A Note on Optimal Subset Selection Procedures PDF Download

Author: Shanti S. Gupta
Publisher:
ISBN:
Category :
Languages : en
Pages : 10

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Book Description
This paper concerns the construction of optimal subset selection procedures. Some classical selection procedures are considered as special cases.

Author: Jan Fredrik Bjornstad
Publisher:
ISBN:
Category :
Languages : en
Pages : 244

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Author: Shanti Swarup Gupta
Publisher:
ISBN:
Category :
Languages : en
Pages : 10

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In this paper, we are concerned with the construction of 'optimal' subset selection procedures. Some classical selection procedures are considered as special cases. (Author).

Author: David Boyce
Publisher: Springer Science & Business Media
ISBN: 3642463118
Category : Mathematics
Languages : en
Pages : 203

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Book Description
In the course of one's research, the expediency of meeting contractual and other externally imposed deadlines too often seems to take priority over what may be more significant research findings in the longer run. Such is the case with this volume which, despite our best intentions, has been put aside time and again since 1971 in favor of what seemed to be more urgent matters. Despite this delay, to our knowledge the principal research results and documentation presented here have not been superseded by other publications. The background of this endeavor may be of some historical interest, especially to those who agree that research is not a straightforward, mechanistic process whose outcome or even direction is known in ad vance. In the process of this brief recounting, we would like to express our gratitude to those individuals and organizations who facilitated and supported our efforts. We were introduced to the Beale, Kendall and Mann algorithm, the source of all our efforts, quite by chance. Professor Britton Harris suggested to me in April 1967 that I might like to attend a CEIR half-day seminar on optimal regression being given by Professor M. G. Kendall in Washington. D. C. I agreed that the topic seemed interesting and went along. Had it not been for Harris' suggestion and financial support, this work almost certainly would have never begun.

Author: Deng-Yuan Huang
Publisher:
ISBN:
Category :
Languages : en
Pages : 14

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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 S. Gupta
Publisher:
ISBN:
Category :
Languages : en
Pages : 16

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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: SIAM
ISBN: 0898715326
Category : Mathematics
Languages : en
Pages : 592

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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.

Author: Dimitris Bertsimas
Publisher:
ISBN: 9781733788502
Category : Machine learning
Languages : en
Pages : 589

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Author: Alan J. Miller
Publisher: Chapman and Hall/CRC
ISBN:
Category : Computers
Languages : en
Pages : 248

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Book Description
Most scientific computing packages contain facilities for stepwise regression and often for 'all subsets' and other techniques for finding 'best-fitting' subsets of regression variables. The application of standard theory can be very misleading in such cases when the model has not been chosen a priori, but from the data. There is widespread awareness that considerable over-fitting occurs and that prediction equations obtained after extensive 'data dredging' often perform poorly when applied to new data. This monograph relates almost entirely to least-squares methods of finding and fitting subsets of regression variables, though most of the concepts are presented in terms of the interpretation and statistical properties of orthogonal projections. An early chapter introduces these methods, which are still not widely known to users of least-squares methods. Existing methods are described for testing whether any useful improvement can be obtained by using any of a set of predictors. Spjotvoll's method for comparing two arbitrary subsets of predictor variables is illustrated and described in detail. When the selected model is the 'best-fitting' in some sense, conventional fitting methods give estimates of regression coefficients which are usually biased in the direction of being too large. The extent of this bias is demonstrated for simple cases. Various ad hoc methods for correcting the bias are discussed (ridge regression, James-Stein shrinkage, jack-knifing, etc.), together with the author's maximum likelihood technique. Areas in which further research is needed are also outlined.

Author: Shanti S. Gupta
Publisher:
ISBN:
Category :
Languages : en
Pages : 10

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Book Description
In the paper, the authors are interested in deriving a procedure which selects a random size subset containing all the t best populations, with a probability not less than P*, a specified constant.