Doubly penalized estimation in additive regression with high-dimensional data

DOI10.1214/18-AOS1757zbMath1436.62358arXiv1704.07229OpenAlexW2966000184MaRDI QIDQ2328052

Yanyan Li

Publication date: 9 October 2019

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

Full work available at URL: https://arxiv.org/abs/1704.07229

zbMATH Keywords

total variationSobolev spacehigh-dimensional datametric entropyreproducing kernel Hilbert spaceadditive modelpenalized estimationANOVA modeltrend filteringbounded variation space

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Parametric tolerance and confidence regions (62F25) Asymptotic distribution theory in statistics (62E20) Linear regression; mixed models (62J05) Generalized linear models (logistic models) (62J12) Robustness and adaptive procedures (parametric inference) (62F35) Hilbert spaces with reproducing kernels (= (proper) functional Hilbert spaces, including de Branges-Rovnyak and other structured spaces) (46E22) Analysis of variance and covariance (ANOVA) (62J10)

Related Items

Improved Estimation of High-dimensional Additive Models Using Subspace Learning, Extreme eigenvalues of nonlinear correlation matrices with applications to additive models, Hierarchical Total Variations and Doubly Penalized ANOVA Modeling for Multivariate Nonparametric Regression, Grouped variable selection with discrete optimization: computational and statistical perspectives, Sharp lower bound for regression with measurement errors and its implication for ill-posedness of functional regression, Linear and nonlinear signal detection and estimation in high-dimensional nonparametric regression under weak sparsity, Large Scale Prediction with Decision Trees, Doubly penalized estimation in additive regression with high-dimensional data

Uses Software

• gss

Cites Work

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