Performances of some high dimensional regression methods: sparse principal component regression

DOI10.1080/03610918.2021.1898638zbMath1497.62171OpenAlexW3138134847MaRDI QIDQ5082719

Fatma Sevinç Kurnaz

Publication date: 21 June 2022

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610918.2021.1898638

zbMATH Keywords

high-dimensional datasparsityprincipal component regressionLasso type penalty

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Nonparametric regression and quantile regression (62G08) Factor analysis and principal components; correspondence analysis (62H25) Ridge regression; shrinkage estimators (Lasso) (62J07) Linear regression; mixed models (62J05)

Related Items

Penalised, post‐pretest, and post‐shrinkage strategies in nonlinear growth models

Uses Software

• chemometrics

Cites Work

• The Adaptive Lasso and Its Oracle Properties
• Sparse principal component regression with adaptive loading
• Regularization and Variable Selection Via the Elastic Net
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