ON INFERENCE IN PARAMETRIC SURVIVAL-DATA MODELS
ON INFERENCE IN PARAMETRIC SURVIVAL-DATA MODELS
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DOI：
10.2307/1403683
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发表时间：
1992-12-01
影响因子：
2
通讯作者：
HJORT, NL
中科院分区：
文献类型：
--
作者：
HJORT, NL
常用的生存数据参数模型如下所示。假设某些参数指定的危险率α (s, θ)可能被截短的随机寿命乘以X1(0)，…。X (n) 0;我们只观察到X(i) = min {X(i)0, c(i)}和delta(i) = i {X(i)0小于或等于c(i)}对于某些给定的或来自某些审查分布的审查时间c(i)。我们研究了以下问题：当真实风险率α (s)不同于参数风险率时，最大似然估计器和其他估计器真正估计了什么？在这种模型外情况下，估计量的极限分布是什么？如何使传统的基于模型的分析具有模型鲁棒性？模型不可知论的观点会带来其他的评估方法吗？执行基于模型和模型鲁棒自举的结果是什么？理论和经验的影响函数如何推广到有删减数据的情况？方法和结果如何延续到更复杂的生活史数据模型，如回归模型和马尔可夫链？
The usual parametric models for survival data are of the following form. Some parametrically specified hazard rate alpha(s, theta) is assumed for possibly censored random fife times X1(0), . . . , X(n)0; one observes only X(i) = min {X(i)0 , c(i)} and delta(i) = I{X(i)0 less-than-or-equal-to c(i)} for certain censoring times c(i) that either are given or come from some censoring distribution. We study the following problems: What do the maximum likelihood estimator and other estimators really estimate when the true hazard rate alpha(s) is different from the parametric hazard rates? What is the limit distribution of an estimator under such outside-the-model circumstances? How can traditional model-based analyses be made model-robust? Does the model-agnostic viewpoint invite alternative estimation approaches? What are the consequences of carrying out model-based and model-robust bootstrapping? How do theoretical and empirical influence functions generalise to situations with censored data? How do methods and results carry over to more complex models for fife history data like regression models and Markov chains?