Coelho-Barros, EA, Achcar, JA, Martinez, EZ et al. (2 more authors) (2019) Bayesian Inference For The Segmented Weibull Distribution. Revista Colombiana de Estadistica, 42 (2). pp. 225-243. ISSN 0120-1751
Abstract
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 effects 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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019, Revista Colombiana de Estadística. This is an open access article under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) (https://creativecommons.org/licenses/by/4.0/) |
Keywords: | Bayesian methods; Censored data; Change-points; Covariates; Segmented Weibull distribution |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Medicine and Health (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 23 Sep 2019 11:01 |
Last Modified: | 23 Sep 2019 11:32 |
Status: | Published |
Publisher: | Universidad Nacional de Colombia |
Identification Number: | 10.15446/rce.v42n2.76815 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:151180 |