Subset Selection Procedures: A Review and an Assessment

Gupta, Shanti S.; Panchapakesan, S.

doi:10.21236/ada140266

Cited by 6 publications

(9 citation statements)

References 98 publications

(122 reference statements)

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“…Comprehensive bibliography and explanations on these two different approaches to selection procedures can be found, for instance, in [6]. Leading research is carried out by Chen (cf.…”

Section: Historical Background On Subset Selectionmentioning

confidence: 99%

On the minimization of multinomial tails and the Gupta–Nagel conjecture

Gastaldi

2005

Journal of Multivariate Analysis

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This paper is primarily concerned with the open problem of minimizing the lower tail of the multinomial distribution. During the study of that specific problem, we have developed an approach which reveals itself useful for solving a general class of problems involving multinomial probabilities. Concerning the main problem, we provide a self-contained proof that the minimum of the multinomial lower tail is reached, as conjectured by Gupta and Nagel (Sankhya Ser. B 29 (1967) 1) (within the framework of subset-selection problems) at the equal probability configuration, i.e., when the cell probabilities are equal to one another. We also point out some novel inequalities and general properties involving multinomial probabilities and multinomial coefficients.

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“…Comprehensive bibliography and explanations on these two different approaches to selection procedures can be found, for instance, in [6]. Leading research is carried out by Chen (cf.…”

Section: Historical Background On Subset Selectionmentioning

confidence: 99%

On the minimization of multinomial tails and the Gupta–Nagel conjecture

Gastaldi

2005

Journal of Multivariate Analysis

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“…This is an extensive research of the area of ranking and selection. A general review on this area is given by Gupta· and Panchapakesan ([1], [2]), Hochberg and Tamhane [4] and Hsu [5]. The goal of SCI approach is to classify treatments into the groups of better-than-the-average, worse-than-the-average and not-much-difference-from the average.…”

Section: Introductionmentioning

confidence: 99%

Multiple comparison procedures with the average forkdependent normal means

1999

Journal of Statistics and Management Systems

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To cite this article: Shu-Fei Wu (1999) Multiple comparison procedures with the average for k dependent normal means, Journal of Statistics and Management Systems, 2:1, 73-95, ABSTRACTIn Wu and Chen [8], multiple comparison procedures with the average fer k independent normal means when variances are known or unknown are proposed. The exact critical values are obtained when common variances are known or unknown and sample sizes are all equal. However, when k norm'll populations are dependent, the problem becomes more complicated. In this article, we consider the multiple comparison procedures with the average for k normal means when popUlations are not independent by simultaneous confidence interval and subset selection approaches. These procedures have broad applicability in identifying better-than-the-average, worse-than-the.average and not-much·difference·from the average stocks in Dow.Jone industrial stock market and 'it can also select a subset which includes all better.than-the-average treatments within its own group in experimental design if treatments are not independent. These procedures can be a very usefi.!l screening procedure especially when k is large. The comparison of the efficiency between approximation results by Bonferroni inequality ane! simulation results by Monte Carlo method has been studied and it shows Bonferroni approximates are efficient for equal correlation cases. An example of eight biggest mutual ftmds in the United States is also given to illustrate the implementation of these multiple comparison procedures with the average.

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“…Recent overviews are provided by Bechhofer (1985) and Gupta and Panchapakesan (1985). Other useful references with broad coverage are the books by Gibbons et al (1977) and Gupta and Panchapakesan (1979), and the categorized bibliography by Dudewicz and Koo (1982).…”

Section: Introductionmentioning

confidence: 99%