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Optimal Sparse Singular Value Decomposition for High-Dimensional High-Order Data

Publication ,  Journal Article
Zhang, A; Han, R
Published in: Journal of the American Statistical Association
October 2, 2019

Duke Scholars

Published In

Journal of the American Statistical Association

DOI

EISSN

1537-274X

ISSN

0162-1459

Publication Date

October 2, 2019

Volume

114

Issue

528

Start / End Page

1708 / 1725

Publisher

Informa UK Limited

Related Subject Headings

  • Statistics & Probability
  • 1603 Demography
  • 1403 Econometrics
  • 0104 Statistics
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Zhang, A., & Han, R. (2019). Optimal Sparse Singular Value Decomposition for High-Dimensional High-Order Data. Journal of the American Statistical Association, 114(528), 1708–1725. https://doi.org/10.1080/01621459.2018.1527227
Zhang, Anru, and Rungang Han. “Optimal Sparse Singular Value Decomposition for High-Dimensional High-Order Data.” Journal of the American Statistical Association 114, no. 528 (October 2, 2019): 1708–25. https://doi.org/10.1080/01621459.2018.1527227.
Zhang A, Han R. Optimal Sparse Singular Value Decomposition for High-Dimensional High-Order Data. Journal of the American Statistical Association. 2019 Oct 2;114(528):1708–25.
Zhang, Anru, and Rungang Han. “Optimal Sparse Singular Value Decomposition for High-Dimensional High-Order Data.” Journal of the American Statistical Association, vol. 114, no. 528, Informa UK Limited, Oct. 2019, pp. 1708–25. Crossref, doi:10.1080/01621459.2018.1527227.
Zhang A, Han R. Optimal Sparse Singular Value Decomposition for High-Dimensional High-Order Data. Journal of the American Statistical Association. Informa UK Limited; 2019 Oct 2;114(528):1708–1725.

Published In

Journal of the American Statistical Association

DOI

EISSN

1537-274X

ISSN

0162-1459

Publication Date

October 2, 2019

Volume

114

Issue

528

Start / End Page

1708 / 1725

Publisher

Informa UK Limited

Related Subject Headings

  • Statistics & Probability
  • 1603 Demography
  • 1403 Econometrics
  • 0104 Statistics