典型文献
Recent advances in statistical methodologies in evaluating program for high-dimensional data
文献摘要：
The era of big data brings opportunities and challenges to developing new statistical methods and models to evaluate social programs or economic policies or interventions.This paper provides a comprehensive review on some recent advances in statistical methodologies and models to evaluate programs with high-dimensional data.In particular,four kinds of methods for making valid statistical inferences for treatment effects in high dimensions are addressed.The first one is the so-called doubly robust type estimation,which models the outcome regression and propensity score functions simultaneously.The second one is the covariate balance method to construct the treatment effect estimators.The third one is the sufficient dimension reduction approach for causal inferences.The last one is the machine learning procedure directly or indirectly to make statistical inferences to treatment effect.In such a way,some of these methods and models are closely related to the de-biased Lasso type methods for the regression model with high dimensions in the statistical literature.Finally,some future research topics are also discussed.
文献关键词：
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作者姓名：
ZHAN Ming-feng;CAI Zong-wu;FANG Ying;LIN Ming
作者机构：
Wang Yanan Institute for Studies in Economics and Fujian Key Laboratory of Statistical Sciences,Xiamen University,Xiamen 361005,China;Department of Economics,University of Kansas,Lawrence,KS 66045,USA;Department of Statistics and Data Science,Xiamen University,Xiamen 361005,China
文献出处：
引用格式：
[1]ZHAN Ming-feng;CAI Zong-wu;FANG Ying;LIN Ming-.Recent advances in statistical methodologies in evaluating program for high-dimensional data)[J].高校应用数学学报B辑（英文版）,2022(01):131-146
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