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

Abstract Details

Activity Number: 127
Type: Contributed
Date/Time: Monday, August 2, 2010 : 8:30 AM to 10:20 AM
Sponsor: Biometrics Section
Abstract - #307597
Title: Analysis of Constrained Generalized Linear Models with Missing Data
Author(s): Karelyn Davis*+ and Sanjoy Sinha and Chul Gyu Park
Companies: Carleton University/Health Canada and Carleton University and Carleton University
Address: Health Canada, Ottawa, ON, K1A 0K9, Canada
Keywords: Constrained inference ; EM algorithm ; Generalized linear model ; Maximum likelihood

Statisticians often encounter data that is non-normally distributed, and includes some missing observations. For the former case, generalized linear models (GLM) are typically considered in the statistical analysis; whereas missing data are commonly analyzed through imputation or the Expectation-Maximization (EM) algorithm. While many procedures have been proposed for these models, only a few researchers have studied estimation and hypothesis testing in the presence of parameter constraints. The presentation will discuss maximum likelihood inference for such models under linear inequality constraints, using a gradient projection version of the EM algorithm. Simulation results and an application to detection of blood contaminants for an environmental study will also be discussed.

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