| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 10525056 | Journal of Statistical Planning and Inference | 2011 | 11 Pages |
Abstract
⺠The notion of latent information priors is introduced. ⺠Parametric submodels of multinomial models are considered. ⺠Predictive densities are evaluated by the Kullback-Leibler divergence. ⺠Limits of Bayesian predictive densities form an essentially complete class. ⺠Minimax predictive densities are constructed by using latent information priors.
Related Topics
Physical Sciences and Engineering
Mathematics
Applied Mathematics
Authors
Fumiyasu Komaki,