Bias-corrected estimation for conditional Pareto-type distributions with random right censoring

Yuri Goegebeur*, Jing Qin, Armelle Guillou

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

We consider bias-reduced estimation of the extreme value index in conditional Pareto-type models with random covariates when the response variable is subject to random right censoring. The bias-correction is obtained by fitting the extended Pareto distribution locally to the relative excesses over a high threshold using the maximum likelihood method. Convergence in probability and asymptotic normality of the estimators are established under suitable assumptions. The finite sample behaviour is illustrated with a simulation experiment and the method is applied to two real datasets.

Original languageEnglish
JournalExtremes
Volume22
Issue number3
Pages (from-to)459-498
ISSN1386-1999
DOIs
Publication statusPublished - 15. Sep 2019

Keywords

  • 62G08
  • 62G20
  • 62G32
  • Bias-reduction
  • Local estimation
  • Pareto-type model
  • Random covariate
  • Random right censoring

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