Integrative sparse reduced-rank regression via orthogonal rotation for analysis of high-dimensional multi-source data
Author:
Funder
Samsung Science and Technology Foundation
Publisher
Springer Science and Business Media LLC
Subject
Computational Theory and Mathematics,Statistics, Probability and Uncertainty,Statistics and Probability,Theoretical Computer Science
Link
https://link.springer.com/content/pdf/10.1007/s11222-023-10322-3.pdf
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3. Bing, X., Wegkamp, M.H.: Adaptive estimation of the rank of the coefficient matrix in high-dimensional multivariate response regression models. Ann. Stat. 47(6), 3157–3184 (2019). https://doi.org/10.1214/18-AOS1774
4. Bunea, F., She, Y., Wegkamp, M.H.: Optimal selection of reduced rank estimators of high-dimensional matrices. Ann. Stat. 39(2), 1282–1309 (2011). https://doi.org/10.1214/11-AOS876
5. Bunea, F., She, Y., Wegkamp, M.H.: Joint variable and rank selection for parsimonious estimation of high-dimensional matrices. Ann. Stat. 40(5), 2359–2388 (2012). https://doi.org/10.1214/12-AOS1039
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