Slice sampler algorithm for generalized pareto distribution

Citation

Rostami, Mohammad and Adam, Mohd Bakri and Yahya, Mohamed Hisham and Ibrahim, Noor Akma (2018) Slice sampler algorithm for generalized pareto distribution. Hacettepe Journal of Mathematics and Statistics, 47 (4). Jan-32. ISSN 1303-5010

Abstract

In this paper, we developed the slice sampler algorithm for the generalized Pareto distribution (GPD) model. Two simulation studies have shown the performance of the peaks over given threshold (POT) and GPD density function on various simulated data sets. The results were compared with another commonly used Markov chain Monte Carlo (MCMC) technique called Metropolis-Hastings algorithm. Based on the results, the slice sampler algorithm provides closer posterior mean values and shorter 95% quantile based credible intervals compared to the Metropolis-Hastings algorithm. Moreover, the slice sampler algorithm presents a higher level of stationarity in terms of the scale and shape parameters compared with the Metropolis-Hastings algorithm. Finally, the slice sampler algorithm was employed to estimate the re- turn and risk values of investment in Malaysian gold market.

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Additional Metadata

Item Type:	Article
Divisions:	Faculty of Economics and Management Faculty of Science Institute for Mathematical Research
DOI Number:	https://doi.org/10.15672/HJMS.2017.441
Publisher:	Hacettepe University
Keywords:	Extreme value theory; Markov chain Monte Carlo; Slice sampler, Metropolis-hastings algorithm; Bayesian analysis; Gold price
Depositing User:	Ms. Nida Hidayati Ghazali
Date Deposited:	17 May 2020 17:30
Last Modified:	17 May 2020 17:30
Altmetrics:	https://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.15672/HJMS.2017.441
URI:	http://psasir.upm.edu.my/id/eprint/73905
Statistic Details:	View Download Statistic

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