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

Goedele Dierckx (), Yuri Goegebeur () and Armelle Guillou ()
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Goedele Dierckx: KU Leuven, campus Brussel
Yuri Goegebeur: University of Southern Denmark
Armelle Guillou: Université de Strasbourg et CNRS

Sankhya A: The Indian Journal of Statistics, 2021, vol. 83, issue 1, No 3, 70-108

Abstract: 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: Pareto-type distribution; Random censoring; Density power divergence; Local estimation; 62G05; 62G20; 62G32; 62G35 (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s13171-019-00169-0

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