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A0534 - Status: Accepted
Type of publication: Only Abstract Type of presentation: Invited Talk for an Organized Session Session: Latest development of dependence modeling Invited by: Jean-David Fermanian Title: About the estimation of the conditional Kendall's tau and Kendall's Regression Authors:  Alexis Derumigny - ENSAE-CREST (France) [presenting]
Jean-David Fermanian - Ensae-Crest (France)
Keywords: models of dependence,mathematical statistics,weather events,nonparametric methods Abstract: The estimation of the conditional Kendall's tau is considered. The conditional Kendall's tau is a conditional dependence parameter between two variables conditionally to some observed covariates. We propose a nonparametric estimator using kernels. Under a pseudo-GLM specification, we also propose a parametric estimator for the (possibly sparse) vector of coefficients in the model. We study the theoretical properties of both estimators, and prove non-asymptotic bounds that holds with high probability. Comments: