ABSTRACT Bivariate current status data occur in many areas and many authors have discussed their analysis and proposed many inference procedures [Jewell, N.P., van der Laan, M.J., and Lei, X. (2005), ‘Bivariate Current Status Data with Univariate Monitoring Times’, Biometrika, 92, 847–862; Wang, N., Wang, L., and McMahan, C.S. (2015), ‘Regression Analysis of Bivariate Current Status Data Under the Gammafrailty Proportional Hazards Model Using the Em Algorithm’, Computational Statistics & Data Analysis, 83, 140–150; Hu, T., Zhou, Q., and Sun, J. (2017), ‘Regression Analysis of Bivariate Current Status Data Under the Proportional Hazards Model’, The Canadian Journal of Statistics, 45, 410–424]. However, most of these methods are for the situation where the observation or censoring is non-informative and sometimes one may face informative censoring [Zhang, Z., Sun, J., and Sun, L. (2005), ‘Statistical Analysis of Current Data with Informative Observation Times’, Statistics in Medicine, 24, 1399–1407; Chen, C.M., Lu, T.F.C., Chen, M.H., and Hsu, C.M. (2012), ‘Semiparametric Transformation Models for Current Status Data with Informative Censoring’, Biometrical Journal, 19, 641–656; Ma, L., Hu, T., and Sun, J. (2015), ‘Sieve Maximum Likelihood Regression Analysis of Dependent Current Status Data’, Biometrika, 85, 649–658], where one has to deal with three correlated random variables. In this paper, a vine copula approach is developed for regression analysis of bivariate current status data in the presence of informative censoring. The proposed estimators are shown to be strongly consistent and the asymptotic normality and efficiency of the estimated regression parameter are also established. Numerical results suggest that the proposed method works well in practice.

中文翻译：

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使用信息删失对双变量当前状态数据进行回归分析的藤蔓 copula 方法

摘要 双变量当前状态数据出现在许多领域，许多作者讨论了他们的分析并提出了许多推理程序 [Jewell, NP, van der Laan, MJ, and Lei, X. (2005), 'Bivariate Current Status Data with Univariate Monitoring Times ', Biometrika, 92, 847–862; Wang, N.、Wang, L. 和 McMahan, CS (2015)，“使用 Em 算法在 Gammafrailty 比例风险模型下对双变量当前状态数据进行回归分析”，计算统计与数据分析，83、140–150；Hu, T., Zhou, Q. 和 Sun, J. (2017)，“比例风险模型下双变量现状数据的回归分析”，加拿大统计杂志，45, 410–424]。然而，这些方法中的大多数是针对观察或审查不提供信息的情况，有时可能会面临信息审查 [Zhang, Z., Sun, J., and Sun, L. (2005), 'Statistical Analysis of Current Data with Informative Observation Times', Statistics in Medicine, 24, 1399–1407; Chen, CM, Lu, TFC, Chen, MH, 和 Hsu, CM (2012), 'Semiparametric Transformation Models for Current Status Data with Informative Censoring', Biometrical Journal, 19, 641–656; Ma, L.、Hu, T. 和 Sun, J. (2015)，“相关当前状态数据的筛法最大似然回归分析”，Biometrika, 85, 649–658]，其中必须处理三个相关随机变量。在本文中，开发了一种藤蔓 copula 方法，用于在存在信息审查的情况下对双变量当前状态数据进行回归分析。所提出的估计量被证明是高度一致的，并且还建立了估计回归参数的渐近正态性和效率。数值结果表明，所提出的方法在实践中效果很好。