A Disaggregate Negative Binomial Regression Procedure for Count Data Analysis

Venkatram Ramaswamy, Eugene W. Anderson and Wayne S. DeSarbo
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Venkatram Ramaswamy: School of Business Administration, The University of Michigan, Ann Arbor, Michigan 48109
Eugene W. Anderson: School of Business Administration, The University of Michigan, Ann Arbor, Michigan 48109
Wayne S. DeSarbo: School of Business Administration, The University of Michigan, Ann Arbor, Michigan 48109

Management Science, 1994, vol. 40, issue 3, 405-417

Abstract: Various research areas face the methodological problems presented by nonnegative integer count data drawn from heterogeneous populations. We present a disaggregate negative binomial regression procedure for analysis of count data observed for a heterogeneous sample of cross-sections, possibly over some fixed time periods. This procedure simultaneously pools or groups cross-sections while estimating a separate negative binomial regression model for each group. An E-M algorithm is described within a maximum likelihood framework to estimate the group proportions, the group-specific regression coefficients, and the degree of overdispersion in event rates within each derived group. The proposed procedure is illustrated with count data entailing nonnegative integer counts of purchases (events) for a frequently bought consumer good.

Keywords: negative binomial regression; count data; stochastic models; maximum likelihood; E-M algorithm (search for similar items in EconPapers)
Date: 1994
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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:40:y:1994:i:3:p:405-417

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