Estimation of a tail index based on minimum density power divergence

Authors:
Moosup Kim;Sangyeol Lee
Affiliations:
Department of Statistics, Seoul National University, Seoul, 151-742, Republic of Korea;Department of Statistics, Seoul National University, Seoul, 151-742, Republic of Korea
Venue:
Journal of Multivariate Analysis
Year:
2008

Citing 1
Cited 2

A robust estimator for the tail index of Pareto-type distributions

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Review: Divergence measures for statistical data processing-An annotated bibliography

Signal Processing
An asymptotically unbiased minimum density power divergence estimator for the Pareto-tail index

Journal of Multivariate Analysis

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Abstract

In this paper, we consider the minimum density power divergence estimator for the tail index of heavy tailed distributions in strong mixing processes. It is shown that the estimator is consistent and asymptotically normal under regularity conditions. The simulation results demonstrate that the estimator is robust in the presence of outliers.