Bias-corrected maximum likelihood estimator of the intraclass correlation parameter for binary data
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Updated: Aug 2, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
Published on: October 11, 2018
Krishna K Saha1, Sudhir R Paul
1Department of Mathematics and Statistics, University of Windsor, 401 Sunset, Windsor, Ont., Canada N9B 3P4. smjp@uwindsor.ca
Abstract:
A popular model to analyse over/under-dispersed proportions is to assume the extended beta-binomial model with dispersion (intraclass correlation) parameter phi and then to estimate this parameter by maximum likelihood. However, it is well known that maximum likelihood estimate (MLE) may be biased when the sample size n or the total Fisher information is small. In this paper we obtain a bias-corrected maximum likelihood (BCML) estimator of the intraclass correlation parameter and compare it, by simulation, in terms of bias and efficiency, with the MLE, an estimator Q(2) based on optimal quadratic estimating equations of Crowder and recommended by Paul et al. and a double extended quasi-likelihood (DEQL) estimator proposed by Lee. The BCML estimator has superior bias and efficiency properties in most instances. Analyses of a set of toxicological data from Paul and a set of medical data pertaining to chromosomal abnormalities among survivors of the atomic bomb in Hiroshima from Otake and Prentice show, in general, much improvement in standard errors of the BCML estimates over the other three estimates.
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