基于广义马歇尔-奥尔金模型的同时死亡时间依赖性普查

IF 1.4 3区 数学 Q2 STATISTICS & PROBABILITY Journal of Multivariate Analysis Pub Date : 2024-07-25 DOI:10.1016/j.jmva.2024.105347
Mikael Escobar-Bach, Salima Helali
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引用次数: 0

摘要

在本文中,我们考虑了失败时间相等的正概率依存剔除模型问题。在这种情况下,我们建议考虑马歇尔-奥尔金类型的模型,并研究了相关生存协方差在剔除数据应用中的一些特性。我们还介绍了不同方案下边际分布和联合生存概率的估计值,并说明了它们在适当条件下的渐近正态性。最后,我们通过对合成数据和真实数据应用的小型模拟研究,评估了我们方法的有限样本性能。
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Dependent censoring with simultaneous death times based on the Generalized Marshall–Olkin model

In this paper, we consider the problem of dependent censoring models with a positive probability that the times of failure are equal. In this context, we propose to consider the Marshall–Olkin type model and studied some properties of the associated survival copula in its application to censored data. We also introduce estimators for the marginal distributions and the joint survival probabilities under different schemes and show their asymptotic normality under appropriate conditions. Finally, we evaluate the finite-sample performance of our approach relying on a small simulation study with synthetic data real data applications.

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来源期刊
Journal of Multivariate Analysis
Journal of Multivariate Analysis 数学-统计学与概率论
CiteScore
2.40
自引率
25.00%
发文量
108
审稿时长
74 days
期刊介绍: Founded in 1971, the Journal of Multivariate Analysis (JMVA) is the central venue for the publication of new, relevant methodology and particularly innovative applications pertaining to the analysis and interpretation of multidimensional data. The journal welcomes contributions to all aspects of multivariate data analysis and modeling, including cluster analysis, discriminant analysis, factor analysis, and multidimensional continuous or discrete distribution theory. Topics of current interest include, but are not limited to, inferential aspects of Copula modeling Functional data analysis Graphical modeling High-dimensional data analysis Image analysis Multivariate extreme-value theory Sparse modeling Spatial statistics.
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