Abstract
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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
