Statistical inference for high-dimensional generalized estimating equations

by   Lu Xia, et al.

We propose a novel inference procedure for linear combinations of high-dimensional regression coefficients in generalized estimating equations, which have been widely used for correlated data analysis for decades. Our estimator, obtained via constructing a system of projected estimating equations, is shown to be asymptotically normally distributed under certain regularity conditions. We also introduce a data-driven cross-validation procedure to select the tuning parameter for estimating the projection direction, which is not addressed in the existing procedures. We demonstrate the robust finite-sample performance, especially in estimation bias and confidence interval coverage, of the proposed method via extensive simulations, and apply the method to gene expression data on riboflavin production with Bacillus subtilis.


page 1

page 2

page 3

page 4


Statistical inference for high dimensional regression via Constrained Lasso

In this paper, we propose a new method for estimation and constructing c...

Inference in High-dimensional Multivariate Response Regression with Hidden Variables

This paper studies the inference of the regression coefficient matrix un...

Semi-Supervised Statistical Inference for High-Dimensional Linear Regression with Blockwise Missing Data

Blockwise missing data occurs frequently when we integrate multisource o...

Doubly Distributed Supervised Learning and Inference with High-Dimensional Correlated Outcomes

This paper presents a unified framework for supervised learning and infe...

A Unified Theory of Confidence Regions and Testing for High Dimensional Estimating Equations

We propose a new inferential framework for constructing confidence regio...

Statistical Inference on Partially Linear Panel Model under Unobserved Linearity

A new statistical procedure, based on a modified spline basis, is propos...

A Distributed and Integrated Method of Moments for High-Dimensional Correlated Data Analysis

This paper is motivated by a regression analysis of electroencephalograp...

Please sign up or login with your details

Forgot password? Click here to reset