Inference for a dependent competing risks model on Marshall-Olkin bivariate Lomax-geometric distribution

The study of competing risk models is of great significance to survival and reliability analysis in statistics and it is more reasonable that they are assumed to have dependent failure causes in the actual situation. Therefore, statistical inference of five-parameter Marshall–Olkin bivariate Lomax-geometric distribution is considered in combination with dependent failure causes in this article. From the perspective of classical frequency, the estimates of unknown parameters are derived by the EM algorithm and the existence and uniqueness of solutions are also proved. In the Bayesian framework, a rather flexible class of prior distributions and the importance sampling technique are considered to obtain the estimates of all parameters for two types of data under the squared error loss function, and the credible intervals are also constructed. Finally, some simulation results and real data analysis are provided to show the effectiveness of the constructed model and the performance of various methods.