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A bayesian via laplace approximation on log-gamma model with censored data


Yusuf, Madaki Umar and Abu Bakar, Mohd Rizam and Husain, Qasim Nasir and Ibrahim, Noor Akma and Arasan, Jayanthi (2016) A bayesian via laplace approximation on log-gamma model with censored data. Modern Applied Science, 11 (1). 14 - 23. ISSN 1913-1844; ESSN: 1913-1852


Log-gamma distribution is the extension of gamma distribution which is more flexible, versatile and provides a great fit to some skewed and censored data. Problem/Objective: In this paper we introduce a solution to closed forms of its survival function of the model which shows the suitability and flexibility towards modelling real life data. Methods/Analysis: Alternatively, Bayesian estimation by MCMC simulation using the Random-walk Metropolis algorithm was applied, using AIC and BIC comparison makes it the smallest and great choice for fitting the survival models and simulations by Markov Chain Monte Carlo Methods. Findings/Conclusion: It shows that this procedure and methods are better option in modelling Bayesian regression and survival/reliability analysis integrations in applied statistics, which based on the comparison criterion log-gamma model have the least values. However, the results of the censored data have been clarified with the simulation results.

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Additional Metadata

Item Type: Article
Divisions: Faculty of Science
Institute for Mathematical Research
DOI Number: https://doi.org/10.5539/mas.v11n1p14
Publisher: Canadian Center of Science and Education
Keywords: Bayesian analysis; Censored data; Laplace approximation; Log-gamma distribution; Simulation; Survival analysis
Depositing User: Mohd Hafiz Che Mahasan
Date Deposited: 04 Apr 2018 04:55
Last Modified: 04 Apr 2018 04:55
Altmetrics: http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.5539/mas.v11n1p14
URI: http://psasir.upm.edu.my/id/eprint/54806
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