Model selection for high-dimensional linear regression with dependent observations

DOI10.1214/19-AOS1872zbMath1459.62172arXiv1906.07395OpenAlexW3049332904MaRDI QIDQ2215720

Ching-Kang Ing

Publication date: 14 December 2020

Published in: The Annals of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1906.07395

zbMATH Keywords

time seriesorthogonal greedy algorithmbest \(m\)-term approximationshigh-dimensional Akaike's information criterionsparsity conditions

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Linear regression; mixed models (62J05) Statistical ranking and selection procedures (62F07) Statistical aspects of information-theoretic topics (62B10)

Related Items (4)

Greedy Variable Selection for High-Dimensional Cox Models ⋮ A resampling approach for confidence intervals in linear time-series models after model selection ⋮ Estimation and variable selection for high-dimensional spatial dynamic panel data models ⋮ On asymptotic risk of selecting models for possibly nonstationary time-series

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