On efficient prediction and predictive density estimation for normal and spherically symmetric models
メタデータ
- 公開日
- 2019-01-01
- DOI
-
- 10.5446/58002
- 10.14288/1.0383293
- 公開者
- Banff International Research Station (BIRS) for Mathematical Innovation and Discovery
- データ作成者 (e-Rad)
-
- Strawderman, William
説明
Let $X ~ Nd(q,s2I)$, $Y ~ Nd(q, s2I)$, $U ~ Nk(q, s2I)$ be independently distributed, or more generally let $(X, Y, U)$ have a spherically symmetric distribution with density $hd+k/2f (h(||x â q||2+ ||u||2+ ||y â cq||2))$ with unknown parameters $h \in Rd$, and with known density $f( . )$ and constant $c > 0$. Based on observing $X = x, U = u$, we consider the problem of obtaining a predictive density $q_hat( â ¢ ; x, u)$ for $Y$ with risk measured by the expected Kullbackâ Leibler loss. A benchmark procedure is the minimum risk equivariant density $
Author affiliation: Rutgers University
Unreviewed
Faculty
Non UBC