Robust estimation of the Pickands dependence function under random right censoring

Yuri Goegebeur*, Armelle Guillou, Jing Qin

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

1 Downloads (Pure)

Abstract

We consider robust nonparametric estimation of the Pickands dependence function under random right censoring. The estimator is obtained by applying the minimum density power divergence criterion to properly transformed bivariate observations. The asymptotic properties are investigated by making use of results for Kaplan–Meier integrals. We investigate the finite sample properties of the proposed estimator with a simulation experiment and illustrate its practical applicability on a dataset of insurance indemnity losses.

Original languageEnglish
JournalInsurance: Mathematics and Economics
Volume87
Pages (from-to)101-114
ISSN0167-6687
DOIs
Publication statusPublished - 1. Jul 2019

Keywords

  • Censoring
  • Density power divergence
  • Insurance indemnity losses
  • Kaplan–Meier integral
  • Pickands dependence function

Fingerprint Dive into the research topics of 'Robust estimation of the Pickands dependence function under random right censoring'. Together they form a unique fingerprint.

Cite this