Exact minimax strategies for predictive density estimation, data compression and model selection

被引：0
|
作者：
Liang, F [1 ]
Barron, AR [1 ]
机构：
[1] Duke Univ, Inst Stat & Decis Sci, Durham, NC 27708 USA
来源：
ISIT: 2002 IEEE INTERNATIONAL SYMPOSIUM ON INFORMATION THEORY, PROCEEDINGS | 2002年
关键词：
D O I：
10.1109/ISIT.2002.1023421
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The Bayes procedure with uniform prior on location (and log-scale) parameters is shown to be exact minimax optimal for location and scale families in problems of universal data compression, predictive density estimation and model selection.
引用
收藏
页码：149 / 149
页数：1
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