An RKHS-based approach to double-penalized regression in high-dimensional partially linear models

DOI10.1016/j.jmva.2018.07.013zbMath1401.62057OpenAlexW2883706650WikidataQ129461820 ScholiaQ129461820MaRDI QIDQ1795582

Wenquan Cui, Haoyang Cheng, Jiajing Sun

Publication date: 16 October 2018

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jmva.2018.07.013

zbMATH Keywords

high-dimensional datareproducing kernel Hilbert spaceoracle propertypartially linear modelSacks-Ylvisaker conditionsrepresenter theoremeigen-analysisSCAD (smoothly clipped absolute deviation) penalty

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Nonparametric regression and quantile regression (62G08) Ridge regression; shrinkage estimators (Lasso) (62J07) Asymptotic properties of nonparametric inference (62G20)

Related Items (1)

Sparse high-dimensional semi-nonparametric quantile regression in a reproducing kernel Hilbert space

Uses Software

• gss

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