Likelihood Inference for Copula Models Based on Left-Truncated and Competing Risks Data from Field Studies
Likelihood Inference for Copula Models Based on Left-Truncated and Competing Risks Data from Field Studies
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DOI：
10.3390/math10132163
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发表时间：
2022-06
期刊：
影响因子：
2.4
通讯作者：
H. Michimae;T. Emura
中科院分区：
文献类型：
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作者：
H. Michimae;T. Emura
生存和可靠性分析处理的是不完全失效时间数据，如删失和截断数据。最近，经典的左截断方法被推广到分析“现场数据”，即在固定周期内收集的样本。然而，现有的处理左截断现场数据的竞争风险模型不够灵活。我们提出了基于Copula的潜在失效时间的竞争风险模型，允许一个灵活的参数形式。给出了威布尔分布、对数正态分布和伽马分布下潜在失效时间的极大似然估计方法。我们通过仿真来检验所提出的方法的性能。最后给出了一个真实的数据实例。我们提供了R代码来再现模拟和数据分析结果。
Survival and reliability analyses deal with incomplete failure time data, such as censored and truncated data. Recently, the classical left-truncation scheme was generalized to analyze “field data”, defined as samples collected within a fixed period. However, existing competing risks models dealing with left-truncated field data are not flexible enough. We propose copula-based competing risks models for latent failure times, permitting a flexible parametric form. We formulate maximum likelihood estimation methods under the Weibull, lognormal, and gamma distributions for the latent failure times. We conduct simulations to check the performance of the proposed methods. We finally give a real data example. We provide the R code to reproduce the simulations and data analysis results.