Work

Asymptotically minimax Bayesian predictive densities for multinomial models

Year: 2012

Type: article

Abstract: One-step ahead prediction for the multinomial model is considered. The performance of a predictive density is evaluated by the average Kullback-Leibler divergence from the true density to the predictive density. Asymptotic approximations of risk functions of Bayesian predictive densities based on Dirichlet priors are obtained. It is shown that a Bayesian predictive density based on a specific Dirichlet prior is asymptotically minimax. The asymptotically minimax prior is different from known objective priors such as the Jeffreys prior or the uniform prior.

Source: Electronic Journal of Statistics

DOI: 10.1214/12-ejs700

Author: Fumiyasu Komaki

Institutions: RIKEN Center for Brain Science, RIKEN, The University of Tokyo

Language: English

Topic: Statistical Methods and Bayesian Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $675

Funders: Council for Science and Technology Policy, Japan Society for the Promotion of Science