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Robust Nonparametric Copula Based Dependence Estimators
journal contribution
posted on 2011-12-01, 00:00 authored by Barnabas Poczos, Sergey Krishner, David Pal, Csaba Szepesvari, Jeff SchneiderA fundamental problem in statistics is the estimation of dependence between random variables. While information theory provides standard measures of dependence (e.g. Shannon-, Renyi-, Tsallis-mutual information), it is still unknown how to estimate these quantities from i.i.d. samples in the most efficient way. In this presentation we review some of our recent results on copula based nonparametric dependence estimators and demonstrate their robustness to outliers both theoretically in terms of finite-sample breakdown points and by numerical experiments in independent subspace analysis and image registration.