Abstract: Methods, apparatus, and systems are disclosed for estimating panel attrition. An example apparatus includes at least one memory, machine readable instructions, and processor circuitry to execute the machine readable instructions to determine a beta distribution of a non-parametric survival curve estimate based on panel meter data associated with a cohort of panelists, determine confidence intervals for a set of beta distribution parameters associated with the survival curve estimate, and output a panelist attrition estimate generated based on the confidence intervals for the survival curve estimate, the panelist attrition estimate to represent panelist retention over time based on an installation date of the panel meter.

Abstract: Methods and apparatus to estimate cardinality of users represented across multiple bloom filter arrays are disclosed. Examples includes processor circuitry to execute and/or instantiate instructions to generate a first composite Bloom filter array based on first and second Bloom filter arrays. The processor circuitry is to generate a final composite Bloom filter array based on the first composite Bloom filter array and a third Bloom filter array. Different ones of the first, second, and third Bloom filter arrays representative of different sets of users who accessed media. The first, second, and third Bloom filter arrays including differential privacy noise. The processor circuitry to estimate a cardinality of a union of the first, second, and third Bloom filter arrays based on the final composite Bloom filter array.

Abstract: Methods and apparatus to estimate cardinality across multiple datasets represented using Bloom filter arrays are disclosed. Disclosed examples include processor circuitry to execute and/or instantiate instructions to determine an inclusion-exclusion expression that defines an audience size for a user group of interest. Terms in the inclusion-exclusion expression corresponding to either a first cardinality of a first one of at least three Bloom filter arrays or a second cardinality of a union of two or more of the Bloom filter arrays. Different ones of the Bloom filter arrays representative of different sets of users who accessed media. The at least one processor further to estimate, based on the inclusion-exclusion expression, the audience size of the user group of interest.

Abstract: Methods and systems to estimate audience measurement metrics based on users represented in Bloom filter arrays are disclosed. An apparatus includes a communications interface to receive a first Bloom filter array from a first computer of a first database proprietor. The first Bloom filter array is representative of first users who accessed media. The first users are registered with the first database proprietor. The first Bloom filter array includes a first array of first elements. Values of respective ones of the first elements are either a 0 or a 1 based on whether quantities of the first users allocated to the respective ones of the first elements are even or odd. The apparatus further includes a Bloom filter array analyzer to estimate a first cardinality for the first Bloom filter array. The first cardinality is indicative of a total number of the first users who accessed the media.

Abstract: Methods, apparatus, systems, and articles of manufacture to estimate audience measurement metrics based on users represented in Bloom filter arrays are disclosed. An apparatus includes a communications interface to receive a first Bloom filter array from a first computer of a first database proprietor. The first Bloom filter array is representative of first users who accessed media. The first users are registered with the first database proprietor. The first Bloom filter array includes a first array of first elements. Values of respective ones of the first elements are either a 0 or a 1 based on whether quantities of the first users allocated to the respective ones of the first elements are even or odd. The apparatus further includes a Bloom filter array analyzer to estimate a first cardinality for the first Bloom filter array. The first cardinality is indicative of a total number of the first users who accessed the media.

Abstract: Methods and apparatus to estimate cardinality of users represented across multiple bloom filter arrays are disclosed. Examples includes processor circuitry to execute and/or instantiate instructions to generate a first composite Bloom filter array based on first and second Bloom filter arrays. The processor circuitry is to generate a final composite Bloom filter array based on the first composite Bloom filter array and a third Bloom filter array. Different ones of the first, second, and third Bloom filter arrays representative of different sets of users who accessed media. The first, second, and third Bloom filter arrays including differential privacy noise. The processor circuitry to estimate a cardinality of a union of the first, second, and third Bloom filter arrays based on the final composite Bloom filter array.

Abstract: Methods, apparatus, and systems are disclosed for estimating panel attrition. An example apparatus includes at least one memory, machine readable instructions, and processor circuitry to execute the machine readable instructions to determine a beta distribution of a non-parametric survival curve estimate based on panel meter data associated with a cohort of panelists, determine confidence intervals for a set of beta distribution parameters associated with the survival curve estimate, and output a panelist attrition estimate generated based on the confidence intervals for the survival curve estimate, the panelist attrition estimate to represent panelist retention over time based on an installation date of the panel meter.

Abstract: Methods, apparatus, and systems are disclosed for audience-based deduplication using vector-of-counts (VOC) central moments. An example system to determine an audience size for media based on vector of counts sketch data, the system including memory, programmable circuitry, and instructions to cause the programmable circuitry to apply a hash function to first vector of counts sketch data, second vector of counts sketch data, and third vector of counts sketch data to remove personally identifiable information, determine a first covariance matrix associated with intersection cardinalities of at least two of the first, second, or third vector of counts, determine a second covariance matrix associated with disjoint cardinalities of the at least two of the first, second, or third vector of counts, and determine the audience size based on the first covariance matrix or the second covariance matrix.

Abstract: Methods and apparatus to estimate audience sizes using deduplication based on vector of counts sketch data are disclosed. An example apparatus to determine an audience size for media based on vector of counts sketch data includes: a coefficient analyzer to determine coefficient values of a polynomial based on variances, a covariance, and cardinalities corresponding to a first vector of counts from a first database and a second vector of counts from a second database; an overlap analyzer to determine a real root of the polynomial, the real root corresponding to an estimate of an overlap between the first vector of counts and the second vector of counts; and a report generator to estimate the audience size based on the estimate of the overlap and the cardinalities of the first vector of counts and the second vector of counts.

Abstract: Methods and apparatus to estimate cardinality across multiple datasets represented using Bloom filter arrays are disclosed. Disclosed examples include processor circuitry to execute and/or instantiate instructions to determine an inclusion-exclusion expression that defines an audience size for a user group of interest. Terms in the inclusion-exclusion expression corresponding to either a first cardinality of a first one of at least three Bloom filter arrays or a second cardinality of a union of two or more of the Bloom filter arrays. Different ones of the Bloom filter arrays representative of different sets of users who accessed media. The at least one processor further to estimate, based on the inclusion-exclusion expression, the audience size of the user group of interest.

Abstract: Methods and apparatus to estimate audience sizes using deduplication based on binomial sketch data are disclosed. An apparatus includes processor circuitry to instantiate coefficient analyzer circuitry to determine coefficient values of a polynomial based on (i) variances in values in the first sketch data and second sketch data, (ii) a first cardinality of the first sketch data, and (iii) a second cardinality of the second sketch data. The processor circuitry to instantiate overlap analyzer circuitry to determine a real root of the polynomial, the real root corresponding to the quantity of the second subscribers that are duplicates of the first subscribers. The processor circuitry to instantiate report generator circuitry to estimate a deduplicated audience size based on the estimate of the quantity of the second subscribers that are duplicates of the first subscribers and the first and second cardinalities.

Abstract: Methods and apparatus to estimate audience sizes using deduplication based on binomial sketch data are disclosed. An apparatus to estimate an audience size for media based on binomial sketch data includes a coefficient analyzer to determine coefficient values of a polynomial based on variances, a covariance, and cardinalities corresponding to first binomial sketch data from a first database and second binomial sketch data from a second database. The apparatus also includes an overlap analyzer to determine a real root of the polynomial. The real root corresponds to an estimate of an overlap between the first binomial sketch data and the second binomial sketch data. The apparatus further includes a report generator to estimate the audience size based on the estimate of the overlap and the cardinalities of the first and second binomial sketch data.

Abstract: Methods, apparatus, systems, and articles of manufacture to estimate audience measurement metrics based on users represented in Bloom filter arrays are disclosed. An apparatus includes a communications interface to receive a first Bloom filter array from a first computer of a first database proprietor. The first Bloom filter array is representative of first users who accessed media. The first users are registered with the first database proprietor. The first Bloom filter array includes a first array of first elements. Values of respective ones of the first elements are either a 0 or a 1 based on whether quantities of the first users allocated to the respective ones of the first elements are even or odd. The apparatus further includes a Bloom filter array analyzer to estimate a first cardinality for the first Bloom filter array. The first cardinality is indicative of a total number of the first users who accessed the media.

Abstract: 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.

Abstract: Methods and apparatus to estimate cardinality of users represented across multiple bloom filter arrays are disclosed. Examples includes processor circuitry to execute and/or instantiate instructions to generate a first composite Bloom filter array based on first and second Bloom filter arrays. The processor circuitry is to generate a final composite Bloom filter array based on the first composite Bloom filter array and a third Bloom filter array. Different ones of the first, second, and third Bloom filter arrays representative of different sets of users who accessed media. The first, second, and third Bloom filter arrays including differential privacy noise. The processor circuitry to estimate a cardinality of a union of the first, second, and third Bloom filter arrays based on the final composite Bloom filter array.

Abstract: A user is afforded the ability to set permanent default settings on a Graphical User Interface (GUI). When the user holds down a selector for a GUI setting for a predetermined period of time, or for a predetermined number of times, that selected GUI setting is set as the default setting for that user.