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不同移动速度的马尔可夫链混合模型估计AI 解读

This article considers a new mixture of time-homogeneous finite Markov chains where the mixing is on the rate of movement and develops the EM algorithm for maximum likelihood estimation of the parameters of the mixture. Continuous- and discrete-time versions of the mixture are defined, and their estimation is considered separately. The developed methods are illustrated with an application to modeling bond ratings migration. The class of mixture models proposed in this…

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区间删失观测下马尔可夫疾病—死亡过程的非参数估计AI 解读

The nonparametric estimation of the cumulative transition intensity functions in a threestate time-nonhomogeneous Markov process with irreversible transitions, an ‘illness-death’ model, is considered when times of the intermediate transition, e.g. onset of a disease, are interval-censored. The times of ‘death’ are assumed to be known exactly or to be right-censored. In addition the observed process may be left-truncated. Data of this type arise when the process is sampled…

区间删失与截断观测下分布函数非参数估计的说明AI 解读

SUMMARY This paper is concerned with the nonparametric estimation of the distribution function F from interval-censored and truncated data as discussed by Turnbull. It is observed that Turnbull's characterization of F̂, the maximum likelihood estimator of F, is not applicable in general when the data are interval censored and truncated. It is shown, however, that Turnbull's characterization can be easily modified to obtain the appropriate characterization of F̂.

定期观测三状态马尔可夫过程的非参数估计及艾滋病应用AI 解读

SUMMARY Estimation in a three-state Markov process with irreversible transitions in the presence of interval-censored data is considered. A nonparametric maximum likelihood procedure for the estimation of the cumulative transition intensities is presented. A self-consistent estimator of the parameters is defined and it is shown that the maximum likelihood estimator is a self-consistent estimator. This extends the idea of self-consistency introduced by Efron to the estimation…

迁移者—留驻者模型的最大似然估计AI 解读

The discrete time mover-stayer model, a special mixture of two independent Markov chains, has been widely used in modeling the dynamics of social processes. The problem of maximum likelihood estimation of its parameters from the data, however, which consist of a sample of independent realizations of this process, has not been considered in the literature. I present a maximum likelihood procedure for the estimation of the parameters of the mover-stayer model and develop a…