Admissible predictive density estimation
| dc.creator | Brown, Lawrence D. | |
| dc.creator | George, Edward I. | |
| dc.creator | Xu, Xinyi | |
| dc.date | 2008-06-18 | |
| dc.date.accessioned | 2026-07-07T12:19:35Z | |
| dc.date.available | 2026-07-07T12:19:35Z | |
| dc.description | Let $X|μ\sim N_p(μ,v_xI)$ and $Y|μ\sim N_p(μ,v_yI)$ be independent $p$-dimensional multivariate normal vectors with common unknown mean $μ$. Based on observing $X=x$, we consider the problem of estimating the true predictive density $p(y|μ)$ of $Y$ under expected Kullback--Leibler loss. Our focus here is the characterization of admissible procedures for this problem. We show that the class of all generalized Bayes rules is a complete class, and that the easily interpretable conditions of Brown and Hwang [Statistical Decision Theory and Related Topics (1982) III 205--230] are sufficient for a formal Bayes rule to be admissible. | |
| dc.description | Published in at http://dx.doi.org/10.1214/07-AOS506 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org) | |
| dc.identifier | https://arxiv.org/abs/0806.2914 | |
| dc.identifier | http://arxiv.org/abs/0806.2914 | |
| dc.identifier | Annals of Statistics 2008, Vol. 36, No. 3, 1156-1170 | |
| dc.identifier | doi:10.1214/07-AOS506 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/212808 | |
| dc.subject | Statistics Theory | |
| dc.subject | 62C15 (Primary) 62C07, 62C10, 62C20 (Secondary) | |
| dc.title | Admissible predictive density estimation | |
| dc.type | text |