报告题目：Robust Inference for High-dimensional Single Index Models        

报告时间：2021年11月22日下午13：30

会议链接：https://meeting.tencent.com/dm/jgBKYQED1TCV

会议 ID：311 487 388

主办单位：科研处/数学与统计学院

主讲人：韩东啸

韩东啸简介：博士毕业于中国科学院大学，在美国西北大学、香港中文大学从事博士后研究工作，现工作于南开大学，主持国家自然科学基金青年基金一项。主要研究方向为生存分析、变量选择和高维统计推断。研究成果发表于Joe、Biometrics等国际知名学术期刊。

报告摘要：We propose a robust inference method for high-dimensional single index models with an unknown link function and covariates satisfying the linearity in expectation condition,focusing on signal recovery and post-selection inference. The proposed method is built on the Huber loss and the estimation of the unknown link function is avoided. The l_1 and l_2 consistency of a Lasso estimator up to a multiplicative scalar is established.When the covariance matrix of the predictors satisfies the irrepresentable condition,our method is shown to recover the signed support of the true parameter under mild conditions. Based on a debiased Lasso estimator, we study the pointwise and group post-selection inference for the high-dimensional index parameter.

The finite-sample performance of our method is evaluated through  extensive simulation studies.An application to a riboflavin production dataset is provided to illustrate the proposed method.