Robust Estimation of Mean in Partially Linear Model With Missing Responses - 文章详情页

作者：

Guo, D. (Guo, D..) | Xue, L. (Xue, L..) | Hu, Y. (Hu, Y..)

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摘要：

To　improve　the　robustness　of　an　estimator,　based　on　the　covariate　balancing　propensity　score　and　the　augmented　inverse　probability　weighted　methods,　a　robust　estimator　of　the　population　mean　was　obtained　for　the　partially　linear　model,　when　the　responses　were　missing　at　random.　It　is　proved　that　the　proposed　estimator　is　asymptotically　normal,　and　hence　it　can　be　applied　to　constructing　the　confidence　region　of　the　population　mean.　©　2017,　Editorial　Department　of　Journal　of　Beijing　University　of　Technology.　All　right　reserved.

关键词：

Augmented inverse probability weighted; Covariate balancing propensity score; Generalized method of moments (GMM); Missing at random; Partially linear model; Robust estimation

作者机构：

[ 1 ] [Guo, D.]College of Applied Sciences, Beijing University of Technology, Beijing, 100124, China
[ 2 ] [Guo, D.]School of Mathematics and Information Science, Shangqiu Normal University, Shangqiu, Henan 476000, China
[ 3 ] [Xue, L.]College of Applied Sciences, Beijing University of Technology, Beijing, 100124, China
[ 4 ] [Hu, Y.]College of Applied Sciences, Beijing University of Technology, Beijing, 100124, China
[ 5 ] [Hu, Y.]School of Data Science, Zhejiang University of Finance and Economics, Hangzhou, 310018, China

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