自适应设计下基于COX回归模型的序贯压缩估计研究

Sequential Shrinkage Estimate Based on COX Regression Model under Adaptive Design

摘要: 在COX回归模型的应用中，我们经常会遇到包含太多变量的数据集，而这些变量中只有少数变量对模型有贡献。因此，在推断过程中估计“无效”变量会浪费大量的样本。在本文中，我们提出一种基于自适应压缩估计的序贯抽样策略来构造“有效”参数的固定长度的置信集，这样在忽略模型中的“无效”变量影响的同时，使用最少样本将模型中的“有效”变量快速地识别出来。最后，在自适应设计下对我们所提出的序贯抽样策略进行数值模拟并且数值模拟达到了预期的效果。

Abstract: In the applications of COX regression models, we always encounter the data sets which contain too many variables that only a few of them contribute to the model. Therefore, it will waste much more samples to estimate the “non-effective” variables in the inference. In this paper, we use a sequential procedure for constructing the fixed size confidence set for the “effective” parameters to the model based on an adaptive shrinkage estimate such that the “effective” coefficients can be efficiently identified with the minimum sample size. Adaptive design is considered for numerical simulation.