Local Robust Estimation of Pareto-Type Tails with Random Right Censoring

Goedele Dierckx, Yuri Goegebeur*, Armelle Guillou

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

We propose a nonparametric robust estimator for the tail index of a conditional Pareto-type distribution in the presence of censoring and random covariates. The censored distribution is also of Pareto-type and the index is estimated locally within a narrow neighbourhood of the point of interest in the covariate space using the minimum density power divergence method. The main asymptotic properties of our robust estimator are derived under mild regularity conditions and its finite sample performance is illustrated on a small simulation study. A real data example is included to illustrate the practical applicability of the estimator.

Original languageEnglish
JournalSankhya A
Number of pages39
ISSN0976-836X
DOIs
Publication statusE-pub ahead of print - 4. Jun 2019

Fingerprint

Random Censoring
Right Censoring
Robust Estimators
Robust Estimation
Pareto
Covariates
Tail
Power Divergence
Tail Index
Nonparametric Estimator
Censoring
Regularity Conditions
Asymptotic Properties
Simulation Study
Estimator
Robust estimators
Robust estimation
Finite sample
Asymptotic properties
Divergence

Keywords

  • 62G05
  • 62G20
  • 62G32
  • 62G35
  • Density power divergence
  • Local estimation
  • Pareto-type distribution
  • Random censoring

Cite this

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title = "Local Robust Estimation of Pareto-Type Tails with Random Right Censoring",
abstract = "We propose a nonparametric robust estimator for the tail index of a conditional Pareto-type distribution in the presence of censoring and random covariates. The censored distribution is also of Pareto-type and the index is estimated locally within a narrow neighbourhood of the point of interest in the covariate space using the minimum density power divergence method. The main asymptotic properties of our robust estimator are derived under mild regularity conditions and its finite sample performance is illustrated on a small simulation study. A real data example is included to illustrate the practical applicability of the estimator.",
keywords = "62G05, 62G20, 62G32, 62G35, Density power divergence, Local estimation, Pareto-type distribution, Random censoring",
author = "Goedele Dierckx and Yuri Goegebeur and Armelle Guillou",
year = "2019",
month = "6",
day = "4",
doi = "10.1007/s13171-019-00169-0",
language = "English",
journal = "Sankhya. Series A",
issn = "0976-836X",
publisher = "Springer",

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Local Robust Estimation of Pareto-Type Tails with Random Right Censoring. / Dierckx, Goedele; Goegebeur, Yuri; Guillou, Armelle.

In: Sankhya A, 04.06.2019.

Research output: Contribution to journalJournal articleResearchpeer-review

TY - JOUR

T1 - Local Robust Estimation of Pareto-Type Tails with Random Right Censoring

AU - Dierckx, Goedele

AU - Goegebeur, Yuri

AU - Guillou, Armelle

PY - 2019/6/4

Y1 - 2019/6/4

N2 - We propose a nonparametric robust estimator for the tail index of a conditional Pareto-type distribution in the presence of censoring and random covariates. The censored distribution is also of Pareto-type and the index is estimated locally within a narrow neighbourhood of the point of interest in the covariate space using the minimum density power divergence method. The main asymptotic properties of our robust estimator are derived under mild regularity conditions and its finite sample performance is illustrated on a small simulation study. A real data example is included to illustrate the practical applicability of the estimator.

AB - We propose a nonparametric robust estimator for the tail index of a conditional Pareto-type distribution in the presence of censoring and random covariates. The censored distribution is also of Pareto-type and the index is estimated locally within a narrow neighbourhood of the point of interest in the covariate space using the minimum density power divergence method. The main asymptotic properties of our robust estimator are derived under mild regularity conditions and its finite sample performance is illustrated on a small simulation study. A real data example is included to illustrate the practical applicability of the estimator.

KW - 62G05

KW - 62G20

KW - 62G32

KW - 62G35

KW - Density power divergence

KW - Local estimation

KW - Pareto-type distribution

KW - Random censoring

U2 - 10.1007/s13171-019-00169-0

DO - 10.1007/s13171-019-00169-0

M3 - Journal article

AN - SCOPUS:85067262534

JO - Sankhya. Series A

JF - Sankhya. Series A

SN - 0976-836X

ER -