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A new approach to Cholesky-based covariance regularization in high dimensions

Citations

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Cited by:

  1. Cao, Xuan & Khare, Kshitij & Ghosh, Malay, 2020. "Consistent Bayesian sparsity selection for high-dimensional Gaussian DAG models with multiplicative and beta-mixture priors," Journal of Multivariate Analysis, Elsevier, vol. 179(C).
  2. Yumou Qiu & Song Xi Chen, 2015. "Bandwidth Selection for High-Dimensional Covariance Matrix Estimation," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(511), pages 1160-1174, September.
  3. Qiu, Yumou & Chen, Songxi, 2012. "Test for Bandedness of High Dimensional Covariance Matrices with Bandwidth Estimation," MPRA Paper 46242, University Library of Munich, Germany.
  4. Gunawan, David & Kohn, Robert & Nott, David, 2021. "Variational Bayes approximation of factor stochastic volatility models," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1355-1375.
  5. Lars Heinrich & Antoniya Shivarova & Martin Zurek, 2021. "Factor investing: alpha concentration versus diversification," Journal of Asset Management, Palgrave Macmillan, vol. 22(6), pages 464-487, October.
  6. Liang, Wanfeng & Ma, Xiaoyan, 2024. "A new approach for ultrahigh-dimensional covariance matrix estimation," Statistics & Probability Letters, Elsevier, vol. 204(C).
  7. Benjamin Poignard & Manabu Asai, 2023. "Estimation of high-dimensional vector autoregression via sparse precision matrix," The Econometrics Journal, Royal Economic Society, vol. 26(2), pages 307-326.
  8. Dengke Xu & Zhongzhan Zhang & Liucang Wu, 2014. "Bayesian analysis of joint mean and covariance models for longitudinal data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(11), pages 2504-2514, November.
  9. Lam, Clifford, 2020. "High-dimensional covariance matrix estimation," LSE Research Online Documents on Economics 101667, London School of Economics and Political Science, LSE Library.
  10. Deirdre Bloome & Daniel Schrage, 2021. "Covariance Regression Models for Studying Treatment Effect Heterogeneity Across One or More Outcomes: Understanding How Treatments Shape Inequality," Sociological Methods & Research, , vol. 50(3), pages 1034-1072, August.
  11. Jacob Bien & Florentina Bunea & Luo Xiao, 2016. "Convex Banding of the Covariance Matrix," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(514), pages 834-845, April.
  12. Jian Zhang & Jie Li, 2022. "Factorized estimation of high‐dimensional nonparametric covariance models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(2), pages 542-567, June.
  13. Zhang, Qihu & Chung, Jongik & Park, Cheolwoo, 2025. "Joint estimation of precision matrices for long-memory time series," Computational Statistics & Data Analysis, Elsevier, vol. 212(C).
  14. Chen, Songxi, 2012. "Two Sample Tests for High Dimensional Covariance Matrices," MPRA Paper 46026, University Library of Munich, Germany.
  15. Guanghui Cheng & Zhengjun Zhang & Baoxue Zhang, 2017. "Test for bandedness of high-dimensional precision matrices," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 29(4), pages 884-902, October.
  16. Wang, Luheng & Chen, Zhao & Wang, Christina Dan & Li, Runze, 2020. "Ultrahigh dimensional precision matrix estimation via refitted cross validation," Journal of Econometrics, Elsevier, vol. 215(1), pages 118-130.
  17. Kang, Xiaoning & Wang, Mingqiu, 2021. "Ensemble sparse estimation of covariance structure for exploring genetic disease data," Computational Statistics & Data Analysis, Elsevier, vol. 159(C).
  18. Yi, Feng & Zou, Hui, 2013. "SURE-tuned tapering estimation of large covariance matrices," Computational Statistics & Data Analysis, Elsevier, vol. 58(C), pages 339-351.
  19. Chen, Xin & Yang, Dan & Xu, Yan & Xia, Yin & Wang, Dong & Shen, Haipeng, 2023. "Testing and support recovery of correlation structures for matrix-valued observations with an application to stock market data," Journal of Econometrics, Elsevier, vol. 232(2), pages 544-564.
  20. Fang, Qian & Yu, Chen & Weiping, Zhang, 2020. "Regularized estimation of precision matrix for high-dimensional multivariate longitudinal data," Journal of Multivariate Analysis, Elsevier, vol. 176(C).
  21. Zhaohui (Zoey) Jiang & Jun Li & Dennis Zhang, 2025. "A High-Dimensional Choice Model for Online Retailing," Management Science, INFORMS, vol. 71(4), pages 3320-3339, April.
  22. He, Jing & Chen, Song Xi, 2016. "Testing super-diagonal structure in high dimensional covariance matrices," Journal of Econometrics, Elsevier, vol. 194(2), pages 283-297.
  23. Lopes, Hedibert F. & McCulloch, Robert E. & Tsay, Ruey S., 2022. "Parsimony inducing priors for large scale state–space models," Journal of Econometrics, Elsevier, vol. 230(1), pages 39-61.
  24. Liang, Wanfeng & Wu, Yue & Ma, Xiaoyan, 2022. "Robust sparse precision matrix estimation for high-dimensional compositional data," Statistics & Probability Letters, Elsevier, vol. 184(C).
  25. Seungkyu Kim & Johan Lim & Donghyeon Yu, 2025. "Synthetic data generation method providing enhanced covariance matrix estimation," Computational Statistics, Springer, vol. 40(7), pages 4007-4035, September.
  26. Mauro Bernardi & Daniele Bianchi & Nicolas Bianco, 2024. "Variational Inference for Large Bayesian Vector Autoregressions," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 42(3), pages 1066-1082, July.
  27. Ziqi Chen & Man†Lai Tang & Wei Gao, 2018. "A profile likelihood approach for longitudinal data analysis," Biometrics, The International Biometric Society, vol. 74(1), pages 220-228, March.
  28. Xue, Lingzhou & Zou, Hui, 2013. "Minimax optimal estimation of general bandable covariance matrices," Journal of Multivariate Analysis, Elsevier, vol. 116(C), pages 45-51.
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