Bayesian Inference using Multiple-try Metropolis Hastings Scheme for the Efficiency of Estimating Gumbel Distribution Parameters

Authors

  • Nor Azrita Mohd Amin UNIVERSITI MALAYSIA PERLIS
  • Mohd Bakri Adam Institute of Mathematical Research Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia
  • Noor Akma Ibrahim Institute of Mathematical Research Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia

DOI:

https://doi.org/10.11113/matematika.v31.n1.743

Abstract

This paper aims to explore the efficiency for estimating the parameters of Gumbel simulated data using Multiple-try Metropolis algorithm (MTM). Several goodness-of-fit tests are used to compare the performance of MTM and the former, Metropolis-Hastings algorithm (MH). Concerning for a fair comparison, this study uses the equivalent starting point, the similar number of iterations and also the same length of burn-in periods. The numerical studies show that the MTM method performs slightly better than MH method after 5000 iterations to meet the stationary distribution. More candidates in the proposals lead to a higher accuracy of MTM estimation.

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Published

28-07-2015

How to Cite

Mohd Amin, N. A., Adam, M. B., & Ibrahim, N. A. (2015). Bayesian Inference using Multiple-try Metropolis Hastings Scheme for the Efficiency of Estimating Gumbel Distribution Parameters. MATEMATIKA, 31(1), 25–36. https://doi.org/10.11113/matematika.v31.n1.743

Issue

Volume 31 (June 2015)

Section

Articles

License

Copyright of articles that appear in MATEMATIKA: MJIAM belongs exclusively to Penerbit UTM Press, Universiti Teknologi Malaysia. This copyright covers the rights to reproduce the article, including reprints, electronic reproductions or any other reproductions of similar nature.