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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).
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).
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.
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.
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).
Author: Shanti S. Gupta Publisher: ISBN: Category : Languages : en Pages : 10
Book Description
This paper concerns the construction of optimal subset selection procedures. Some classical selection procedures are considered as special cases.
Author: Deng-Yuan Huang Publisher: ISBN: Category : Languages : en Pages : 14
Book Description
Some optimal subset selection procedures for model 1 problem are derived to select a subset which contains all 'positive' populations while controlling 'false' positives. For model 2 problem, the optimal subset selections procedure are to select all positive populations while rejecting all negative ones. The Gamma-minimax selection procedures are considered for the general family of distributions. (Author).
Author: Shanti Swarup Gupta Publisher: ISBN: Category : Languages : en Pages : 24
Book Description
This paper deals with the classical Gupta (1956,65)-approach (Minimize the expected subset size under the P*-condition) in the case of three normal populations with a common known variance and equal sample sizes n. By the method of Lagrangian (undetermined) multipliers a function (involving psi and phi-terms only) is derived which is a convenient tool to find optimal procedures within Seal's (1955,57) class. Numerical work together with asymptotical results lead to the conclusion that for every fixed P* and mean vector mu, Gupta's (1956) means procedure is optimal within Seal's class for sufficiently large sample size n. (Author).
Author: Stanford University. Department of Statistics Publisher: ISBN: Category : Languages : en Pages : 46
Book Description
The paper is concerned with certain multiple-decision procedures based on ranks which have been proposed for analyzing data in a one-way layout: X sub ij = theta sub i + epsilon sub ij, i = 1 ..., k, j =1 ..., n where the errors (epsilon sub ij) are independent, have the same known cumulative distribution function (cdf) F and where theta = (theta sub 1 ..., theta sub k) is unknown. Two problems are considered: (1) Select the indices of the t largest theta-values. (2) Select a subset containing the index of the largest theta-value. (Author).
Author: Shanti S. Gupta
Author: Shanti S. Gupta
Author: S. S. Gupta
Author: Jan Fredrik Bjornstad
Author: S. S. Gupta
Author: Deng-Yuan Huang
Author: Deng-Yuan Huang
Author: Shanti S. Gupta
Author: Deng-Yuan Huang
Author: Shanti Swarup Gupta
Author: Stanford University. Department of Statistics