Statistical Inference for High-Dimensional Matrix-Variate Factor Models
Author:
Affiliation:
1. EECS, University of California, Berkeley, CA;
2. ORFE, Princeton University, Princeton, NJ
Funder
Division of Mathematical Sciences
Foundation for the National Institutes of Health
NSF
ONR
Publisher
Informa UK Limited
Subject
Statistics, Probability and Uncertainty,Statistics and Probability
Link
https://www.tandfonline.com/doi/pdf/10.1080/01621459.2021.1970569
Reference34 articles.
1. Eigenvalue Ratio Test for the Number of Factors
2. Inferential Theory for Factor Models of Large Dimensions
3. Determining the Number of Factors in Approximate Factor Models
4. Cai, C., Chen, R., and Xiao, H. (2019), “KOPA: Automated Kronecker Product Approximation,” arXiv no. 1912.02392.
5. Rate-optimal perturbation bounds for singular subspaces with applications to high-dimensional statistics
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