Inference on Marshall–Olkin Bivariate Exponential in the Presence of Dependent Left Censoring

This paper deals with the dependent left censoring scheme when the survival time variable and censoring variable are dependent and have Marshal–Olkin bivariate exponential distribution. We use the expectation–conditional maximization algorithm for finding the maximum likelihood estimates of the unknown parameters. From Bayesian point of view, based on a particular choice of hyperparameters of prior distribution we obtain the exact Bayes estimates of the unknown parameters. We employ importance sampling MCMC technique and an approximate Bayes estimation method to compute Bayes estimates of the unknown parameters. Finally, a Monte Carlo simulation and a real data analysis are studied.