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Activity Number: 345 - High-Dimensional Statistics
Type: Contributed
Date/Time: Tuesday, July 30, 2019 : 10:30 AM to 12:20 PM
Sponsor: IMS
Abstract #306939 Presentation
Title: Debiased Inference in High-Dimensional Single-Index Models Under Gaussian Design
Author(s): Hamid Eftekhari* and Moulinath of Banerjee and Ya'acov Ritov
Companies: University of Michigan and university of michigan and university of michigan
Keywords: Sparsity; Asymptotic normality; Non-linear link function; Semiparametric regression; Average Partial Effect

We consider the problem of statistical inference of a single covariate in a single-index model with p > n covariates and unknown link function under Gaussian design. The estimator of the coefficient is similar to the de-biased lasso in the standard linear model and is square-root consistent and asymptotically normally distributed.

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

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