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A class of proper priors for Bayesian simultaneous prediction of independent Poisson observables


  • Komaki, Fumiyasu


Simultaneous prediction and parameter inference for the independent Poisson observables model are considered. A class of proper prior distributions for Poisson means is introduced. Bayesian predictive densities and estimators based on priors in the introduced class dominate the Bayesian predictive density and estimator based on the Jeffreys prior under Kullback-Leibler loss.

Suggested Citation

  • Komaki, Fumiyasu, 2006. "A class of proper priors for Bayesian simultaneous prediction of independent Poisson observables," Journal of Multivariate Analysis, Elsevier, vol. 97(8), pages 1815-1828, September.
  • Handle: RePEc:eee:jmvana:v:97:y:2006:i:8:p:1815-1828

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    References listed on IDEAS

    1. Barhen, A. & Daudin, J. J., 1995. "Generalization of the Mahalanobis Distance in the Mixed Case," Journal of Multivariate Analysis, Elsevier, vol. 53(2), pages 332-342, May.
    2. Wai-Yin Poon & Sik-Yum Lee, 1987. "Maximum likelihood estimation of multivariate polyserial and polychoric correlation coefficients," Psychometrika, Springer;The Psychometric Society, vol. 52(3), pages 409-430, September.
    3. Edward J. Bedrick & Jodi Lapidus & Joseph F. Powell, 2000. "Estimating the Mahalanobis Distance from Mixed Continuous and Discrete Data," Biometrics, The International Biometric Society, vol. 56(2), pages 394-401, June.
    4. de Leon, A. R. & Carrière, K. C., 2005. "A generalized Mahalanobis distance for mixed data," Journal of Multivariate Analysis, Elsevier, vol. 92(1), pages 174-185, January.
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    Cited by:

    1. Komaki, Fumiyasu, 2015. "Simultaneous prediction for independent Poisson processes with different durations," Journal of Multivariate Analysis, Elsevier, vol. 141(C), pages 35-48.


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