The Nielsen Comapny (us), LLC

The Nielson Company (us), LLC

Michael R Sheppard

Jonathan Lee Sullivan

Edward Murphy

Alejandro Terrazas

Peter Lipa

Molly Poppie

Ludo Daemen

Michael D Morgan

Diane Morovati Lopez

Jake Ryan Dailey

Peter Campbell Doe

Matthew B Reid

Dongbo Cui

Christie Nicole Summers

Paul Donato

Damien Forthomme

Balachander Shankar

Frank Downing

Peng Fei Yi

Jessica D Brinson

Chris Samuel Redino

Albert Ronald Perez

Dipti Umesh Shah

Brian Schiller

PengFei Yi

Jessica Lynn White

David Forteguerre

Jing Liu

Wei Xie

Beate Sissenich

Ajete Junuzi

Kevin Keqiang Deng

Stephen S Bell

Remy Spoentgen

Michael Allen Bivins

Rachel Worth Olson

Edmond Wong

Lauren Elizabeth Lubeck

Matt Reid

Peter Otto Robert Lipa

Steve Jones

DongBo Cui

Samantha Dawson Edds

Xiaoqi Cui

Alex Terrazas

John Charles Torres

Albert Robert Perez

Marissa Joseph

Susan Kim

Matthew Zipoy

Olabode O Oyeneye

Disclosed examples to estimate audience sizes of media include a coefficient generator to determine coefficient values for a polynomial based on normalized weighted sums of variances, a normalized weighted sum of covariances, and cardinalities corresponding to a first plurality of vectors of counts from a first database proprietor and a second plurality of vectors of counts from a second database proprietor, a real roots solver to determine a real root value of the polynomial, the real root value indicative of a number of audience members represented in the first plurality of vectors of counts that are also represented in the second plurality of vectors of counts, and an audience size generator to determine the audience size based on the real root value and the cardinalities of the first plurality of vectors of counts and the second plurality of vectors of counts.

Methods and apparatus to estimate audience sizes of media using deduplication based on multiple vectors of counts

Example methods, apparatus, systems, and articles of manufacture are disclosed to estimate de-duplicated unknown total audience sizes based on partial information of known audiences. An example apparatus includes an association controller to generate a tree structure association corresponding to a union of first and second margins of time; and one or more commercial solvers to: perform parallel computations on a processor to determine multipliers by solving equations corresponding to the tree structure association, the multipliers corresponding to the first total audience size for the union, the second total audience size for the first margin, and the third total audience size for the second margin; and determine an estimate for the third total audience size for the second margin of time based on the multipliers.

Methods and apparatus to estimate de-duplicated unknown total audience sizes based on partial information of known audiences

Methods, apparatus, systems, and articles of manufacture to estimate cardinality of users represented in arbitrarily distributed bloom filter arrays are disclosed. A system includes a communication interface to: access a first Bloom filter array representative of first entries in a first database, the first entries allocated to ones of first elements in the first Bloom filter array based on a non-uniform distribution of outputs of a hash function applied to the first entries, and access a second Bloom filter array representative of second entries in a second database. The system also includes machine readable instructions to cause one or more processors to estimate a cardinality of a union of the first and second entries based on the non-uniform distribution of the outputs of the hash function.

Methods and apparatus to estimate cardinality of users represented in arbitrarily distributed bloom filters

Methods, apparatus, and articles of manufacture are disclosed to estimate population reach from marginal ratings with missing information. An example apparatus includes a pseudo universe calculator to (i) determine a first pseudo universe estimate for a first audience of media corresponding to a first union of first margins, the first margins corresponding to known audience totals, and (ii) determine a second pseudo universe estimate for a second audience of the media corresponding to a second union of second margins, the determination based on an audience constant, the second margins including a margin corresponding to an unknown audience total. The example apparatus also includes a reach calculator to (i) determine an estimate of the unknown audience total of the second audience based on the second pseudo universe estimate for the second audience, and (ii) determine a reach estimate for the second union based on the first and second pseudo universe estimates.

Methods and apparatus to estimate population reach from marginal ratings with missing information

Methods, apparatus, systems, and articles of manufacture are disclosed to estimate an audience population. An example apparatus includes processor circuitry; characteristic identifier instructions to be executed by the processor circuitry to determine whether respective ones of respondents are associated with a characteristic; recapture probability estimator instructions to be executed by the processor circuitry to select a recapture probability of the respective ones of respondents; and population estimator instructions to be executed by the processor circuitry to in response to the recapture probability satisfying a recapture threshold, determine a population estimate having the characteristics based on a first model; and in response to the recapture probability not satisfying the recapture threshold, determine the population estimate having the characteristics based on a second model, the second model different than the first model.

Methods, systems, articles of manufacture, and apparatus to estimate audience population

Methods and apparatus to determine probabilistic media viewing metrics are disclosed herein. An example apparatus includes memory including machine reachable instructions; and processor circuitry to execute the instructions to calculate a first probability for respective ones of a plurality of panelists as having viewed media based on viewing data, the viewing data including incomplete viewing data for one or more of the panelists relative to the media; identify respective ones of a plurality of panelists as included in a demographic subgroup based on demographic data for the panelists; assign a sampling weight to the respective ones of the plurality of panelists based on the demographic data; and calculate a second probability of the demographic subgroup having viewed the media based on the first probabilities and the sampling weights for the respective ones of the plurality of panelists in the demographic subgroup.

Methods and apparatus to determine probabilistic media viewing metrics

Methods and apparatus to correct for deterioration of a demographic model to associate demographic information with media impression information are disclosed. An example method includes estimating first and second ages of audience members based on demographic information; estimating a third age of an audience member who is not included in the audience members; applying a window function to the second ages to determine a distribution of ages based on the third age; multiplying window values by the first ages to determine corrected first age components; dividing a total of the corrected first age components by a sum of the window values to determine an estimated age of the audience member at a first time; and determining the corrected age of the audience member at a second time based on the estimated age of the audience member at the first time and a time difference between the first and second times.

Methods and apparatus to correct for deterioration of a demographic model to associate demographic information with media impression information

Methods, apparatus, systems, and articles of manufacture to identify inconsistencies in audience measurement data are disclosed. Example apparatus disclosed herein are to compare ones of a first set of cumulative audience metrics with one or more limits based on a second set of event-level audience metrics to detect an inconsistency in at least one of the first set of cumulative audience metrics or the second set of event-level audience metrics. Disclosed example apparatus are further to generate a report of the inconsistency in the at least one of the first set of event-level audience metrics or the second set of cumulative audience metrics.

Methods and apparatus to identify inconsistencies in audience measurement data

Disclosed examples determine a unique audience exposed to media while reducing memory resources of a computing device. Example instructions program programmable circuitry to log a plurality of impressions corresponding to media accessed at client devices; generate a first value based on a count of the impressions and a count of users of a database proprietor that accessed the media; generate a second value based on the first value and a count of demographic impressions logged by the database proprietor; and determine a unique audience size based on the second value.

Methods and apparatus for estimating total unique audiences

Methods, apparatus, systems, and articles of manufacture are disclosed to estimate population reach from marginals. An example apparatus includes memory including computer readable instructions; and a processor to execute the instructions to: iteratively converge on an output estimate of a pseudo universe estimate of a recorded audience of first media based on (A) a recorded reach for the recorded audience of the first media and (B) first marginal ratings for the recorded audience of the first media; determine a panel representation value based on the pseudo universe estimate of the recorded audience of the first media; and iteratively converge on an output estimate of a final reach of second media for a population audience based on the panel representation value and second marginal ratings for the population audience of the second media.

Methods and apparatus to estimate population reach from marginals

Example methods, apparatus, systems and articles of manufacture (e.g., physical storage media) to assign respondents to geographic locations for audience measurement are disclosed. Example apparatus disclosed herein are to determine a set of constraints based on aggregate values of demographic features associated with respective ones of the geographic locations. Disclosed example apparatus are also to construct a model to return probabilities that respective ones the respondents are associated with the respective ones of the geographic locations, the model to have a set of parameters, respective ones of the parameters to be associated with respective ones of the constraints. Disclosed example apparatus are further to evaluate the model based on values of the set of parameters and values of the demographic features for a first one of the respondents to determine a set of probabilities that the first respondent is to be assigned to respective ones of the geographic locations.

Assigning synthetic respondents to geographic locations for audience measurement

Methods, apparatus, systems and articles of manufacture (e.g., physical storage media) to estimate and use nested merge ratios (e.g., nested Fréchet ratios) for audience measurement are disclosed herein. Example apparatus disclosed herein are to identify a first merged media event for which a first census audience value is to be determined, the first merged media event based on a union including a second merged media event. Disclosed example apparatus are also to determine whether the first merged media event satisfies a mutual exclusivity condition. Disclosed example apparatus are further to, in response to the first merged media event satisfying the mutual exclusivity condition, estimate the first census audience value for the first merged media event based on at least a merge ratio and a second census audience value previously estimated for the second merged media event.

Estimation and use of nested merge ratios for audience measurement

Methods, apparatus, systems, and articles of manufacture to virtually estimate cardinality with global registers are disclosed. An example apparatus includes processor circuitry to s assign subsets of a sample dataset to a shared global register array, the shared global register array having a first number of registers, the sample dataset selected from a reference dataset of media assets; identify a virtual register array from the shared global register array that includes data elements associated with a label value, the virtual register array including a second number of registers less than the first number of registers; determine a maximum rank value of the label value across the virtual register array; and calculate a cardinality estimate of the label value across the virtual register array based on the second number of registers and the maximum rank value.

Methods and apparatus to virtually estimate cardinality with global registers

Example methods, apparatus, systems, and articles of manufacture are disclosed to estimate population reach for different unions based on marginal ratings. An example apparatus includes memory and processor circuitry to determine a population reach estimate of a union of time intervals for which media ratings data is available, the population reach estimate based on a pseudo universe estimate of a population audience corresponding to the union of the time intervals; determine a pseudo universe estimate of a recorded audience corresponding to the union of the time intervals; determine the pseudo universe estimate of the population audience based on the pseudo universe estimate of the recorded audience; and in response to a decision to update the pseudo universe estimate of the population audience to reduce an error of the population reach estimate output the population reach estimate of the union when the error of the population reach estimate satisfies a threshold.

Methods and apparatus to estimate population reach from different marginal rating unions

Methods, apparatus, systems to determine a conditional probability based on audience member probability distributions for media audience measurement are disclosed. Disclosed example methods for media audience measurement include determining a first audience probability distribution for a first member of a household and determining a second audience probability distribution for a second member of the household. Disclosed example methods also include calculating probabilities for audience combinations of the first member and the second member of the household based on the first audience probability distribution and the second audience probability distribution. Disclosed example methods further include determining a household audience characteristic probability based on the calculated probabilities of the audience combinations of the household. The household audience characteristic indicates likelihoods of different possible audience compositions of the household for a media event.

Methods and apparatus to determine a conditional probability based on audience member probability distributions for media audience measurement

Growing community of inventors

Holland, MI, United States of America

Michael R Sheppard