Permutation methods for factor analysis and PCA

DOI10.1214/19-AOS1907zbMath1460.62093arXiv1710.00479OpenAlexW3087034383MaRDI QIDQ2215761

Edgar Dobriban

Publication date: 14 December 2020

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

Full work available at URL: https://arxiv.org/abs/1710.00479

zbMATH Keywords

factor analysisprincipal component analysis (PCA)high-dimensional asymptoticspermutation methodsparallel analysis

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Estimation in multivariate analysis (62H12) Signal detection and filtering (aspects of stochastic processes) (60G35) Signal theory (characterization, reconstruction, filtering, etc.) (94A12)

Related Items (9)

Estimating Number of Factors by Adjusted Eigenvalues Thresholding ⋮ Biwhitening Reveals the Rank of a Count Matrix ⋮ Statistical inference for principal components of spiked covariance matrices ⋮ A CLT for the LSS of large-dimensional sample covariance matrices with diverging spikes ⋮ Rapid evaluation of the spectral signal detection threshold and Stieltjes transform ⋮ Matrix Denoising for Weighted Loss Functions and Heterogeneous Signals ⋮ Estimating change-point latent factor models for high-dimensional time series ⋮ A note on the likelihood ratio test in high-dimensional exploratory factor analysis ⋮ Consistency of invariance-based randomization tests

Uses Software

• nFactors
• ElemStatLearn
• OptShrink

Cites Work

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