具有参数阿基米德风险依赖的半参数竞争风险模型的单风险方法

IF 1.4 3区数学 Q2 STATISTICS & PROBABILITY

Journal of Multivariate Analysis Pub Date : 2023-11-24 DOI:10.1016/j.jmva.2023.105276

Simon M.S. Lo , Ralf A. Wilke

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引用次数: 0

摘要

本文考虑一个相互依赖的竞争风险模型，其中一个风险的分布是半参数比例风险模型，而其他风险的模型和阿基米德联结的风险依赖程度是未知的。当至少有一个协变量具有至少两个值时，显示可识别性。估计是通过一个n一致的半参数两步过程来完成的。通过仿真验证了该方法的适用性和良好的有限样本性能。对失业持续时间的应用证实了估计而不是假设风险依赖的重要性。

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A single risk approach to the semiparametric competing risks model with parametric Archimedean risk dependence

This paper considers a dependent competing risks model with the distribution of one risk being a semiparametric proportional hazards model, whereas the model for the other risks and the degree of risk dependence of an Archimedean copula are unknown. Identifiability is shown when there is at least one covariate with at least two values. Estimation is done by means of a $\sqrt{n}$ -consistent semiparametric two-step procedure. Applicability and attractive finite sample performance are demonstrated with the help of simulations. An application to unemployment duration confirms the importance of estimating rather than assuming risk dependence.

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来源期刊

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.