The Affinely Invariant Distance Correlation

• Activity Number: 5
• Type: Invited
• Date/Time: Sunday, August 3, 2014 : 2:00 PM to 3:50 PM
• Sponsor: IMS
• Author(s): Donald Richards*+ and Johannes Dueck and Dominic Edelmann and Tilmann Gneiting
• Companies: Penn State and Heidelberg University and Heidelberg University and Heidelberg University
• Keywords: Affine invariance ; Autocorrelation function ; Cross-correlation function ; Multivariate normal distribution ; Wind vectors ; Zonal polynomials

Abstract

This talk is based on joint work with J. Dueck, D. Edelmann, and T. Gneiting, of Heidelberg University.

Szekely, Rizzo and Bakirov (2007) and Szekely and Rizzo (2009), in two seminal papers, introduced the powerful concept of distance correlation as a measure of dependence between sets of random variables. We study in this paper an affinely invariant version of the distance correlation and an empirical version of that distance correlation, and we establish the consistency of the empirical quantity. In the case of sub-vectors of a multivariate normally distributed random vector, we provide exact expressions for the distance correlation in both finite-dimensional and asymptotic settings. We illustrate these results by considering time series of wind vectors at the Stateline wind energy center in Oregon and Washington, and we derive the empirical auto- and cross- distance correlation functions between wind vectors at distinct meteorological stations.