Improved empirical bayes estimation in group testing procedure for small proportions

Xiang Fang, Walter W. Stroup, Shunpu Zhang

Research output: Contribution to journalArticle

1 Citation (Scopus)

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
StatePublished - Dec 1 2007

Fingerprint

Empirical Bayes Estimation
Squared Error Loss Function
Group Testing
Proportion
Property Testing
Estimator
Mutually exclusive
Bayes Estimator
Testing
Mean Squared Error
Bayesian Approach
Maximum Likelihood Estimator
Classify
Maximum likelihood
Unit

Keywords

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

ASJC Scopus subject areas

  • Statistics and Probability

Cite this

Improved empirical bayes estimation in group testing procedure for small proportions. / Fang, Xiang; Stroup, Walter W.; Zhang, Shunpu.

In: Communications in Statistics - Theory and Methods, Vol. 36, No. 16, 01.12.2007, p. 2937-2944.

Research output: Contribution to journalArticle

Fang, Xiang ; Stroup, Walter W. ; Zhang, Shunpu. / Improved empirical bayes estimation in group testing procedure for small proportions. In: Communications in Statistics - Theory and Methods. 2007 ; Vol. 36, No. 16. pp. 2937-2944.
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