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首页> 外文期刊>Statistics >ESTIMATION IN A MODEL FOR A SEMI-MARKOV PROCESS WITH COVARIATES UNDER RIGHT-CENSORING
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ESTIMATION IN A MODEL FOR A SEMI-MARKOV PROCESS WITH COVARIATES UNDER RIGHT-CENSORING

机译：右删失下带有协变量的半马尔可夫过程模型的估计

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摘要

A semi-Markov model with covariates is proposed for a multi-state process with a finite number of states such that the transition probabilities between the states and the distribution functions of the duration times between the occurrence of two states depend on a discrete covariate. The hazard rates for the time elapsed between two successive states depend on the covariate through a proportional hazards model involving a set of regression parameters, while the transition probabilities depend on the covariate in an unspecified way. We propose estimators for these parameters and for the cumulative hazard functions of the sojourn times. A difficulty comes from the fact that when a sojourn lime in a state is right-censored, the next state is unknown. We prove that our estimators are consistent and asymptotically Gaussian under the model constraints.
机译：针对具有有限数量状态的多状态过程，提出了具有协变量的半马尔可夫模型，使得状态之间的转换概率和两个状态发生之间的持续时间的分布函数取决于离散协变量。通过包含一组回归参数的比例风险模型，两个连续状态之间经过的时间的风险率取决于协变量，而转换概率以不确定的方式取决于协变量。我们为这些参数和停留时间的累积危害函数提供估计器。困难来自以下事实：当某个州的生石灰被右删减时，下一个州是未知的。我们证明了在模型约束下我们的估计是一致的并且渐近高斯的。

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