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首页> 外文期刊>Journal of applied statistics >Analysis of discrete lifetime data under middle-censoring and in the presence of covariates
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Analysis of discrete lifetime data under middle-censoring and in the presence of covariates

机译：在中间删失和存在协变量的情况下分析离散生命数据

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摘要

'Middle censoring' is a very general censoring scheme where the actual value of an observation in the data becomes unobservable if it falls inside a random interval (L, R) and includes both left and right censoring. In this paper, we consider discrete lifetime data that follow a geometric distribution that is subject to middle censoring. Two major innovations in this paper, compared to the earlier work of Davarzani and Parsian, include (ⅰ) an extension and generalization to the case where covariates are present along with the data and (ⅱ) an alternate approach and proofs which exploit the simple relationship between the geometric and the exponential distributions, so that the theory is more in line with the work of Iyer et al. It is also demonstrated that this kind of discretization of life times gives results that are close to the original data involving exponential life times. Maximum likelihood estimation of the parameters is studied for this middle-censoring scheme with covariates and their large sample distributions discussed. Simulation results indicate how well the proposed estimation methods work and an illustrative example using time-to-pregnancy data from Baird and Wilcox is included.
机译：“中间检查”是一种非常通用的检查方案，其中，如果观察值的实际值落在随机间隔（L，R）内并且同时包含左检查和右检查，则该数据的实际值将变得不可观察。在本文中，我们考虑离散的生命周期数据，这些数据遵循受中间检查的几何分布。与Davarzani和Parsian的早期工作相比，本文的两个主要创新包括（ⅰ）对协变量与数据一起出现的情况的扩展和推广，以及（ⅱ）利用简单关系的替代方法和证明在几何分布和指数分布之间，因此该理论更符合Iyer等人的工作。还证明了这种寿命的离散化所得到的结果接近于涉及指数寿命的原始数据。对于这种带有协变量的中删失方案及其最大样本分布，研究了参数的最大似然估计。仿真结果表明了所提出的估算方法的工作效果，并包括了一个使用Baird和Wilcox的怀孕时间数据的示例。

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