Constrained Factor Models for High-Dimensional Matrix-Variate Time Series

DOI10.1080/01621459.2019.1584899zbMath1445.62143arXiv1710.06075OpenAlexW2963413336MaRDI QIDQ5130622

Ruey S. Tsay, Rong Chen, Elynn Y. Chen

Publication date: 28 October 2020

Published in: Journal of the American Statistical Association (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1710.06075

zbMATH Keywords

dimension reductionfactor modelmatrix-variate time seriesconstrained eigen-analysisconvergence in L2-norm

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Classification and discrimination; cluster analysis (statistical aspects) (62H30)

Related Items (11)

Tensor Canonical Correlation Analysis With Convergence and Statistical Guarantees ⋮ Matrix Autoregressive Spatio-Temporal Models ⋮ One-way or two-way factor model for matrix sequences? ⋮ Determining the number of factors in constrained factor models via Bayesian information criterion ⋮ Matrix-variate data analysis by two-way factor model with replicated observations ⋮ An identity for expectations and characteristic function of matrix variate skew-normal distribution with applications to associated stochastic orderings ⋮ On singular values of data matrices with general independent columns ⋮ Factor Models for High-Dimensional Tensor Time Series ⋮ Matrix Factor Analysis: From Least Squares to Iterative Projection ⋮ Statistical Inference for High-Dimensional Matrix-Variate Factor Models ⋮ Projected estimation for large-dimensional matrix factor models

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