摘要
本文旨在建立一个存在不可忽略的无响应缺失时高维协变量向量的协变量选择方法.由于有不可忽略的缺失响应数据,必须建立一种新的协变量选择方法来删除既与响应变量也与缺失机制无关的协变量.一旦冗余协变量被删除,现有的缺失机制估计和其他基于逆缺失机制加权的分析方法可以被应用.我们提供了一些模拟结果来展示我们方法的有效性.
This paper aims at developing a covariate selection approach for high-dimensional covariate vector in the presence of nonignorable nonresponse.Because of nonignorable missing responses,a novel covariate selection method has to be developed to eliminate covariates associated with neither the response variable nor the nonresponse mechanism.Once the redundant covariates are removed,existing methods for propensity estimation and other analyses by inverse propensity weighting can be applied.We provide some simulation results to show the effectiveness of our approach.
作者
邵军
王磊
SHAO Jun;WANG Lei(School of Statistics,East China Normal University,Shanghai,200062,China;Department of Statistics,University of Wisconsin-Madison,Madison,WI 53706,USA;School of Statistics and Data Science&LPMC,Nankai University,Tianjin,300071,China)
出处
《应用概率统计》
CSCD
北大核心
2024年第2期287-297,共11页
Chinese Journal of Applied Probability and Statistics
基金
supported by the Fundamental Research Funds for the Central Universities and the National Natural Science Foundation of China(Grant No.12271272).
关键词
构造的响应变量
高维
非随机丢失
缺失机制
半参数方法
created responses
high dimensionality
missing not at random
propensity
semiparametric method
