A Double Regression Method for Graphical Modeling of High-dimensional Nonlinear and Non-Gaussian Data

TL;DR

This paper introduces a novel double regression approach for learning graphical models from high-dimensional, nonlinear, and non-Gaussian data, utilizing nonparametric tests and deep neural networks, with proven consistency.

Contribution

It develops a new double regression method that effectively handles high-dimensional nonlinear and non-Gaussian data for graphical model learning, with theoretical guarantees.

Findings

Method performs well on high-dimensional nonlinear data

Consistent under mild conditions

Effective in non-Gaussian settings

Abstract

Graphical models have long been studied in statistics as a tool for inferring conditional independence relationships among a large set of random variables. The most existing works in graphical modeling focus on the cases that the data are Gaussian or mixed and the variables are linearly dependent. In this paper, we propose a double regression method for learning graphical models under the high-dimensional nonlinear and non-Gaussian setting, and prove that the proposed method is consistent under mild conditions. The proposed method works by performing a series of nonparametric conditional independence tests. The conditioning set of each test is reduced via a double regression procedure where a model-free sure independence screening procedure or a sparse deep neural network can be employed. The numerical results indicate that the proposed method works well for high-dimensional nonlinear and non-Gaussian data.