, Volume 58, Issue 3, pp 489–509 | Cite as

Estimating latent distributions in recurrent choice data

  • Ulf Böckenholt


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.

Key words

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


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Copyright information

© The Psychometric Society 1993

Authors and Affiliations

  • Ulf Böckenholt
    • 1
  1. 1.Department of PsychologyUniversity of Illinois at Urbana-ChampaignChampaign

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