A Class of Semiparametric Tail Index Estimators and Its Applications


Cite item

Full Text

Open Access Open Access
Restricted Access Access granted
Restricted Access Subscription Access

Abstract

A new class of semiparametric estimators of the tail index is proposed. These estimators are based on a rather general class of semiparametric statistics. Their asymptotic normality is proved. The new estimators are compared with several other recently introduced estimators of the tail index in terms of the asymptotic mean-square error. An algorithm to calculate the new estimators is developed and then applied to several real data sets.

About the authors

M. Vaičiulis

Vilnius University

Author for correspondence.
Email: marijus.vaiciulis@mii.vu.lt
Lithuania, Vilnius

N. M. Markovich

Trapeznikov Institute of Control Sciences

Email: marijus.vaiciulis@mii.vu.lt
Russian Federation, Moscow

Supplementary files

Supplementary Files
Action
1. JATS XML

Copyright (c) 2019 Pleiades Publishing, Inc.