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Licensed Unlicensed Requires Authentication Published by De Gruyter February 2, 2021

Body tail adaptive kernel density estimation for nonnegative heavy-tailed data

Yasmina Ziane, Nabil Zougab and Smail Adjabi


In this paper, we consider the procedure for deriving variable bandwidth in univariate kernel density estimation for nonnegative heavy-tailed (HT) data. These procedures consider the Birnbaum–Saunders power-exponential (BS-PE) kernel estimator and the bayesian approach that treats the adaptive bandwidths. We adapt an algorithm that subdivides the HT data set into two regions, high density region (HDR) and low-density region (LDR), and we assign a bandwidth parameter for each region. They are derived by using a Monte Carlo Markov chain (MCMC) sampling algorithm. A series of simulation studies and real data are realized for evaluating the performance of a procedure proposed.

MSC 2010: 62G07; 62G99


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Received: 2019-02-28
Revised: 2020-12-10
Accepted: 2021-01-12
Published Online: 2021-02-02
Published in Print: 2021-03-01

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