title: Hypothesis testing for differentially correlated features.
abstract: In a multivariate setting, we consider the task of identifying features whose correlations with the other features differ across conditions. Such correlation shifts may occur independently of mean shifts, or differences in the means of the individual features across conditions. Previous approaches for detecting correlation shifts consider features simultaneously, by computing a correlation-based test statistic for each feature. However, since correlations involve two features, such approaches do not lend themselves to identifying which feature is the culprit. In this article, we instead consider a serial testing approach, by comparing columns of the sample correlation matrix across two conditions, and removing one feature at a time. Our method provides a novel perspective and favorable empirical results compared with competing approaches.
authors.affiliation: Department of Biostatistics, University of Washington, Seattle, WA, USA.
authors.forename: Elisa
authors.initials: E
authors.lastname: Sheng
authors.affiliation: Department of Biostatistics, University of Washington, Seattle, Washington, USA and Department of Statistics, University of Washington, Seattle, Washington, USA.
authors.forename: Daniela
authors.initials: D
authors.lastname: Witten
authors.affiliation: Department of Biostatistics, University of Washington, Seattle, Washington, USA and Veterans Affairs Seattle Medical Center, Seattle, Washington, USA azhou@uw.edu.
authors.forename: Xiao-Hua
authors.initials: XH
authors.lastname: Zhou
mesh_terms: D056808:Biostatistics / Q000379:methods*; D003627:Data Interpretation, Statistical; D006801:Humans; D008962:Models, Theoretical; D012107:Research Design
keywords: Correlation matrix; Differential correlation; Feature selection; Hypothesis testing; Wald test