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Activity Number: 452
Type: Contributed
Date/Time: Tuesday, August 2, 2016 : 3:05 PM to 3:50 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #321642
Title: High-Dimensional Inference for Partial Linear Models
Author(s): Zhuqing Yu*
Keywords: Partial Linear Model ; Debiased Lasso ; Statistical Inference

We consider high dimensional partial linear models, where the dimension of parametric components is allowed to be exponentially high w.r.t. sample size. We propose a semiparametric version of de-biased lasso estimator. In the high dimensional regime, this new estimate is shown to be asymptotically normal and achieve the semiparametric efficiency bound. Based on this distributional result, we further conduct simultaneous hypothesis testing. Interesting applications such as support recovery and multiple testing with family-wise error rate control will also be discussed.

Authors who are presenting talks have a * after their name.

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