Regression based thresholds in principal loading analysis

DOI10.1016/j.jmva.2022.105103OpenAlexW4295809331MaRDI QIDQ2101461

Jan O. Bauer, Bernhard Drabant

Publication date: 6 December 2022

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

Full work available at URL: https://arxiv.org/abs/2103.06691

zbMATH Keywords

principal component analysisvariable selectiondimensionality reductionmultivariate linear regressionmatrix perturbation theoryordinary least squares regression

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Linear regression; mixed models (62J05) Eigenvalues, singular values, and eigenvectors (15A18) Perturbations of nonlinear operators (47H14) Multivariate analysis (62Hxx)

Uses Software

• prinvars

Cites Work

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• Principal loading analysis
• The asymptotic variance matrix of the sample correlation matrix
• Asymptotic theory for the principal component analysis of a vector random function: Some applications to statistical inference
• Clustering and disjoint principal component analysis
• Practical methods for computing power in testing the multivariate general linear hypothesis
• Asymptotics of eigenvalues and unit-length eigenvectors of sample variance and correlation matrices
• Latent Root Regression Analysis
• A Biometrics Invited Paper. The Analysis and Selection of Variables in Linear Regression
• Clustering of Variables Around Latent Components
• Refined Perturbation Bounds for Eigenvalues of Hermitian and Non-Hermitian Matrices
• A useful variant of the Davis–Kahan theorem for statisticians
• Dimensions of Well-Being and Their Statistical Measurements
• The Rotation of Eigenvectors by a Perturbation. III
• Asymptotic Theory for Principal Component Analysis

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