scholarly journals A Restricted Subset Selection Approach to Ranking and Selection Problems

1975 ◽  
Vol 3 (2) ◽  
pp. 334-349 ◽  
Author(s):  
Thomas J. Santner
Keyword(s):  
Subset Selection ◽  
Ranking And Selection ◽  
Selection Problems ◽  
Selection Approach

scholarly journals Selection Procedures: A New Statistical Methodology and Its Applications for Marketing Research

1981 ◽  
Vol 18 (4) ◽  
pp. 449-455 ◽  
Author(s):  
Jean Dickinson Gibbons ◽  
Oded Gur-Arie
Keyword(s):  
Marketing Research ◽  
Subset Selection ◽  
Statistical Methodology ◽  
Traditional Methods ◽  
Selection Procedures ◽  
Indifference Zone ◽  
Selection Problems ◽  
Methods Of Analysis ◽  
Selection Approach

Traditional methods of analysis do not provide the researcher with means for selecting the “best” among several alternatives while having control over the probability of being correct. The authors present the indifference zone approach and the subset-selection approach to selection problems. The findings of several recent studies are used as illustrative examples.

Multi-Goal Subset Selection Problems under Uncertainty

1979 ◽  
Vol 11 (1) ◽  
pp. 61-69 ◽  
Author(s):  
Pat R. Odom ◽  
Robert E. Shannon ◽  
Billy P. Buckles
Keyword(s):  
Subset Selection ◽  
Selection Problems

scholarly journals ON THE IDENTIFICATION AND MITIGATION OF WEAKNESSES IN THE KNOWLEDGE GRADIENT POLICY FOR MULTI-ARMED BANDITS

2016 ◽  
Vol 31 (2) ◽  
pp. 239-263 ◽  
Author(s):  
James Edwards ◽  
Paul Fearnhead ◽  
Kevin Glazebrook
Keyword(s):  
Decision Making ◽  
Exponential Family ◽  
Numerical Study ◽  
Ranking And Selection ◽  
Gittins Index ◽  
Selection Problems ◽  
Index Policies ◽  
Online Decision Making ◽  
New Policies ◽  
Knowledge Gradient

The knowledge gradient (KG) policy was originally proposed for online ranking and selection problems but has recently been adapted for use in online decision-making in general and multi-armed bandit problems (MABs) in particular. We study its use in a class of exponential family MABs and identify weaknesses, including a propensity to take actions which are dominated with respect to both exploitation and exploration. We propose variants of KG which avoid such errors. These new policies include an index heuristic, which deploys a KG approach to develop an approximation to the Gittins index. A numerical study shows this policy to perform well over a range of MABs including those for which index policies are not optimal. While KG does not take dominated actions when bandits are Gaussian, it fails to be index consistent and appears not to enjoy a performance advantage over competitor policies when arms are correlated to compensate for its greater computational demands.

scholarly journals An overview on selection methods

1982 ◽  
Vol 1 (2) ◽  
pp. 91-96 ◽  
Author(s):  
J. W. H. Swanepoel
Keyword(s):  
Major Part ◽  
Ranking And Selection ◽  
Regression Analyses ◽  
Selection Methods ◽  
Selection Procedures ◽  
Indifference Zone ◽  
Selection Problems ◽  
Fixed Sample ◽  
Sequential Procedures ◽  
Ranking And Selection Procedures

In many studies the experimenter has under consideration several (two or more) alternatives, and is studying them in order to determine which is the best (with regard to certain specified criteria of “goodness”). Such an experimenter does not wish basically to test hypotheses, or construct confidence intervals, or perform regression analyses (though these may be appropriate parts of his analysis); he does wish to select the best of several alternatives, and the major part of his analysis should therefore be directed towards this goal. It is precisely for this problem that ranking and selection procedures were developed. This paper presents an overview of some recent work in this field, with emphasis on aspects important to experimenters confronted with selection problems. Fixed sample size and sequential procedures for both the indifference zone and subset formulations of the selection problem are discussed.

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