Escobar L A, Meeker W Q
Department of Experimental Statistics, Louisiana State University, Baton Rouge 70803.
Biometrics. 1992 Jun;48(2):507-28.
In this paper we show how to evaluate the effect that perturbations to the model, data, or case weights have on maximum likelihood estimates from censored survival data. The ideas and methods also apply to other nonlinear estimation problems. We review the ideas behind using log-likelihood displacement and local influence methods. We describe new interpretations for some local influence statistics and show how these statistics extend and complement traditional case deletion influence statistics for linear least squares. These statistics identify individual and combinations of cases that have important influence on estimates of parameters and functions of these parameters. We illustrate the methods by reanalyzing the Stanford Heart Transplant data with a parametric regression model.
在本文中，我们展示了如何评估对模型、数据或病例权重的扰动对删失生存数据的最大似然估计的影响。这些思想和方法也适用于其他非线性估计问题。我们回顾了使用对数似然位移和局部影响方法背后的思想。我们描述了一些局部影响统计量的新解释，并展示了这些统计量如何扩展和补充线性最小二乘的传统病例删除影响统计量。这些统计量识别出对参数估计及其函数有重要影响的个体病例和病例组合。我们通过使用参数回归模型重新分析斯坦福心脏移植数据来说明这些方法。
