Covariate-balancing-propensity-score-based inference for linear models with missing responses - 文章详情页

作者：

Guo, Donglin (Guo, Donglin.) | Xue, Liugen (Xue, Liugen.) (学者：薛留根) | Hu, Yuqin (Hu, Yuqin.)

收录：

Scopus SCIE

摘要：

Based　on　covariate　balancing　propensity　score　(CBPS),　improved　estimators　for　the　regression　coefficients　and　population　mean　of　linear　models　are　obtained,　when　the　responses　are　missing　at　random.　It　is　proved　that　the　proposed　estimators　are　asymptotically　normal.　(C)　2016　Elsevier　B.V.　All　rights　reserved.

关键词：

Augmented inverse probability weighted Missing at random GMM Covariate balancing propensity score Robust estimation Linear model

作者机构：

[ 1 ] [Guo, Donglin]Beijing Univ Technol, Coll Appl Sci, Beijing 100124, Peoples R China
[ 2 ] [Xue, Liugen]Beijing Univ Technol, Coll Appl Sci, Beijing 100124, Peoples R China
[ 3 ] [Hu, Yuqin]Beijing Univ Technol, Coll Appl Sci, Beijing 100124, Peoples R China
[ 4 ] [Guo, Donglin]Shangqiu Normal Univ, Sch Math & Informat Sci, Shangqiu 476000, Peoples R China
[ 5 ] [Hu, Yuqin]Zhejiang Univ Finance & Econ, Sch Data Sci, Hangzhou 310018, Peoples R China

通讯作者信息：

[Guo, Donglin]Beijing Univ Technol, Coll Appl Sci, Beijing 100124, Peoples R China

电子邮件地址：

gd11105@emails.bjut.edu.cn

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