# Testing for high-dimensional network parameters in auto-regressive models

## TL;DR

This paper develops statistical inference methods, including confidence intervals, for high-dimensional auto-regressive network models with sub-Gaussian noise, extending beyond Gaussian assumptions and addressing dependence challenges.

## Contribution

It introduces convergence in distribution results and confidence intervals for high-dimensional AR(p) models with sub-Gaussian noise, broadening applicability beyond Gaussian assumptions.

## Key findings

- Convergence results hold when T scales as (s ∨ ρ)^2 log^2 M.
- Provides novel concentration bounds for dependent sub-Gaussian quadratic forms.
- Validates theoretical results through simulations on structured networks.

## Abstract

High-dimensional auto-regressive models provide a natural way to model influence between $M$ actors given multi-variate time series data for $T$ time intervals. While there has been considerable work on network estimation, there is limited work in the context of inference and hypothesis testing. In particular, prior work on hypothesis testing in time series has been restricted to linear Gaussian auto-regressive models. From a practical perspective, it is important to determine suitable statistical tests for connections between actors that go beyond the Gaussian assumption. In the context of \emph{high-dimensional} time series models, confidence intervals present additional estimators since most estimators such as the Lasso and Dantzig selectors are biased which has led to \emph{de-biased} estimators. In this paper we address these challenges and provide convergence in distribution results and confidence intervals for the multi-variate AR(p) model with sub-Gaussian noise, a generalization of Gaussian noise that broadens applicability and presents numerous technical challenges. The main technical challenge lies in the fact that unlike Gaussian random vectors, for sub-Gaussian vectors zero correlation does not imply independence. The proof relies on using an intricate truncation argument to develop novel concentration bounds for quadratic forms of dependent sub-Gaussian random variables. Our convergence in distribution results hold provided $T = \Omega((s \vee \rho)^2 \log^2 M)$, where $s$ and $\rho$ refer to sparsity parameters which matches existed results for hypothesis testing with i.i.d. samples. We validate our theoretical results with simulation results for both block-structured and chain-structured networks.