Bayesian inference for rare errors in populations with unequal unit sizes | Zendy

Laws David J. | Zendy; O'Hagan Anthony | Zendy

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Bayesian inference for rare errors in populations with unequal unit sizes

Author(s) -

Laws David J.,

O'Hagan Anthony

Publication year - 2000

Publication title -

journal of the royal statistical society: series c (applied statistics)

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.205

H-Index - 72

eISSN - 1467-9876

pISSN - 0035-9254

DOI - 10.1111/1467-9876.00213

Subject(s) - covariate , inference , bayesian probability , computer science , categorization , econometrics , bayesian inference , population , statistics , small area estimation , audit , set (abstract data type) , frequentist inference , nonparametric statistics , data mining , machine learning , artificial intelligence , mathematics , accounting , estimator , demography , economics , sociology , programming language

We describe a Bayesian model for a scenario in which the population of errors contains many 0s and there is a known covariate. This kind of structure typically occurs in auditing, and we use auditing as the driving application of the method. Our model is based on a categorization of the error population together with a Bayesian nonparametric method of modelling errors within some of the categories. Inference is through simulation. We conclude with an example based on a data set provided by the UK's National Audit Office.

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