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We present a nonparametric family of estimators for the tail index of aPareto-type distribution when covariate information is available. Ourestimators are based on a weighted sum of the log-spacings between someselected observations. This selection is achieved through a moving windowapproach on the covariate domain and a random threshold on the variable ofinterest. Asymptotic normality is proved under mild regularity conditions andillustrated for some weight functions. Finite sample performances are presentedon a real data study.

A moving window approach for nonparametric estimation of the conditional tail index