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Generalized Thresholding of Large Covariance Matrices
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Cited by:
- Xin Wang & Lingchen Kong & Liqun Wang & Zhaoqilin Yang, 2023. "High-Dimensional Covariance Estimation via Constrained L q -Type Regularization," Mathematics, MDPI, vol. 11(4), pages 1-20, February.
- Natalia Bailey & George Kapetanios & M. Hashem Pesaran, 2019.
"Exponent of Cross-sectional Dependence for Residuals,"
Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 81(1), pages 46-102, September.
- Natalia Bailey & George Kapetanios & M. Hashem Pesaran, 2018. "Exponent of Cross-sectional Dependence for Residuals," CESifo Working Paper Series 7223, CESifo.
- Natalia Bailey & George Kapetanios & M. Hashem Pesaran, 2018. "Exponent of cross-sectional dependence for residuals," Monash Econometrics and Business Statistics Working Papers 13/18, Monash University, Department of Econometrics and Business Statistics.
- Bo Zhou & David E. Moorman & Sam Behseta & Hernando Ombao & Babak Shahbaba, 2016. "A Dynamic Bayesian Model for Characterizing Cross-Neuronal Interactions During Decision-Making," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(514), pages 459-471, April.
- Sung, Bongjung & Lee, Jaeyong, 2023. "Covariance structure estimation with Laplace approximation," Journal of Multivariate Analysis, Elsevier, vol. 198(C).
- Chen, Dachuan & Mykland, Per A. & Zhang, Lan, 2024. "Realized regression with asynchronous and noisy high frequency and high dimensional data," Journal of Econometrics, Elsevier, vol. 239(2).
- Ziqi Chen & Chenlei Leng, 2016. "Dynamic Covariance Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(515), pages 1196-1207, July.
- Jonas Krampe & Efstathios Paparoditis, 2021. "Sparsity concepts and estimation procedures for high‐dimensional vector autoregressive models," Journal of Time Series Analysis, Wiley Blackwell, vol. 42(5-6), pages 554-579, September.
- Maurizio Daniele & Winfried Pohlmeier & Aygul Zagidullina, 2018.
"Sparse Approximate Factor Estimation for High-Dimensional Covariance Matrices,"
Working Paper Series of the Department of Economics, University of Konstanz
2018-07, Department of Economics, University of Konstanz.
- Maurizio Daniele & Winfried Pohlmeier & Aygul Zagidullina, 2020. "Sparse Approximate Factor Estimation for High-Dimensional Covariance Matrices," Working Paper series 20-03, Rimini Centre for Economic Analysis.
- Maurizio Daniele & Winfried Pohlmeier & Aygul Zagidullina, 2019. "Sparse Approximate Factor Estimation for High-Dimensional Covariance Matrices," Papers 1906.05545, arXiv.org.
- 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.
- Qiu, Yumou & Chen, Song Xi, 2014. "Band Width Selection for High Dimensional Covariance Matrix Estimation," MPRA Paper 59641, University Library of Munich, Germany.
- Avagyan, Vahe & Nogales, Francisco J., 2015. "D-trace Precision Matrix Estimation Using Adaptive Lasso Penalties," DES - Working Papers. Statistics and Econometrics. WS 21775, Universidad Carlos III de Madrid. Departamento de EstadÃstica.
- Yang, Yihe & Dai, Hongsheng & Pan, Jianxin, 2023. "Block-diagonal precision matrix regularization for ultra-high dimensional data," Computational Statistics & Data Analysis, Elsevier, vol. 179(C).
- Chen, Jia & Li, Degui & Linton, Oliver, 2019.
"A new semiparametric estimation approach for large dynamic covariance matrices with multiple conditioning variables,"
Journal of Econometrics, Elsevier, vol. 212(1), pages 155-176.
- Chen, J. & Li, D. & Linton, O., 2018. "A New Semiparametric Estimation Approach for Large Dynamic Covariance Matrices with Multiple Conditioning Variables," Cambridge Working Papers in Economics 1876, Faculty of Economics, University of Cambridge.
- Jia Chen & Degui Li & Oliver Linton, 2018. "A New Semiparametric Estimation Approach for Large Dynamic Covariance Matrices with Multiple Conditioning Variables," Discussion Papers 18/14, Department of Economics, University of York.
- Qiu, Yumou & Chen, Songxi, 2012. "Test for Bandedness of High Dimensional Covariance Matrices with Bandwidth Estimation," MPRA Paper 46242, University Library of Munich, Germany.
- Wang, Xin & Kong, Lingchen & Wang, Liqun, 2024. "Estimation of sparse covariance matrix via non-convex regularization," Journal of Multivariate Analysis, Elsevier, vol. 202(C).
- Gao, Zhenguo & Wang, Xinye & Kang, Xiaoning, 2023. "Ensemble LDA via the modified Cholesky decomposition," Computational Statistics & Data Analysis, Elsevier, vol. 188(C).
- Jianqing Fan & Yingying Li & Ke Yu, 2012.
"Vast Volatility Matrix Estimation Using High-Frequency Data for Portfolio Selection,"
Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(497), pages 412-428, March.
- Jianqing Fan & Yingying Li & Ke Yu, 2010. "Vast Volatility Matrix Estimation using High Frequency Data for Portfolio Selection," Papers 1004.4956, arXiv.org.
- Alain Hecq & Marie Ternes & Ines Wilms, 2021. "Hierarchical Regularizers for Mixed-Frequency Vector Autoregressions," Papers 2102.11780, arXiv.org, revised Mar 2022.
- Jia, Shengji & Zhang, Chunming & Lu, Haoran, 2022. "Covariance function versus covariance matrix estimation in efficient semi-parametric regression for longitudinal data analysis," Journal of Multivariate Analysis, Elsevier, vol. 187(C).
- Shaoxin Wang & Hu Yang & Chaoli Yao, 2019. "On the penalized maximum likelihood estimation of high-dimensional approximate factor model," Computational Statistics, Springer, vol. 34(2), pages 819-846, June.
- Bai, Jushan & Liao, Yuan, 2012. "Efficient Estimation of Approximate Factor Models," MPRA Paper 41558, University Library of Munich, Germany.
- Fan, Jianqing & Liao, Yuan & Shi, Xiaofeng, 2015.
"Risks of large portfolios,"
Journal of Econometrics, Elsevier, vol. 186(2), pages 367-387.
- Fan, Jianqing & Liao, Yuan & Shi, Xiaofeng, 2013. "Risks of large portfolios," MPRA Paper 44206, University Library of Munich, Germany.
- Jianqing Fan & Yuan Liao & Xiaofeng Shi, 2013. "Risks of Large Portfolios," Papers 1302.0926, arXiv.org.
- Aït-Sahalia, Yacine & Xiu, Dacheng, 2017. "Using principal component analysis to estimate a high dimensional factor model with high-frequency data," Journal of Econometrics, Elsevier, vol. 201(2), pages 384-399.
- Cui, Ying & Leng, Chenlei & Sun, Defeng, 2016. "Sparse estimation of high-dimensional correlation matrices," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 390-403.
- Yang, Yihe & Zhou, Jie & Pan, Jianxin, 2021. "Estimation and optimal structure selection of high-dimensional Toeplitz covariance matrix," Journal of Multivariate Analysis, Elsevier, vol. 184(C).
- Huang Lin & Merete Eggesbø & Shyamal Das Peddada, 2022. "Linear and nonlinear correlation estimators unveil undescribed taxa interactions in microbiome data," Nature Communications, Nature, vol. 13(1), pages 1-16, December.
- Yang, Guangren & Liu, Yiming & Pan, Guangming, 2019. "Weighted covariance matrix estimation," Computational Statistics & Data Analysis, Elsevier, vol. 139(C), pages 82-98.
- Luo, June & Kulasekera, K.B., 2013. "Error covariance matrix estimation using ridge estimator," Statistics & Probability Letters, Elsevier, vol. 83(1), pages 257-264.
- Rutger van der Spek & Alexis Derumigny, 2022. "Fast estimation of Kendall's Tau and conditional Kendall's Tau matrices under structural assumptions," Papers 2204.03285, arXiv.org, revised Dec 2024.
- 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.
- Lee, Kyoungjae & Jo, Seongil & Lee, Jaeyong, 2022. "The beta-mixture shrinkage prior for sparse covariances with near-minimax posterior convergence rate," Journal of Multivariate Analysis, Elsevier, vol. 192(C).
- Jian, Zhihong & Deng, Pingjun & Zhu, Zhican, 2018. "High-dimensional covariance forecasting based on principal component analysis of high-frequency data," Economic Modelling, Elsevier, vol. 75(C), pages 422-431.
- Lee, Kwangmin & Lee, Jaeyong, 2023. "Post-processed posteriors for sparse covariances," Journal of Econometrics, Elsevier, vol. 236(1).
- Sung Hoon Choi & Donggyu Kim, 2023. "Large Global Volatility Matrix Analysis Based on Observation Structural Information," Papers 2305.01464, arXiv.org, revised Feb 2024.
- Zhang, Qiang & Ip, Edward H. & Pan, Junhao & Plemmons, Robert, 2017. "Individual-specific, sparse inverse covariance estimation in generalized estimating equations," Statistics & Probability Letters, Elsevier, vol. 122(C), pages 96-103.
- Jianqing Fan & Yuan Liao & Martina Mincheva, 2013.
"Large covariance estimation by thresholding principal orthogonal complements,"
Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 75(4), pages 603-680, September.
- Fan, Jianqing & Liao, Yuan & Mincheva, Martina, 2011. "Large covariance estimation by thresholding principal orthogonal complements," MPRA Paper 38697, University Library of Munich, Germany.
- Lee Woojoo & Lee Donghwan & Lee Youngjo & Pawitan Yudi, 2011. "Sparse Canonical Covariance Analysis for High-throughput Data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 10(1), pages 1-24, July.
- Cai, T. Tony & Zhang, Anru, 2016. "Inference for high-dimensional differential correlation matrices," Journal of Multivariate Analysis, Elsevier, vol. 143(C), pages 107-126.
- Denis Belomestny & Mathias Trabs & Alexandre Tsybakov, 2017. "Sparse covariance matrix estimation in high-dimensional deconvolution," Working Papers 2017-25, Center for Research in Economics and Statistics.
- 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.
- Na Huang & Piotr Fryzlewicz, 2019. "NOVELIST estimator of large correlation and covariance matrices and their inverses," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(3), pages 694-727, September.
- Krampe, J. & Paparoditis, E. & Trenkler, C., 2023. "Structural inference in sparse high-dimensional vector autoregressions," Journal of Econometrics, Elsevier, vol. 234(1), pages 276-300.
- Gonçalves, Sílvia & Perron, Benoit, 2020.
"Bootstrapping factor models with cross sectional dependence,"
Journal of Econometrics, Elsevier, vol. 218(2), pages 476-495.
- Sílvia GONÇALVES & Benoit PERRON, 2018. "Bootstrapping Factor Models With Cross Sectional Dependence," Cahiers de recherche 10-2018, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
- GONÇALVES, Sílvia & PERRON, Benoit, 2018. "Bootstrapping factor models with cross sectional dependence," Cahiers de recherche 2018-07, Universite de Montreal, Departement de sciences economiques.
- Gautam Sabnis & Debdeep Pati & Anirban Bhattacharya, 2019. "Compressed Covariance Estimation with Automated Dimension Learning," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 81(2), pages 466-481, December.
- Paolo Giordani & Xiuyan Mun & Robert Kohn, 2012. "Efficient Estimation of Covariance Matrices using Posterior Mode Multiple Shrinkage," Journal of Financial Econometrics, Oxford University Press, vol. 11(1), pages 154-192, December.
- Kang, Xiaoning & Wang, Mingqiu, 2021. "Ensemble sparse estimation of covariance structure for exploring genetic disease data," Computational Statistics & Data Analysis, Elsevier, vol. 159(C).
- Avagyan, Vahe & Nogales, Francisco J., 2014. "Improving the graphical lasso estimation for the precision matrix through roots ot the sample convariance matrix," DES - Working Papers. Statistics and Econometrics. WS ws141208, Universidad Carlos III de Madrid. Departamento de EstadÃstica.
- Seunghwan Lee & Sang Cheol Kim & Donghyeon Yu, 2023. "An efficient GPU-parallel coordinate descent algorithm for sparse precision matrix estimation via scaled lasso," Computational Statistics, Springer, vol. 38(1), pages 217-242, March.
- Yi, Feng & Zou, Hui, 2013. "SURE-tuned tapering estimation of large covariance matrices," Computational Statistics & Data Analysis, Elsevier, vol. 58(C), pages 339-351.
- 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.
- Konrad Furmańczyk, 2021. "Estimation of autocovariance matrices for high dimensional linear processes," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 84(4), pages 595-613, May.
- Jianqing Fan & Yuan Liao & Han Liu, 2016. "An overview of the estimation of large covariance and precision matrices," Econometrics Journal, Royal Economic Society, vol. 19(1), pages 1-32, February.
- Banerjee, Sayantan & Akbani, Rehan & Baladandayuthapani, Veerabhadran, 2019. "Spectral clustering via sparse graph structure learning with application to proteomic signaling networks in cancer," Computational Statistics & Data Analysis, Elsevier, vol. 132(C), pages 46-69.
- Li, Peili & Xiao, Yunhai, 2018. "An efficient algorithm for sparse inverse covariance matrix estimation based on dual formulation," Computational Statistics & Data Analysis, Elsevier, vol. 128(C), pages 292-307.
- Yan Zhang & Jiyuan Tao & Zhixiang Yin & Guoqiang Wang, 2022. "Improved Large Covariance Matrix Estimation Based on Efficient Convex Combination and Its Application in Portfolio Optimization," Mathematics, MDPI, vol. 10(22), pages 1-15, November.
- Choi, Sung Hoon & Kim, Donggyu, 2023.
"Large volatility matrix analysis using global and national factor models,"
Journal of Econometrics, Elsevier, vol. 235(2), pages 1917-1933.
- Sung Hoon Choi & Donggyu Kim, 2022. "Large Volatility Matrix Analysis Using Global and National Factor Models," Papers 2208.12323, arXiv.org, revised Dec 2022.
- Binyan Jiang, 2015. "An empirical estimator for the sparsity of a large covariance matrix under multivariate normal assumptions," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 67(2), pages 211-227, April.
- Fan, Jianqing & Wang, Weichen & Zhong, Yiqiao, 2019. "Robust covariance estimation for approximate factor models," Journal of Econometrics, Elsevier, vol. 208(1), pages 5-22.
- Ikeda, Yuki & Kubokawa, Tatsuya, 2016. "Linear shrinkage estimation of large covariance matrices using factor models," Journal of Multivariate Analysis, Elsevier, vol. 152(C), pages 61-81.
- Alexander Robitzsch, 2022. "Comparing the Robustness of the Structural after Measurement (SAM) Approach to Structural Equation Modeling (SEM) against Local Model Misspecifications with Alternative Estimation Approaches," Stats, MDPI, vol. 5(3), pages 1-42, July.
- Ruili Sun & Tiefeng Ma & Shuangzhe Liu & Milind Sathye, 2019. "Improved Covariance Matrix Estimation for Portfolio Risk Measurement: A Review," JRFM, MDPI, vol. 12(1), pages 1-34, March.
- Banerjee, Sayantan & Ghosal, Subhashis, 2015. "Bayesian structure learning in graphical models," Journal of Multivariate Analysis, Elsevier, vol. 136(C), pages 147-162.
- Joo, Young C. & Park, Sung Y., 2021. "Optimal portfolio selection using a simple double-shrinkage selection rule," Finance Research Letters, Elsevier, vol. 43(C).
- Liang, Wanfeng & Ma, Xiaoyan, 2024. "A new approach for ultrahigh-dimensional covariance matrix estimation," Statistics & Probability Letters, Elsevier, vol. 204(C).
- Wang, Shaoxin, 2021. "An efficient numerical method for condition number constrained covariance matrix approximation," Applied Mathematics and Computation, Elsevier, vol. 397(C).
- He, Kevin & Kang, Jian & Hong, Hyokyoung G. & Zhu, Ji & Li, Yanming & Lin, Huazhen & Xu, Han & Li, Yi, 2019. "Covariance-insured screening," Computational Statistics & Data Analysis, Elsevier, vol. 132(C), pages 100-114.
- Rasoul Lotfi & Davood Shahsavani & Mohammad Arashi, 2022. "Classification in High Dimension Using the Ledoit–Wolf Shrinkage Method," Mathematics, MDPI, vol. 10(21), pages 1-13, November.
- Li, Degui, 2024. "Estimation of Large Dynamic Covariance Matrices: A Selective Review," Econometrics and Statistics, Elsevier, vol. 29(C), pages 16-30.
- Aaron J Molstad & Adam J Rothman, 2018. "Shrinking characteristics of precision matrix estimators," Biometrika, Biometrika Trust, vol. 105(3), pages 563-574.
- Ikeda, Yuki & Kubokawa, Tatsuya & Srivastava, Muni S., 2016. "Comparison of linear shrinkage estimators of a large covariance matrix in normal and non-normal distributions," Computational Statistics & Data Analysis, Elsevier, vol. 95(C), pages 95-108.
- Yuki Ikeda & Tatsuya Kubokawa, 2015. "Linear Shrinkage Estimation of Large Covariance Matrices with Use of Factor Models," CIRJE F-Series CIRJE-F-958, CIRJE, Faculty of Economics, University of Tokyo.
- Farnè, Matteo & Montanari, Angela, 2020. "A large covariance matrix estimator under intermediate spikiness regimes," Journal of Multivariate Analysis, Elsevier, vol. 176(C).
- Zeyu Wu & Cheng Wang & Weidong Liu, 2023. "A unified precision matrix estimation framework via sparse column-wise inverse operator under weak sparsity," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 75(4), pages 619-648, August.
- 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.
- Benjamin Poignard & Manabu Asai, 2021. "Estimation of High Dimensional Vector Autoregression via Sparse Precision Matrix," Discussion Papers in Economics and Business 21-03, Osaka University, Graduate School of Economics.
- Bailey, Natalia & Pesaran, M. Hashem & Smith, L. Vanessa, 2019.
"A multiple testing approach to the regularisation of large sample correlation matrices,"
Journal of Econometrics, Elsevier, vol. 208(2), pages 507-534.
- Natalia Bailey & M. Hashem Pesaran & L. Vanessa Smith, 2014. "A Multiple Testing Approach to the Regularisation of Large Sample Correlation Matrices," CESifo Working Paper Series 4834, CESifo.
- Natalia Bailey & M. Hashem Pesaran & L. Vanessa Smith, 2015. "A Multiple Testing Approach to the Regularisation of Large Sample Correlation Matrices," Working Papers 764, Queen Mary University of London, School of Economics and Finance.
- Natalia Bailey & Vanessa Smith & M. Hashem Pesaran, 2014. "A multiple testing approach to the regularisation of large sample correlation matrices," Cambridge Working Papers in Economics 1413, Faculty of Economics, University of Cambridge.
- Huiqin Xin & Sihai Dave Zhao, 2023. "A compound decision approach to covariance matrix estimation," Biometrics, The International Biometric Society, vol. 79(2), pages 1201-1212, June.
- Chen, Shuo & Kang, Jian & Xing, Yishi & Zhao, Yunpeng & Milton, Donald K., 2018. "Estimating large covariance matrix with network topology for high-dimensional biomedical data," Computational Statistics & Data Analysis, Elsevier, vol. 127(C), pages 82-95.
- Cai, T. Tony & Zhang, Anru, 2016. "Minimax rate-optimal estimation of high-dimensional covariance matrices with incomplete data," Journal of Multivariate Analysis, Elsevier, vol. 150(C), pages 55-74.
- Esra Ulasan & A. Özlem Önder, 2023. "Large portfolio optimisation approaches," Journal of Asset Management, Palgrave Macmillan, vol. 24(6), pages 485-497, October.
- Richard Luger, 2024. "Regularizing stock return covariance matrices via multiple testing of correlations," Papers 2407.09696, arXiv.org.
- Abadir, Karim M. & Distaso, Walter & Žikeš, Filip, 2014. "Design-free estimation of variance matrices," Journal of Econometrics, Elsevier, vol. 181(2), pages 165-180.
- Dai, Chaoxing & Lu, Kun & Xiu, Dacheng, 2019. "Knowing factors or factor loadings, or neither? Evaluating estimators of large covariance matrices with noisy and asynchronous data," Journal of Econometrics, Elsevier, vol. 208(1), pages 43-79.
- Huang, Na & Fryzlewicz, Piotr, 2018. "NOVELIST estimator of large correlation and covariance matrices and their inverses," LSE Research Online Documents on Economics 89055, London School of Economics and Political Science, LSE Library.
- Fisher, Thomas J. & Sun, Xiaoqian, 2011. "Improved Stein-type shrinkage estimators for the high-dimensional multivariate normal covariance matrix," Computational Statistics & Data Analysis, Elsevier, vol. 55(5), pages 1909-1918, May.
- Peng, Liuhua & Chen, Song Xi & Zhou, Wen, 2016. "More powerful tests for sparse high-dimensional covariances matrices," Journal of Multivariate Analysis, Elsevier, vol. 149(C), pages 124-143.
- Han-Chung Yang & Chuan-Yi Wang & Jia-Xue Yang, 2014. "Applying image recording and identification for measuring water stages to prevent flood hazards," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 74(2), pages 737-754, November.
- Daniel Borup & Martin Thyrsgaard, 2017. "Statistical tests for equal predictive ability across multiple forecasting methods," CREATES Research Papers 2017-19, Department of Economics and Business Economics, Aarhus University.
- Qiang Sun & Hongtu Zhu & Yufeng Liu & Joseph G. Ibrahim, 2015. "SPReM: Sparse Projection Regression Model For High-Dimensional Linear Regression," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 289-302, March.
- Yin, Jianxin & Li, Hongzhe, 2012. "Model selection and estimation in the matrix normal graphical model," Journal of Multivariate Analysis, Elsevier, vol. 107(C), pages 119-140.
- Jin-Chuan Duan & Weimin Miao, 2016. "Default Correlations and Large-Portfolio Credit Analysis," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(4), pages 536-546, October.
- Fourdrinier, Dominique & Mezoued, Fatiha & Wells, Martin T., 2016. "Estimation of the inverse scatter matrix of an elliptically symmetric distribution," Journal of Multivariate Analysis, Elsevier, vol. 143(C), pages 32-55.
- Piotr Zwiernik & Caroline Uhler & Donald Richards, 2017. "Maximum likelihood estimation for linear Gaussian covariance models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(4), pages 1269-1292, September.
- Kashlak, Adam B., 2021. "Non-asymptotic error controlled sparse high dimensional precision matrix estimation," Journal of Multivariate Analysis, Elsevier, vol. 181(C).
- Choi, Young-Geun & Lim, Johan & Roy, Anindya & Park, Junyong, 2019. "Fixed support positive-definite modification of covariance matrix estimators via linear shrinkage," Journal of Multivariate Analysis, Elsevier, vol. 171(C), pages 234-249.
- Ding, Yi & Li, Yingying & Zheng, Xinghua, 2021. "High dimensional minimum variance portfolio estimation under statistical factor models," Journal of Econometrics, Elsevier, vol. 222(1), pages 502-515.
- Chang, Jinyuan & Hu, Qiao & Liu, Cheng & Tang, Cheng Yong, 2024. "Optimal covariance matrix estimation for high-dimensional noise in high-frequency data," Journal of Econometrics, Elsevier, vol. 239(2).
- Jingying Yang, 2024. "Element Aggregation for Estimation of High-Dimensional Covariance Matrices," Mathematics, MDPI, vol. 12(7), pages 1-16, March.
- Wang, Hanchao & Peng, Bin & Li, Degui & Leng, Chenlei, 2021. "Nonparametric estimation of large covariance matrices with conditional sparsity," Journal of Econometrics, Elsevier, vol. 223(1), pages 53-72.
- Xue, Lingzhou & Zou, Hui, 2013. "Minimax optimal estimation of general bandable covariance matrices," Journal of Multivariate Analysis, Elsevier, vol. 116(C), pages 45-51.
- Yu, Xiufan & Yao, Jiawei & Xue, Lingzhou, 2024. "Power enhancement for testing multi-factor asset pricing models via Fisher’s method," Journal of Econometrics, Elsevier, vol. 239(2).
- Xi Luo, 2011. "Recovering Model Structures from Large Low Rank and Sparse Covariance Matrix Estimation," Papers 1111.1133, arXiv.org, revised Mar 2013.
- Mian Huang & Shangbing Yu & Weixin Yao, 2022. "Regularized Factor Portfolio for Cross-sectional Multifactor Models," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 84(2), pages 427-449, August.
- Lan, Wei & Wang, Hansheng & Tsai, Chih-Ling, 2012. "A Bayesian information criterion for portfolio selection," Computational Statistics & Data Analysis, Elsevier, vol. 56(1), pages 88-99, January.
- Du, Lilun & Lan, Wei & Luo, Ronghua & Zhong, Pingshou, 2018. "Factor-adjusted multiple testing of correlations," Computational Statistics & Data Analysis, Elsevier, vol. 128(C), pages 34-47.
- Lam, Clifford, 2020. "High-dimensional covariance matrix estimation," LSE Research Online Documents on Economics 101667, London School of Economics and Political Science, LSE Library.
- Roman Vershynin, 2012. "How Close is the Sample Covariance Matrix to the Actual Covariance Matrix?," Journal of Theoretical Probability, Springer, vol. 25(3), pages 655-686, September.
- Rajaratnam, Bala & Salzman, Julia, 2013. "Best permutation analysis," Journal of Multivariate Analysis, Elsevier, vol. 121(C), pages 193-223.
- Vahe Avagyan & Andrés M. Alonso & Francisco J. Nogales, 2018. "D-trace estimation of a precision matrix using adaptive Lasso penalties," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 12(2), pages 425-447, June.
- Daniel Felix Ahelegbey & Luis Carvalho & Eric D. Kolaczyk, 2020. "A Bayesian Covariance Graph And Latent Position Model For Multivariate Financial Time Series," DEM Working Papers Series 181, University of Pavia, Department of Economics and Management.
- Zhou Tang & Zhangsheng Yu & Cheng Wang, 2020. "A fast iterative algorithm for high-dimensional differential network," Computational Statistics, Springer, vol. 35(1), pages 95-109, March.