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Abstract Details

Activity Number: 127
Type: Contributed
Date/Time: Monday, August 2, 2010 : 8:30 AM to 10:20 AM
Sponsor: Biometrics Section
Abstract - #307551
Title: Maximum Likelihood Estimation in Generalized Linear Models with Censored Covariate Data
Author(s): Ryan May*+ and Joseph G. Ibrahim and Haitao Chu
Companies: The University of North Carolina at Chapel Hill and The University of North Carolina at Chapel Hill and The University of North Carolina at Chapel Hill
Address: , Chapel Hill, NC, 27510,
Keywords: EM Algorithm ; Gibbs Sampling ; Maximum likelihood estimation ; Missing data mechanism ; Monte Carlo EM

The analysis of data subject to detection limits is becoming increasingly necessary in many environmental and laboratory studies. Covariates subject to detection limits are often left censored due to a measurement device having a minimal lower limit of detection. We propose a Monte Carlo version of the EM algorithm similar to that in Wei and Tanner (1990) to handle an unlimited number of covariates subject to detection limits. Following Lipsitz and Ibrahim (1996), the covariate distribution is modeled via a sequence of one-dimensional conditional distributions. Censored covariate values are sampled using the Adaptive Rejection Metropolis Algorithm of Gilks, Best, and Tan (1995). Parameter estimation is obtained by a maximization of the weighted likelihood. We show that the proposed approach can lead to a significant reduction in variance for parameter estimates in such models.

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