Bayesian inference for polyhazard models in the presence of covariates

Computational Statistics and Data Analysis

Volumn 38, Issue 1, 2001, Pages 1-14

  Mazucheli, Josmar ^a   Louzada Neto, Francisco ^b   Achcar, Jorge A ^c  

^a STATE UNIVERSITY OF MARINGÁ   (Brazil)

^b FEDERAL UNIVERSITY OF SÃO CARLOS   (Brazil)

^c UNIVERSITY OF SÃO PAULO   (Brazil)

Author keywords

Arms algorithm; Artificial variables; Bayes factor; Bayesian inference; Gibbs sampling algorithm; Kaplan Meier survival curves; Polyhazard models

Indexed keywords

DATA REDUCTION; HAZARDS; MODAL ANALYSIS; WEIBULL DISTRIBUTION;

BAYESIAN INFERENCE; POLYHAZARD MODELS;

STATISTICAL METHODS;

BAYESIAN ANALYSIS; NUMERICAL METHOD;

EID: 0035965631     PISSN: 01679473     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0167-9473(01)00027-5     Document Type: Article

References (29)

1. Bayesian analysis for masked system failure data using non-identical Weibull Models
2. Log-logistic regression models for survival data
3. Bayesian analysis for the Poly-Weibull distribution
4. Marginal likelihood from the Gibbs output
5. Understanding the Metropolis-Hastings algorithm
6. Regression models and life-tables (with discussion)
7. Gibbs sampling for Bayesian non-conjugate and hierarchical models by using auxiliary variables
8. Inference for the poly-Weibull models
9. Maximum likelihood from incomplete data via EM algorithm (with discussion)
10. Model determination using sampling-based methods
11. Derivative-free adaptive rejection sampling for Gibbs sampling
12. Full conditional distributions
13. Adaptive rejection Metropolis sampling within Gibbs sampling
14. Corrigendum: Adaptive rejection metropolis sampling within Gibbs sampling
15. Adaptive rejection sampling for Gibbs sampling
16. R: A language for data analysis and graphics
17. Bayes factor
18. Bayesian reliability modeling for masked system lifetime
19. Use the tumors lethality to interpret tumorigenicity experiments lacking cause-of-death data
20. Polyhazard regression models for lifetime data
21. Optimum accelerated life tests with a nonconstant scale parameter
22. Hypothesis testing and model selection
23. A general model for accelerated life testing with time-dependent covariates
24. The calculation of posterior distributions by data augmentation