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

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  • Komaki, Fumiyasu

Abstract

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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    Cited by:

    1. Hamura, Yasuyuki & Kubokawa, Tatsuya, 2020. "Bayesian shrinkage estimation of negative multinomial parameter vectors," Journal of Multivariate Analysis, Elsevier, vol. 179(C).
    2. Li, Xiao, 2024. "Nearly minimax empirical Bayesian prediction of independent Poisson observables," Statistics & Probability Letters, Elsevier, vol. 208(C).
    3. 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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