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Enhancing
multiple testing: two applications of the probability of
correct selection statistic
Erin Irwin and Jason Wilson
Vol. 8 (2015), No. 2, 181–194
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Abstract |
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The calculation of the probability of correct selection (PCS) shows how likely it is that the populations chosen as “best” truly are the top populations, according to a well-defined standard. PCS is useful for the researcher with limited resources or the statistician attempting to test the quality of two different statistics. This paper explores the theory behind two selection goals for PCS, -best and -best, and how they improve previous definitions of PCS for massive datasets. This paper also calculates PCS for two applications that have already been analyzed by multiple testing procedures in the literature. The two applications are in neuroimaging and econometrics. It is shown through these applications that PCS not only supports the multiple testing conclusions but also provides further information about the statistics used. |
Keywords
probability of correct selection (PCS), $d$-best, $G$-best,
ranking and selection, neuroimaging, econometrics
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Mathematical Subject Classification 2010
Primary: 46N30, 47N30
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Milestones
Received: 29 August 2011
Revised: 12 July 2013
Accepted: 26 July 2013
Published: 3 March 2015
Communicated by Timothy O'Brien
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Authors |
| Erin Irwin | |
| Mathematics and Computer Science
Department Biola University 13800 Biola Avenue La Mirada, CA 90639 United States |
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| Jason Wilson | |
| Mathematics and Computer Science
Department Biola University 13800 Biola Avenue La Mirada, CA 90639 United States |
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