1. Cai, T., Liu, W. and Luo, X. (2011), A constrained L1 Minimization Approach to Sparse Precision Matrix Estimation, Journal of the American Statistical Association, 106(494), 594-607. [ DOI:10.1198/jasa.2011.tm10155] 2. Friedman, J., Hastie, T. and Tibshirani, R. (2008), Sparse Inverse Covariance Estimation with the Graphical Lasso, Biostatistics, 9(3), 432-441. [ DOI:10.1093/biostatistics/kxm045] [ PMID] [ ] 3. Gupta, S.D. (1977), Tests on Multiple Correlation Coefficient and Multiple Partial Correlation Coefficient, Journal of Multivariate Analysis, 7(1), 82-88. [ DOI:10.1016/0047-259X(77)90033-1] 4. Hoeffding, W. (1952), The Large-sample Power of Tests Based on Permutations of Observations, The Annals of Mathematical Statistics, 23(2), 169-192. [ DOI:10.1214/aoms/1177729436] 5. Hsieh, C.J., Sustik, M.A., Dhillon, I.S., Ravikumar, P.K., and Poldrack, R. (2013), Big & quic: Sparse Inverse Covariance Estimation for a Million Variables, In Advances in Neural Information Processing Systems, 3165-3173. 6. Liang, J., Tang, M.L. and Chan, P.S. (2009), A Generalized Shapiro-wilk W Statistic for Testing High-dimensional Normality, Computational Statistics & Data Analysis, 53(11), 3883-3891. [ DOI:10.1016/j.csda.2009.04.016] 7. Liu, W. and Luo, X. (2015), Fast and Adaptive Sparse Precision Matrix Estimation in High Dimensions, Journal of Multivariate Analysis, 135, 153-162. [ DOI:10.1016/j.jmva.2014.11.005] [ PMID] [ ] 8. Muirhead, R. (2005), Aspects of Multivariate Statistical Theory, John Wiley and Sons, New York. 9. Najarzadeh, D. (2020), A Simple Test for Zero Multiple Correlation Coefficient in High-dimensional Normal Data using Random Projection, Computational Statistics & Data Analysis, 148, 106955. [ DOI:10.1016/j.csda.2020.106955] 10. Najarzadeh, D. (2022), An Optimal Projection Test for Zero Multiple Correlation Coefficient in High-dimensional Normal Data, Communications in Statistics Theory & Methods, 51(4), 1011-1028. [ DOI:10.1080/03610926.2020.1757111] 11. Pourahmadi, M. (2013), High-dimensional Covariance Estimation: with High dimensional Data, John Wiley and Sons, New York. [ DOI:10.1002/9781118445112.stat07373] 12. Provost, S.B. (1987), Testing for the Nullity of the Multiple Correlation Coefficient with Incomplete Multivariate Data, In Advances in the Statistical Sciences: Foundations of Statistical Inference, 149-161. [ DOI:10.1007/978-94-009-4788-7_14] 13. Romano, J.P. and Wolf, M. (2005), Exact and Approximate Stepdown Methods for Multiple Hypothesis Testing, Journal of the American Statistical Association, 100(469), 94-108. [ DOI:10.1198/016214504000000539] 14. Tan, M., Fang, H.B., Tian, G.L. and Wei, G. (2005), Testing Multivariate Normality in Incomplete Data of Small Sample Size, Journal of Multivariate Analysis, 93(1), 164-179. [ DOI:10.1016/j.jmva.2004.02.014] 15. Wang, C. and Jiang, B. (2019), EQUAL: An Efficient ADMM Algorithm for High Dimensional Precision Matrix Estimation via Penalized Quadratic Loss, R package version 1.2. [ DOI:10.1016/j.csda.2019.106812] 16. Wang, C. and Jiang, B. (2020), An Efficient Admm Algorithm for High Dimensional Precision Matrix Estimation via Penalized Quadratic Loss, Computational Statistics & Data Analysis, 142, 106812. [ DOI:10.1016/j.csda.2019.106812] 17. Zheng, S., Jiang, D., Bai, Z. and He, X. (2014), Inference on Multiple Correlation Coefficients with Moderately High Dimensional Data, Biometrika, 101(3), 748-754. [ DOI:10.1093/biomet/asu023]
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