diag ( p ) x ) - ( x T p ) ( p T x ) = ( i = 1 n p i x i
2 ) - ( i = 1 n p i x i ) 2 = Var ( X ) ##EQU00003##
[0021] Therefore, in order to minimize variance for a set of
weights one would need to minimize x for Var(X)=x.sup.T Hx, where
H, as defined above, is conventionally known as the Hessian matrix.
As shown above, variance is expressed in quadratic form. In this
manner, the solution that minimizes the quadratic equation is the
same solution that minimizes variance. Although the Hessian, H, may
be arbitrary, examples disclosed herein specifically define H such
that x.sup.T Hx equals the variance.
[0022] Example methods, apparatus, and articles of manufacture
disclosed herein determine a solution to a linear system to find an
optimal set of weights based on a set of constraints without
storing the linear system into memory. The linear system is solved
analytically using various techniques to break the linear system
into smaller problems to be solved separately, thus conserving
memory. In this manner, each calculation used to solve the linear
system involves matrices no larger than the dimensions of a
constraint matrix based on the set of constraints. The various
techniques involve repeated application of block-inversion formula
and Sherman-Morrison formula.
[0023] The audience measurement entity enlists a plurality of media
consumers (often called panelists) to cooperate in an audience
measurement study (often called a panel) for a predefined length of
time. The media consumption habits and demographic data associated
with these enlisted media consumers is collected and used to
statistically determine the size and demographics of the entire
audience of the media presentation. In order to determine media
exposure data for demographics related to a total population (e.g.,
universe) of users, a separate weight may be applied (e.g.,
multiplied) to each panelist such that after the weights are
applied, the sample matches the population. For example, if there
are 4 panelists (e.g., 2 male and 2 female) for a total population
of 100 people (e.g., 40 males and 60 females), there are a variety
of weights that can be applied to the panelist to represent the
total population. For example:
TABLE-US-00001 Male 1 Male 2 Female 1 Female 2 Combination 1 40 0
60 0 Combination 2 0 40 0 60 Combination 3 10 30 10 50 Combination
4 20 20 30 30
[0024] Although there are many solutions, there is only one unique
solution that has all non-negative entries that minimizes variance
(e.g., the optimal solution). In order to determine the unique
solution, multiple linear equations must be solved recursively
until an arbitrary set of weights converges to a desired unique set
of weights. The linear equations are based on the population, the
panel (e.g., the panelists and the demographics), and the set of
constraints (e.g., non-negative, bounded, minimum variance, etc.).
However, as the number of panelists and demographics increases, the
size of the linear systems required to determine the unique
solution becomes larger. For medium-scaled to large-scaled problems
the number of entries is too large to store into memory of modern
computer systems.
[0025] Example methods, apparatus, and articles of manufacture
disclosed herein determine a unique set of weights for a panel of
panelists based on a set of constraints without storing linear
systems into memory. The linear systems (e.g., a predictor linear
system and a corrector linear system) are solved analytically using
various techniques to break the linear systems into smaller
problems to be solved separately, thus conserving memory. In this
manner, each calculation used to solve the linear system involves
matrices no larger than the dimensions of a constraint matrix based
on the set of constraints. The various techniques involve repeated
application of block-inversion formula and Sherman-Morrison
formula. Once the solution to the predictor linear system is found
the solution is applied to the corrector linear system to determine
a set of adjustments, the set of adjustments are applied to an
arbitrary set of weights and the process is repeated until the
arbitrary set of weights converges to a unique solution. The unique
solution (e.g., optimal weights) may be applied (e.g., multiplied)
to the panel of panelists. Data from the weighted panelists are
used to represent the total population.
[0026] An example method for determining a plurality of weights for
a plurality of panelist representative of a population is described
herein. Such disclosed examples include storing a constraint matrix
and a population matrix into a memory, the constraint matrix
representing first demographics associated with a plurality of
panelists in a population, the population matrix representing
second demographics of the population. Such disclosed examples
include determining, via a processor, a linear system based on a
set of constraints including the constraint matrix and the
population matrix. Such disclosed examples include determining, via
a processor, an inverse of a matrix of the linear system, the
determination of the inverse including breaking down the matrix
into blocks, the dimensions of the blocks being no larger than
dimensions of the constraint matrix. Such disclosed examples
include outputting a set of weights based on the inverse of the
linear system, the set of weights satisfying the set of
constraints.
[0027] An example method disclosed herein includes storing a
constraint matrix and a population matrix into a memory, the
population matrix representing a population related to data of the
constraint matrix. Such disclosed examples include determining, via
a processor, a linear system based on a set of constraints
including the constraint matrix and the population matrix. Such
disclosed examples include determining, via a processor, an inverse
of a matrix of the linear system, the determination of the inverse
including breaking down the matrix into blocks, the dimensions of
the blocks being no larger than dimensions of the constraint
matrix. Such disclosed examples include outputting a set of weights
based on the inverse of the linear system, the set of weights
satisfying the set of constraints.
[0028] Turning to the figures, FIG. 1 illustrates an example
environment in which a unique set of weights that minimize variance
is determined based on a sample of panelists in a total population.
FIG. 1 includes an example total population 100, an example sample
102, example panelist data 104, example population data 106, an
example audience measurement entity (e.g., AME) 108, an example
collector 110, and an example sample calibrator 112.
[0029] The example total population 100 is a total population of
users of a particular device (e.g., an audience). For example, the
total population 100 may be a total population of television
viewers, computing device users, mobile device users, radio
listeners, Internet users, video game users, and/or any population
of media users. Data, including demographic data, may be obtained
and/or known for the example total population 100. The data from
the example total population 100 is represented in the population
data 106. The example population data 106 is obtained and stored.
In some examples, population data may be obtained from a database
proprietor that provides service to large numbers of subscribers.
Such service may include, but is not limited to, cable television
services, email services, social networking services, news media
services, cloud storage services, streaming music services,
streaming video services, satellite radio services, cellular
services, video gaming services, online retail shopping services,
credit monitoring services, etc. In some examples, the database
proprietor maintains user account records corresponding to users
registered for the media services provided by the database
proprietors. The user account records may include demographic
information (e.g., gender, age, income, location, education level,
occupation, etc.). In some examples, however, media usage data may
not be known for the total population 100. Alternatively, media
usage data may not be released to the example AME 108.
Additionally, although general demographics may be known (e.g., the
number of 16-25 year olds, the number of males, the number of users
whose income is less than $50,000), complex demographics (e.g., the
number of 16-25 year old males making less than $50,000) may not be
known or released to the example AME 108.
[0030] In order to determine the media usage behavior and/or
complex demographics of the example total population 100, an
example sample 102 may be used. The example sample 102 is a group
(e.g., a panel) of monitored panelist within the total population
100. Data from the panelist may be acquired using local people
meters, portable people meters, surveys, and/or any other means for
obtaining data from a panelist. The data, including media usage
data and detailed demographic data, of the example sample 102 are
represented in the example panelist data 104. The example panelist
data 104 is obtained, stored, and calibrated to represent the total
population 100.
[0031] In the illustrated example, the AME 108 does not provide the
media to the total population 100 and is a trusted (e.g., neutral)
third party (e.g., The Nielsen Company, LLC) for providing accurate
media access (e.g., exposure) statistics. The AME 108 establishes a
panel of users (e.g., the example sample 102) who have agreed to
provide their demographic information and to have their media
exposure activities monitored. When an individual joins the sample
102 (e.g., a panel), the individual (e.g., panelist) provides
detailed information concerning the person's identity and
demographics (e.g., gender, age, ethnicity, income, home location,
occupation, etc.) to the AME 108.
[0032] The example AME 108 includes the example collector 110 and
the example sample calibrator 112. The example collector 110
collects the example panelist data 104 (e.g., media exposure data
and demographic data from the sample 102) and the example
population data 106 (e.g., demographics data from the total
population 100). In some examples, the panelist data 104 and the
population data 106 are transmitted to the example AME 108 via a
network (e.g., a local area network, a wide area network, the
Internet, a cloud, or any other type of communications network).
The sample collector 110 sends the collected panelist data 104 and
population data 106 to the example sample calibrator 112. The
example sample calibrator 112 calibrates the panelist data by
determining a set of weights that, when applied to the panelist
data in the sample 102, optimally represent the total population
100, as further described in FIG. 2. For example, the sample
calibrator 112 may determine that the optimal weight for a 25-25
year old male panelist is 12.2. In such examples, if the panelist
is exposed a single media event (e.g., viewing a television show),
the single event is credited to 25-35 year old males 12.2
times.
[0033] FIG. 2 is a block diagram of an example implementation of
the example sample calibrator 112 of FIG. 1, disclosed herein, to
create an optimal set of weights that, when applied to the sample
102, cause the weighted sample to accurately represent the total
population 100 while minimizing variance. As previously described,
although the optimal solution described herein is the solution that
follows a set of constraints while minimizing variance, the optimal
solution may optimize any criteria. The sample calibrator 112 of
FIG. 2 includes an example panelist data receiver 200, an example
panelist demographics determiner 202, an example constraint
determiner 204, an example population data receiver 206, an example
population demographics determiner 208, an example constrained
population determiner 210, example memory 212, an example weight
determiner 214, and an example reporter 216.
[0034] The example panelist data receiver 200 receives the example
panelist data 104 stored in, or received by, the example AME 108 of
FIG. 1 and sends the panelist data 104 to the example panelist
demographics determiner 202. The panelist demographic determiner
202 divides the panelist data into multiple demographic buckets
(e.g., male or female) for at least one demographic group (e.g.,
gender). The amount and/or type of demographics may be preset or
based on user and/or administer preferences. For example, the
panelist demographics determiner 202 may divide each panelist in
the sample 102 into demographic buckets for each of the three
demographics: gender, income, and location. In this example, the
example panelist demographics determiner 202 may determine a
gender, an income bracket, and a location for each panelist based
on the panelist data 104, as further described in FIG. 3A.
[0035] Once the example panelist demographics determiner 202
divides the panelist into the demographic buckets for each
demographic, the constraint determiner 204 creates a constraint
matrix based the panelists and demographic data. The dimensions of
the constraint matrix are (c, n) where c is the number of
demographic constraints (e.g., Male, Female, low income, medium
income, high income, etc.) and n is the panelists in the sample
panel. Once the example constraint determiner 204 creates the
constraint matrix, the constraint matrix is stored into example
memory 212.
[0036] The example population data receiver 206 receives the
example population data 106 stored in the example AME 108 of FIG. 1
and sends the population data 106 to the example population
demographics determiner 208. The population demographic determiner
208 determines the total number of users in the example population
100 for each demographic bucket used by the example panelist
demographic determiner 202. The amount and/or type of demographics
may be preset or based on user and/or administer preferences. For
example, the population demographics determiner 208 may determine a
total number of people from the population that fit into
demographic buckets for three demographics: gender, income, and
location. The demographic totals for each bucket is determined
based on the population data 106, as further described in FIG.
3B.
[0037] Once the example population demographics determiner 208
determines population totals for the demographic buckets for each
demographic, the constrained population determiner 210 creates a
constrained population matrix based on the totals. The dimensions
of the constrained population matrix are (c, 1) where c is the
number of constraints (e.g., based on the number of demographic
buckets). Once the example constrained population determiner 210
creates the constrained population matrix, the constrained
population matrix is stored into the example memory 212.
[0038] The example weight determiner 214 gathers the constraint
matrix and the constrained population matrix from the example
memory 212 to determine and output a set of optimal weights that,
when applied to the panelists of the example sample 102, accurately
match the example total population 100 while minimizing variance.
Additionally, the weight determiner 214 may receive various other
constraints including an equality constraint (e.g., an equation
where the product of the constraint matrix and an unknown weight
matrix equals the constrained population matrix) a non-negative
constraint (e.g., the weights need to be positive), and/or a
boundary constraint (e.g., the weights are bounded above and/or
below a threshold amount). The example weight determiner 214
determines an initial set of weights to optimize and example linear
systems (e.g., a predictor and corrector linear system) based on a
minimum variance condition and the set of constraints. For example,
the linear system may be based on the following three
conditions:
1 ) minimize x Var ( x ) = x T Hx , subject to 2 ) Ax = B ( e . g .
, equality constraint ) and 3 ) x .gtoreq. 0 ( e . g . , non -
negative constraint ) ##EQU00004##
[0039] Where A is the constraint matrix, B is the constrained
population matrix, x is a matrix of the unknown weights, and H is a
Hessian matrix as previously described.
[0040] In this manner, a predictor linear system is created to
determine how to adjust an arbitrary set of weights to minimize
variance given an equality constraint and a non-negative
constraint. An example predictor linear system is shown below:
[ - H A T I A 0 0 D z 0 D x ] [ d x d y d z ] = [ r d r p r cz ]
##EQU00005##
[0041] Where D.sub.z, for a column slackvector z (e.g., defined as
z=A.sup.Tx-B, where z>0), is a diagonal matrix with values equal
to z, D.sub.x, for a column Lagrange multiplier vector x (e.g.,
based on the inequality constraint), is a diagonal matrix with
values equal to x, r.sub.d, r.sub.p, and r.sub.cz are residual
vectors based on the population data, and d.sub.x, d.sub.y, and
d.sub.z are vectors to related to an amount of adjustment to apply
to an arbitrary set of weights to lead to the optimal solution.
Additionally, the solutions (e.g., d.sub.x, d.sub.y, and d.sub.z)
may be corrected to satisfy other constraints in the predictor
step. In this manner, the solutions from the predictor linear
equation are used to solve a second corrector linear system.
Solving the corrector linear system adjusts the predictor solutions
to satisfy constraints. An example corrector linear system is shown
below:
[ - H A T I A 0 0 D z 0 D x ] [ d x ' d y ' d z ' ] = [ r d r p r c
- D z d x D x d z e + .sigma..mu. e ] ##EQU00006##
[0042] Where d'.sub.x, d'.sub.y, and d'.sub.z are the corrector
solutions used to adjust the initial set of weights toward an
optimal solution. Additionally,
.sigma. = ( z + .alpha. d z ) T ( y + .alpha. d y ) z 0 T y 0 ,
.mu. = z o T y o m , ##EQU00007##
where m is the number of inequality constraints, and a can be
defined by solving the following linear system:
.alpha. .gtoreq. y d y , .alpha. .gtoreq. z d z ##EQU00008##
[0043] In some examples, the linear system may be based on a bound
constraint (e.g., the solutions must be above a lower bound, L, and
an upper bound, U). In this manner, the linear system that
satisfies the minimum variance, equality constraint, and bound
constraint is:
[ - H A T I - I A 0 0 0 I 0 D szL 0 - I 0 0 D szU ] [ d x d y d z 1
d z 2 ] = [ r d r p r cz 1 r cz 2 ] ##EQU00009##
[0044] Where D.sub.szL is, for a column Lagrange multiplier vector
szL (e.g., based on the lower bound), is a diagonal matrix with
values equal to szL, D.sub.szU is, for a column Lagrange multiplier
vector szU (e.g., based on the upper bound), is a diagonal matrix
with values equal to szU.
[0045] The example weight determiner 214 solves the linear equation
by taking the inverse of the matrix on the left (e.g., defined
herein as the Jacobian matrix) analytically such that the Jacobian
does not need to be stored in memory 212. Alternatively, the method
shown below may be used to invert and N.times.N matrix
[ d x d y d z ] = [ - H A T I A 0 0 D z 0 D x ] - 1 [ r d r p r cz
] .fwdarw. yields [ - H A T I A 0 0 D z 0 D x ] - 1 = [ B 1 B 2 B 3
B 4 B 5 B 6 B 7 B 8 B 9 ] ##EQU00010##
[0046] As the number of constraints and panelists increases, the
Jacobian increases exponentially. For example, for a linear
equation containing a bound constraint which has 10,000 panelists
with 100 constraints, the Jacobian will have 9.times.108 entries,
far exceeding the memory available on a computer. The example
weight determiner 214 finds a solution to the linear system
analytically, such that neither the Jacobian nor the Hessian needs
to be stored into memory or computing using conventional techniques
(e.g., taking the inverse of the Jacobian numerically and
multiplying by the residual vectors). In this manner, the example
weight determiner 214 is able to solve large scale problems that
were previously unsolvable by a computer, as further describe in
FIG. 6.
[0047] The example reporter 216 of FIG. 2 generates reports
including data related to the optimal weights. In some examples,
the reports include data relating to the example panelist data 104,
the example population data 106, the weights, data relating to
applying the weights, etc. The reports may further include audience
ratings, media content access metrics, specific demographic data,
usage profiles, or any other type of audience measurement data.
[0048] FIG. 3A is an illustration of example panelist data 104 from
the example sample 102 of FIG. 1. In the illustrated example, the
sample 102 includes twelve panelists. In some examples, the sample
102 may include any number of panelists. The example panelist data
104 includes an example identifier 302, a first example demographic
304, a second example demographic 306, and a third example
demographic 308.
[0049] The example identifier 302 is a number, name, and/or code
used to identify a panelist. In some examples, the identifier 302
may be a device identifier (e.g., a number and/or code associated
with a device, etc.), a web browser unique identifier (e.g., a
cookie), a user identifier (e.g., a user name, a login ID, etc.),
identification information stored in an HTML5 datastore (where HTML
is an abbreviation for hypertext markup language), and/or any other
identifier that the AME 108 stores in association with demographic
information about panelists in the example sample 102. In this
manner, when the AME 108 receives the example identifier 302, the
AME 108 may obtain demographic information corresponding to a
panelist based on the identifier 302 that the AME 108 receives from
a device associated with the panelist. In some examples, the
identifier 302 may be encrypted (e.g., hashed) at the device so
that only an intended final recipient of the example identifier 302
can decrypt the hashed identifier. For example, if the example
identifier 302 is a cookie that is set in the device associated
with the panelist by the AME 108, the example identifier 302 can be
hashed so that only the AME 108 can decrypt the identifier 302. If
the identifier 302 is an international mobile equipment identity
(e.g., IMEI) number, the device can hash the example identifier 302
so that only a wireless carrier (e.g., the database proprietor) can
decrypt the hashed identifier 302 to recover the IMEI for use in
accessing demographic information corresponding to the panelist in
the example sample 102. By hashing the example identifier 302, an
intermediate party (e.g., an intermediate server or entity on the
Internet) receiving the beacon request cannot directly identify a
user of the example sample 102.
[0050] In the example illustration of FIG. 3A, three different
demographics 304, 306, 308 are determined for each panelist. For
example, the first example demographic 304 represents gender (e.g.,
M is male and F is female), the second example demographic 306
represents income level (e.g., L is low income, M is medium income,
and H is high income), and the third example demographic 308
represents location (e.g., R is rural and U is urban). Based on the
information in the example panelist data 104, it can be determined
that the panelist associated with ID `12` is a female in the high
income range living in an urban environment. Alternatively, any
number and/or type of demographics may be used in addition and/or
instead of the example demographics to describe the sample 102.
[0051] FIG. 3B is an illustration of example population data 106
from the example total population 100 of FIG. 1. In the illustrated
example, the total population 100 includes a total number of 590
users. In some examples, the total population 100 may include any
number of users. The example population data 106 includes an
example demographic breakdown 312 and an example population
breakdown 314 for the three example demographics 304, 306, 308 of
FIG. 3A. As previously described, the example population breakdown
314 may be gathered and/or obtained from the example total
population 100. In some examples, the population breakdown 314 may
be obtained from a database proprietor that provides service to
large numbers of subscribers. Such service may include, but are not
limited to cable television services, email services, social
networking services, news media services, cloud storage services,
streaming music services, streaming video services, satellite radio
services, cellular services, video gaming services, online retail
shopping services, credit monitoring services, etc. In some
examples, the database proprietor maintains user account records
corresponding to users registered for the media services provided
by the database proprietors. The user account records may include
demographic information (e.g., gender, age, income, location,
education level, occupation, etc.). In this example, the population
data 106 is broken up into a total population for each of three
example demographics (e.g., gender 304, income 306, and location
308). Alternatively, the total population for any number and/or
types of demographics may be determined.
[0052] Based on the information from the population data 106 of
FIG. 3B it known that the total number of females in the example
total population 100 is 260 and the total number of males in the
example total population 100 is 330 (e.g., summing to the total 590
users). Additionally, it is known that the example total population
100 contains 100 low income users, 240 medium income users, and 250
high income users (e.g., summing to the total 590 users). In this
example, it is known that the example total population 100 contains
300 users located in rural areas and 290 users located in urban
areas. As previously described, the example constraint determiner
204 creates a constraint matrix and the example constrained
population determiner 210 creates a constrained population matrix
as shown in FIGS. 4A and 4B.
[0053] FIG. 4A is an example constraint matrix 400 created by the
example constraint determiner 204 based on the example panelist
data 104 of FIG. 3A. The example constraint matrix 400 includes
example columns representing demographic buckets for each
demographic of FIG. 3A and example rows representing the different
panelists of FIG. 3A. For example, example column 402 represents
males, example column 404 represents females, example column 406
represents low income, example column 408 represents medium income,
example column 410 represents high income, example column 412
represents rural location, and example column 414 represents urban
location. In this example, row 416 represents a panelist associated
with the example identifier `12` of FIG. 3A. The example panelist
`12` is a female with high income in an urban area as represented
by placing a `1` in the example female column 404, the example high
income column 408, and the example urban column 414. A `0` will be
entered in the other example columns within the example row 416
(e.g., since panelist `12` does not belong to those demographic
buckets).
[0054] FIG. 4B is an example constrained population matrix 418
determined by the example constrained population determiner 204
based on the example population data 106 of FIG. 3A. In this
example, the example constrained population matrix 418 is defined
as matrix B. The example constrained population matrix 218 includes
an example totals column 420 representing to the total number of
users for each demographic group of FIG. 3B. For example, the
example column 420 includes 260 males, 330 females, 100 low income
users, 240 medium income users, 250 high income users, 300 rural
users, and 290 urban users to represent the totals of FIG. 3B.
[0055] The example sample calibrator 112 determines the optimal
weights (e.g., the weights that satisfy the constraints and
minimize variance) that, when applied to the sample, accurately
represent the population, without requiring a processor to store
and/or calculate a matrix whose dimensions are larger than the
constraint matrix 400 of FIG. 4A. The illustrated example of FIGS.
4A and 4B is a small-scale problem (e.g., the Jacobian will have
only 441 entries) that can easily be solved by a processor.
However, when the number of panelists and/or demographics
increases, the number of entries of the Jacobian quickly becomes
too large to store in memory of a modern computer. For example, a
Jacobian for a problem with 100,000 panelists with 100 different
demographics has more than 4.times.10.sup.10 entries. The example
sample calibrator 112 may determine the optimal weights without
storing the large and unmanageable Jacobian associated with the
optimal weights, which is exponentially larger than the constraint
matrix 400, as further described in FIGS. 5-7B. The result is a
determination of weights for large scale problems that previously
would be impossible to determine.
[0056] While example manners of implementing the example sample
calibrator 112 of FIG. 1 are illustrated in FIG. 2, elements,
processes and/or devices illustrated in FIG. 2 may be combined,
divided, re-arranged, omitted, eliminated and/or implemented in any
other way. Further, the example panelist data receiver 200, the
example panelist demographics determiner 202, the example
constraint determiner 204, the example population data receiver
206, the example populations demographics determiner 208, the
example constrained population determiner 210, the example weight
determiner 214, the example reporter 216, and/or, more generally,
the example sample calibrator 112 of FIG. 2 may be implemented by
hardware, machine readable instructions, software, firmware and/or
any combination of hardware, machine readable instructions,
software and/or firmware. Thus, for example, any of the example
panelist data receiver 200, the example panelist demographics
determiner 202, the example constraint determiner 204, the example
population data receiver 206, the example populations demographics
determiner 208, the example constrained population determiner 210,
the example weight determiner 214, the example reporter 216,
and/or, more generally, the example sample calibrator 112 of FIG. 2
could be implemented by analog and/or digital circuit(s), logic
circuit(s), programmable processor(s), application specific
integrated circuit(s) (ASIC(s)), programmable logic device(s)
(PLD(s)) and/or field programmable logic device(s) (FPLD(s)). When
reading any of the apparatus or system claims of this patent to
cover a purely software and/or firmware implementation, at least
one of the example panelist data receiver 200, the example panelist
demographics determiner 202, the example constraint determiner 204,
the example population data receiver 206, the example populations
demographics determiner 208, the example constrained population
determiner 210, the example weight determiner 214, the example
reporter 216, and/or, more generally, the example sample calibrator
112 of FIG. 2 is/are hereby expressly defined to include a tangible
computer readable storage device or storage disk such as a memory,
a digital versatile disk (DVD), a compact disk (CD), a Blu-ray
disk, etc. storing the software and/or firmware. Further still, the
example sample calibrator 112 of FIG. 2 include elements, processes
and/or devices in addition to, or instead of, those illustrated in
FIGS. 5-7B, and/or may include more than one of any or all of the
illustrated elements, processes and devices.
[0057] Flowcharts representative of example machine readable
instructions for implementing the example sample calibrator 112 of
FIG. 2 are shown in FIGS. 5-7B. In the examples, the machine
readable instructions comprise a program for execution by a
processor such as the processor 812 shown in the example processor
platform 800 discussed below in connection with FIG. 8. The program
may be embodied in machine readable instructions stored on a
tangible computer readable storage medium such as a CD-ROM, a
floppy disk, a hard drive, a digital versatile disk (DVD), a
Blu-ray disk, or a memory associated with the processor 812, but
the entire program and/or parts thereof could alternatively be
executed by a device other than the processor 812 and/or embodied
in firmware or dedicated hardware. Further, although the example
program is described with reference to the flowcharts illustrated
in FIGS. 5-7B, many other methods of implementing the example
sample calibrator 112 of FIG. 2 may alternatively be used. For
example, the order of execution of the blocks may be changed,
and/or some of the blocks described may be changed, eliminated, or
combined.
[0058] As mentioned above, the example processes of FIGS. 5-7B may
be implemented using coded instructions (e.g., computer and/or
machine readable instructions) stored on a tangible computer
readable storage medium such as a hard disk drive, a flash memory,
a read-only memory (ROM), a compact disk (CD), a digital versatile
disk (DVD), a cache, a random-access memory (RAM) and/or any other
storage device or storage disk in which information is stored for
any duration (e.g., for extended time periods, permanently, for
brief instances, for temporarily buffering, and/or for caching of
the information). As used herein, the term tangible computer
readable storage medium is expressly defined to include any type of
computer readable storage device and/or storage disk and to exclude
propagating signals and to exclude transmission media. As used
herein, "tangible computer readable storage medium" and "tangible
machine readable storage medium" are used interchangeably.
Additionally or alternatively, the example processes of FIGS. 5-7B
may be implemented using coded instructions (e.g., computer and/or
machine readable instructions) stored on a non-transitory computer
and/or machine readable medium such as a hard disk drive, a flash
memory, a read-only memory, a compact disk, a digital versatile
disk, a cache, a random-access memory and/or any other storage
device or storage disk in which information is stored for any
duration (e.g., for extended time periods, permanently, for brief
instances, for temporarily buffering, and/or for caching of the
information). As used herein, the term non-transitory computer
readable medium is expressly defined to include any type of
computer readable storage device and/or storage disk and to exclude
propagating signals and to exclude transmission media. As used
herein, when the phrase "at least" is used as the transition term
in a preamble of a claim, it is open-ended in the same manner as
the term "comprising" is open ended.
[0059] The example machine readable instructions illustrated in
FIG. 5 may be executed to cause the sample calibrator 112 of FIG. 2
to determine a set of weights that, when applied to the sample,
accurately represent the population based on a set of constraints
while minimizing variance. Although the flowchart of FIG. 5 depicts
example steps in a given order, these steps are not exhaustive,
various changes and modifications may be affected by one skilled in
the art within the spirit and scope of the disclosure. For example,
blocks illustrated in the flowchart may be performed in an
alternative order or may be performed in parallel.
[0060] FIG. 5 is a flowchart representative of example machine
readable instructions that may be executed to implement the sample
calibrator 112 of FIG. 2 to find a minimum variance solution for a
constrained weight problem in a scalable and memory efficient
manner. At block 500, the example panelist data receiver 200
receives panelist data 104 from the example collector 110. The
panelist data 104 includes data relating to demographics for each
panelist as well as viewing data for each panelist. At block 502,
the example population data receiver 206 receives example
population data 106 from the example collector 110. The population
data 106 includes data relating to a total number of users that
belong to each of the demographics. The number and/or type of
demographics may be entered by a user and/or administrator.
Alternatively, the number and/or type of demographics may be preset
and/or automatically selected by the example sample calibrator
112.
[0061] At block 504, the example population demographics determiner
208 determines the total number users in the example population 100
that belong to each demographic group based on the received
population data 106. For example, the population demographics
determiner 208 may determine that there are 575,000 males and
492,000 females in a population of television users. The example
panelist demographic determiner 202 determines demographics for
each panelist based on the received panelist data 104 (block 506).
For example, the panelist demographic determiner 202 may determine
that a first panelist is male, a second panelist is female, etc. At
block 508, the example sample calibrator 112 calibrates the example
panelist data 104 to match the population data 106 based on a set
of constraints while minimizing variance, as further described in
FIG. 6. The example reporter 216 generates a report including the
example panelist data 104, the example population data 106, and
data relating to the calibration (e.g., the optimal weights,
demographic data, etc.)(block 510).
[0062] The example machine readable instructions illustrated in
FIG. 6 may be executed to cause the sample calibrator 112 of FIG. 2
to determine a set of weights that, when applied to the sample,
accurately represent the population based on a set of constraints
while minimizing variance. Although the flowchart of FIG. 6 depicts
example steps in a given order, these steps are not exhaustive,
various changes and modifications may be affected by one skilled in
the art within the spirit and scope of the disclosure. For example,
blocks illustrated in the flowchart may be performed in an
alternative order or may be performed in parallel.
[0063] FIG. 6 is a flowchart representative of example machine
readable instructions that may be executed to implement the sample
calibrator 112 of FIG. 2 to calibrate example panelist data 104 to
match the example population data 106 as shown in block 508 of FIG.
5. At block 600, the example constraint determiner 204 receives the
demographic data from the example panelist demographics determiner
202 and the example constrained population determiner 208 receives
the population demographic totals from the population demographics
determiner 208. As previously described, the number and/or type of
demographics may have been created by a user and/or administrator
based on the preferences of the user and/or administrator.
Alternatively, the number and/or type of demographics may be preset
based on the data received from the example panelist demographic
determiner 202.
[0064] At block 602, the example constraint determiner 204 creates
a constraint matrix based on the received demographic data from the
sample of panelists, as previously described in FIG. 4A. The
example constrained population determiner 210 creates a constrained
population matrix based on population totals for each demographic
(block 604), as previously described in FIG. 4B. Once the
constraint matrix and the constrained population matrix have been
created, the constraint matrix and the constrained population
matrix are stored into the example memory 212 (block 606). As
described throughout, in order to conserver memory and find optimal
solutions in a scalable manner, the example weight determiner 214
can determine the optimal set of weights analytically without
storing or calculating matrices whose dimensions are larger than
the constraint matrix and the constrained population matrix.
[0065] At block 608, the example weight determiner 214 determines a
predictor linear system and a corrector linear system based on the
stored constraint matrix and the constrained population matrix
stored in the example memory 212. In some examples, the linear
systems are further based on other constraints (e.g., a
non-negative constraint, a bound constraint, etc.). The other
constraints may be based on a preset setting or a preference from a
user and/or administrator depending on the purpose of the linear
system.
[0066] At block 610, the example weight determiner 214 creates an
initial set of weights. In some examples, the initial set of
weights is a matrix (e.g., (c, 1)) in which every weight is 1.
Alternatively, the initial values in the initial set of weights may
be any numbers. The initial values are irrelevant since multiple
iterations of linear systems (e.g., applying the initial values to
a predictor and corrector linear system) will adjust (e.g.,
converge) the initial set of weights to the optimal values.
[0067] At block 612, the example weight determiner 214 solves the
linear systems analytically to find a set of adjustments that, when
applied to the initial set of weights data, brings the initial set
of weights one step closer to an optimal set of weights. The linear
systems include a predictor linear system and a corrector linear
system as further described in FIGS. 7A and 7B. To conserve the
example memory 212, the example weight determiner 214 determines
adjustment values by solving the linear systems without using a
calculation larger than the dimensions of the constraint matrix
(e.g., (10,000, 100) for medium-scaled problem, (100,000, 100) for
a large-scaled problem, etc.).
[0068] Once the adjustments have been determined by solving the
linear equations, the example weight determiner 214 adjusts the
initial set of weights based on the determined adjustments (block
614). As previously described, the adjustments bring the initial
set of weights closer to the optimal set of weights. At block 616,
the example weight determiner 214 determines if the adjusted set of
weights are satisfactory. If the set of weights are not
satisfactory, the example weight determiner 214 runs an additional
iteration by solving the predictor and corrector linear system
using the adjusted set of weights. In this manner, the set of
weights can continue to converge (e.g., get closer) to the optimal
set of weights. In some examples, the example weight determiner 214
may determine that the set of weights are satisfactory based on a
number (e.g., count of iterations). For example, the example weight
determiner 214 may run 10,000 iterations before it determines that
the set of weights are satisfactory. Alternatively, the example
weight determiner 214 may determine that the set of weights is
satisfactory based on a threshold tolerance level (e.g., when the
corrector solutions are less than 10-16). The determination of what
constitutes as satisfactory may be determined based on user and/or
manufacture settings. If the example weight determiner 214
determines that the weights are satisfactory, the process continues
to block 510 of FIG. 5.
[0069] In some examples, the example weight determiner 214 applies
the weights to the panelists. For example, if the example weight
determiner 214 determines that an optimal weight for a panelist is
25.1, the example weight determiner 214 may credit demographics
associated with the panelist 25.1 times for a single media exposure
event. In some examples, once the weights are determined and/or
applied to the example sample 102, the example reporter 216 may
output a report regarding the optimal solution. The report may
further include the panelist data, the population data, the
constraints, the calculations, etc.
[0070] The example machine readable instructions illustrated in
FIGS. 7A and 7B may be executed to cause the sample calibrator 112
of FIG. 2 to solve a predictor linear system based on an arbitrary
set of weights and apply the solution to a corrector linear system
to adjust the solution based on the set of constraints. Although
the flowcharts of FIGS. 7A and B depict example steps in a given
order, these steps are not exhaustive, various changes and
modifications may be affected by one skilled in the art within the
spirit and scope of the disclosure. For example, blocks illustrated
in the flowchart may be performed in an alternative order or may be
performed in parallel.
[0071] FIGS. 7A and 7B are flowcharts representative of example
machine readable instructions that may be executed to implement the
sample calibrator 112 of FIG. 2 to solve a predictor linear system
and a corrector linear system analytically, as shown in block 610
of FIG. 6. In this manner, solving the linear systems analytically
does not require storing the large linear system into the example
memory 212. As previously described, determining the predictor
adjustments involves finding the inverse of a Jacobian matrix
associated with the linear system, as shown below:
[ d x d y d z ] = [ - H A T I A 0 0 D z 0 D x ] - 1 [ r d r p r cz
] .fwdarw. [ - H A T I A 0 0 D z 0 D x ] - 1 = [ B 1 B 2 B 3 B 4 B
5 B 6 B 7 B 8 B 9 ] ##EQU00011##
[0072] Where B.sub.1-B.sub.9 are analytical blocks used to
calculate the predictor adjustments.
[0073] At block 700, the weight determiner 214 separates components
of the Jacobian matrix into blocks, as shown below:
[ - H A T I A 0 0 - - - D z 0 D x ] - 1 .fwdarw. [ E F - - G J ] -
1 = M 1 - 1 ##EQU00012##
[0074] In this example, -H, A.sup.T, A, and 0 become E, I and 0
become F, D.sub.Z and 0 become G, and D.sub.x becomes J. As shown
above, the block representation of the Jacobian is represented by
M.sub.1.
[0075] At block 702, the example Jacobian (e.g., M.sub.1) is
inverted using block-wise inversion, as shown below:
M 1 - 1 = [ ( E - FJ - 1 G ) - 1 ( E - FJ - 1 G ) - 1 FJ - 1 -- --
-- -- - -- -- -- -- -- -- - - J - 1 G ( E - FJ - 1 G ) - 1 J - 1 +
J - 1 G ( E - FJ - 1 G ) - 1 FJ - 1 ] ##EQU00013##
[0076] Since E is a matrix including the Hessian matrix H, E may be
further broken up to reduce the dimensions of E. In this example,
each component of M.sub.1.sup.-1 contains (E-FJ.sup.-1G).sup.-1,
therefore (E-FJ.sup.-1G).sup.-1 may be separated into blocks in a
similar fashion to block 700. At block 704, each element of
(E-FJ.sup.-1G).sup.-1 is separated into blocks as shown below:
( E - FJ - 1 G ) - 1 = [ - ( H + D y ) A T -- -- - - A 0 ] - 1
.fwdarw. [ E 2 F 2 - - G 2 J 2 ] - 1 = M 2 - 1 ##EQU00014##
[0077] Where the separation of (E-FJ.sup.-1G).sup.-1 is labeled as
M.sub.2.sup.-1.
[0078] At block 706, M.sub.2.sup.-1 is inverted using block-wise
inversion, as shown below:
M 2 - 1 = [ E 2 - 1 + E 2 - 1 F 2 WG 2 E 2 - 1 - E 2 - 1 F 2 W --
-- -- -- - -- -- - WG 2 E 2 - 1 W ] ##EQU00015##
[0079] where W=(J.sub.2-G.sub.2E.sub.2.sup.-1F.sub.2).sup.-1,
F.sub.2=A.sup.T, G.sub.2=A.
[0080] In this example E.sub.2 is the only component containing the
Hessian matrix (e.g., E.sub.2=-(H+D.sub.y)). Since H is defined as
diag(p)-pp.sup.T, where
p = n N ##EQU00016##
and n represents frequency counts and N represents the total sum of
the frequency counts, then
H+D.sub.y=[diag(p)-pp.sup.T]+[diag(y)].fwdarw.diag(p+y)-pp.sup.T.
Therefore, by using Sherman-Morrison formula, the inverse of
H+D.sub.y is calculated without computing the large Hessian H
resulting in an expression for E.sub.2 whose dimensions (e.g., [n,
1]) are smaller than a threshold size (e.g., [c, n]) (e.g., the
size of the constraint matrix) (block 708), as shown below:
E 2 - 1 = - ( H + D Y ) - 1 = diag ( - g ) - ww T 1 - w T p , where
w = pg , g = 1 p + y , and y = z x ##EQU00017##
[0081] Once the reduced expression for E.sub.2 is calculated, it is
substituted for E.sub.2 (block 710). At block 712, M.sub.2.sup.-1
is replaced for (E-FJ.sup.-1G).sup.-1 in M.sub.1.sup.-1, giving our
final expression for the inverse of the Jacobian and the analytical
blocks, as shown below:
M 1 - 1 = [ M 2 - 1 - M 2 - 1 FJ - 1 -- -- - -- -- -- -- - - J - 1
GM 2 - 1 J - 1 + J - 1 GM 2 - 1 FJ - 1 ] = [ B 1 B 2 B 3 B 4 B 5 B
6 B 7 B 8 B 9 ] ##EQU00018##
[0082] By calculating the each element of M.sub.1.sup.-1, the
following values for B.sub.1-B.sub.9 are acquired:
B.sub.1=L
B.sub.2=-VA.sup.TW
B.sub.3=-LD.sub.x.sup.-1
B.sub.4=-WAV
B.sub.5=W
B.sub.6=WAVD.sub.x.sup.-1
B.sub.7=-D.sub.x.sup.-1D.sub.zL
B.sub.8=D.sub.x.sup.-1D.sub.z(VA.sup.TW)
B.sub.9=D.sub.x.sup.-1+D.sub.x.sup.-1(D.sub.zL)D.sub.x.sup.-1
[0083] Where L=V+VA.sup.TW AV,
V = diag ( - g ) - ww T 1 - w T p , ##EQU00019##
W=Q.sup.-1, and Q=-AV A.sup.T
[0084] In this manner, the dimensions of V are (n, n) (e.g.,
because the number of entries in g depend on the number of entries
in n), the dimensions for Q are (c, c) (e.g.,
-A.sub.cxnV.sub.nxnA.sub.nxc.sup.T=Q.sub.cxc), the dimensions for W
are (c, c), the dimensions for L are (n, n) (e.g.,
V.sub.nxn+V.sub.xnxA.sub.nxc.sup.TW.sub.cxcA.sub.cxnV.sub.nxn=L.sub.nxn),
and the dimensions of D.sub.x and D.sub.z are (n, n). Since V is
always multiplied by some vector, i, creating a new vector, a
subroutine may be applied to determine the product of V and the
vector without storing V into the example memory 212, as shown
below:
Vd = - ( duag ( g ) + ww T 1 - w T p ) d = - ( diag ( - g ) d - w (
w T d ) 1 - w T p ) ##EQU00020##
[0085] Where the first term may be expressed as element-product of
two column vector, the product of (w.sup.Td) is a dot product of
two vectors (e.g., creating a scalar), and the denominator is a
scalar. Therefore, none of the calculations involve matrices whose
dimensions are larger than (c, n). Additionally, the only inverse
that needs to be calculated based on these results is (Q.sup.-1)d,
which is small (e.g., (c, c)) and can be solved using standard
optimizing software without taking the inverse.
[0086] At block 714, the weight determiner 214 multiplies the
analytical blocks (e.g., B1-B9) to the residual vectors (e.g.,
r.sub.d, r.sub.p, r.sub.c) representative of the example population
100 resulting in the predictor adjustments, as shown below:
[ d x d y d z ] = [ B 1 B 2 B 3 B 4 B 5 B 6 B 7 B 8 B 9 ] [ r d r p
r c ] = [ B 1 r d + B 2 r p + B 3 r c B 4 r d + B 5 r p + B 6 r c B
7 r d + B 8 r p + B 9 r c ] ##EQU00021##
[0087] As previously described, determining the corrector
adjustments (e.g., to adjust the predictor adjustments based on
constraints) involves finding the inverse of a Jacobian matrix
associated with a linear system based on the predictor adjustments,
as shown below (block 716):
[ d x ' d y ' d z ' ] = [ - H A T I A 0 0 D z 0 D x ] - 1 [ r d r p
r c - D z d x - D x d z ] , where [ - H A T I A 0 0 D z 0 D x ] - 1
= [ B 1 B 2 B 3 B 4 B 5 B 6 B 7 B 8 B 9 ] ##EQU00022##
[0088] Where B.sub.1-B.sub.9 are analytical blocks used to
calculate the corrector adjustments.
[0089] At block 718, the weight determiner 214 separates components
of the Jacobian matrix into blocks, as shown below:
[ - H A T I A 0 0 - - - D z 0 D x ] - 1 .fwdarw. [ E F - - G J ] -
1 = M 1 - 1 ##EQU00023##
[0090] In this example, -H, A.sup.T, A, and 0 become E, I and 0
become F, D.sub.Z and 0 become G, and D.sub.x becomes J. As shown
above, the block representation of the Jacobian is represented by
M.sub.1.
[0091] At block 720, the example Jacobian (e.g., M.sub.1) is
inverted using block-wise inversion, as shown below:
M 1 - 1 = [ ( E - FJ - 1 G ) - 1 ( E - FJ - 1 G ) - 1 FJ - 1 -- --
-- -- - -- -- -- -- -- -- - - J - 1 G ( E - FJ - 1 G ) - 1 J - 1 +
J - 1 G ( E - FJ - 1 G ) - 1 FJ - 1 ] ##EQU00024##
[0092] Since E is a matrix including the Hessian matrix H, E may be
further broken up to reduce the dimensions of E. In this example,
each component of M.sub.1.sup.-1 contains (E-FJ.sup.-1G).sup.-1,
therefore (E-FJ.sup.-1G).sup.-1 may be separated into blocks in a
similar fashion to block 718. At block 722, each element of
(E-FJ.sup.-1G).sup.-1 is separated into blocks as shown below:
( E - FJ - 1 G ) - 1 = [ - ( H + D y ) A T -- -- - - A 0 ] - 1
.fwdarw. [ E 2 F 2 - - G 2 J 2 ] - 1 = M 2 - 1 ##EQU00025##
[0093] Where the separation of (E-FJ.sup.-1G).sup.-1 is labeled as
M.sub.2.sup.-1.
[0094] At block 724, M.sub.2.sup.-1 is inverted using block-wise
inversion, as shown below:
M 2 - 1 = [ E 2 - 1 + E 2 - 1 F 2 WG 2 E 2 - 1 - E 2 - 1 F 2 W --
-- -- -- - -- -- - WG 2 E 2 - 1 W ] ##EQU00026##
[0095] where W=(J.sub.2-G.sub.2E.sub.2.sup.-1F.sub.2).sup.-1,
F.sub.2=A.sup.T, G.sub.2=A.
[0096] In this example E.sub.2 is the only component containing the
Hessian matrix (e.g., E.sub.2=-(H+D.sub.y)). Since H is defined as
diag(p)-pp.sup.T where
p = n N ##EQU00027##
and n represents frequency counts and N represents the total sum of
the frequency counts, then
H+D.sub.y=[diag(p)-pp.sup.T]+[diag(y)].fwdarw.diag(p+y)-pp.sup.T.
Therefore, by using Sherman-Morrison formula, the inverse of
H+D.sub.y is calculated without computing the large Hessian H
resulting in an expression for E.sub.2 whose dimensions (e.g., [n,
1]) are smaller than a threshold size (e.g., [c, n]) (e.g., the
size of the constraint matrix) (block 726), as shown below:
E 2 - 1 = - ( H + D Y ) - 1 = diag ( - g ) - ww T 1 - w T p , where
w = pg , g = 1 p + y , and y = z x ##EQU00028##
[0097] Once the reduced expression for E.sub.2 is calculated, it is
substituted for E.sub.2 (block 728). At block 730, M.sub.2.sup.-1
is replaced for (E-FJ.sup.-1G).sup.-1 in M.sub.1.sup.-1, giving our
final expression for the inverse of the Jacobian and the
analytical, as shown below:
M 1 - 1 = [ M 2 - 1 - M 2 - 1 FJ - 1 -- -- - -- -- -- -- - - J - 1
GM 2 - 1 J - 1 + J - 1 GM 2 - 1 FJ - 1 ] = [ B 1 B 2 B 3 B 4 B 5 B
6 B 7 B 8 B 9 ] ##EQU00029##
[0098] By calculating the each element of M.sub.1.sup.-1, the
following values for B.sub.1-B.sub.9 are acquired:
B.sub.1=L
B.sub.2=-VA.sup.TW
B.sub.3=-LD.sub.x.sup.-1
B.sub.4=-WAV
B.sub.5=W
B.sub.6=WAVD.sub.x.sup.-1
B.sub.7=-D.sub.x.sup.-1D.sub.zL
B.sub.8=D.sub.x.sup.-1D.sub.z(VA.sup.TW)
B.sub.9=D.sub.x.sup.-1+D.sub.x.sup.-1(D.sub.zL)D.sub.x.sup.-1
[0099] Where L=V+V A.sup.TW AV,
V = diag ( - g ) - ww T 1 - w T p , ##EQU00030##
W=Q.sup.-1, and Q=-AV A.sup.T
[0100] In this manner, the dimensions of V are (n, n) (e.g.,
because the number of entries in g depend on the number of entries
in n), the dimensions for Q are (c, c) (e.g.,
-A.sub.cxnV.sub.nxnA.sub.nxc.sup.T=Q.sub.cxc), the dimensions for W
are (c, c), the dimensions for L are (n, n) (e.g.,
V.sub.nxn+V.sub.xnxA.sub.nxc.sup.TW.sub.cxcA.sub.cxnV.sub.nxn=L.sub.nxn),
and the dimensions of D.sub.x and D.sub.z are (n, n). Since V is
always multiplied by some vector, i, creating a new vector, a
subroutine may be applied to determine the product of V and the
vector without storing V into the example memory 212, as shown
below:
Vd = - ( diag ( g ) + ww T 1 - w T p ) d = - ( diag ( - g ) d - w (
w T d ) 1 - w T p ) ##EQU00031##
[0101] Where the first term may be expressed as element-product of
two column vector, the product of (w.sup.Td) is a dot product of
two vectors (e.g., creating a scalar), and the denominator is a
scalar. Therefore, none of the calculations involve matrices whose
dimensions are larger than (c, n). Additionally, the only inverse
that needs to be calculated based on these results is (Q.sup.-1)d,
which is small (e.g., (c, c)) and can be solved using standard
optimizing software without taking the inverse.
[0102] At block 732, the weight determiner 214 multiplies the
analytical blocks (e.g., B1-B9) to the residual vectors (e.g.,
r.sub.d, r.sub.p, r.sub.c-D.sub.zd.sub.x-D.sub.xd.sub.z)
representative of the example population 100 resulting in the
predictor adjustments, as shown below:
[ d x d y d z ] = [ B 1 B 2 B 3 B 4 B 5 B 6 B 7 B 8 B 9 ] [ r d r p
r c - D z d x - D x d z ] = [ B 1 r d + B 2 r p + B 3 r c - D z d x
- D x d z B 4 r d + B 5 r p + B 6 r c - D z d x - D x d z B 7 r d +
B 8 r p + B 9 r c - D z d x - D x d z ] ##EQU00032##
[0103] The solution to the corrector linear equation is a set of
adjustments that are applied to the arbitrary set of weights, as
previously described in FIG. 6. In this manner, there is no need to
calculate or store any matrix whose dimensions are larger than the
constraint matrix and/or the example constrained population matrix.
The result is a set of adjustments that slightly minimizes variance
that is memory efficient and scalable. As previously described in
FIG. 6, the process of FIGS. 7A and 7B is repeated multiple times
until the arbitrary set of weights converges to the optimal set of
weights.
[0104] While the example manners describe a particular technique to
find set of optimal weights based on the equality constraint and
the non-negative constraint, the technique may be used for linear
systems accommodating any set of constraints. For example, if the
predictor linear system includes an upper and lower bound
constraint, the linear system will be slightly different, but the
technique will be the same. In the upper and lower bound example,
the analytical blocks may be determined based on the following
predictor linear system:
[ d x d y d z 1 d z 2 ] = [ - H A T I - I A 0 0 0 I 0 D szL 0 - I 0
0 D szU ] - 1 [ r d r p r cz 1 r cz 2 ] .fwdarw. [ - H A T I - I A
0 0 0 I 0 D szL 0 - I 0 0 D szU ] - 1 = [ B 11 B 12 B 13 B 14 B 21
B 22 B 23 B 24 B 31 B 32 B 33 B 34 B 41 B 42 B 43 B 44 ]
##EQU00033##
[0105] In this manner, the example Jacobian will be broken up into
the following blocks:
[ - H A T I - I A 0 0 0 -- - -- - -- - -- -- I 0 D szL 0 - I 0 0 D
szU ] - 1 = [ E F - - G J ] - 1 = M 1 - 1 ##EQU00034##
[0106] At this point, the steps of FIGS. 7A and 7B may be followed
to obtain the following set of analytical values for
B.sub.11-B.sub.44:
B.sub.11=L
B.sub.12=-VA.sup.TW
B.sub.13=-LD.sub.sz1.sup.-1
B.sub.14=LD.sub.sz2.sup.-1
B.sub.21=-WAV
B.sub.22=W
B.sub.23=WAVD.sub.sz1.sup.-
B.sub.24=-WAVD.sub.sz2.sup.-1
B.sub.31=-D.sub.sz1.sup.-1L
B.sub.32=D.sub.sz1.sup.-1(VA.sup.TW)
B.sub.33-D.sub.sz1.sup.-1+D.sub.sz1.sup.-1LD.sub.sz1.sup.-1
B.sub.34=-D.sub.sz1.sup.-1LD.sub.sz2.sup.-1
B.sub.41=D.sub.sz2.sup.-1L
B.sub.42=-D.sub.sz2.sup.-1(VA.sup.TW)
B.sub.43=-D.sub.sz2.sup.-1LD.sub.sz1.sup.-1
B.sub.44=D.sub.sz2.sup.-1+D.sub.sz2.sup.-1LD.sub.sz2.sup.-1
Where L=V+V A.sup.TW AV,
[0107] V = diag ( - g ) - ww T 1 - w T p , ##EQU00035##
W=Q.sup.-1, Q=-AV A.sup.T D.sub.sz1=diag(sz1),
D.sub.sz2=diag(sz2),
sz 1 = s 1 , i z 1 , i , sz 2 = s 2 , i z 2 , i , y i = 1 sz 1 i +
1 sz 2 i , g i = 1 p i + y i , ##EQU00036##
and w.sub.i=p.sub.ig.sub.i. In some examples, a corrector linear
solution may be constructed for the bounded problem in a similar
manner.
[0108] FIG. 8 is a block diagram of an example processor platform
800 capable of executing the instructions of FIGS. 5-7B to
implement the example memory controller 202 of FIG. 2. The
processor platform 800 can be, for example, a server, a personal
computer, a mobile device (e.g., a cell phone, a smart phone, a
tablet such as an iPad.TM.), a personal digital assistant (PDA), an
Internet appliance, or any other type of computing device.
[0109] The processor platform 800 of the illustrated example
includes a processor 812. The processor 812 of the illustrated
example is hardware. For example, the processor 812 can be
implemented by integrated circuits, logic circuits, microprocessors
or controllers from any desired family or manufacturer.
[0110] The processor 812 of the illustrated example includes the
example memory 212 (e.g., a cache). The example processor 812 of
FIG. 8 executes the instructions of FIGS. 5-7B to implement the
example panelist data receiver 200, the example panelist
demographics determiner 202, the example constraint determiner 204,
the example population data receiver 206, the example populations
demographics determiner 208, the example constrained population
determiner 210, the example weight determiner 214, and the example
reporter 216 of FIG. 2 to implement the example sample calibrator
112. The processor 812 of the illustrated example is in
communication with a main memory including a volatile memory 814
and a non-volatile memory 816 via a bus 818. The volatile memory
814 may be implemented by Synchronous Dynamic Random Access Memory
(SDRAM), Dynamic Random Access Memory (DRAM), RAMBUS Dynamic Random
Access Memory (RDRAM) and/or any other type of random access memory
device. The non-volatile memory 816 may be implemented by flash
memory and/or any other desired type of memory device. Access to
the main memory 814, 816 is controlled by a memory controller.
[0111] The processor platform 800 of the illustrated example also
includes an interface circuit 820. The interface circuit 820 may be
implemented by any type of interface standard, such as an Ethernet
interface, a universal serial bus (USB), and/or a PCI express
interface.
[0112] In the illustrated example, one or more input devices 822
are connected to the interface circuit 820. The input device(s) 822
permit(s) a user to enter data and commands into the processor 812.
The input device(s) can be implemented by, for example, a sensor, a
microphone, a camera (still or video), a keyboard, a button, a
mouse, a touchscreen, a track-pad, a trackball, isopoint and/or a
voice recognition system.
[0113] One or more output devices 824 are also connected to the
interface circuit 820 of the illustrated example. The output
devices 824 can be implemented, for example, by display devices
(e.g., a light emitting diode (LED), an organic light emitting
diode (OLED), a liquid crystal display, a cathode ray tube display
(CRT), a touchscreen, a tactile output device, and/or speakers).
The interface circuit 820 of the illustrated example, thus,
typically includes a graphics driver card, a graphics driver chip
or a graphics driver processor.
[0114] The interface circuit 820 of the illustrated example also
includes a communication device such as a transmitter, a receiver,
a transceiver, a modem and/or network interface card to facilitate
exchange of data with external machines (e.g., computing devices of
any kind) via a network 826 (e.g., an Ethernet connection, a
digital subscriber line (DSL), a telephone line, coaxial cable, a
cellular telephone system, etc.).
[0115] The processor platform 800 of the illustrated example also
includes one or more mass storage devices 828 for storing software
and/or data. Examples of such mass storage devices 828 include
floppy disk drives, hard drive disks, compact disk drives, Blu-ray
disk drives, RAID systems, and digital versatile disk (DVD)
drives.
[0116] The coded instructions 832 of FIGS. 5-7B may be stored in
the mass storage device 828, in the volatile memory 814, in the
non-volatile memory 816, and/or on a removable tangible computer
readable storage medium such as a CD or DVD.
[0117] From the foregoing, it would be appreciated that the above
disclosed methods, apparatus, and articles of manufacture determine
a minimum variance solution for a constrained weight problem in a
scalable and memory efficient manner. Using the examples disclosed
herein, linear systems are used to adjust a set of weights to an
optimal set of weights (e.g., whose variance is minimized) based on
a set of constraints. In some examples, the optimal set of weights
is a set of weights that is unique, satisfies all constraints, and
minimizes variance. In some examples, the linear systems are solved
analytically so that a matrix whose dimensions are larger than the
constraint matrix does not need to be calculated or stored into
memory. In some examples, blockwise inversion and Sherman-Morrison
formula are used to determine the optimal solution. In some
example, the optimal set of weights is applied to a sample to
represent a population.
[0118] Conventional techniques to solve a linear system are not
scalable or memory efficient. In fact, there is not enough
available memory to solve medium-to-large scale problems using
conventional techniques. The methods, apparatus, and articles of
manufacture disclosed herein eliminate the need to store a
potentially large linear equation associated with the optimal
weights by solving the predictor and corrector linear systems
analytically to adjust a set of weights to a desired set of weights
by minimizing variance such that the amount of memory required to
solve the linear system is dramatically reduced. The methods,
apparatus, and articles of manufacture provide a technical solution
acquiring such weights that could previously not be acquired.
[0119] The methods, apparatus, and articles of manufacture
disclosed herein are able to solve a technical problem associated
with solving large-scaled panelist-to-total population problems on
a computer. Since a sample panel usually include more than 10,000
panelists, determining a set of weights based on a large number of
panelists is desirable. Conventional techniques, however, cannot be
executed by modern computers due to lack of memory. The methods,
apparatus, and articles of manufacture disclosed herein are able to
determine weights for a large number of panelists without storing
large matrices associated with conventional techniques. In this
manner, weights for large panels may be determined to represent a
population of users.
[0120] Although certain example methods, apparatus and articles of
manufacture have been described herein, the scope of coverage of
this patent is not limited thereto. On the contrary, this patent
covers all methods, apparatus and articles of manufacture fairly
falling within the scope of the claims of this patent.
* * * * *