Slice sampler algorithm for generalized Pareto distribution | Zendy

Mohammad Rostami | Zendy; Mohd Bakri Adam Yahya | Zendy; Mohamed Hisham Yahya | Zendy; Noor Akma Ibrahim | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Slice sampler algorithm for generalized Pareto distribution

Author(s) -

Mohammad Rostami,

Mohd Bakri Adam Yahya,

Mohamed Hisham Yahya,

Noor Akma Ibrahim

Publication year - 2017

Publication title -

hacettepe journal of mathematics and statistics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.312

H-Index - 26

ISSN - 1303-5010

DOI - 10.15672/hjms.2017.441

Subject(s) - mathematics , pareto principle , generalized pareto distribution , pareto distribution , distribution (mathematics) , algorithm , lomax distribution , mathematical optimization , statistics , mathematical analysis , extreme value theory

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 return and risk values of investment in Malaysian gold market.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research