Please join us for this week’s Biostatistics & Medical Informatics seminar on Friday February 18th at noon

Time: Friday, February 18 2022 at 12:00-1:00pm

Zoom Link:


Please note that this seminar is fully virtual.

Title: Decorrelated Local Linear Estimator: Inference for Non-linear Effects in High-dimensional Additive Models

Presenter: Dr. Zijian Guo, Department of Statistics, Rutgers University

Abstract: Additive models play an essential role in studying non-linear relationships. Despite many recent advances in estimation, there is a lack of methods and theories for inference in high-dimensional additive models, including confidence interval construction and hypothesis testing. Motivated by inference for non-linear treatment effects, we consider the high-dimensional additive model and make inference for the derivative of the function of interest. We propose a novel decorrelated local linear estimator and establish its asymptotic normality. The asymptotic variance of our proposed estimator matches with the optimal rate in the univariate setting. The main novelty is the construction of the decorrelation weights, which is instrumental in reducing the error inherited from estimating the high-dimensional additive model. We construct the confidence interval for the function derivative and conduct the related hypothesis testing. We demonstrate our proposed method over large-scale simulation studies and apply it to motif regression. This is based on joint work with Wei Yuan and Cun-Hui Zhang.

Link to Poster