Author: Lloyd William KoenigPublisher:
ISBN:
Category : Population
Languages : en
Pages : 224
Book Description
Subset-selection Procedures for Normal Populations with Unknown Variances
Author: Lloyd William KoenigPublisher:
ISBN:
Category : Population
Languages : en
Pages : 224
Book Description
Subset Selection Procedures for the Means of Normal Populations with Unequal Variances: Unequal Sample Sizes Case
Author: S. S. GuptaSelecting and Ordering Populations
Author: Jean Dickinson GibbonsPublisher: SIAM
ISBN: 0898714397
Category : Mathematics
Languages : en
Pages : 589
Book Description
Provides a compendium of applied aspects of ordering and selection procedures.
Multiple Decision Procedures
Author: Shanti S. GuptaPublisher: SIAM
ISBN: 0898715326
Category : Mathematics
Languages : en
Pages : 592
Book Description
An encyclopaedic coverage of the literature in the area of ranking and selection procedures. It also deals with the estimation of unknown ordered parameters. This book can serve as a text for a graduate topics course in ranking and selection. It is also a valuable reference for researchers and practitioners.
On the Performance of Subset Selection Procedures Under Normality
Author: Shanti S. GuptaPublisher:
ISBN:
Category :
Languages : en
Pages : 25
Book Description
From k normal populations N(t1,t1(2)) ..., N(tk, tk(2), where the means t1,tk in R are unknown, and the variances t1(2) ..., tk(2)> 0 are known, independent random samples of sizes n1 ..., nk, respectively, are drawn. Based on these observations, a non-empty subset of these k populations of preferably small size has to be selected, which contains the population with the largest mean with probability of the lest P() at every parameter configuration. Several subset selection procedures which have been proposed in the literature are compared with Bayes selection procedures for normal priors under two natural type of loss functions. Two new subset selection procedures are considered.
On Subset Selection Procedures for the Largest Mean from Normal Populations Having a Common Known Coefficient of Variation
Author: Shanti S. GuptaPublisher:
ISBN:
Category :
Languages : en
Pages : 20
Book Description
The problem of selecting a subset of k normal populations which includes the population associated with the largest mean is considered for the situation in which the normal populations have a common known coefficient of variation. Subset selection rules based on best asymptotically normal (BAN) estimators of the mean have been studied in the literature and tables based on large sample theory for implementing these rules exist. The authors have compared these rules to a selection rule based on sample variances, and limited study suggest that, when n is large, the difference between the rules based on BAN estimates and the variance rule, in terms of the expected proportion of the selected subset, is minimal. Moreover, since the exact distribution theory for BAN estimates is too complicated, and these BAN estimates are much harder to compute than the sample variances, the selection rule based on the sample variances may be preferred. (Author).
On the Performance of Subset Selection Procedures Under Normality
Author: Shanti S. GuptaPublisher:
ISBN:
Category :
Languages : en
Pages : 0
Book Description
From k normal populations N(t1,t1(2)) ..., N(tk, tk(2), where the means t1,tk in R are unknown, and the variances t1(2) ..., tk(2)> 0 are known, independent random samples of sizes n1 ..., nk, respectively, are drawn. Based on these observations, a non-empty subset of these k populations of preferably small size has to be selected, which contains the population with the largest mean with probability of the lest P() at every parameter configuration. Several subset selection procedures which have been proposed in the literature are compared with Bayes selection procedures for normal priors under two natural type of loss functions. Two new subset selection procedures are considered.
Selection Procedures for the Means and Variances of Normal Populations When the Sample Sizes are Unequal
Author: Shanti S. GuptaPublisher:
ISBN:
Category :
Languages : en
Pages : 25
Book Description
Let (Pi sub 1), ..., (Pi sub k) be k independent normal populations with means (mu sub 1), ..., (mu sub k) and variances (sigma sub 1, sup 2), ..., (sigma sub k, sup 2), respectively. The authors interest is to select a non-empty subset of the k populations containing the best when the populations are ranked in terms of (i) the means (mu sub i), when (sigma sub i sup 2) = (sigma sup 2), known or unknown, and (ii) the variance (sigma sub i sup 2), when the (mu sub i) are known or unknown. Procedures and results are derived for the case when sample sizes are unequal. The authors also discuss gamma populations with scale parameter, and selection for normal means that are better than control. (Author).
Some Contributions to Subset Selection Procedures
Author: Wing-Yue WongPublisher:
ISBN:
Category :
Languages : en
Pages : 122
Book Description
The main purpose of this paper is to propose and study the subset selection approach for some new problems and make contributions. Chapter 1 deals with some selection and ranking procedures for the largest unknown mean of k normal populations with unequal variances. In chapter 2 some nonparametric subset selection procedures based on U-statistics for selecting the largest of the k location parameters are proposed and studied. Chapter 3 discusses some subset selection procedures for Poisson processes. Chapter 4 deals with a class of selection rules for finite schemes. Chapter 5 discusses some subset selection procedures for a negative multinomial distribution. An inverse sampling rule for selecting the cell with largest cell-probability from a multinomial distribution is considered.
Some Results on Subset Selection Problems
Author: Yoon-Kwai LeongPublisher:
ISBN:
Category :
Languages : en
Pages : 125
Book Description
The main purpose of this paper is to propose and study the subset selection approach for some new problems. Chapter I deals with some subset selection procedures for Poisson populations. A procedure of the type discussed by Seal is proposed and compared with the main proposed procedure which is an unconditional rule. Some selection procedure for populations better than a standard are also investigated. In Chapter II, a subset selection procedure based on the sample median for selecting the largest of the k location parameters of double exponential (Laplace) distributions is studied. For this distribution the problem of selection for the scale parameters is also investigated. A test of homogeneity is proposed which is based on the range of sample medians. An indifference zone approach to the problem of selecting the populations with the t-largest unknown means is also studied. Chapter III discusses some classification rules for k univariate normal populations using the subset selection approach. The classification problem is studied in terms of (i) the mean (ii) the variance and (iii) the reciprocal of the coefficient of variation. Chapter IV deals with a conditional and an unconditional procedure for selecting a subset which contains the negative binomial population with the smallest unknown probability of a success. Selection of populations better than a standard is also investigated. An application of the procedure to reliability theory is described. (Author).