Cover Image

Dimension estimation in PCA model using high-dimensional data augmentation

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Radojicic, Una, Virta, Joni
Format:	Preprint
Published:	2025
Subjects:	Statistics Theory Methodology
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

New M-estimator of the leading principal component
by: Virta, Joni, et al.
Published: (2025)

Unsupervised linear discrimination using skewness
by: Radojicic, Una, et al.
Published: (2025)

Asymptotic testing of covariance separability for matrix elliptical data
by: Virta, Joni, et al.
Published: (2026)

Large-Sample Properties of Non-Stationary Source Separation for Gaussian Signals
by: Bachoc, François, et al.
Published: (2022)

A method for sparse and robust independent component analysis
by: Heinonen, Lauri, et al.
Published: (2025)

Optimal covariance matrix estimation for high-dimensional noise in high-frequency data
by: Chang, Jinyuan, et al.
Published: (2018)

Weight-calibrated estimation for factor models of high-dimensional time series
by: Qiao, Xinghao, et al.
Published: (2025)

Robust functional PCA for relative data
by: Oguamalam, Jeremy, et al.
Published: (2024)

Latent confounding in high-dimensional nonlinear models
by: Wang, Yuhao, et al.
Published: (2025)

Controlling the false discovery rate in high-dimensional linear models using model-X knockoffs and $p$-values
by: Chang, Jinyuan, et al.
Published: (2025)

Minimum regularized covariance trace estimator and outlier detection for functional data
by: Oguamalam, Jeremy, et al.
Published: (2023)

Testing independence in the presence of missing data: high-dimensional case
by: Cuparić, Marija, et al.
Published: (2026)

Does PCA Work for Rough Functional Data?
by: Kutta, Tim, et al.
Published: (2026)

Robust covariance estimation and explainable outlier detection for matrix-valued data
by: Mayrhofer, Marcus, et al.
Published: (2024)

Determining the signal dimension in second order source separation
by: Virta, Joni, et al.
Published: (2018)

Empirical Bayes inference in sparse high-dimensional generalized linear models
by: Tang, Yiqi, et al.
Published: (2023)

A nonparametric test of spherical symmetry applicable to high dimensional data
by: Banerjee, Bilol, et al.
Published: (2024)

Uncertainty of high-dimensional genetic data prediction with polygenic risk scores
by: Fu, Haoxuan, et al.
Published: (2024)

Selecting the number of components in PCA via random signflips
by: Hong, David, et al.
Published: (2020)

Robust mean change point testing in high-dimensional data with heavy tails
by: Li, Mengchu, et al.
Published: (2023)

Exact distribution-free tests of spherical symmetry applicable to high dimensional data
by: Banerjee, Bilol, et al.
Published: (2024)

Dimension-free bounds in high-dimensional linear regression via error-in-operator approach
by: Noskov, Fedor, et al.
Published: (2025)

Sufficient conditions for proper posteriors in fully-Bayesian Functional PCA
by: Sartini, Joseph, et al.
Published: (2026)

On the modelling and prediction of high-dimensional functional time series
by: Chang, Jinyuan, et al.
Published: (2024)

Covariance scanning for adaptively optimal change point detection in high-dimensional linear models
by: Cho, Haeran, et al.
Published: (2025)

Fitting sparse high-dimensional varying-coefficient models with Bayesian regression tree ensembles
by: Ghosh, Soham, et al.
Published: (2025)

Sparse PCA: A New Scalable Estimator Based On Integer Programming
by: Behdin, Kayhan, et al.
Published: (2021)

Testing common structure in high-dimensional factor models: change-point and two-sample procedures
by: Düker, Marie-Christine, et al.
Published: (2024)

Asymmetric canonical correlation analysis of Riemannian and high-dimensional data
by: Buenfil, James, et al.
Published: (2024)

PCA for Point Processes
by: Picard, Franck, et al.
Published: (2024)

Deflated HeteroPCA: Overcoming the curse of ill-conditioning in heteroskedastic PCA
by: Zhou, Yuchen, et al.
Published: (2023)

Statistical inference for high-dimensional spectral density matrix
by: Chang, Jinyuan, et al.
Published: (2022)

Two-Stage Testing in a high dimensional setting
by: Jonker, Marianne A, et al.
Published: (2024)

Adaptive L-statistics for high dimensional test problem
by: Ma, Huifang, et al.
Published: (2024)

HEDE: Heritability estimation in high dimensions by Ensembling Debiased Estimators
by: Song, Yanke, et al.
Published: (2024)

An autocovariance-based learning framework for high-dimensional functional time series
by: Chang, Jinyuan, et al.
Published: (2020)

Trade-off between predictive performance and FDR control for high-dimensional Gaussian model selection
by: Lacroix, Perrine, et al.
Published: (2023)

Spatial depth for data in metric spaces
by: Joni Virta
Published: (2026)

Detection and inference of changes in high-dimensional linear regression with non-sparse structures
by: Cho, Haeran, et al.
Published: (2024)

Test for high-dimensional mean vectors via the weighted $L_2$-norm
by: Li, Jianghao, et al.
Published: (2024)