Abstract
Estimation of the predictive probability function of a negative binomial distribution is addressed under the Kullback—Leibler risk. An identity that relates Bayesian predictive probability estimation to Bayesian point estimation is derived. Such identities are known in the cases of normal and Poisson distributions, and the paper extends the result to the negative binomial case. By using the derived identity, a dominance property of a Bayesian predictive probability is studied when the parameter space is restricted.
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Hamura, Y., Kubokawa, T. Bayesian Predictive Distribution for a Negative Binomial Model. Math. Meth. Stat. 28, 1–17 (2019). https://doi.org/10.3103/S1066530719010010
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DOI: https://doi.org/10.3103/S1066530719010010
Keywords
- Bayes
- dominance
- estimation
- Jeffreys prior
- Kullback-Leibler divergence
- negative binomial distribution
- prediction
- restricted parameter space