Robust estimation of the Pickands dependence function under random right censoring

Yuri Goegebeur*, Armelle Guillou, Jing Qin

*Kontaktforfatter for dette arbejde

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Resumé

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.

OriginalsprogEngelsk
TidsskriftInsurance: Mathematics and Economics
Vol/bind87
Sider (fra-til)101-114
ISSN0167-6687
DOI
StatusUdgivet - 1. jul. 2019

Fingeraftryk

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

Citer dette

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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",
author = "Yuri Goegebeur and Armelle Guillou and Jing Qin",
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Robust estimation of the Pickands dependence function under random right censoring. / Goegebeur, Yuri; Guillou, Armelle; Qin, Jing.

I: Insurance: Mathematics and Economics, Bind 87, 01.07.2019, s. 101-114.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

TY - JOUR

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

AU - Goegebeur, Yuri

AU - Guillou, Armelle

AU - Qin, Jing

PY - 2019/7/1

Y1 - 2019/7/1

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

U2 - 10.1016/j.insmatheco.2019.03.008

DO - 10.1016/j.insmatheco.2019.03.008

M3 - Journal article

VL - 87

SP - 101

EP - 114

JO - Insurance: Mathematics and Economics

JF - Insurance: Mathematics and Economics

SN - 0167-6687

ER -