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

Yuri Goegebeur*, Jing Qin, Armelle Guillou

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Resumé

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.

OriginalsprogEngelsk
TidsskriftExtremes
Vol/bind22
Udgave nummer3
Sider (fra-til)459-498
Antal sider40
ISSN1386-1999
DOI
StatusUdgivet - 15. sep. 2019

Fingeraftryk

Extreme Value Index
Random Censoring
Convergence in Probability
Right Censoring
Bias Correction
Pareto Distribution
Maximum Likelihood Method
Pareto
Asymptotic Normality
Maximum likelihood
Simulation Experiment
Excess
Covariates
Estimator
Experiments
Model
Censoring
Simulation experiment
Finite sample
Bias correction

Citer dette

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Bias-corrected estimation for conditional Pareto-type distributions with random right censoring. / Goegebeur, Yuri; Qin, Jing; Guillou, Armelle.

I: Extremes, Bind 22, Nr. 3, 15.09.2019, s. 459-498.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

TY - JOUR

T1 - Bias-corrected estimation for conditional Pareto-type distributions with random right censoring

AU - Goegebeur, Yuri

AU - Qin, Jing

AU - Guillou, Armelle

PY - 2019/9/15

Y1 - 2019/9/15

N2 - 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.

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KW - 62G08

KW - 62G20

KW - 62G32

KW - Bias-reduction

KW - Local estimation

KW - Pareto-type model

KW - Random covariate

KW - Random right censoring

U2 - 10.1007/s10687-019-00341-7

DO - 10.1007/s10687-019-00341-7

M3 - Journal article

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EP - 498

JO - Extremes

JF - Extremes

SN - 1386-1999

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