JSM 2005 Online Program

Abstract #304461

This is the preliminary program for the 2005 Joint Statistical Meetings in Minneapolis, Minnesota. Currently included in this program is the "technical" program, schedule of invited, topic contributed, regular contributed and poster sessions; Continuing Education courses (August 7-10, 2005); and Committee and Business Meetings. This on-line program will be updated frequently to reflect the most current revisions.

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Activity Number:	227
Type:	Contributed
Date/Time:	Tuesday, August 9, 2005 : 8:30 AM to 10:20 AM
Sponsor:	Section on Bayesian Statistical Science
Abstract - #304461
Title:	A Flexible Bayesian Generalized Linear Model for Dichotomous Response Data with an Application to Text Categorization
Author(s):	David Madigan*+ and Susana Eyeramendy
Companies:	Rutgers, The State University of New Jersey and Oxford University
Address:	30 Sunset Drive, Chatham, NJ, 07928, United States
Keywords:	glm ; asymmetric link ; EM algorithm
Abstract:	Under the generalized linear models settings, the standard approach to model the dependence of binary data on explanatory variables is through a cumulative density function (cdf). The most commonly used are the logistic and normal cdfs. For the logistic and normal cdfs, the corresponding probability density functions are symmetric around $0$. This implies that the cdfs approach $1$ at the same rate they approach $0$, which may not always be reasonable to fit data. In this study, we present a class of binary regression models that include probit and logistic regression as special cases and offer extra flexibility. We provide an EM algorithm for learning the parameters of these models from data. Finally, we present empirical results demonstrating the predictive benefits of the new class of models in a particular text classification application.

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