Author: T. LiangPublisher:
ISBN:
Category :
Languages : en
Pages : 171
Book Description
Some Contributions to Empirical Bayes, Sequential and Locally Optimal Subset Selection Rules
Author: T. LiangAdvances in Statistical Decision Theory and Applications
Author: S. PanchapakesanPublisher: Springer Science & Business Media
ISBN: 1461223083
Category : Mathematics
Languages : en
Pages : 478
Book Description
Shanti S. Gupta has made pioneering contributions to ranking and selection theory; in particular, to subset selection theory. His list of publications and the numerous citations his publications have received over the last forty years will amply testify to this fact. Besides ranking and selection, his interests include order statistics and reliability theory. The first editor's association with Shanti Gupta goes back to 1965 when he came to Purdue to do his Ph.D. He has the good fortune of being a student, a colleague and a long-standing collaborator of Shanti Gupta. The second editor's association with Shanti Gupta began in 1978 when he started his research in the area of order statistics. During the past twenty years, he has collaborated with Shanti Gupta on several publications. We both feel that our lives have been enriched by our association with him. He has indeed been a friend, philosopher and guide to us.
Dissertation Abstracts International
Author: Publisher:
ISBN:
Category : Dissertations, Academic
Languages : en
Pages : 698
Book Description
Some Contributions to Fixed Sample and Sequential Multiple Decision (Selection and Ranking) Theory
Author: Deng-Yuan HuangPublisher:
ISBN:
Category :
Languages : en
Pages : 78
Book Description
The report makes some contributions to the subset selection procedures - both for the fixed sample and the sequential case. Chapter 1 deals with some subset selection procedures for binomial populations in terms of the entropy functions, which is different from the usual selection problem in terms of the success probabilities. In Chapter 2, some fixed sample optimal subset selection procedures are discussed for model I and II problems in the analysis of variance in treatments versus control, and a method for constructing some subset selection procedures is derived. Chapter 3 discusses a method for constructing some sequential subset selection procedures and some optimal sequential subset selection procedure in treatments versus control. An upper bound on the expected sample size for Bechhofer-Kiefer-Sobel sequential selection procedure with indifference zone approach is also derived. (Author).
Comprehensive Dissertation Index
Author: Publisher:
ISBN:
Category : Dissertations, Academic
Languages : en
Pages : 1016
Book Description
Bulletin - Institute of Mathematical Statistics
Author: Institute of Mathematical StatisticsPublisher:
ISBN:
Category : Mathematical statistics
Languages : en
Pages : 646
Book Description
Some Locally Optimal Subset Selection Rules for Comparison with a Control
Author: Deng-Yuan HuangPublisher:
ISBN:
Category :
Languages : en
Pages : 18
Book Description
The goal is to select from pi sub 1, ..., pi sub k (experimental treatments) those populations, if any, that are better (suitably defined) than pi sub 0 which is the control population. A locally optimal rule is derived in the class of rules for which Pr(pi sub i is selected) = gamma sub i, 1 = 1, ..., k, when theta sub 0 = theta sub 1 = ... = theta sub k. The criterion used for local optimality amounts to maximizing the efficiency in a certain sense of the rule in picking out the superior populations for specific configurations of theta = (theta sub 0, ..., theta sub k) in a neighborhood of an equiparameter configuration. The general result is then applied to the following special cases: (a) normal means comparison - common known variance, (b) normal means comparison - common unknown variance, (c) gamma scale parameters comparison - known (unequal) shape parameters, and (d) comparison of regression slopes. In all these cases, the rule is obtained based on samples of unequal sizes.
Locally Optimal Subset Selection Procedures Based on Ranks
Author: Shanti S. GuptaPublisher:
ISBN:
Category :
Languages : en
Pages : 16
Book Description
This paper deals with subset selection rules based on ranks in the pooled sample. The procedures satisfy the P-condition and also locally maximize the probability of a correct selection. An application to a problem in regression analysis is provided. (Author).
Some locally optimal Subset Selection Rules
Author: Deng-Yuan HuangPublisher:
ISBN:
Category :
Languages : en
Pages : 21
Book Description
Let pi(o), pi(1), ..., pi(k) be k = 1 independent populations where pi(i) has the associated density function f(x, theta sub i) with the unknown parameter belonging to an interval H of the real line. Two types of problems are studied: (1) to select from pi(1), ..., pi(k) those populations, if any, that are better (to be suitably defined) than pi(o) which is the control population; and (2) to select from pi(1), ..., pi(k) a subset preferably of small size so as to contain the best population. For both problems, some locally optimal selection rules are derived. The optimality criteria employed in the two problems are different. Further, the procedure for the second problem is based on ranks though the densities are assumed to be known but for the values of the parameters. The rule in the first case is applied to the special cases of (1) normal means comparison with common known variance and unequal sample sizes; (2) normal means comparison with common unknown variance and unequal sample sizes, and (3) gamma scale parameters comparison with unequal shape parameters. The rank procedure is specialized to the case of logistic distributions. (Author).
Author: