Bayesian analysis of 3-components Kumaraswamy mixture model: Quadrature method vs. Importance sampling
This paper presents the Bayesian inference of the 3-component Kumaraswamy mixture distribution. The paper formulates the 3-component model, provides Bayesian estimates and their respective posterior risks assuming non-informative prior under type-I censoring. Since the Bayes estimates are not in clo...
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doaj-c36ec822ef934ac9bf1b37a8834874522021-06-02T18:54:15ZengElsevierAlexandria Engineering Journal1110-01682020-08-0159427532763Bayesian analysis of 3-components Kumaraswamy mixture model: Quadrature method vs. Importance samplingMaryam Khalid0Muhammad Aslam1Tabassum Naz Sindhu2Department of Mathematics and Statistics, Riphah International University, Islamabad, PakistanDepartment of Mathematics and Statistics, Riphah International University, Islamabad, PakistanDepartment of Sciences and Humanities, FAST – National University, Islamabad, Pakistan; Department of Statistics, Quaid-i-Azam University 45320, Islamabad 44000, Pakistan; Corresponding author at: Department of Sciences and Humanities, FAST – National University, Islamabad, Pakistan.This paper presents the Bayesian inference of the 3-component Kumaraswamy mixture distribution. The paper formulates the 3-component model, provides Bayesian estimates and their respective posterior risks assuming non-informative prior under type-I censoring. Since the Bayes estimates are not in closed form, the paper provides a comparative analysis of the estimates and their respective risks derived under quadrature method and importance sampling using different loss functions, assuming different sample sizes, test termination times and mixture probabilities. Finally, the proposed 3 component mixture model is then applied to the real life data to assess its validity. The convergence of the estimates under importance sampling is rapidly observed as compared to quadrature methods.http://www.sciencedirect.com/science/article/pii/S11100168203023253-Component mixture distributionImportance samplingNon-informative priorLoss functionTest termination time |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Maryam Khalid Muhammad Aslam Tabassum Naz Sindhu |
spellingShingle |
Maryam Khalid Muhammad Aslam Tabassum Naz Sindhu Bayesian analysis of 3-components Kumaraswamy mixture model: Quadrature method vs. Importance sampling Alexandria Engineering Journal 3-Component mixture distribution Importance sampling Non-informative prior Loss function Test termination time |
author_facet |
Maryam Khalid Muhammad Aslam Tabassum Naz Sindhu |
author_sort |
Maryam Khalid |
title |
Bayesian analysis of 3-components Kumaraswamy mixture model: Quadrature method vs. Importance sampling |
title_short |
Bayesian analysis of 3-components Kumaraswamy mixture model: Quadrature method vs. Importance sampling |
title_full |
Bayesian analysis of 3-components Kumaraswamy mixture model: Quadrature method vs. Importance sampling |
title_fullStr |
Bayesian analysis of 3-components Kumaraswamy mixture model: Quadrature method vs. Importance sampling |
title_full_unstemmed |
Bayesian analysis of 3-components Kumaraswamy mixture model: Quadrature method vs. Importance sampling |
title_sort |
bayesian analysis of 3-components kumaraswamy mixture model: quadrature method vs. importance sampling |
publisher |
Elsevier |
series |
Alexandria Engineering Journal |
issn |
1110-0168 |
publishDate |
2020-08-01 |
description |
This paper presents the Bayesian inference of the 3-component Kumaraswamy mixture distribution. The paper formulates the 3-component model, provides Bayesian estimates and their respective posterior risks assuming non-informative prior under type-I censoring. Since the Bayes estimates are not in closed form, the paper provides a comparative analysis of the estimates and their respective risks derived under quadrature method and importance sampling using different loss functions, assuming different sample sizes, test termination times and mixture probabilities. Finally, the proposed 3 component mixture model is then applied to the real life data to assess its validity. The convergence of the estimates under importance sampling is rapidly observed as compared to quadrature methods. |
topic |
3-Component mixture distribution Importance sampling Non-informative prior Loss function Test termination time |
url |
http://www.sciencedirect.com/science/article/pii/S1110016820302325 |
work_keys_str_mv |
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