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Model selection and estimation in the Gaussian graphical model

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Cited by:

  1. Azam Kheyri & Andriette Bekker & Mohammad Arashi, 2022. "High-Dimensional Precision Matrix Estimation through GSOS with Application in the Foreign Exchange Market," Mathematics, MDPI, vol. 10(22), pages 1-19, November.
  2. Yujie Xue & Masanobu Taniguchi, 2020. "Modified LASSO estimators for time series regression models with dependent disturbances," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 29(4), pages 845-869, December.
  3. Wang, Ke & Franks, Alexander & Oh, Sang-Yun, 2023. "Learning Gaussian graphical models with latent confounders," Journal of Multivariate Analysis, Elsevier, vol. 198(C).
  4. 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.
  5. Fan, Xinyan & Zhang, Qingzhao & Ma, Shuangge & Fang, Kuangnan, 2021. "Conditional score matching for high-dimensional partial graphical models," Computational Statistics & Data Analysis, Elsevier, vol. 153(C).
  6. Ning Zhang & Jin Yang, 2023. "Sparse precision matrix estimation with missing observations," Computational Statistics, Springer, vol. 38(3), pages 1337-1355, September.
  7. Craig, Ben & Karamysheva, Madina & Salakhova, Dilyara, 2023. "Do market-based networks reflect true exposures between banks?," Working Paper Series 2867, European Central Bank.
  8. 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.
  9. Shizhe Chen & Ali Shojaie & Daniela M. Witten, 2017. "Network Reconstruction From High-Dimensional Ordinary Differential Equations," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(520), pages 1697-1707, October.
  10. 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).
  11. Qiuyan Zhang & Lingrui Li & Hu Yang, 2024. "Application of fused graphical lasso to statistical inference for multiple sparse precision matrices," PLOS ONE, Public Library of Science, vol. 19(5), pages 1-26, May.
  12. Rothfelder, Mario, 2018. "Three essays on time-varying parameters and time series networks," Other publications TiSEM fc7a10c0-7eee-479a-ac22-b, Tilburg University, School of Economics and Management.
  13. Lichun Wang & Yuan You & Heng Lian, 2015. "Convergence and sparsity of Lasso and group Lasso in high-dimensional generalized linear models," Statistical Papers, Springer, vol. 56(3), pages 819-828, August.
  14. 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.
  15. 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.
  16. Yu, Philip L.H. & Wang, Xiaohang & Zhu, Yuanyuan, 2017. "High dimensional covariance matrix estimation by penalizing the matrix-logarithm transformed likelihood," Computational Statistics & Data Analysis, Elsevier, vol. 114(C), pages 12-25.
  17. Chatterjee, A. & Gupta, S. & Lahiri, S.N., 2015. "On the residual empirical process based on the ALASSO in high dimensions and its functional oracle property," Journal of Econometrics, Elsevier, vol. 186(2), pages 317-324.
  18. Yifan He & Yang Feng & Xinyuan Song, 2023. "Variable selection for high‐dimensional generalized linear model with block‐missing data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 50(3), pages 1279-1297, September.
  19. Kevin H. Lee & Qian Chen & Wayne S. DeSarbo & Lingzhou Xue, 2022. "Estimating Finite Mixtures of Ordinal Graphical Models," Psychometrika, Springer;The Psychometric Society, vol. 87(1), pages 83-106, March.
  20. Yoshida, Wataru & Hirose, Kei, 2024. "Fast same-step forecast in SUTSE model and its theoretical properties," Computational Statistics & Data Analysis, Elsevier, vol. 190(C).
  21. Sung, Bongjung & Lee, Jaeyong, 2023. "Covariance structure estimation with Laplace approximation," Journal of Multivariate Analysis, Elsevier, vol. 198(C).
  22. Duo Jiang & Thomas Sharpton & Yuan Jiang, 2021. "Microbial Interaction Network Estimation via Bias-Corrected Graphical Lasso," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 13(2), pages 329-350, July.
  23. Ziqi Chen & Chenlei Leng, 2016. "Dynamic Covariance Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(515), pages 1196-1207, July.
  24. Victor Chernozhukov & Wolfgang K. Hardle & Chen Huang & Weining Wang, 2018. "LASSO-Driven Inference in Time and Space," Papers 1806.05081, arXiv.org, revised May 2020.
  25. Tan, Kean Ming & Witten, Daniela & Shojaie, Ali, 2015. "The cluster graphical lasso for improved estimation of Gaussian graphical models," Computational Statistics & Data Analysis, Elsevier, vol. 85(C), pages 23-36.
  26. 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.
  27. Li, Gaorong & Xue, Liugen & Lian, Heng, 2011. "Semi-varying coefficient models with a diverging number of components," Journal of Multivariate Analysis, Elsevier, vol. 102(7), pages 1166-1174, August.
  28. 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.
  29. 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.
  30. Áureo de Paula & Imran Rasul & Pedro C L Souza, 2025. "Identifying Network Ties from Panel Data: Theory and an Application to Tax Competition," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 92(4), pages 2691-2729.
  31. Suprateek Kundu & Benjamin B. Risk, 2021. "Scalable Bayesian matrix normal graphical models for brain functional networks," Biometrics, The International Biometric Society, vol. 77(2), pages 439-450, June.
  32. Banerjee, Sayantan & Ghosal, Subhashis, 2015. "Bayesian structure learning in graphical models," Journal of Multivariate Analysis, Elsevier, vol. 136(C), pages 147-162.
  33. Saverio Ranciati & Alberto Roverato & Alessandra Luati, 2021. "Fused graphical lasso for brain networks with symmetries," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(5), pages 1299-1322, November.
  34. Chang, Jinyuan & Qiu, Yumou & Yao, Qiwei & Zou, Tao, 2018. "Confidence regions for entries of a large precision matrix," Journal of Econometrics, Elsevier, vol. 206(1), pages 57-82.
  35. Xiaoping Zhou & Dmitry Malioutov & Frank J. Fabozzi & Svetlozar T. Rachev, 2014. "Smooth monotone covariance for elliptical distributions and applications in finance," Quantitative Finance, Taylor & Francis Journals, vol. 14(9), pages 1555-1571, September.
  36. Lam, Clifford, 2008. "Estimation of large precision matrices through block penalization," LSE Research Online Documents on Economics 31543, London School of Economics and Political Science, LSE Library.
  37. Anton Rask Lundborg & Rajen D. Shah & Jonas Peters, 2022. "Conditional independence testing in Hilbert spaces with applications to functional data analysis," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(5), pages 1821-1850, November.
  38. Yuanrong Wang & Tomaso Aste, 2022. "Sparsification and Filtering for Spatial-temporal GNN in Multivariate Time-series," Papers 2203.03991, arXiv.org.
  39. Popovic, Gordana C. & Hui, Francis K.C. & Warton, David I., 2018. "A general algorithm for covariance modeling of discrete data," Journal of Multivariate Analysis, Elsevier, vol. 165(C), pages 86-100.
  40. Nezakati, Ensiyeh & Pircalabelu, Eugen, 2021. "Unbalanced distributed estimation and inference for precision matrices," LIDAM Discussion Papers ISBA 2021031, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
  41. Siliang Zhang & Yunxiao Chen, 2024. "A Note on Ising Network Analysis with Missing Data," Psychometrika, Springer;The Psychometric Society, vol. 89(4), pages 1186-1202, December.
  42. Áureo de Paula & Imran Rasul & Pedro CL Souza, 2018. "Recovering social networks from panel data: identification, simulations and an application," CeMMAP working papers CWP17/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  43. Jarod Smith & Mohammad Arashi & Andriëtte Bekker, 2022. "Empowering differential networks using Bayesian analysis," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-19, January.
  44. Gabriele Torri & Rosella Giacometti & Sandra Paterlini, 2019. "Sparse precision matrices for minimum variance portfolios," Computational Management Science, Springer, vol. 16(3), pages 375-400, July.
  45. Johannes Lederer & Christian L. Müller, 2022. "Topology Adaptive Graph Estimation in High Dimensions," Mathematics, MDPI, vol. 10(8), pages 1-10, April.
  46. Yang Ni & Veerabhadran Baladandayuthapani & Marina Vannucci & Francesco C. Stingo, 2022. "Bayesian graphical models for modern biological applications," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(2), pages 197-225, June.
  47. Perrot-Dockès, Marie & Lévy-Leduc, Céline & Sansonnet, Laure & Chiquet, Julien, 2018. "Variable selection in multivariate linear models with high-dimensional covariance matrix estimation," Journal of Multivariate Analysis, Elsevier, vol. 166(C), pages 78-97.
  48. Giraud Christophe & Huet Sylvie & Verzelen Nicolas, 2012. "Graph Selection with GGMselect," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(3), pages 1-52, February.
  49. Yang, Yuehan & Xia, Siwei & Yang, Hu, 2023. "Multivariate sparse Laplacian shrinkage for joint estimation of two graphical structures," Computational Statistics & Data Analysis, Elsevier, vol. 178(C).
  50. Kudraszow, Nadia L. & Vahnovan, Alejandra V. & Ferrario, Julieta & Fasano, M. Victoria, 2025. "Robust generalized canonical correlation analysis based on scatter matrices," Computational Statistics & Data Analysis, Elsevier, vol. 206(C).
  51. Jinsong Chen, 2020. "A Partially Confirmatory Approach to the Multidimensional Item Response Theory with the Bayesian Lasso," Psychometrika, Springer;The Psychometric Society, vol. 85(3), pages 738-774, September.
  52. Avagyan, Vahe, 2016. "D-Trace precision matrix estimator with eigenvalue control," DES - Working Papers. Statistics and Econometrics. WS 23410, Universidad Carlos III de Madrid. Departamento de Estadística.
  53. Amedeo Argentiero & Giovanni Bonaccolto & Giulio Pedrini, 2024. "Green finance: Evidence from large portfolios and networks during financial crises and recessions," Corporate Social Responsibility and Environmental Management, John Wiley & Sons, vol. 31(3), pages 2474-2495, May.
  54. Laura Freijeiro‐González & Manuel Febrero‐Bande & Wenceslao González‐Manteiga, 2022. "A Critical Review of LASSO and Its Derivatives for Variable Selection Under Dependence Among Covariates," International Statistical Review, International Statistical Institute, vol. 90(1), pages 118-145, April.
  55. He, Yong & Zhang, Xinsheng & Wang, Pingping & Zhang, Liwen, 2017. "High dimensional Gaussian copula graphical model with FDR control," Computational Statistics & Data Analysis, Elsevier, vol. 113(C), pages 457-474.
  56. Jana Janková & Sara Geer, 2017. "Honest confidence regions and optimality in high-dimensional precision matrix estimation," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 26(1), pages 143-162, March.
  57. Lim Hao Shen Keith, 2024. "Covariance Matrix Analysis for Optimal Portfolio Selection," Papers 2407.08748, arXiv.org.
  58. 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.
  59. Soloveychik, Ilya & Tarokh, Vahid, 2023. "Region selection in Markov random fields: Gaussian case," Journal of Multivariate Analysis, Elsevier, vol. 196(C).
  60. 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.
  61. Arnab Chakrabarti & Rituparna Sen, 2018. "Some Statistical Problems with High Dimensional Financial data," Papers 1808.02953, arXiv.org.
  62. McGillivray, Annaliza & Khalili, Abbas & Stephens, David A., 2020. "Estimating sparse networks with hubs," Journal of Multivariate Analysis, Elsevier, vol. 179(C).
  63. Zhang, Siliang & Chen, Yunxiao, 2024. "A note on Ising network analysis with missing data," LSE Research Online Documents on Economics 123984, London School of Economics and Political Science, LSE Library.
  64. Ding, Wenliang & Shu, Lianjie & Gu, Xinhua, 2023. "A robust Glasso approach to portfolio selection in high dimensions," Journal of Empirical Finance, Elsevier, vol. 70(C), pages 22-37.
  65. Nanshan, Muye & Zhang, Nan & Xun, Xiaolei & Cao, Jiguo, 2022. "Dynamical modeling for non-Gaussian data with high-dimensional sparse ordinary differential equations," Computational Statistics & Data Analysis, Elsevier, vol. 173(C).
  66. Lam, Clifford & Fan, Jianqing, 2009. "Sparsistency and rates of convergence in large covariance matrix estimation," LSE Research Online Documents on Economics 31540, London School of Economics and Political Science, LSE Library.
  67. Zheng, Zemin & Li, Liwan & Zhou, Jia & Kong, Yinfei, 2020. "Innovated scalable dynamic learning for time-varying graphical models," Statistics & Probability Letters, Elsevier, vol. 165(C).
  68. Sheng, Tianhong & Li, Bing & Solea, Eftychia, 2023. "On skewed Gaussian graphical models," Journal of Multivariate Analysis, Elsevier, vol. 194(C).
  69. Liu, Jianyu & Yu, Guan & Liu, Yufeng, 2019. "Graph-based sparse linear discriminant analysis for high-dimensional classification," Journal of Multivariate Analysis, Elsevier, vol. 171(C), pages 250-269.
  70. Avagyan, Vahe & Alonso Fernández, Andrés Modesto & 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.
  71. Vahe Avagyan, 2022. "Precision matrix estimation using penalized Generalized Sylvester matrix equation," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 31(4), pages 950-967, December.
  72. Aaron J Molstad & Adam J Rothman, 2018. "Shrinking characteristics of precision matrix estimators," Biometrika, Biometrika Trust, vol. 105(3), pages 563-574.
  73. Bertsimas, Dimitris & Koukouvinos, Thodoris, 2024. "Robust linear algebra," European Journal of Operational Research, Elsevier, vol. 314(3), pages 1174-1184.
  74. Tengteng Xu & Ping Deng & Riquan Zhang & Weihua Zhao, 2025. "Robust Estimation of Multivariate Time Series Data Based on Reduced Rank Model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(2), pages 474-484, March.
  75. Chang, Jinyuan & Qiu, Yumou & Yao, Qiwei & Zou, Tao, 2018. "Confidence regions for entries of a large precision matrix," LSE Research Online Documents on Economics 87513, London School of Economics and Political Science, LSE Library.
  76. Nystrup, Peter & Lindström, Erik & Møller, Jan K. & Madsen, Henrik, 2021. "Dimensionality reduction in forecasting with temporal hierarchies," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1127-1146.
  77. 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).
  78. Lee, Wonyul & Liu, Yufeng, 2012. "Simultaneous multiple response regression and inverse covariance matrix estimation via penalized Gaussian maximum likelihood," Journal of Multivariate Analysis, Elsevier, vol. 111(C), pages 241-255.
  79. Khai X. Chiong & Hyungsik Roger Moon, 2017. "Estimation of Graphical Models using the $L_{1,2}$ Norm," Papers 1709.10038, arXiv.org, revised Oct 2017.
  80. Li, Lijie & Yu, Yang & Liang, Wanfeng & Zou, Feng, 2025. "A novel approach for estimating multi-attribute Gaussian copula graphical models," Statistics & Probability Letters, Elsevier, vol. 222(C).
  81. Rieser, Christopher & Filzmoser, Peter, 2023. "Extending compositional data analysis from a graph signal processing perspective," Journal of Multivariate Analysis, Elsevier, vol. 198(C).
  82. Jewson, Jack & Li, Li & Battaglia, Laura & Hansen, Stephen & Rossell, David & Zwiernik, Piotr, 2022. "Graphical model inference with external network data," CEPR Discussion Papers 17638, C.E.P.R. Discussion Papers.
  83. Tan, Xueping & Sirichand, Kavita & Vivian, Andrew & Wang, Xinyu, 2022. "Forecasting European carbon returns using dimension reduction techniques: Commodity versus financial fundamentals," International Journal of Forecasting, Elsevier, vol. 38(3), pages 944-969.
  84. Murat Genç, 2022. "A new double-regularized regression using Liu and lasso regularization," Computational Statistics, Springer, vol. 37(1), pages 159-227, March.
  85. Xiao Guo & Hai Zhang, 2020. "Sparse directed acyclic graphs incorporating the covariates," Statistical Papers, Springer, vol. 61(5), pages 2119-2148, October.
  86. 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.
  87. Alexandre Belloni & Mingli Chen & Victor Chernozhukov, 2016. "Quantile Graphical Models: Prediction and Conditional Independence with Applications to Systemic Risk," Papers 1607.00286, arXiv.org, revised Oct 2019.
  88. 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.
  89. Li-Pang Chen, 2022. "Network-Based Discriminant Analysis for Multiclassification," Journal of Classification, Springer;The Classification Society, vol. 39(3), pages 410-431, November.
  90. 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.
  91. Xingqi Du & Subhashis Ghosal, 2018. "Bayesian Discriminant Analysis Using a High Dimensional Predictor," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 80(1), pages 112-145, December.
  92. Gao, Zhenguo & Wang, Xinye & Kang, Xiaoning, 2023. "Ensemble LDA via the modified Cholesky decomposition," Computational Statistics & Data Analysis, Elsevier, vol. 188(C).
  93. Pei Wang & Shunjie Chen & Sijia Yang, 2022. "Recent Advances on Penalized Regression Models for Biological Data," Mathematics, MDPI, vol. 10(19), pages 1-24, October.
  94. Pircalabelu, Eugen & Claeskens, Gerda, 2021. "Linear manifold modeling and graph estimation based on multivariate functional data with different coarseness scales," LIDAM Discussion Papers ISBA 2021032, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
  95. Le, Khuyen T. & Chaux, Caroline & Richard, Frédéric J.P. & Guedj, Eric, 2020. "An adapted linear discriminant analysis with variable selection for the classification in high-dimension, and an application to medical data," Computational Statistics & Data Analysis, Elsevier, vol. 152(C).
  96. Guan Yu & Yufeng Liu, 2016. "Sparse Regression Incorporating Graphical Structure Among Predictors," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(514), pages 707-720, April.
  97. 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.
  98. 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.
  99. S Klaassen & J Kueck & M Spindler & V Chernozhukov, 2023. "Uniform inference in high-dimensional Gaussian graphical models," Biometrika, Biometrika Trust, vol. 110(1), pages 51-68.
  100. Pötscher, Benedikt M. & Schneider, Ulrike, 2007. "On the distribution of the adaptive LASSO estimator," MPRA Paper 6913, University Library of Munich, Germany.
  101. Zhou, Jia & Li, Yang & Zheng, Zemin & Li, Daoji, 2022. "Reproducible learning in large-scale graphical models," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
  102. 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.
  103. Byrd, Michael & Nghiem, Linh H. & McGee, Monnie, 2021. "Bayesian regularization of Gaussian graphical models with measurement error," Computational Statistics & Data Analysis, Elsevier, vol. 156(C).
  104. Maurya, Ashwini, 2014. "A joint convex penalty for inverse covariance matrix estimation," Computational Statistics & Data Analysis, Elsevier, vol. 75(C), pages 15-27.
  105. Kang, Xiaoning & Kang, Lulu & Chen, Wei & Deng, Xinwei, 2022. "A generative approach to modeling data with quantitative and qualitative responses," Journal of Multivariate Analysis, Elsevier, vol. 190(C).
  106. Kang, Xiaoning & Wang, Mingqiu, 2021. "Ensemble sparse estimation of covariance structure for exploring genetic disease data," Computational Statistics & Data Analysis, Elsevier, vol. 159(C).
  107. Zhang Haixiang & Zheng Yinan & Yoon Grace & Zhang Zhou & Gao Tao & Joyce Brian & Zhang Wei & Schwartz Joel & Vokonas Pantel & Colicino Elena & Baccarelli Andrea & Hou Lifang & Liu Lei, 2017. "Regularized estimation in sparse high-dimensional multivariate regression, with application to a DNA methylation study," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 16(3), pages 159-171, August.
  108. Lafit, Ginette & Nogales Martín, Francisco Javier, 2017. "Robust and sparse estimation of high-dimensional precision matrices via bivariate outlier detection," DES - Working Papers. Statistics and Econometrics. WS 24534, Universidad Carlos III de Madrid. Departamento de Estadística.
  109. 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.
  110. Avagyan, Vahe & Alonso Fernández, Andrés Modesto & 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 Estadística.
  111. Souvik Seal & Qunhua Li & Elle Butler Basner & Laura M Saba & Katerina Kechris, 2023. "RCFGL: Rapid Condition adaptive Fused Graphical Lasso and application to modeling brain region co-expression networks," PLOS Computational Biology, Public Library of Science, vol. 19(1), pages 1-26, January.
  112. Zhigang Li & Katherine Lee & Margaret R. Karagas & Juliette C. Madan & Anne G. Hoen & A. James O’Malley & Hongzhe Li, 2018. "Conditional Regression Based on a Multivariate Zero-Inflated Logistic-Normal Model for Microbiome Relative Abundance Data," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 10(3), pages 587-608, December.
  113. Yuta Umezu & Yusuke Shimizu & Hiroki Masuda & Yoshiyuki Ninomiya, 2019. "AIC for the non-concave penalized likelihood method," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 71(2), pages 247-274, April.
  114. 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.
  115. Rong Zhang & Zhao Ren & Wei Chen, 2018. "SILGGM: An extensive R package for efficient statistical inference in large-scale gene networks," PLOS Computational Biology, Public Library of Science, vol. 14(8), pages 1-14, August.
  116. Siwei Xia & Yuehan Yang & Hu Yang, 2022. "Sparse Laplacian Shrinkage with the Graphical Lasso Estimator for Regression Problems," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 31(1), pages 255-277, March.
  117. Huangdi Yi & Qingzhao Zhang & Cunjie Lin & Shuangge Ma, 2022. "Information‐incorporated Gaussian graphical model for gene expression data," Biometrics, The International Biometric Society, vol. 78(2), pages 512-523, June.
  118. Li‐Pang Chen, 2024. "Estimation of Graphical Models: An Overview of Selected Topics," International Statistical Review, International Statistical Institute, vol. 92(2), pages 194-245, August.
  119. Lam, Clifford, 2020. "High-dimensional covariance matrix estimation," LSE Research Online Documents on Economics 101667, London School of Economics and Political Science, LSE Library.
  120. Meinshausen, Nicolai, 2008. "A note on the Lasso for Gaussian graphical model selection," Statistics & Probability Letters, Elsevier, vol. 78(7), pages 880-884, May.
  121. Byol Kim & Song Liu & Mladen Kolar, 2021. "Two‐sample inference for high‐dimensional Markov networks," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(5), pages 939-962, November.
  122. Laurenţiu Cătălin Hinoveanu & Fabrizio Leisen & Cristiano Villa, 2020. "A loss‐based prior for Gaussian graphical models," Australian & New Zealand Journal of Statistics, Australian Statistical Publishing Association Inc., vol. 62(4), pages 444-466, December.
  123. Alexandre D'Aspremont, 2010. "Identifying small mean-reverting portfolios," Quantitative Finance, Taylor & Francis Journals, vol. 11(3), pages 351-364.
  124. Ines Wilms & Jacob Bien, 2021. "Tree-based Node Aggregation in Sparse Graphical Models," Papers 2101.12503, arXiv.org.
  125. Samuel Vaiter & Charles Deledalle & Jalal Fadili & Gabriel Peyré & Charles Dossal, 2017. "The degrees of freedom of partly smooth regularizers," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 69(4), pages 791-832, August.
  126. Tomokaze Shiratori & Yuichi Takano, 2024. "DC algorithm for estimation of sparse Gaussian graphical models," PLOS ONE, Public Library of Science, vol. 19(12), pages 1-23, December.
  127. Brownlees, Christian & Hans, Christina & Nualart, Eulalia, 2021. "Bank credit risk networks: Evidence from the Eurozone," Journal of Monetary Economics, Elsevier, vol. 117(C), pages 585-599.
  128. Katayama, Shota & Imori, Shinpei, 2014. "Lasso penalized model selection criteria for high-dimensional multivariate linear regression analysis," Journal of Multivariate Analysis, Elsevier, vol. 132(C), pages 138-150.
  129. Jianyu Liu & Wei Sun & Yufeng Liu, 2019. "Joint skeleton estimation of multiple directed acyclic graphs for heterogeneous population," Biometrics, The International Biometric Society, vol. 75(1), pages 36-47, March.
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