Open AccessBayesian Inference For The Segmented Weibull Distribution
Author(s) -
Emílio Augusto Coelho-Barros,
Jorge Alberto Achcar,
Edson Zangiacomí Martínez,
Nasser Davarzani,
Heike Grabsch
Publication year - 2019
Publication title -
revista colombiana de estadística/revista colombiana de estadistica
Language(s) - English
Resource type - Journals
eISSN - 2389-8976
pISSN - 0120-1751
DOI - 10.15446/rce.v42n2.76815
Subject(s) - covariate , markov chain monte carlo , weibull distribution , bayesian probability , bayesian inference , statistics , inference , computer science , survival analysis , point estimation , mathematics , reversible jump markov chain monte carlo , econometrics , artificial intelligence
In this paper, we introduce a Bayesian approach for segmented Weibull distributions which could be a good alternative to analyze medical survival data in the presence of censored observations and covariates. With the obtained Bayesian estimated change-points we could get an excellent fit of the proposed model to any data sets. With the proposed methodology, it is also possible to identify survival times intervals where a covariate could have significantly different efects when compared to other lifetime intervals, an important point under a clinical view. The obtained Bayesian estimates are obtained using standard Markov Chain Monte Carlo methods. Some examples with real data sets illustrate the proposed methodology and its potential clinical value.