Variable selection for high dimensional Gaussian copula regression model: an adaptive hypothesis testing procedure

DOI10.1016/j.csda.2018.03.003zbMath1469.62078OpenAlexW2791175438WikidataQ130097407 ScholiaQ130097407MaRDI QIDQ1662864

Yong He, Xin Sheng Zhang, Li-wen Zhang

Publication date: 20 August 2018

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2018.03.003

zbMATH Keywords

variable selectionmultiple testingFDR/FDVGaussian copula regression

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Nonparametric regression and quantile regression (62G08) Paired and multiple comparisons; multiple testing (62J15)

Related Items

Robust factor number specification for large-dimensional elliptical factor model ⋮ Efficient and feasible inference for high-dimensional normal copula regression models ⋮ Copula shrinkage and portfolio allocation in ultra-high dimensions

Uses Software

• gcmr
• CoSaMP
• hgam

Cites Work

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