The change-plane Cox model is a popular tool for the subgroup analysis of the survival data. Despite the rich literature on this model, there has been limited investigation on the asymptotic properties of the estimators of the finite dimensional parameter. Particularly, the convergence rate, not to mention the asymptotic distribution, remains an unsolved problem for the general model where classification is based on multiple covariates. To bridge this theoretical gap, this study proposes a maximum smoothed partial likelihood estimator and establishes the following asymptotic properties. First, it shows that the convergence rate for the classification parameter can be arbitrarily close to n^-1 up to a logarithmic factor, depending on a choice of tuning parameter. Second, it establishes the asymptotic normality for the regression parameter.
翻译:更改平面 Cox 模型是分组分析生存数据的一个受欢迎的工具。 尽管该模型的文献丰富, 但对于有限维参数估计参数的无症状特性的调查有限。 特别是, 趋同率, 更不用提无症状分布, 对于基于多个共变量进行分类的一般模型来说, 仍然是尚未解决的问题 。 为了缩小这一理论差距, 本研究建议了最大平滑的部分概率估计器, 并设定了以下的无症状特性 。 首先, 它表明分类参数的趋同率可以任意接近 n ⁇ -1, 直至对数系数, 取决于调参数的选择 。 其次, 它确定了回归参数的无症状常度 。