Computational Methods in Survival Analysis
Computational Methods in Survival Analysis
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DOI：
10.1007/978-3-642-21551-3_27
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
T. Kamakura
中科院分区：
文献类型：
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作者：
T. Kamakura
生存分析广泛应用于医学、制药、可靠性和金融工程等领域，用于分析由特定事件发生定义的正随机现象。在可靠性领域，我们关注的是一些物理组件（如电子设备或机器部件）的失效时间。本文从统计计算方法的角度简要地介绍了最近发展起来的统计生存技术，着重于通过基于一阶矩和条件似然的简单计算来获得分布参数的良好估计，以消除干扰参数和近似似然。偏似然法（考克斯1972，1975）最初是从条件似然的观点提出的，目的是避免估计基线危险的讨厌参数，以获得结构参数的简单而良好的估计。然而，在故障时间的重关系的情况下，计算偏似然不成功。然后研究了偏似然的近似，这将在后面的部分中描述，并将解释一种好的近似方法。我们相信，更好的近似方法和更好的统计模型将在大大减轻计算负担方面发挥重要作用。
Survival analysis is widely used in the fields of medical science, pharmaceutics, reliability and financial engineering, and many others to analyze positive random phenomena defined by event occurrences of particular interest. In the reliability field, we are concerned with the time to failure of some physical component such as an electronic device or a machine part. This article briefly describes statistical survival techniques developed recently from the standpoint of statistical computational methods focussing on obtaining the good estimates of distribution parameters by simple calculations based on the first moment and conditional likelihood for eliminating nuisance parameters and approximation of the likelihoods. The method of partial likelihood (Cox 1972, 1975) was originally proposed from the view point of conditional likelihood for avoiding estimating the nuisance parameters of the baseline hazards for obtaining simple and good estimates of the structure parameters. However, in case of heavy ties of failure times calculating the partial likelihood does not succeed. Then the approximations of the partial likelihood have been studied, which will be described in the later section and a good approximation method will be explained. We believe that the better approximation method and the better statistical model should play an important role in lessening the computational burdens greatly.