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Locally Optimal Subset Selection Rules Based on Ranks Under Joint Type II Censoring

Locally Optimal Subset Selection Rules Based on Ranks Under Joint Type II Censoring PDF Author: S. S. Gupta
Publisher:
ISBN:
Category :
Languages : en
Pages : 26

Book Description
This paper deals with the derivation of subset selection rules which satisfy the basic P-condition and which locally maximize the probability of a correct selection among all invariant subset selection rules based on the ranks under the joint type II censoring. Following the earlier setup of Gupta, Huang and Nagel (1979), a locally optimal subset selection rule R1 is derived. The property of local monotonicity related to the rule R1 is discussed.

Locally Optimal Subset Selection Rules Based on Ranks Under Joint Type II Censoring

Locally Optimal Subset Selection Rules Based on Ranks Under Joint Type II Censoring PDF Author: S. S. Gupta
Publisher:
ISBN:
Category :
Languages : en
Pages : 26

Book Description
This paper deals with the derivation of subset selection rules which satisfy the basic P-condition and which locally maximize the probability of a correct selection among all invariant subset selection rules based on the ranks under the joint type II censoring. Following the earlier setup of Gupta, Huang and Nagel (1979), a locally optimal subset selection rule R1 is derived. The property of local monotonicity related to the rule R1 is discussed.

Locally Optimal Subset Selection Rules Absed on Ranks Under Joint Type II Censoring

Locally Optimal Subset Selection Rules Absed on Ranks Under Joint Type II Censoring PDF Author: S. S. Gupta
Publisher:
ISBN:
Category :
Languages : en
Pages : 23

Book Description


Scientific and Technical Aerospace Reports

Scientific and Technical Aerospace Reports PDF Author:
Publisher:
ISBN:
Category : Aeronautics
Languages : en
Pages : 984

Book Description


Advances in Statistical Decision Theory and Applications

Advances in Statistical Decision Theory and Applications PDF Author: S. Panchapakesan
Publisher: 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.

Some locally optimal Subset Selection Rules

Some locally optimal Subset Selection Rules PDF Author: Deng-Yuan Huang
Publisher:
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).

Locally Optimal Subset Selection Procedures Based on Ranks

Locally Optimal Subset Selection Procedures Based on Ranks PDF Author: Shanti S. Gupta
Publisher:
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).

Quality Control and Reliability

Quality Control and Reliability PDF Author: P. R. Krishnaiah
Publisher:
ISBN:
Category : Mathematics
Languages : en
Pages : 528

Book Description
This volume covers an area of statistics dealing with complex problems in the production of goods and services, maintenance and repair, and management and operations. The opening chapter is by W. Edwards Deming, pioneer in statistical quality control, who was involved in the quality control movement in Japan and helped the country in its rapid industrial development. He gives a 14-point program for management to keep a country in an ascending path of industrial development.

Some Locally Optimal Subset Selection Rules for Comparison with a Control

Some Locally Optimal Subset Selection Rules for Comparison with a Control PDF Author: Deng-Yuan Huang
Publisher:
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.

Statistical Theory and Method Abstracts

Statistical Theory and Method Abstracts PDF Author:
Publisher:
ISBN:
Category : Statistics
Languages : en
Pages : 1032

Book Description


Government Reports Announcements & Index

Government Reports Announcements & Index PDF Author:
Publisher:
ISBN:
Category : Science
Languages : en
Pages : 1014

Book Description