[1] Zhou H, Li L. Regularized matrix regression [J]. Journal of the Royal Statistical Society, Series B, 2014, 76: 463-483.
[2] Wainwright M J. Structured regularizers for high-dimensional problems: statistical and computational issues [J]. Annual Review of Statistics and its Applications, 2014, 1: 233-253.
[3] Obozinski G, Wainwright M J, Jordan M I. Support union recovery in high-dimensional multivariate regression [J]. Annals of Statistics, 2011, 39(1): 1-47.
[4] Negahban S, Wainwright M J. Estimation of (near) low-rank matrices with noise and high-dimensional scaling [J]. Annals of Statistics, 2011, 39(2): 1069-1097.
[5] Peng J, Zhu J, Bergamaschi A, et al. Regularized multivariate regression for identifying master predictors with application to integrative genomics study of breast cancer [J]. The Annals of Applied Statistics, 2010, 4(1): 53-77.
[6] Yin J., Li H. Model selection and estimation in the matrix normal graphical model [J]. Journal of Multivariate Analysis, 2012, 107: 119-140.
[7] Reiss P, Ogden R. Functional generalized linear models with images as predictors [J]. Biometrics, 2010, 66: 61-69.
[8] Yin X, Li L. Hypothesis testing of matrix graph model with application to brain connectivity analysis [J]. arXiv:1511.00718v1, 2 November, 2015.
[9] Leng C, Tang C Y. Sparse matrix graphical models [J]. Journal of the American Statistical Association, 2012, 107(499): 1187-1200.
[10] Jordan M I, Mitchell T. Machine learning: trends, perspectives, and prospects [J]. Science, 2015, 349: 255-260.
[11] Pollack J R, T S{\o}rlie T, Perou C M, et al. Microarray analysis reveals a major direct role of DNA copy number alteration in the transcriptional program of human breast tumors [C]// Proceedings of the National Academy of Sciences of the United States of America, 2002, 99: 12963-12968.
[12] Jeong H, Mason S P, Barab\acute{a}si A L, et al. Lethality and centrality in protein networks [J]. Nature, 2002, 411: 41-42.
[13] Gardner T S, Diego D B, David L, et al. Inferring genetic networks and identifying compound mode of action via expression profiling [J]. Science, 2003, 301: 102-105.
[14] Zhang X L, Begleiter H, Porjesz B, et al. Event related potentials during object recognition tasks [J]. Brain Research Bulletin, 1995, 38: 531-538.
[15] Chen Y, Wainwright M J. Fast low-rank estimation by projected gradient descent: general statistical and algorithmic guarantees [J]. arXiv: 1509.03025, 10 September, 2015.
[16] Wang W, Liang Y, Eric Xing, Block regularized lasso for multivariate multi-response linear regression [C]//Proceedings of the 16th International Conference on Artificial Intelligence and Statistics, 2013, 31: 608-617.
[17] Li Y, Nan B, Zhu J. Multivariate sparse group lasso for the multivariate multiple linear regression with an arbitrary group structure [J]. Biometrics, 2015, 71(2): 354-363.
[18] Katayama S, Imori S. Lasso penalized model selection criteria for high-dimensional multivariate linear regression analysis [J]. Journal of Multivariate Analysis, 2014, 132: 138-150.
[19] Chen B Z, Kong L C. High-dimensional Least Square Matrix Regression via Elastic Net Penalty [J]. Pacific Journal of Optimization, 2017, 13(2): 185-196.
[20] Cook R D, Li B, Chiaromonte F. Envelope models for parsimonious and efficient multivariate linear regression (with discussion) [J]. Statistica Sinica, 2010, 20: 927-1010.
[21] Su Z, Cook R D. Inner envelopes: efficient estimation in multivariate linear regression [J]. Biometrika, 2012, 99: 687-702.
[22] Cook R D, Su Z. Scaled envelopes: Scale invariant and efficient estimation in multivariate linear regression [J]. Biometrika, 2013, 100: 921-938.
[23] Li L, Zhang X. Parsimonious tensor response regression [J]. arXiv:1501.07815v1, 30 January, 2015. |