Improved empirical bayes estimation in group testing procedure for small proportions

Xiang Fang, Walter W. Stroup, Shunpu Zhang

Research output: Contribution to journalArticle

1 Scopus citations

Abstract

Group testing has been long recognized as an efficient method to classify all the experimental units into two mutually exclusive categories: defective or not defective. In recent years, more attention has been brought to the estimation of the population prevalence rate p of a disease, or of some property, using group testing. In this article, we propose two scaled squared-error loss functions, which improve the Bayesian approach to estimating p in terms of minimizing the mean squared error (MSE) of the Bayes estimators of p for small p. We show that the new estimators are preferred over the estimator from the usual squared-error loss function and the maximum likelihood estimator (MLE) for small p.

Original languageEnglish (US)
Pages (from-to)2937-2944
Number of pages8
JournalCommunications in Statistics - Theory and Methods
Volume36
Issue number16
DOIs
Publication statusPublished - Dec 1 2007

    Fingerprint

Keywords

  • Emperical Bayes estimation
  • Group testing
  • Loss function
  • Proportion

ASJC Scopus subject areas

  • Statistics and Probability

Cite this