Hashimoto Elizabeth M, Ortega Edwin M M, Cordeiro Gauss M, Barreto Mauricio L
Departamento de Ciências Exatas, Universidade de São Paulo, São Paulo, Brazil.
J Biopharm Stat. 2012;22(1):141-59. doi: 10.1080/10543406.2010.509527.
The log-Burr XII regression model for grouped survival data is evaluated in the presence of many ties. The methodology for grouped survival data is based on life tables, where the times are grouped in k intervals, and we fit discrete lifetime regression models to the data. The model parameters are estimated by maximum likelihood and jackknife methods. To detect influential observations in the proposed model, diagnostic measures based on case deletion, so-called global influence, and influence measures based on small perturbations in the data or in the model, referred to as local influence, are used. In addition to these measures, the total local influence and influential estimates are also used. We conduct Monte Carlo simulation studies to assess the finite sample behavior of the maximum likelihood estimators of the proposed model for grouped survival. A real data set is analyzed using a regression model for grouped data.
在存在大量节点的情况下，对分组生存数据的对数 - 伯尔 XII 回归模型进行评估。分组生存数据的方法基于生命表，其中时间被划分为 k 个区间，并且我们对数据拟合离散寿命回归模型。模型参数通过最大似然法和刀切法进行估计。为了在所提出的模型中检测有影响的观测值，使用了基于案例删除的诊断度量（即所谓的全局影响）以及基于数据或模型中的小扰动的影响度量（称为局部影响）。除了这些度量之外，还使用了总局部影响和有影响估计。我们进行蒙特卡罗模拟研究，以评估所提出的分组生存模型的最大似然估计量的有限样本行为。使用分组数据的回归模型对一个真实数据集进行分析。
