Compound P-Value Statistics for Multiple Testing Procedures

• Activity Number: 581
• Type: Contributed
• Date/Time: Wednesday, August 3, 2011 : 2:00 PM to 3:50 PM
• Sponsor: ENAR
• Author(s): Joshua D. Habiger*+ and Edsel A. Pena
• Companies: Oklahoma State University and University of South Carolina
• Keywords: Multiple Testing ; p-value ; False Discovery Rate ; Family Wise Error Rate ; Sample Splitting ; microarray

Abstract

Many multiple testing procedures make use of the p-values from the individual pairs of hypothesis tests, and are valid if the p-value statistics are independent and uniformly distributed under the null hypotheses. However, works such as Sun and Cai(2007) and Storey(2007) have shown that these types of multiple testing procedures are inefficient since such p-values do not depend upon all of the available data. This talk will provide tools for constructing compound p-value statistics, which are those that depend upon all of the available data, but still satisfy the independence and uniformity conditions. As an example, a class of compound p-value statistics for testing for location shifts is developed. It will be shown that multiple testing procedures are more powerful when applied to these compound p-values rather than the usual p-values, and at the same time still guarantee control of the desired type I error rate. Methods are used to analyze a microarray data set.