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    题名: Parameter estimation for the composite dynamic systems based on sequential order statistics from Burr type XII mixture distribution
    作者: Tsai, Tzong-Ru;Lio, Yuhlong;Xin, Hua;Pham, Hoang
    关键词: composite dynamical systems;hazard rate;Markov chain Monte Carlo;mixture distribution;sequential order statistics
    日期: 2021-04-08
    上传时间: 2021-06-11 12:13:38 (UTC+8)
    出版者: MDPI AG
    摘要: Considering the impact of the heterogeneous conditions of the mixture baseline distribution on the parameter estimation of a composite dynamical system (CDS), we propose an approach to infer the model parameters and baseline survival function of CDS using the maximum likelihood estimation and Bayesian estimation methods. The power-trend hazard rate function and Burr type XII mixture distribution as the baseline distribution are used to characterize the changes of the residual lifetime distribution of surviving components. The Markov chain Monte Carlo approach via using a new Metropolis–Hastings within the Gibbs sampling algorithm is proposed to overcome the computation complexity when obtaining the Bayes estimates of model parameters. A numerical example is generated from the proposed CDS to analyze the proposed procedure. Monte Carlo simulations are conducted to investigate the performance of the proposed methods, and results show that the proposed Bayesian estimation method outperforms the maximum likelihood estimation method to obtain reliable estimates of the model parameters and baseline survival function in terms of the bias and mean square error
    關聯: Mathematics 9(8), 810
    DOI: 10.3390/math9080810
    显示于类别:[統計學系暨研究所] 期刊論文

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