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On the strong consistency of the kernel estimator of extreme conditional quantiles
Abstract : Nonparametric regression quantiles can be obtained by inverting a kernel estimator of the conditional distribution. The asymptotic properties of this estimator are well-known in the case of ordinary quantiles of fixed order. The goal of this paper is to establish the strong consistency of the estimator in case of extreme conditional quantiles. In such a case, the probability of exceeding the quantile tends to zero as the sample size increases, and the extreme conditional quantile is thus located in the distribution tails.
Cited literature [30 references]
https://hal.inria.fr/hal-00956351
Contributor : Stephane Girard <>
Submitted on : Tuesday, August 26, 2014 - 5:02:42 PM
Last modification on : Tuesday, February 9, 2021 - 3:20:37 PM
Long-term archiving on: : Thursday, November 27, 2014 - 4:30:33 PM
Contributor : Stephane Girard <>
Submitted on : Tuesday, August 26, 2014 - 5:02:42 PM
Last modification on : Tuesday, February 9, 2021 - 3:20:37 PM
Long-term archiving on: : Thursday, November 27, 2014 - 4:30:33 PM
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