Penalized regression analysis with individual-specific patterns of missing covariates

作     者：Liu, Zhishuai Zhan, Zishu Lin, Cunjie 

作者机构：Renmin Univ China Ctr Appl Stat Beijing Peoples R China Renmin Univ China Sch Stat Beijing Peoples R China 

出 版 物：《COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION》 (统计学通讯：模拟与计算)

年 卷 期：2024年第53卷第7期

页      面：3126-3142页

核心收录：

学科分类：0202[经济学-应用经济学] 02[经济学] 020208[经济学-统计学] 07[理学] 0714[理学-统计学（可授理学、经济学学位）] 

基　　金：Fundamental Research Funds for the Central Universities Research Funds of Renmin University of China [19XNB014] 

主　　题：High missing rate Individual-specific missing Variable selection Iterative algorithm 

摘      要：High-dimensional but incomplete data are common in many settings. With such data, regularized estimation and variable selection techniques developed for complete data fail to produce reliable results due to the missingness. To address this problem, we propose an iterative penalized least squares estimation (IPLSE) method, which customizes existing penalized regression techniques for data with high missing rates and individual-specific missing patterns. The proposed method simultaneously conducts missing value imputation, parameter estimation, and variable selection. Statistical properties are rigorously established, and a simulation demonstrates its competitive performance under various missing patterns, especially when there is missingness concerning important variables. An analysis of China s provincial economic data further supports the merits of the proposed method.