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A0585
Title: Valid inference on semiparametric estimators in high-dimensions Authors:  Shaojun Guo - Institute of Statistics and Big Data, Renmin Unversity of China (China) [presenting]
Abstract: Inference after model selection is a fundamental problem in high dimensional settings. A selective overview of post-selection inference in statistical and economic fields will be given, and why statistical inference after model selection is challenging will be discussed. Second, we will focus on a specific topic of post-selection inference. To be specific, we will try to deal with the problem where covariates are generated through high dimensional regularization. It turns out that the regularization step has a very serious effect for valid inference on parameters of interest. Our primary interest is to develop a novel regularized approach to generate covariates. The proposed estimator can be shown to be asymptotically normal. To illustrate that, we provide several examples to demonstrate the superiority of the proposed approach. This approach is also applicable to linear or nonlinear functionals in other sparse nonparametric high dimensional regression models such as additive or varying coefficient models.