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

Ziane Yasmina (), Zougab Nabil () and Adjabi Smail ()
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Ziane Yasmina: Research Unit LaMOS, Faculty of Exact Sciences, Bejaia University, 06000Bejaia, Algeria
Zougab Nabil: Research Unit LaMOS, Faculty of Exact Sciences, Bejaia University, 06000Bejaia, Algeria
Adjabi Smail: Research Unit LaMOS, Faculty of Exact Sciences, Bejaia University, 06000Bejaia, Algeria

Monte Carlo Methods and Applications, 2021, vol. 27, issue 1, 57-69

Abstract: 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.

Keywords: Bayesian bandwidth selector; BS-PE kernel; cross validation; heavy-tailed data; kernel density estimation; MCMC method; prior distribution (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1515/mcma-2021-2082

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