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2016 Performance bounds for parameter estimates of high-dimensional linear models with correlated errors
Wei-Biao Wu, Ying Nian Wu
Electron. J. Statist. 10(1): 352-379 (2016). DOI: 10.1214/16-EJS1108


This paper develops a systematic theory for high-dimensional linear models with dependent errors and/or dependent covariates. To study properties of estimates of the regression parameters, we adopt the framework of functional dependence measures ([43]). For the covariates two schemes are addressed: the random design and the deterministic design. For the former we apply the constrained $\ell_{1}$ minimization approach, while for the latter the Lasso estimation procedure is used. We provide a detailed characterization on how the error rates of the estimates depend on the moment conditions that control the tail behaviors, the dependencies of the underlying processes that generate the errors and the covariates, the dimension and the sample size. Our theory substantially extends earlier ones by allowing dependent and/or heavy-tailed errors and the covariates. As our main tools, we derive exponential tail probability inequalities for dependent sub-Gaussian errors and Nagaev-type inequalities for dependent non-sub-Gaussian errors that arise from linear or non-linear processes.


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Wei-Biao Wu. Ying Nian Wu. "Performance bounds for parameter estimates of high-dimensional linear models with correlated errors." Electron. J. Statist. 10 (1) 352 - 379, 2016.


Received: 1 March 2015; Published: 2016
First available in Project Euclid: 17 February 2016

zbMATH: 1333.62172
MathSciNet: MR3466186
Digital Object Identifier: 10.1214/16-EJS1108

Rights: Copyright © 2016 The Institute of Mathematical Statistics and the Bernoulli Society


Vol.10 • No. 1 • 2016
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