Title

Simulation Study on the Probability of Correct Selection for Large k Populations

School/Department

School of Science Technology and Health

Publication Date

4-2009

Abstract

An increasing number of contemporary datasets are high dimensional. Applications require these datasets be screened (or filtered) to select a subset for further study. Multiple testing is the standard tool in such applications, although alternatives have begun to be explored. In order to assess the quality of selection in these high-dimensional contexts, Cui and Wilson (2008b4. Cui , X. , Wilson , J. ( 2008b ). On the probability of correct selection for large k populations with application to microarray data . Biometrical Journal 50 ( 5 ): 870 – 883 . [CrossRef], [PubMed], [Web of Science ®]View all references) proposed two viable methods of calculating the probability that any such selection is correct (PCS). PCS thereby serves as a measure of the quality of competing statistics used for selection. The first simulation study of this article investigates the two PCS statistics of the above article. It shows that in the high-dimensional case PCS can be accurately estimated and is robust under certain conditions. The second simulation study investigates a nonparametric estimator of PCS.

Keywords

Bootstrap; PCS; Probability of correct selection; Simulation

Publication Title

Communication in Statistics- Simulation and Computation

Volume

38

Issue

6

First Page

1244

Last Page

1255

DOI of Published Version

10.1080/03610910902898457

Link to Full Text

Share

COinS