Bayesian survival and hazard estimates for Weibull regression with censored data using modified Jeffreys prior
In this study, firstly, consideration is given to the traditional maximum likelihood estimator and the Bayesian estimator by employing Jeffreys prior and Extension of Jeffreys prior information on the Weibull distribution with a given shape under right censored data. We have formulated equatio...
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主要作者: | |
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格式: | Thesis |
语言: | English |
出版: |
2013
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主题: | |
在线阅读: | http://psasir.upm.edu.my/id/eprint/66635/1/FS%202013%2052%20IR.pdf |
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总结: | In this study, firstly, consideration is given to the traditional maximum likelihood
estimator and the Bayesian estimator by employing Jeffreys prior and Extension of
Jeffreys prior information on the Weibull distribution with a given shape under right
censored data. We have formulated equations for the scale parameter, the survival
function and the hazard functionunder Bayesian with extension of Jeffreys prior.
Next we consider both the scale and shape parameters to be unknown under
censored data. It is observed that the estimate of the shape parameter under the
maximum likelihood method cannot be obtained in closed form, but can be solved
by the application of numerical methods. With the application of the Bayesian
estimates for the parameters, the survival function and hazard function, we realised
that the posterior distribution from which Bayesian inference is drawn cannot be obtained analytically. Due to this, we have employed Lindley’s approximation
technique and then compared it to the maximum likelihood approach.
We then incorporate covariates into the Weibull model. Under this regression model
with regards to Bayesian, the usual method was not possible. Thus we develop an
approach to accommodate the covariate terms in the Jeffreys and Modified of
Jeffreys prior by employingGauss quadrature method.
Subsequently, we use Markov Chain Monte Carlo (MCMC) method in the Bayesian
estimator of the Weibull distributionand Weibull regression model with shape
unknown. For the Weibull model with right censoring and unknown shape, the full
conditional distribution for the scale and shape parameters are obtained via Gibbs
sampling and Metropolis-Hastings algorithm from which the survival function and
hazard function are estimated. For Weibull regression model of both Jeffreys priors
with covariates, importance sampling technique has been employed. Mean squared
error (MSE) and absolute bias are obtained and used to compare the Bayesian and
the maximum likelihood estimation through simulation studies.
Lastly, we use real data to assess the performance of the developed models based on
Gauss quadrature and Markov Chain Monte Carlo (MCMC) methods together with
the maximum likelihood approach. The comparisons are done by using standard
error and the confidence interval for maximum likelihood method and credible
interval for the Bayesian method. |
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