Nonparametric Testing for Nonhomogeneous Correlation

• Activity Number: 281
• Type: Topic Contributed
• Date/Time: Tuesday, August 5, 2014 : 8:30 AM to 10:20 AM
• Sponsor: Section on Nonparametric Statistics
• Author(s): Joseph S. Verducci*+ and Stephen Bamattre and Rex Hu
• Companies: Ohio State University and Google and Ohio State University
• Keywords: Frank Copula ; Mallows' Model ; Multistage Ranking Model ; Permutations ; Gaussian Copula ; Spearman's Footrule

Abstract

In the presence of weak overall correlation, it may be useful to investigate if the correlation is significantly and substantially more pronounced over a subpopulation. Two different testing procedures are compared. Both are based on the rankings of the values of two variables from a data set with a large number n of observations. The first maintains its level against Gaussian copulas; the second adapts to general alternatives in the sense that that the number of parameters used in the test grows with n. An example illustrates how the test statistics may be used to discover pairs of genes whose expressions in rat hepatocyte cells are substantially more correlated over those cells that have been exposed to certain classes of chemicals.