On Examining Complex Systems Using the q-Weibull Distribution in Classical and Bayesian Paradigms
- DOI
- 10.2991/jsta.d.200825.001How to use a DOI?
- Keywords
- Bayesian analysis; MCMC simulations; ML estimation; Model selection criteria; Posterior estimates; q-Weibull distribution
- Abstract
The q-Weibull distribution is a generalized form of the Weibull distribution and has potential to model complex systems and life time datasets. Bayesian inference is the modern statistical technique that can accommodate uncertainty associated with the model parameters in the form of prior distributions. This study presents Bayesian analysis of the q-Weibull distribution using uninformative and informative priors and the results are compared with those produced by the classical maximum likelihood (ML) and least-squares (LS) estimation methods. A simulation study is also made to compare the competing methods. Different model selection criteria and predicted datasets are considered to compare the inferential methods under study. Posterior analyses include evaluating posterior means, medians, credible intervals of highest density regions, and posterior predictive distributions. The entire analysis is carried out using Markov chain Monte Carlo (MCMC) setup using WinBUGS package. The Bayesian method has proved to be superior to its classical counterparts. A real dataset is used to illustrate the entire inferential procedure.
- Copyright
- © 2020 The Authors. Published by Atlantis Press B.V.
- Open Access
- This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).
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TY - JOUR AU - Nasir Abbas PY - 2020 DA - 2020/09/03 TI - On Examining Complex Systems Using the q-Weibull Distribution in Classical and Bayesian Paradigms JO - Journal of Statistical Theory and Applications SP - 368 EP - 382 VL - 19 IS - 3 SN - 2214-1766 UR - https://doi.org/10.2991/jsta.d.200825.001 DO - 10.2991/jsta.d.200825.001 ID - Abbas2020 ER -