
Accession Number : ADA313657
Title : Selecting Good Exponential Populations Compared with a Control: A Nonparametric Empirical Bayes Approach.
Descriptive Note : Technical rept.,
Corporate Author : PURDUE UNIV LAFAYETTE IN DEPT OF STATISTICS
Personal Author(s) : Gupta, Shanti S. ; Liang, TaChen
PDF Url : ADA313657
Report Date : JUL 1996
Pagination or Media Count : 24
Abstract : This paper deals with empirical Bayes selection procedures for selecting good exponential populations compared with a control. Based on the accumulated historical data, an empirical Bayes selection procedure delta(*) is constructed by mimicking the behavior of a Bayes selection procedure. The empirical Bayes selection procedure delta(*) is proved to be asympototically optimal. The analysis shows that the rate of convergence of delta(*) is influenced by the tail probabilities of the underlying distributions. It is shown that under certain regularity conditions on the moments of the prior distribution, the empirical Bayes selection procedure delta(*) is asymptotically optimal of order O(n(lambda/2)) for some 0<lambda<2. A lower bound with rate of convergence of order O(n(1)) is also established for the regret Bayes risk of the empirical Bayes selection procedure delta(*). This result suggests that a rate of order O(n(1)) might be the best possible rate of convergence for this empirical Bayes selection problem.
Descriptors : *NONPARAMETRIC STATISTICS, *BAYES THEOREM, OPTIMIZATION, PROBABILITY DISTRIBUTION FUNCTIONS, RANDOM VARIABLES, STATISTICAL DATA, PROBABILITY DENSITY FUNCTIONS, CONVERGENCE, EXPONENTIAL FUNCTIONS, ASYMPTOTIC NORMALITY, POPULATION(MATHEMATICS).
Subject Categories : Statistics and Probability
Distribution Statement : APPROVED FOR PUBLIC RELEASE