Nonparametric Estimation of Extreme Conditional Quantiles with Functional Covariate

Feng Yang He*, Ye Bin Cheng, Tiejun TONG

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Estimation of the extreme conditional quantiles with functional covariate is an important problem in quantile regression. The existing methods, however, are only applicable for heavy-tailed distributions with a positive conditional tail index. In this paper, we propose a new framework for estimating the extreme conditional quantiles with functional covariate that combines the nonparametric modeling techniques and extreme value theory systematically. Our proposed method is widely applicable, no matter whether the conditional distribution of a response variable Y given a vector of functional covariates X is short, light or heavy-tailed. It thus enriches the existing literature.

Original languageEnglish
Pages (from-to)1589-1610
Number of pages22
JournalActa Mathematica Sinica, English Series
Volume34
Issue number10
DOIs
Publication statusPublished - 1 Oct 2018

Scopus Subject Areas

  • Mathematics(all)
  • Applied Mathematics

User-Defined Keywords

  • 62G32
  • Extreme conditional quantile
  • extreme value theory
  • functional covariate
  • nonparametric modeling

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