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Multivariate Discrete Distributions with a Product-Type Dependence

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  • Becker, Niels G.
  • Utev, Sergey

Abstract

A discrete multivariate probability distribution for dependent random variables, which contains the Poisson and Geometric conditionals distributions as particular cases, is characterized by means of conditional expectations of arbitrary one-to-one functions. Independence of the random variables is also characterized in terms of these conditional expectations. For certain exchangeable and partially exchangeable random variables with a joint distribution of this form it is shown that maximum likelihood estimates coincide with the simple method of moments estimates, suggesting that these models offer a pragmatic way to analyze certain dependent data.

Suggested Citation

  • Becker, Niels G. & Utev, Sergey, 2002. "Multivariate Discrete Distributions with a Product-Type Dependence," Journal of Multivariate Analysis, Elsevier, vol. 83(2), pages 509-524, November.
    • Handle: RePEc:eee:jmvana:v:83:y:2002:i:2:p:509-524
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    References listed on IDEAS

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    1. Theofanis Sapatinas, 1995. "Identifiability of mixtures of power-series distributions and related characterizations," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 47(3), pages 447-459, September.
    2. Wesolowski, J., 1995. "Bivariate Discrete Measures via a Power Series Conditional Distribution and a Regression," Journal of Multivariate Analysis, Elsevier, vol. 55(2), pages 219-229, November.
    3. Gupta, Arjun K. & Nguyen, Truc T. & Wang, Yinning & Wesolowski, Jacek, 2001. "Identifiability of Modified Power Series Mixtures via Posterior Means," Journal of Multivariate Analysis, Elsevier, vol. 77(2), pages 163-174, May.
    4. Papageorgiou, H. & Wesolowski, Jacek, 1997. "Posterior mean identifies the prior distribution in nb and related models," Statistics & Probability Letters, Elsevier, vol. 36(2), pages 127-134, December.
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    Cited by:

    1. Ramesh Gupta, 2011. "Bivariate odds ratio and association measures," Statistical Papers, Springer, vol. 52(1), pages 125-138, February.

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