Bayesian Analysis in Generalized Log-Gamma Censored Regression Model

  • Younshik chung (Department of Statistics, Pusan National University) ;
  • Yoomi Kang (Department of Statistics, Pusan National University)
  • 발행 : 1998.12.01

초록

For industrial and medical lifetime data, the generalized log-gamma regression model is considered. Then the Bayesian analysis for the generalized log-gamma regression with censored data are explained and following the data augmentation (Tanner and Wang; 1987), the censored data is replaced by simulated data. To overcome the complicated Bayesian computation, Makov Chain Monte Carlo (MCMC) method is employed. Then some modified algorithms are proposed to implement MCMC. Finally, one example is presented.

키워드

참고문헌

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