Estimating latent distributions in recurrent choice data

Ulf Böckenholt*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)489-509
Number of pages21
Issue number3
StatePublished - Sep 1 1993


  • Dirichlet distribution
  • EM algorithm
  • Poisson distribution
  • count data
  • empirial Bayes estimation
  • gamma distribution
  • latent class models

ASJC Scopus subject areas

  • Psychology(all)
  • Applied Mathematics


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