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Estimating Latent Distributions in Recurrent Choice Data

Published online by Cambridge University Press:  01 January 2025

Ulf Böckenholt
Ulf Böckenholt*
Affiliation:
University of Illinois at Urbana-Champaign
*
Requests for reprints should be sent to Ulf Böckenholt, Department of Psychology, University of Illinois at Urbana-Champaign, Champaign, IL 61820.
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Abstract

This paper introduces a flexible class of stochastic mixture models for the analysis and interpretation of individual differences in recurrent choice and other types of count data. These choice models are derived by specifying elements of the choice process at the individual level. Probability distributions are introduced to describe variations in the choice process among individuals and to obtain a representation of the aggregate choice behavior. Due to the explicit consideration of random effect sources, the choice models are parsimonious and readily interpretable. An easy to implement EM algorithm is presented for parameter estimation. Two applications illustrate the proposed approach.

Keywords

latent class modelsPoisson distributiongamma distributionDirichlet distributionempirial Bayes estimationcount dataEM algorithm
Type
Original Paper
Information
Psychometrika , Volume 58 , Issue 3 , September 1993 , pp. 489 - 509
Copyright
Copyright © 1993 The Psychometric Society

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Footnotes

The author thanks Greg Allenby for helpful discussion and a marketing data set from the A. C. Nielsen Corporation, and four anonymous reviewers and the Editor for their comments on a previous version of the article.

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