Estimating latent distributions in recurrent choice data
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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 wordslatent class models Poisson distribution gamma distribution Dirichlet distribution empirial Bayes estimation count data EM algorithm
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- Bates, G. E., & Neyman, J. (1952).Contributions to the theory of accident proneness, Parts I and II. Berkeley: University of California Press.Google Scholar
- Devroye, L. (1986).Non-uniform random variate generation. New York: Springer-Verlag.Google Scholar
- Ehrenberg, A. S. C. (1988).Repeat-buying. New York: Oxford University Press.Google Scholar
- Johnson, N. L., & Kotz, S. (1969).Distribution in statistics: Discrete distributions. New York: Wiley.Google Scholar
- Lazarsfeld, P. F. (1950). The logical and mathematical foundation of latent structure analysis. In S. A. Stouffer et al. (Eds.),Studies in social psychology in World War II, Vol. IV (pp. 362–412). Princeton: Princeton University Press.Google Scholar
- Luce, R. D. (1959).Individual choice behavior. New York: Wiley.Google Scholar
- Maritz, J. S., & Lwin, T. (1989).Empirical Bayes methods. London: Chapman and Hall.Google Scholar
- Poulsen, C. S. (1983).Latent structure analysis with choice modeling applications. Unpublished dissertation. University of Pennsylvania.Google Scholar
- Pudney, S. (1989).Modeling individual choice: The econometrics of corners, kinks and holes. New York: Basil Blackwell.Google Scholar
- Titterington, D. M., Smith, A. F. M., & Makov, U. E. (1985).Statistical analysis of finite mixture distributions. New York: Wiley.Google Scholar