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A Shrinkage Approach to Large-Scale Covariance Matrix Estimation and Implications for Functional Genomics

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Cited by:

  1. Daniel Bartz & Kerr Hatrick & Christian W Hesse & Klaus-Robert Müller & Steven Lemm, 2013. "Directional Variance Adjustment: Bias Reduction in Covariance Matrices Based on Factor Analysis with an Application to Portfolio Optimization," PLOS ONE, Public Library of Science, vol. 8(7), pages 1-14, July.
  2. Villers Fanny & Schaeffer Brigitte & Bertin Caroline & Huet Sylvie, 2008. "Assessing the Validity Domains of Graphical Gaussian Models in Order to Infer Relationships among Components of Complex Biological Systems," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 7(2), pages 1-37, September.
  3. Sim Aaron & Tsagkrasoulis Dimosthenis & Montana Giovanni, 2013. "Random forests on distance matrices for imaging genetics studies," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 12(6), pages 757-786, December.
  4. Srinivasan, Karthik K. & Prakash, A.A. & Seshadri, Ravi, 2014. "Finding most reliable paths on networks with correlated and shifted log–normal travel times," Transportation Research Part B: Methodological, Elsevier, vol. 66(C), pages 110-128.
  5. Hannart, Alexis & Naveau, Philippe, 2014. "Estimating high dimensional covariance matrices: A new look at the Gaussian conjugate framework," Journal of Multivariate Analysis, Elsevier, vol. 131(C), pages 149-162.
  6. Reiss Philip T. & Huang Lei & Mennes Maarten, 2010. "Fast Function-on-Scalar Regression with Penalized Basis Expansions," The International Journal of Biostatistics, De Gruyter, vol. 6(1), pages 1-30, August.
  7. Montazeri Zahra & Yanofsky Corey M. & Bickel David R., 2010. "Shrinkage Estimation of Effect Sizes as an Alternative to Hypothesis Testing Followed by Estimation in High-Dimensional Biology: Applications to Differential Gene Expression," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 9(1), pages 1-33, June.
  8. Soloveychik, I. & Trushin, D., 2016. "Gaussian and robust Kronecker product covariance estimation: Existence and uniqueness," Journal of Multivariate Analysis, Elsevier, vol. 149(C), pages 92-113.
  9. Ledoit, Olivier & Wolf, Michael, 2017. "Numerical implementation of the QuEST function," Computational Statistics & Data Analysis, Elsevier, vol. 115(C), pages 199-223.
  10. Lèbre Sophie, 2009. "Inferring Dynamic Genetic Networks with Low Order Independencies," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 8(1), pages 1-40, February.
  11. Pesonen, Maiju & Pesonen, Henri & Nevalainen, Jaakko, 2015. "Covariance matrix estimation for left-censored data," Computational Statistics & Data Analysis, Elsevier, vol. 92(C), pages 13-25.
  12. Qing Cheng & Xiao Zhang & Lin S. Chen & Jin Liu, 2022. "Mendelian randomization accounting for complex correlated horizontal pleiotropy while elucidating shared genetic etiology," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
  13. Olivier Ledoit & Michael Wolf, 2019. "Quadratic shrinkage for large covariance matrices," ECON - Working Papers 335, Department of Economics - University of Zurich, revised Dec 2020.
  14. Jain Yashita & Ding Shanshan & Qiu Jing, 2019. "Sliced inverse regression for integrative multi-omics data analysis," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 18(1), pages 1-13, February.
  15. van Wieringen, Wessel N. & Stam, Koen A. & Peeters, Carel F.W. & van de Wiel, Mark A., 2020. "Updating of the Gaussian graphical model through targeted penalized estimation," Journal of Multivariate Analysis, Elsevier, vol. 178(C).
  16. Qing Zhou & Robert Faff, 2017. "The complementary role of cross-sectional and time-series information in forecasting stock returns," Australian Journal of Management, Australian School of Business, vol. 42(1), pages 113-139, February.
  17. Shanika L. Wickramasuriya & George Athanasopoulos & Rob J. Hyndman, 2019. "Optimal Forecast Reconciliation for Hierarchical and Grouped Time Series Through Trace Minimization," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(526), pages 804-819, April.
  18. Haiyan Wang & Michael Akritas, 2010. "Inference from heteroscedastic functional data," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 22(2), pages 149-168.
  19. Olivier Ledoit & Michael Wolf, 2019. "The power of (non-)linear shrinking: a review and guide to covariance matrix estimation," ECON - Working Papers 323, Department of Economics - University of Zurich, revised Feb 2020.
  20. Bala Rajaratnam & Dario Vincenzi, 2016. "A theoretical study of Stein's covariance estimator," Biometrika, Biometrika Trust, vol. 103(3), pages 653-666.
  21. Kwan, Clarence C.Y., 2008. "Estimation error in the average correlation of security returns and shrinkage estimation of covariance and correlation matrices," Finance Research Letters, Elsevier, vol. 5(4), pages 236-244, December.
  22. Christian Bongiorno, 2020. "Bootstraps Regularize Singular Correlation Matrices," Papers 2004.03165, arXiv.org.
  23. Athanasopoulos, George & Hyndman, Rob J. & Kourentzes, Nikolaos & Petropoulos, Fotios, 2017. "Forecasting with temporal hierarchies," European Journal of Operational Research, Elsevier, vol. 262(1), pages 60-74.
  24. Dimitri Yatsenko & Krešimir Josić & Alexander S Ecker & Emmanouil Froudarakis & R James Cotton & Andreas S Tolias, 2015. "Improved Estimation and Interpretation of Correlations in Neural Circuits," PLOS Computational Biology, Public Library of Science, vol. 11(3), pages 1-28, March.
  25. Charbonnier Camille & Chiquet Julien & Ambroise Christophe, 2010. "Weighted-LASSO for Structured Network Inference from Time Course Data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 9(1), pages 1-29, February.
  26. Guibert, Quentin & Lopez, Olivier & Piette, Pierrick, 2019. "Forecasting mortality rate improvements with a high-dimensional VAR," Insurance: Mathematics and Economics, Elsevier, vol. 88(C), pages 255-272.
  27. Thompson, Ryan & Qian, Yilin & Vasnev, Andrey L., 2024. "Flexible global forecast combinations," Omega, Elsevier, vol. 126(C).
  28. Arnab Chakrabarti & Rituparna Sen, 2018. "Some Statistical Problems with High Dimensional Financial data," Papers 1808.02953, arXiv.org.
  29. Victor P Andreev & Gang Liu & Jarcy Zee & Lisa Henn & Gilberto E Flores & Robert M Merion, 2019. "Clustering of the structures by using “snakes-&-dragons” approach, or correlation matrix as a signal," PLOS ONE, Public Library of Science, vol. 14(10), pages 1-27, October.
  30. Zongliang Hu & Zhishui Hu & Kai Dong & Tiejun Tong & Yuedong Wang, 2021. "A shrinkage approach to joint estimation of multiple covariance matrices," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 84(3), pages 339-374, April.
  31. Tumminello, Michele & Lillo, Fabrizio & Mantegna, Rosario N., 2010. "Correlation, hierarchies, and networks in financial markets," Journal of Economic Behavior & Organization, Elsevier, vol. 75(1), pages 40-58, July.
  32. 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 Estadística.
  33. Yang, Baoying & Yin, Xiangrong & Zhang, Nan, 2019. "Sufficient variable selection using independence measures for continuous response," Journal of Multivariate Analysis, Elsevier, vol. 173(C), pages 480-493.
  34. Mr. Jorge A Chan-Lau, 2017. "Variance Decomposition Networks: Potential Pitfalls and a Simple Solution," IMF Working Papers 2017/107, International Monetary Fund.
  35. 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.
  36. Puwasala Gamakumara & Anastasios Panagiotelis & George Athanasopoulos & Rob J Hyndman, 2018. "Probabilisitic forecasts in hierarchical time series," Monash Econometrics and Business Statistics Working Papers 11/18, Monash University, Department of Econometrics and Business Statistics.
  37. Ghelasi, Paul & Ziel, Florian, 2024. "Hierarchical forecasting for aggregated curves with an application to day-ahead electricity price auctions," International Journal of Forecasting, Elsevier, vol. 40(2), pages 581-596.
  38. Hamid Jemila S & Beyene Joseph, 2009. "A Multivariate Growth Curve Model for Ranking Genes in Replicated Time Course Microarray Data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 8(1), pages 1-28, July.
  39. Sumanjay Dutta & Shashi Jain, 2023. "Precision versus Shrinkage: A Comparative Analysis of Covariance Estimation Methods for Portfolio Allocation," Papers 2305.11298, arXiv.org.
  40. Marius Arend & Yizhong Yuan & M. Águila Ruiz-Sola & Nooshin Omranian & Zoran Nikoloski & Dimitris Petroutsos, 2023. "Widening the landscape of transcriptional regulation of green algal photoprotection," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
  41. Zhou, Qing & Faff, Robert & Alpert, Karen, 2014. "Bias correction in the estimation of dynamic panel models in corporate finance," Journal of Corporate Finance, Elsevier, vol. 25(C), pages 494-513.
  42. van Wieringen, Wessel N. & Peeters, Carel F.W., 2016. "Ridge estimation of inverse covariance matrices from high-dimensional data," Computational Statistics & Data Analysis, Elsevier, vol. 103(C), pages 284-303.
  43. Vera Djordjilović & Monica Chiogna & Chiara Romualdi, 2020. "Simulating gene silencing through intervention analysis," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(4), pages 887-907, August.
  44. Farnè, Matteo & Vouldis, Angelos T., 2018. "A methodology for automised outlier detection in high-dimensional datasets: an application to euro area banks' supervisory data," Working Paper Series 2171, European Central Bank.
  45. Boulesteix Anne-Laure, 2006. "Reader's Reaction to "Dimension Reduction for Classification with Gene Expression Microarray Data" by Dai et al (2006)," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 5(1), pages 1-7, June.
  46. 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.
  47. Zhiliang Ma & Adam Cardinal-Stakenas & Youngser Park & Michael Trosset & Carey Priebe, 2010. "Dimensionality Reduction on the Cartesian Product of Embeddings of Multiple Dissimilarity Matrices," Journal of Classification, Springer;The Classification Society, vol. 27(3), pages 307-321, November.
  48. 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.
  49. Wang, Christina Dan & Chen, Zhao & Lian, Yimin & Chen, Min, 2022. "Asset selection based on high frequency Sharpe ratio," Journal of Econometrics, Elsevier, vol. 227(1), pages 168-188.
  50. 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.
  51. Helmut Lütkepohl & Anna Staszewska-Bystrova & Peter Winker, 2018. "Calculating joint confidence bands for impulse response functions using highest density regions," Empirical Economics, Springer, vol. 55(4), pages 1389-1411, December.
  52. Panagiotelis, Anastasios & Athanasopoulos, George & Gamakumara, Puwasala & Hyndman, Rob J., 2021. "Forecast reconciliation: A geometric view with new insights on bias correction," International Journal of Forecasting, Elsevier, vol. 37(1), pages 343-359.
  53. Svetunkov, Ivan & Chen, Huijing & Boylan, John E., 2023. "A new taxonomy for vector exponential smoothing and its application to seasonal time series," European Journal of Operational Research, Elsevier, vol. 304(3), pages 964-980.
  54. 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.
  55. Perreault, Samuel & Duchesne, Thierry & Nešlehová, Johanna G., 2019. "Detection of block-exchangeable structure in large-scale correlation matrices," Journal of Multivariate Analysis, Elsevier, vol. 169(C), pages 400-422.
  56. George Athanasopoulos & Puwasala Gamakumara & Anastasios Panagiotelis & Rob J Hyndman & Mohamed Affan, 2019. "Hierarchical Forecasting," Monash Econometrics and Business Statistics Working Papers 2/19, Monash University, Department of Econometrics and Business Statistics.
  57. Wang, Haiyan & Higgins, James & Blasi, Dale, 2010. "Distribution-free tests for no effect of treatment in heteroscedastic functional data under both weak and long range dependence," Statistics & Probability Letters, Elsevier, vol. 80(5-6), pages 390-402, March.
  58. Zuber Verena & Strimmer Korbinian, 2011. "High-Dimensional Regression and Variable Selection Using CAR Scores," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 10(1), pages 1-27, July.
  59. Lim Johan & Kim Jayoun & Kim Sang-cheol & Yu Donghyeon & Kim Kyunga & Kim Byung Soo, 2012. "Detection of Differentially Expressed Gene Sets in a Partially Paired Microarray Data Set," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(3), pages 1-30, February.
  60. Donatello Telesca & Peter Müller & Steven M. Kornblau & Marc A. Suchard & Yuan Ji, 2012. "Modeling Protein Expression and Protein Signaling Pathways," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(500), pages 1372-1384, December.
  61. Qian, Yilin & Thompson, Ryan & Vasnev, Andrey L, 2022. "Global combinations of expert forecasts," Working Papers BAWP-2022-02, University of Sydney Business School, Discipline of Business Analytics.
  62. Kourentzes, Nikolaos & Saayman, Andrea & Jean-Pierre, Philippe & Provenzano, Davide & Sahli, Mondher & Seetaram, Neelu & Volo, Serena, 2021. "Visitor arrivals forecasts amid COVID-19: A perspective from the Africa team," Annals of Tourism Research, Elsevier, vol. 88(C).
  63. Pan-Jun Kim & Nathan D Price, 2011. "Genetic Co-Occurrence Network across Sequenced Microbes," PLOS Computational Biology, Public Library of Science, vol. 7(12), pages 1-9, December.
  64. Elisa Salviato & Vera Djordjilović & Monica Chiogna & Chiara Romualdi, 2019. "SourceSet: A graphical model approach to identify primary genes in perturbed biological pathways," PLOS Computational Biology, Public Library of Science, vol. 15(10), pages 1-28, October.
  65. Juliane Schäfer, 2008. "Comments on: Augmenting the bootstrap to analyze high dimensional genomic data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 17(1), pages 28-30, May.
  66. Viet Anh Nguyen & Daniel Kuhn & Peyman Mohajerin Esfahani, 2018. "Distributionally Robust Inverse Covariance Estimation: The Wasserstein Shrinkage Estimator," Papers 1805.07194, arXiv.org.
  67. Bergsteinsson, Hjörleifur G. & Møller, Jan Kloppenborg & Nystrup, Peter & Pálsson, Ólafur Pétur & Guericke, Daniela & Madsen, Henrik, 2021. "Heat load forecasting using adaptive temporal hierarchies," Applied Energy, Elsevier, vol. 292(C).
  68. Piga, Angelo & Font-Pomarol, Lluc & Sales-Pardo, Marta & Guimerà, Roger, 2024. "Bayesian estimation of information-theoretic metrics for sparsely sampled distributions," Chaos, Solitons & Fractals, Elsevier, vol. 180(C).
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  72. Panagiotelis, Anastasios & Gamakumara, Puwasala & Athanasopoulos, George & Hyndman, Rob J., 2023. "Probabilistic forecast reconciliation: Properties, evaluation and score optimisation," European Journal of Operational Research, Elsevier, vol. 306(2), pages 693-706.
  73. Lingxue Zhang & Seyoung Kim, 2014. "Learning Gene Networks under SNP Perturbations Using eQTL Datasets," PLOS Computational Biology, Public Library of Science, vol. 10(2), pages 1-20, February.
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