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Sequential multilocation auditing and the New York food stamps program
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This article discusses the use of Bayesian methods in analysing data that evolve over time in sequential multilocation auditing. Using the New York food stamps program as a case study, it proposes a model that incorporates a nonparametric component for the error magnitudes (taints), a hierarchical model for overall error rates across counties and parameters controlling the variation of rates from one year to the next, including an overall trend in error rates. The article first provides an overview of the New York food stamps program, along with the auditing concepts and terminology, before introducing the Bayesian model. This model is used to examine a sample of individual awards of food stamps to see if the value awarded is correct according to the rules of the scheme. The model makes it possible to smooth estimation of error rates and error classes in small counties across counties and through time.
Title: Sequential multilocation auditing and the New York food stamps program
Description:
This article discusses the use of Bayesian methods in analysing data that evolve over time in sequential multilocation auditing.
Using the New York food stamps program as a case study, it proposes a model that incorporates a nonparametric component for the error magnitudes (taints), a hierarchical model for overall error rates across counties and parameters controlling the variation of rates from one year to the next, including an overall trend in error rates.
The article first provides an overview of the New York food stamps program, along with the auditing concepts and terminology, before introducing the Bayesian model.
This model is used to examine a sample of individual awards of food stamps to see if the value awarded is correct according to the rules of the scheme.
The model makes it possible to smooth estimation of error rates and error classes in small counties across counties and through time.
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