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

Tzong-Ru Tsai, Yuhlong Lio and Wei-Chen Ting
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Tzong-Ru Tsai: Department of Statistics, Tamkang University, Tamsui, New Taipei City 251301, Taiwan
Yuhlong Lio: Department of Mathematical Sciences, University of South Dakota, Vermillion, SD 57069, USA
Wei-Chen Ting: Department of Statistics, Tamkang University, Tamsui, New Taipei City 251301, Taiwan

Mathematics, 2021, vol. 9, issue 19, 1-18

Abstract: 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.

Keywords: bootstrap method; EM algorithm; maximum likelihood estimation; mixture distributions model; Monte Carlo simulation (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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