Open Access
April 2020 Lasso guarantees for $\beta$-mixing heavy-tailed time series
Kam Chung Wong, Zifan Li, Ambuj Tewari
Ann. Statist. 48(2): 1124-1142 (April 2020). DOI: 10.1214/19-AOS1840


Many theoretical results for lasso require the samples to be i.i.d. Recent work has provided guarantees for lasso assuming that the time series is generated by a sparse Vector Autoregressive (VAR) model with Gaussian innovations. Proofs of these results rely critically on the fact that the true data generating mechanism (DGM) is a finite-order Gaussian VAR. This assumption is quite brittle: linear transformations, including selecting a subset of variables, can lead to the violation of this assumption. In order to break free from such assumptions, we derive nonasymptotic inequalities for estimation error and prediction error of lasso estimate of the best linear predictor without assuming any special parametric form of the DGM. Instead, we rely only on (strict) stationarity and geometrically decaying $\beta$-mixing coefficients to establish error bounds for lasso for sub-Weibull random vectors. The class of sub-Weibull random variables that we introduce includes sub-Gaussian and subexponential random variables but also includes random variables with tails heavier than an exponential. We also show that, for Gaussian processes, the $\beta$-mixing condition can be relaxed to summability of the $\alpha$-mixing coefficients. Our work provides an alternative proof of the consistency of lasso for sparse Gaussian VAR models. But the applicability of our results extends to non-Gaussian and nonlinear times series models as the examples we provide demonstrate.


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Kam Chung Wong. Zifan Li. Ambuj Tewari. "Lasso guarantees for $\beta$-mixing heavy-tailed time series." Ann. Statist. 48 (2) 1124 - 1142, April 2020.


Received: 1 August 2017; Revised: 1 March 2019; Published: April 2020
First available in Project Euclid: 26 May 2020

zbMATH: 07241583
MathSciNet: MR4102690
Digital Object Identifier: 10.1214/19-AOS1840

Primary: 60K35

Keywords: high-dimensional estimation , Lasso , Mixing , time series

Rights: Copyright © 2020 Institute of Mathematical Statistics

Vol.48 • No. 2 • April 2020
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