Robust estimation of the Pickands dependence function under random right censoring

Yuri Goegebeur, Armelle Guillou, Jing Qin

Research output: Contribution to journalJournal articleResearchpeer-review

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

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Dependence Function
Random Censoring
Right Censoring
Robust Estimation
Power Divergence
Kaplan-Meier
Estimator
Nonparametric Estimation
Insurance
Asymptotic Properties
Simulation Experiment
Robust estimation
Censoring
Observation
Integral
Asymptotic properties
Nonparametric estimation
Finite sample properties
Divergence
Simulation experiment

Keywords

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

Cite this

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title = "Robust estimation of the Pickands dependence function under random right censoring",
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.",
keywords = "Censoring, Density power divergence, Insurance indemnity losses, Kaplan–Meier integral, Pickands dependence function",
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Robust estimation of the Pickands dependence function under random right censoring. / Goegebeur, Yuri; Guillou, Armelle; Qin, Jing.

In: Insurance: Mathematics and Economics, Vol. 87, 01.07.2019, p. 101-114.

Research output: Contribution to journalJournal articleResearchpeer-review

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T1 - Robust estimation of the Pickands dependence function under random right censoring

AU - Goegebeur, Yuri

AU - Guillou, Armelle

AU - Qin, Jing

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N2 - 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.

AB - 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.

KW - Censoring

KW - Density power divergence

KW - Insurance indemnity losses

KW - Kaplan–Meier integral

KW - Pickands dependence function

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DO - 10.1016/j.insmatheco.2019.03.008

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