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Statistical Theory and Related Fields Volume 1, 2017 - Issue 1
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Articles

Cholesky-based model averaging for covariance matrix estimation

Hao ZhengGilead Sciences, Inc., Foster City, CA, USAView further author information
,
Kam-Wah TsuiDepartment of Statistics, University of Wisconsin-Madison, Madison, WI, USAView further author information
,
Xiaoning KangInternational Business College, Dongbei University of Finance and Economics, Dalian, ChinaView further author information
&
Xinwei DengDepartment of Statistics, Virginia Tech, Blacksburg, VA, USACorrespondence[email protected]
View further author information
Pages 48-58 | Received 01 Mar 2017, Accepted 29 May 2017, Published online: 28 Jul 2017

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Chaoxia Yuan, Fang Fang & Jialiang Li. (2024) Model averaging for generalized linear models in diverging model spaces with effective model size. Econometric Reviews 43:1, pages 71-96.
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Wenyu Yang & Xiaoning Kang. (2023) An improved banded estimation for large covariance matrix. Communications in Statistics - Theory and Methods 52:1, pages 141-155.
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Chaoxia Yuan, Yang Wu & Fang Fang. (2022) Model averaging for generalized linear models in fragmentary data prediction. Statistical Theory and Related Fields 6:4, pages 344-352.
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Chunshi Li, Mo Yang, Mingqiu Wang, Hong Kang & Xiaoning Kang. (2021) A Cholesky-based sparse covariance estimation with an application to genes data. Journal of Biopharmaceutical Statistics 31:5, pages 603-616.
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Joris Chau & Rainer von Sachs. (2021) Intrinsic Wavelet Regression for Curves of Hermitian Positive Definite Matrices. Journal of the American Statistical Association 116:534, pages 819-832.
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Xiaoning Kang, Chaoping Xie & Mingqiu Wang. (2020) A Cholesky-based estimation for large-dimensional covariance matrices. Journal of Applied Statistics 47:6, pages 1017-1030.
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Xiaoning Kang & Xinwei Deng. (2020) An improved modified cholesky decomposition approach for precision matrix estimation. Journal of Statistical Computation and Simulation 90:3, pages 443-464.
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Articles from other publishers (10)

Fang Fang, Chaoxia Yuan & Wenling Tian. (2022) AN ASYMPTOTIC THEORY FOR LEAST SQUARES MODEL AVERAGING WITH NESTED MODELS. Econometric Theory 39:2, pages 412-441.
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Shaoxin Wang, Chaoping Xie & Xiaoning Kang. (2022) A novel robust estimation for high‐dimensional precision matrices. Statistics in Medicine 42:5, pages 656-675.
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Xiaoning Kang, Zhiyang Zhang & Xinwei Deng. 2023. Springer Handbook of Engineering Statistics. Springer Handbook of Engineering Statistics 887 900 .
Wanfeng Liang, Yue Wu & Hui Chen. (2022) Sparse covariance matrix estimation for ultrahigh dimensional data. Stat 11:1.
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Chaoxia Yuan, Fang Fang & Lyu Ni. (2022) Mallows model averaging with effective model size in fragmentary data prediction. Computational Statistics & Data Analysis 173, pages 107497.
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Fang Fang, Qiwei Yang & Wenling Tian. (2022) Cross-validation for selecting the penalty factor in least squares model averaging. Economics Letters 217, pages 110683.
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Xiaoning Kang & Mingqiu Wang. (2021) Ensemble sparse estimation of covariance structure for exploring genetic disease data. Computational Statistics & Data Analysis 159, pages 107220.
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Xiaoning Kang & Xinwei Deng. (2020) On variable ordination of Cholesky‐based estimation for a sparse covariance matrix. Canadian Journal of Statistics 49:2, pages 283-310.
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Elias David Nino-Ruiz, Alfonso Mancilla-Herrera, Santiago Lopez-Restrepo & Olga Quintero-Montoya. (2020) A Maximum Likelihood Ensemble Filter via a Modified Cholesky Decomposition for Non-Gaussian Data Assimilation. Sensors 20:3, pages 877.
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Kshitij Khare, Sang-Yun Oh, Syed Rahman & Bala Rajaratnam. (2019) A scalable sparse Cholesky based approach for learning high-dimensional covariance matrices in ordered data. Machine Learning 108:12, pages 2061-2086.
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