Minimaxity in Predictive Density Estimation with Parametric Constraints

Tatsuya Kubokawa, Éric Marchand, William E. Strawderman and Jean-Philippe Turcotte
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Tatsuya Kubokawa: Faculty of Economics, University of Tokyo
Éric Marchand: Département de mathématiques, Université de Sherbrooke,
William E. Strawderman: Department of Statistics and Biostatistics, Rutgers University,
Jean-Philippe Turcotte: Département de mathématiques, Université de Sherbrooke,

No CIRJE-F-843, CIRJE F-Series from CIRJE, Faculty of Economics, University of Tokyo

Abstract: This paper is concerned with estimation of a predictive density with parametric constraints under Kullback-Leibler loss. When an invariance structure is embed- ded in the problem, general and uni ed conditions for the minimaxity of the best equivariant predictive density estimator are derived. These conditions are applied to check minimaxity in various restricted parameter spaces in location and/or scale families. Further, it is shown that the generalized Bayes estimator against the uni- form prior over the restricted space is minimax and dominates the best equivariant estimator in a location family when the parameter is restricted to an interval of the form [a0;1). Similar ndings are obtained for scale parameter families. Finally, the presentation is accompanied by various observations and illustrations, such as normal, exponential location, and gamma model examples.

Pages: 27 pages
Date: 2012-02
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (1)

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