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Concentration Inequalities for High-Dimensional Linear Processes with Dependent Innovations

Year: 2025

Type: article

Abstract: We develop concentration inequalities for the l∞ norm of vector linear processes with sub-Weibull, mixingale innovations.This inequality is used to obtain a concentration bound for the maximum entrywise norm of the lag-h autocovariance matrix of linear processes.We apply these inequalities to sparse estimation of large-dimensional VAR(p) systems and heterocedasticity and autocorrelation consistent (HAC) high-dimensional covariance estimation.

Source: Statistica Sinica

DOI: 10.5705/ss.202023.0364

Authors: Eduardo Fonseca Mendes, Fellipe Lopes Lima Leite

Language: English

Topic: Statistical Methods and Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: bronze

Funder: Fundação de Amparo à Pesquisa do Estado de São Paulo

Award: Econometric modeling and forecasting in high dimensional models

OpenAlex ID: W4406134989

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