Inferences for Weibull Fréchet Distribution Using a Bayesian and Non-Bayesian Methods on Gastric Cancer Survival Times

Comput Math Methods Med. 2021 May 26:2021:9965856. doi: 10.1155/2021/9965856. eCollection 2021.

Abstract

In this article, based on progressively type-II censored schemes, the maximum likelihood, Bayes, and two parametric bootstrap methods are used for estimating the unknown parameters of the Weibull Fréchet distribution and some lifetime indices as reliability and hazard rate functions. Moreover, approximate confidence intervals and asymptotic variance-covariance matrix have been obtained. Markov chain Monte Carlo technique based on Gibbs sampler within Metropolis-Hasting algorithm is used to generate samples from the posterior density functions. Furthermore, Bayesian estimate is computed under both balanced square error loss and balanced linear exponential loss functions. Simulation results have been implemented to obtain the accuracy of the estimators. Finally, application on the survival times in years of a group of patients given chemotherapy and radiation treatment is presented for illustrating all the inferential procedures developed here.

MeSH terms

  • Algorithms
  • Bayes Theorem
  • Computational Biology
  • Computer Simulation
  • Humans
  • Likelihood Functions
  • Markov Chains
  • Monte Carlo Method
  • Proportional Hazards Models
  • Reproducibility of Results
  • Stomach Neoplasms / mortality*
  • Survival Analysis