Modeling and Learning on High-Dimensional Matrix-Variate Sequences-Reference-Cited by-同舟云学术

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Modeling and Learning on High-Dimensional Matrix-Variate Sequences

Published:2024-05-24 Issue: Volume: Page:1-16
ISSN:0162-1459
Container-title:Journal of the American Statistical Association
language:en
Short-container-title:Journal of the American Statistical Association

Author:

Zhang Xu¹²,Liu Catherine C.²,Guo Jianhua³,Yuen K. C.⁴,Welsh A. H.⁵

Affiliation:

1. South China Normal University, Guangzhou, China

2. The Hong Kong Polytechnic University, Hung Hom, HKSAR

3. Beijing Technology and Business University, Beijing, China

4. The University of Hong Kong, Hong Kong, HKSAR

5. The Australian National University, Canberra, Australia

Funder

National Natural Science Foundation of China

Postdoc Fellowship of CAS AMSS-PolyU Joint Laboratory of Applied Mathematics

PolyU Research Grant

National Key Research and Development Program of China

Research Grants Council of the Hong Kong Special Administrative Region, China

Australian Research Council Discovery Project

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/01621459.2024.2344687

Reference43 articles.

1. Generalized low-rank approximation of matrices based on multiple transformation pairs

2. Athreya, K. B., and Lahiri, S. N. (2006), Measure Theory and Probability Theory (1st ed.), New York: Springer.

3. Inferential Theory for Factor Models of Large Dimensions

4. Ben-Israel, A., and Greville, T. N. (2003), Generalized Inverses: Theory and Applications (2nd ed.), Springer.

5. Statistical Inference for High-Dimensional Matrix-Variate Factor Models

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