ON THE CONVERGENCE-RATES OF A MONOTONE EMPIRICAL BAYES TEST FOR UNIFORM DISTRIBUTIONS

被引：4
|
作者：
LIANG, TC
机构：
[1] Department of Mathematics, Wayne State University, Detroit
来源：
JOURNAL OF STATISTICAL PLANNING AND INFERENCE | 1990年 / 26卷 / 01期
关键词：
asymptotically optimal; Bayes rule; empirical Bayes; least-concave majorant; monotone decision problem; rate of convergence;
D O I：
10.1016/0378-3758(90)90092-9
中图分类号：
O21 [概率论与数理统计]; C8 [统计学];
学科分类号：
020208 ; 070103 ; 0714 ;
摘要：
We investigate the convergence rates of a sequence of monotone empirical Bayes tests for the two-action decision problems involving uniform distributions. It is found that the sequence of monotone empirical Bayes tests under study is asymptotically optimal, and the order of associated convergence rate is O(β(n)), where β(n) is such that n -1 2<β(n)<(ln n n) 1 2 and n is the number of accumulated past experience at hand. © 1990.
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页码：25 / 34
页数：10
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