This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 534
Type: Contributed
Date/Time: Wednesday, August 4, 2010 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Computing
Abstract - #306503
Title: Maximum Likelihood Estimation with Binary Data Regression Models: Small-Sample and Large-Sample Features
Author(s): Roland C. Deutsch*+ and John M. Grego and Brian Habing and Walter W. Piegorsch
Companies: The University of North Carolina at Greensboro and University of South Carolina and University of South Carolina and The University of Arizona
Address: Dep. of Mathematics & Statistics, Greensboro, NC, 27402,
Keywords: Binomial Response Models ; Maximum Likelihood Estimator ; Asymptotic Normality ; Complementary Log-Log Link ; Complementary Log Link

Many inferential procedures for generalized linear models rely on the asymptotic normality of the MLE. Fahrmeir & Kaufmann (1985) present mild conditions under which the MLEs in GLiMs are asymptotically normal. Limited study has appeared for binomial response models beyond the familiar logit and probit links, and for more general links such as the complementary log-log link, and the less well-known complementary log link. We verify the asymptotic normality conditions of the MLEs for these models under the assumption of a fixed number of experimental groups and present a simple set of conditions for any twice differentiable monotone link function. We also study the quality of the approximation for constructing asymptotic Wald confidence regions. Our results show that for small sample sizes with certain link functions, the approximation can be problematic (i.e. boundary cases)

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