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Some problems of selection and ranking for the multivariate normal populations are studied. The major part of the paper (Section 2) deals with the selection problem in terms of the population multiple correlation coefficient. Both unconditional and conditional cases are studied for the largest (smallest) multiple correlation. Selection procedures R1, R2, R3, and R4 are proposed for the largest multiple correlation case while procedures R5, R6, R7, and R8 are proposed for the case of the smallest. Asymptotic results are obtained. Properties of the selection procedures are investigated. Sufficient conditions are obtained for the monotonicity of certain probability integrals in terms of the non-centrality parameter. Which is involved in the negative binomial weights (Theorem 2.6). Tables of the percentage points of the statistics which give appropriate constants for procedures R1, R2, R3, R4, R7, and R8 are constructed and appended at the end. Section 3 deals with the selection of p-variate normal populations. When the variables are partitioned into two sets of q1 and q2 components, the criterion of ranking being the generalized conditional variance of the q2-set (q1-set fixed). (Author).
The paper deals with selection and ranking procedures for multivariate normal populations. Procedures for selecting a subset containing the (unknown) population with the smallest generalized variance, the largest Mahalanobis distance function and the largest (smallest) multiple correlation coefficient are described. The paper also surveys other known results in ranking problems for these populations and mentions some unsolved problems. (Modified author abstract).
A problem of subset selection for parameters which are not necessarily scale or location parameters is considered. A general theorem dealing with the infimum of the probability of a correct selection for parameters occurring in densities which are Poisson mixtures of arbitrary densities on the interval (0, infinity) is proved. This theorem is applied to obtain the minimum value of the probability of a correct selection in several cases where multivariate normal populations are ranked according to a given scalar quantity.
Provides a compendium of applied aspects of ordering and selection procedures.
Multiple Decision Procedures: Theory and Methodology of Selecting and Ranking Populations provides an encyclopedic coverage of the literature in the area of ranking and selection procedures, summarizing and surveying in a unified manner a majority of more than 600 main references in the bibliography. It also deals with related problems, such as the estimation of unknown ordered parameters. A separate chapter is devoted to information about several tables available in the literature for carrying out various specific procedures. Examples are given in another chapter illustrating applications of these procedures in various practical contexts. Although several books have appeared to date in this area, many of them deal with specific aspects of the field and a limited number of topics. This book contains substantial material not discussed in other books. Audience: this book can serve as a text for a graduate topics course in ranking and selection (as it has done at Purdue University for more than 30 years). It can also serve as a reference for researchers and practitioners in fields such as agriculture, industry, engineering, and behavioral sciences.
"This useful volume provides a thorough synthesis of second-order asymptotics in multistage sampling methodologies for selection and ranking unifying available second-order results in general and applying them to a host of situations Contains, in each chapter, helpful Notes and Overviews to facilitate comprehension, as well as Complements and Problems for more in-depth study of specific topics!"
Selection and ranking problems have been studied over the last thirty years, generally under one of two formulations: Bechhofer's indifference zone approach and Gupta's subset selection approach. This paper deals with subset selection. Subset selection procedures in multivariate models are briefly reviewed. These include: (1) Procedures for selecting the best component in a multivariate normal population in terms of the component means as well as the component variances; (2) Procedures for selecting the best from several multivariate normal populations in terms of the Mahalanobis distance, the generalized variance, and the multiple correlation coefficient; (3) Procedures (fixed sample size as well as inverse sampling) for selecting the most (least) probable cell in a multinominal distribution; (4) Procedures for selecting the best from several multinomial populations in terms of the Shannon entropy function; and (5) Procedures for choosing the best subset of the predictor variables in a linear regression model. (Author).