An empirical Bayes group-testing approach to estimating small proportions

Joshua M. Tebbs, Christopher R. Bilder, Barry K. Moser

Research output: Contribution to journalArticle

16 Scopus citations

Abstract

Group testing has long been recognized as a safe and sensible alternative to one-at-a-time testing in applications wherein the prevalence rate p is small. In this article, we develop an empirical Bayes (EB) procedure to estimate p using a beta-type prior distribution and a squared-error loss function. We show that the EB estimator is preferred over the usual maximum likelihood estimator (MLE) for small group sizes and small p. In addition, we also discuss interval estimation and consider the use of other loss functions perhaps more appropriate in public health studies. The proposed methods are illustrated using group-testing data from a prospective hepatitis C virus study conducted in Xuzhou City, China.

Original languageEnglish (US)
Pages (from-to)983-995
Number of pages13
JournalCommunications in Statistics - Theory and Methods
Volume32
Issue number5
DOIs
StatePublished - May 2003

Keywords

  • Composite sampling
  • Empirical Bayes estimation
  • Hepatitis C virus
  • Pooling designs
  • Screening experiments

ASJC Scopus subject areas

  • Statistics and Probability

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