Statistical Inference for High-Dimensional Matrix-Variate Factor Models

This article considers the estimation and inference of the low-rank components in high-dimensional matrix-variate factor models, where each dimension of the matrix-variates (p × q) is comparable to or greater than the number of observations (T). We propose an estimation method called α-PCA that pres...

Full description

Saved in:

Bibliographic Details
Published in:	Journal of the American Statistical Association Vol. 118; no. 542; pp. 1038 - 1055
Main Authors:	Chen, Elynn Y., Fan, Jianqing
Format:	Journal Article
Language:	English
Published:	Alexandria Taylor & Francis 03.04.2023 Taylor & Francis Ltd
Subjects:	Adequacy Analysis of covariance Asymptotic normality Convergence covariance data collection Datasets Estimation Factor models High-dimension Inference Latent low rank Matrix-variate Property Regression analysis Research methodology Simulation Statistical inference Statistical methods Statistics variance
ISSN:	0162-1459, 1537-274X, 1537-274X
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!