Robust Nonparametric Copula Based Dependence Estimators (2011)

Barnabas Poczos, Sergey Krishner, David Pal, Csaba Szepesvari, Jeff Schneider

Abstract

A 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.

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Approximate BibTeX Entry

@misc{poczos11NIPScopulaWorkshop,
    Howpublished = {NIPS 2011 Copula workshop},
    Year = {2011},
    Booktitle = {Copulas in machine learning. Nips 2011 Workshop},
    Author = { Barnabas Poczos, Sergey Krishner, David Pal, Csaba Szepesvari, Jeff Schneider },
    Title = {Robust Nonparametric Copula Based Dependence Estimators}
}