EM Algorithm for Mixture Distributions Model with Type-I Hybrid Censoring Scheme

An expectation-maximization (EM) likelihood estimation procedure is proposed to obtain the maximum likelihood estimates of the parameters in a mixture distributions model based on type-I hybrid censored samples when the mixture proportions are unknown. Three bootstrap methods are applied to construct the confidence intervals of the model parameters. Monte Carlo simulations are conducted to evaluate the performance of the proposed methods. Simulation results show that the proposed methods can perform well to obtain reliable point and interval estimation results. Three examples are used to illustrate the applications of the proposed methods.

Automated summary

An expectation-maximization (EM) likelihood estimation procedure is proposed for obtaining maximum likelihood estimates of parameters in a mixture distributions model with unknown mixture proportions based on type-I hybrid censored samples. Three bootstrap methods are utilized for constructing confidence intervals of the model parameters, and Monte Carlo simulations are conducted to evaluate the performance of the proposed methods. Simulation results demonstrate that the proposed methods can effectively provide reliable point and interval estimation results, with three examples illustrating their applications.