High Dimensional Logistic Regression Under Network Dependence

TL;DR

This paper introduces a high-dimensional logistic regression model that accounts for network dependence by incorporating pairwise interactions, enabling consistent parameter estimation even with dependent data.

Contribution

It develops a penalized pseudo-likelihood method for network-dependent high-dimensional logistic regression, extending classical results to dependent data scenarios.

Findings

Estimates achieve classical high-dimensional convergence rates under network dependence.

Method effectively handles high-dimensional covariates and network peer-effects.

Numerical experiments validate theoretical consistency and computational efficiency.

Abstract

Logistic regression is key method for modeling the probability of a binary outcome based on a collection of covariates. However, the classical formulation of logistic regression relies on the independent sampling assumption, which is often violated when the outcomes interact through an underlying network structure, such as over a temporal/spatial domain or on a social network. This necessitates the development of models that can simultaneously handle both the network `peer-effect' and the effect of high-dimensional covariates. In this paper, we develop a framework for incorporating such dependencies in a high-dimensional logistic regression model by introducing a quadratic interaction term, as in the Ising model, designed to capture the pairwise interactions from the underlying network. The resulting model can also be viewed as an Ising model, where the node-dependent external fields linearly encode the high-dimensional covariates. We propose a penalized maximum pseudo-likelihood method for estimating the network peer-effect and the effect of the covariates (the regression coefficients), which, in addition to handling the high-dimensionality of the parameters, conveniently avoids the computational intractability of the maximum likelihood approach. Under various standard regularity conditions, we show that the corresponding estimate attains the classical high-dimensional rate of consistency. Our results imply that even under network dependence it is possible to consistently estimate the model parameters at the same rate as in classical (independent) logistic regression, when the true parameter is sparse and the underlying network is not too dense. We also develop an efficient algorithm for computing the estimates and validate our theoretical results in numerical experiments. An application to selecting genes in clustering spatial transcriptomics data is also discussed.