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Adaptive Thresholding for Sparse Covariance Matrix Estimation
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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.
- Guo, Wenwen & Cui, Hengjian, 2019. "Projection tests for high-dimensional spiked covariance matrices," Journal of Multivariate Analysis, Elsevier, vol. 169(C), pages 21-32.
- 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.
- Shi Yafeng & Ai Chunrong & Yanlong Shi & Ying Tingting & Xu Qunfang, 2023. "Large covariance estimation using a factor model with common and group‐specific factors," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(8), pages 2217-2248, December.
- 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.
- Hu Zongliang & Dong Kai & Dai Wenlin & Tong Tiejun, 2017. "A Comparison of Methods for Estimating the Determinant of High-Dimensional Covariance Matrix," The International Journal of Biostatistics, De Gruyter, vol. 13(2), pages 1-24, November.
- Jonas Krampe & Luca Margaritella, 2021. "Factor Models with Sparse VAR Idiosyncratic Components," Papers 2112.07149, arXiv.org, revised May 2022.
- 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.
- Sven Husmann & Antoniya Shivarova & Rick Steinert, 2019. "Sparsity and Stability for Minimum-Variance Portfolios," Papers 1910.11840, arXiv.org.
- 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.
- Tae-Hwy Lee & Millie Yi Mao & Aman Ullah, 2021.
"Estimation of high-dimensional dynamic conditional precision matrices with an application to forecast combination,"
Econometric Reviews, Taylor & Francis Journals, vol. 40(10), pages 905-918, November.
- Tae-Hwy Lee & Millie Yi Mao & Aman Ullah, 2020. "Estimation of High-Dimensional Dynamic Conditional Precision Matrices with an Application to Forecast Combination," Working Papers 202012, University of California at Riverside, Department of Economics.
- 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.
- Kim, Donggyu & Kong, Xin-Bing & Li, Cui-Xia & Wang, Yazhen, 2018. "Adaptive thresholding for large volatility matrix estimation based on high-frequency financial data," Journal of Econometrics, Elsevier, vol. 203(1), pages 69-79.
- Arnab Chakrabarti & Rituparna Sen, 2018. "Some Statistical Problems with High Dimensional Financial data," Papers 1808.02953, arXiv.org.
- Kim, Donggyu & Song, Xinyu & Wang, Yazhen, 2022.
"Unified discrete-time factor stochastic volatility and continuous-time Itô models for combining inference based on low-frequency and high-frequency,"
Journal of Multivariate Analysis, Elsevier, vol. 192(C).
- Donggyu Kim & Xinyu Song & Yazhen Wang, 2020. "Unified Discrete-Time Factor Stochastic Volatility and Continuous-Time Ito Models for Combining Inference Based on Low-Frequency and High-Frequency," Papers 2006.12039, arXiv.org.
- 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.
- Tae-Hwy Lee & Ekaterina Seregina, 2020.
"Learning from Forecast Errors: A New Approach to Forecast Combination,"
Working Papers
202024, University of California at Riverside, Department of Economics.
- Tae-Hwy Lee & Ekaterina Seregina, 2020. "Learning from Forecast Errors: A New Approach to Forecast Combinations," Papers 2011.02077, arXiv.org, revised May 2021.
- Wang, Xin & Kong, Lingchen & Wang, Liqun, 2024. "Estimation of sparse covariance matrix via non-convex regularization," Journal of Multivariate Analysis, Elsevier, vol. 202(C).
- 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.
- 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.
- Shen, Yanfeng & Lin, Zhengyan, 2015. "An adaptive test for the mean vector in large-p-small-n problems," Computational Statistics & Data Analysis, Elsevier, vol. 89(C), pages 25-38.
- 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.
- Asai, Manabu & McAleer, Michael, 2015.
"Forecasting co-volatilities via factor models with asymmetry and long memory in realized covariance,"
Journal of Econometrics, Elsevier, vol. 189(2), pages 251-262.
- Manabu Asai & Michael McAleer, 2014. "Forecasting Co-Volatilities via Factor Models with Asymmetry and Long Memory in Realized Covariance," Tinbergen Institute Discussion Papers 14-037/III, Tinbergen Institute.
- Manabu Asai & Michael McAleer, 2014. "Forecasting Co-Volatilities via Factor Models with Asymmetry and Long Memory in Realized Covariance," Working Papers in Economics 14/10, University of Canterbury, Department of Economics and Finance.
- Manabu Asai & Michael McAleer, 2014. "Forecasting Co-Volatilities via Factor Models with Asymmetry and Long Memory in Realized Covariance," Documentos de Trabajo del ICAE 2014-05, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
- 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.
- Sven Husmann & Antoniya Shivarova & Rick Steinert, 2019. "Cross-validated covariance estimators for high-dimensional minimum-variance portfolios," Papers 1910.13960, arXiv.org, revised Oct 2020.
- Sung Hoon Choi & Donggyu Kim, 2023. "Large Global Volatility Matrix Analysis Based on Observation Structural Information," Papers 2305.01464, arXiv.org, revised Feb 2024.
- 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.
- Yunxi Zhang & Soeun Kim, 2024. "Gaussian Graphical Model Estimation and Selection for High-Dimensional Incomplete Data Using Multiple Imputation and Horseshoe Estimators," Mathematics, MDPI, vol. 12(12), pages 1-15, June.
- Dutta, Sumanjay & Jain, Shashi, 2024. "Shrinkage and thresholding approaches for expected utility portfolios: An analysis in terms of predictive ability," Finance Research Letters, Elsevier, vol. 64(C).
- Sven Husmann & Antoniya Shivarova & Rick Steinert, 2022. "Sparsity and stability for minimum-variance portfolios," Risk Management, Palgrave Macmillan, vol. 24(3), pages 214-235, September.
- 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.
- Jianqing Fan & Xu Han, 2017. "Estimation of the false discovery proportion with unknown dependence," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(4), pages 1143-1164, September.
- M Hashem Pesaran & Takashi Yamagata, 2024.
"Testing for Alpha in Linear Factor Pricing Models with a Large Number of Securities,"
Journal of Financial Econometrics, Oxford University Press, vol. 22(2), pages 407-460.
- M. Hashem Pesaran & Takashi Yamagata, 2017. "Testing for Alpha in Linear Factor Pricing Models with a Large Number of Securities," Discussion Papers 17/04, Department of Economics, University of York.
- M. Hashem Pesaran & Takashi Yamagata, 2017. "Testing for Alpha in Linear Factor Pricing Models with a Large Number of Securities," CESifo Working Paper Series 6432, CESifo.
- Jianqing Fan & Alex Furger & Dacheng Xiu, 2016. "Incorporating Global Industrial Classification Standard Into Portfolio Allocation: A Simple Factor-Based Large Covariance Matrix Estimator With High-Frequency Data," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(4), pages 489-503, October.
- 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.
- Seonghun Cho & Shota Katayama & Johan Lim & Young-Geun Choi, 2021. "Positive-definite modification of a covariance matrix by minimizing the matrix $$\ell_{\infty}$$ ℓ ∞ norm with applications to portfolio optimization," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 105(4), pages 601-627, December.
- 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.
- Pedro Galeano & Daniel Peña, 2019. "Data science, big data and statistics," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(2), pages 289-329, June.
- Chudik, Alexander & Grossman, Valerie & Pesaran, M. Hashem, 2016.
"A multi-country approach to forecasting output growth using PMIs,"
Journal of Econometrics, Elsevier, vol. 192(2), pages 349-365.
- Alexander Chudik & Valerie Grossman & M. Hashem Pesaran, 2014. "A Multi-Country Approach to Forecasting Output Growth Using PMIs," CESifo Working Paper Series 5100, CESifo.
- Alexander Chudik & Valerie Grossman & M. Hashem Pesaran, 2014. "A multi-country approach to forecasting output growth using PMIs," Globalization Institute Working Papers 213, Federal Reserve Bank of Dallas.
- Chen, Binbin & Huang, Shih-Feng & Pan, Guangming, 2015. "High dimensional mean–variance optimization through factor analysis," Journal of Multivariate Analysis, Elsevier, vol. 133(C), pages 140-159.
- Xiao, Han & Wu, Wei Biao, 2013. "Asymptotic theory for maximum deviations of sample covariance matrix estimates," Stochastic Processes and their Applications, Elsevier, vol. 123(7), pages 2899-2920.
- 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.
- Kwangmin Jung & Donggyu Kim & Seunghyeon Yu, 2022.
"Next generation models for portfolio risk management: An approach using financial big data,"
Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 89(3), pages 765-787, September.
- Kwangmin Jung & Donggyu Kim & Seunghyeon Yu, 2021. "Next Generation Models for Portfolio Risk Management: An Approach Using Financial Big Data," Papers 2102.12783, arXiv.org, revised Feb 2022.
- Sangahn Kim & Mehmet Turkoz & Myong K. Jeong & Elsayed A. Elsayed, 2024. "Monitoring of group-structured high-dimensional processes via sparse group LASSO," Annals of Operations Research, Springer, vol. 340(2), pages 891-911, September.
- 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.
- Jianqing Fan & Kunpeng Li & Yuan Liao, 2020. "Recent Developments on Factor Models and its Applications in Econometric Learning," Papers 2009.10103, arXiv.org.
- Matteo Barigozzi & Marc Hallin, 2017.
"A network analysis of the volatility of high dimensional financial series,"
Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(3), pages 581-605, April.
- Barigozzi, Matteo & Hallin, Marc, 2017. "A network analysis of the volatility of high-dimensionalfinancial series," LSE Research Online Documents on Economics 67456, London School of Economics and Political Science, LSE Library.
- 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.
- Huimin Li & Jinru Wang, 2023. "Differentially Private Sparse Covariance Matrix Estimation under Lower-Bounded Moment Assumption," Mathematics, MDPI, vol. 11(17), pages 1-16, August.
- Lucija v{Z}igni'c & Stjepan Beguv{s}i'c & Zvonko Kostanjv{c}ar, 2024. "Block-diagonal idiosyncratic covariance estimation in high-dimensional factor models for financial time series," Papers 2407.03781, arXiv.org.
- 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.
- Alexander Giessing & Jianqing Fan, 2020. "Bootstrapping $\ell_p$-Statistics in High Dimensions," Papers 2006.13099, arXiv.org, revised Aug 2020.
- Matteo Barigozzi & Marc Hallin, 2015.
"Networks, Dynamic Factors, and the Volatility Analysis of High-Dimensional Financial Series,"
Papers
1510.05118, arXiv.org, revised Jul 2016.
- Matteo Barigozzi & Marc Hallin, 2015. "Networks, Dynamic Factors, and the Volatility Analysis of High-Dimensional Financial Series," Working Papers ECARES ECARES 2015-34, ULB -- Universite Libre de Bruxelles.
- Jungjun Choi & Hyukjun Kwon & Yuan Liao, 2023. "Inference for Low-rank Models without Estimating the Rank," Papers 2311.16440, arXiv.org, revised Oct 2024.
- 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.
- Shujie Ma & Oliver Linton & Jiti Gao, 2017.
"Estimation and inference in semiparametric quantile factor models,"
Monash Econometrics and Business Statistics Working Papers
8/17, Monash University, Department of Econometrics and Business Statistics.
- Ma, S. & Linton, O. & Gao, J., 2019. "Estimation and Inference in Semiparametric Quantile Factor Models," Cambridge Working Papers in Economics 1939, Faculty of Economics, University of Cambridge.
- 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.
- Pan, Yuqing & Mai, Qing, 2020. "Efficient computation for differential network analysis with applications to quadratic discriminant analysis," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).
- 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.
- Liu, Weidong & Luo, Xi, 2015. "Fast and adaptive sparse precision matrix estimation in high dimensions," Journal of Multivariate Analysis, Elsevier, vol. 135(C), pages 153-162.
- 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.
- Tony Cai & Weidong Liu & Yin Xia, 2013. "Two-Sample Covariance Matrix Testing and Support Recovery in High-Dimensional and Sparse Settings," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(501), pages 265-277, March.
- Wei Jiang & Ling Chen & Matthew J. Girgenti & Hongyu Zhao, 2024. "Tuning parameters for polygenic risk score methods using GWAS summary statistics from training data," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
- Long Feng & Tiefeng Jiang & Binghui Liu & Wei Xiong, 2020. "Max-sum tests for cross-sectional dependence of high-demensional panel data," Papers 2007.03911, arXiv.org.
- Ma, Shujie & Linton, Oliver & Gao, Jiti, 2021.
"Estimation and inference in semiparametric quantile factor models,"
Journal of Econometrics, Elsevier, vol. 222(1), pages 295-323.
- Shujie Ma & Oliver Linton & Jiti Gao, 2017. "Estimation and inference in semiparametric quantile factor models," Monash Econometrics and Business Statistics Working Papers 8/17, Monash University, Department of Econometrics and Business Statistics.
- Ma, S. & Linton, O. & Gao, J., 2019. "Estimation and Inference in Semiparametric Quantile Factor Models," Cambridge Working Papers in Economics 1933, Faculty of Economics, University of Cambridge.
- Li, Degui, 2024. "Estimation of Large Dynamic Covariance Matrices: A Selective Review," Econometrics and Statistics, Elsevier, vol. 29(C), pages 16-30.
- 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.
- Yin Xia & Lexin Li, 2017. "Hypothesis testing of matrix graph model with application to brain connectivity analysis," Biometrics, The International Biometric Society, vol. 73(3), pages 780-791, September.
- T. Tony Cai & Weidong Liu, 2016. "Large-Scale Multiple Testing of Correlations," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(513), pages 229-240, March.
- Sumanjay Dutta & Shashi Jain, 2023. "Precision versus Shrinkage: A Comparative Analysis of Covariance Estimation Methods for Portfolio Allocation," Papers 2305.11298, arXiv.org.
- Farnè, Matteo & Montanari, Angela, 2020. "A large covariance matrix estimator under intermediate spikiness regimes," Journal of Multivariate Analysis, Elsevier, vol. 176(C).
- Long Feng & Changliang Zou & Zhaojun Wang, 2016. "Multivariate-Sign-Based High-Dimensional Tests for the Two-Sample Location Problem," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(514), pages 721-735, April.
- Bettache, Nayel & Butucea, Cristina & Sorba, Marianne, 2022. "Fast nonasymptotic testing and support recovery for large sparse Toeplitz covariance matrices," Journal of Multivariate Analysis, Elsevier, vol. 190(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.
- 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.
- Gillen, Benjamin J., 2014. "An empirical Bayesian approach to stein-optimal covariance matrix estimation," Journal of Empirical Finance, Elsevier, vol. 29(C), pages 402-420.
- Jiang, Binyan, 2013. "Covariance selection by thresholding the sample correlation matrix," Statistics & Probability Letters, Elsevier, vol. 83(11), pages 2492-2498.
- 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.
- Bodnar, Taras & Mazur, Stepan & Ngailo, Edward & Parolya, Nestor, 2017. "Discriminant analysis in small and large dimensions," Working Papers 2017:6, Örebro University, School of Business.
- Sven Husmann & Antoniya Shivarova & Rick Steinert, 2021. "Cross-validated covariance estimators for high-dimensional minimum-variance portfolios," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 35(3), pages 309-352, September.
- Li, Huimin & Wang, Jinru, 2024. "Sparse basis covariance matrix estimation for high dimensional compositional data via hard thresholding," Statistics & Probability Letters, Elsevier, vol. 209(C).
- 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.
- Ge, S. & Li, S. & Linton, O. B. & Liu, W. & Su, W., 2024.
"Should We Augment Large Covariance Matrix Estimation with Auxiliary Network Information?,"
Cambridge Working Papers in Economics
2427, Faculty of Economics, University of Cambridge.
- Ge, S. & Li, S. & Linton, O. B. & Liu, W. & Su, W., 2024. "Should We Augment Large Covariance Matrix Estimation with Auxiliary Network Information?," Janeway Institute Working Papers 2416, Faculty of Economics, University of Cambridge.
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