Bayesian statistical studies in the presence of censored data

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This thesis considers Bayesian inference for some recent survival time models in the presence of censoring. The Xlindley model, Zeghdoudi model, and Xexponential model are the intriguing models. With type II right- censored data, the parameters for these three models were calculated using a Bayesian technique under various loss functions, both symmetric (quadratic loss) and asymmetric (LINEX loss and entropy) and balanced. Numerical approaches were used in the classical approach, where the estimators are solutions of a nonlinear system whose solutions are not analytically explicit. Estimators are provided as a ratio of integrals in the Bayesian technique. Simulations and data analysis were conducted using Markov Chain Monte Carlo (MCMC) techniques, namely the Metropolis-Hastings algorithm, to demonstrate the outcomes. Finally, we used the Pitman criterion and the integrated mean square error (IMSE) criterion to compare Bayesian estimators and maximum likelihood estimators.

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