Tuning the Parameters for Precision Matrix Estimation Using Regression Analysis
暂无分享,去创建一个
Jiangtao Xi | Jun Tong | Yanguang Yu | Jiayi Yang | Philip O. Ogunbona | P. Ogunbona | J. Xi | Yanguang Yu | Jun Tong | Jiayi Yang
[1] Jiangtao Xi,et al. Linear shrinkage estimation of covariance matrices using low-complexity cross-validation , 2018, Signal Process..
[2] Olivier Ledoit,et al. A well-conditioned estimator for large-dimensional covariance matrices , 2004 .
[3] Alfred O. Hero,et al. Shrinkage Algorithms for MMSE Covariance Estimation , 2009, IEEE Transactions on Signal Processing.
[4] Jun Fang,et al. Low-Rank Covariance-Assisted Downlink Training and Channel Estimation for FDD Massive MIMO Systems , 2016, IEEE Transactions on Wireless Communications.
[5] Ernst Bonek,et al. A stochastic MIMO channel model with joint correlation of both link ends , 2006, IEEE Transactions on Wireless Communications.
[6] P. Bickel,et al. Covariance regularization by thresholding , 2009, 0901.3079.
[7] R. Tibshirani,et al. Sparse inverse covariance estimation with the graphical lasso. , 2008, Biostatistics.
[8] Gene H. Golub,et al. Generalized cross-validation as a method for choosing a good ridge parameter , 1979, Milestones in Matrix Computation.
[9] Radford M. Neal. Pattern Recognition and Machine Learning , 2007, Technometrics.
[10] Jiangtao Xi,et al. Shrinkage of Covariance Matrices for Linear Signal Estimation Using Cross-Validation , 2016, IEEE Transactions on Signal Processing.
[11] Peng Zhao,et al. On Model Selection Consistency of Lasso , 2006, J. Mach. Learn. Res..
[12] Harrison H. Zhou,et al. Optimal rates of convergence for covariance matrix estimation , 2010, 1010.3866.
[13] Xiaohui Chen,et al. Shrinkage-to-Tapering Estimation of Large Covariance Matrices , 2012, IEEE Transactions on Signal Processing.
[14] Fei Wen,et al. Positive Definite Estimation of Large Covariance Matrix Using Generalized Nonconvex Penalties , 2016, IEEE Access.
[15] Luc Devroye,et al. Distribution-free performance bounds for potential function rules , 1979, IEEE Trans. Inf. Theory.
[16] Arjun K. Gupta,et al. Direct shrinkage estimation of large dimensional precision matrix , 2013, J. Multivar. Anal..
[17] Yumou Qiu,et al. Bandwidth Selection for High-Dimensional Covariance Matrix Estimation , 2014 .
[18] G. Schwarz. Estimating the Dimension of a Model , 1978 .
[19] Don H. Johnson,et al. Statistical Signal Processing , 2009, Encyclopedia of Biometrics.
[20] M. Yuan,et al. Model selection and estimation in the Gaussian graphical model , 2007 .
[21] Lixing Zhu,et al. SHRINKAGE ESTIMATION OF LARGE DIMENSIONAL PRECISION MATRIX USING RANDOM MATRIX THEORY , 2015 .
[22] Jianqing Fan,et al. An Overview of the Estimation of Large Covariance and Precision Matrices , 2015, The Econometrics Journal.
[23] Jie Zhou,et al. Estimation of Large Covariance Matrices by Shrinking to Structured Target in Normal and Non-Normal Distributions , 2018, IEEE Access.
[24] Joseph R. Guerci,et al. Space-Time Adaptive Processing for Radar , 2003 .
[25] Sylvain Arlot,et al. A survey of cross-validation procedures for model selection , 2009, 0907.4728.
[26] Patrice Abry,et al. Covariance Versus Precision Matrix Estimation for Efficient Asset Allocation , 2016, IEEE Journal of Selected Topics in Signal Processing.
[27] Anne H. Schistad Solberg,et al. Regression Approaches to Small Sample Inverse Covariance Matrix Estimation for Hyperspectral Image Classification , 2007, IEEE Transactions on Geoscience and Remote Sensing.
[28] Y. Selen,et al. Model-order selection: a review of information criterion rules , 2004, IEEE Signal Processing Magazine.
[29] Nicholas J. Higham,et al. Restoring Definiteness via Shrinking, with an Application to Correlation Matrices with a Fixed Block , 2016, SIAM Rev..
[30] Rina Foygel,et al. Extended Bayesian Information Criteria for Gaussian Graphical Models , 2010, NIPS.
[31] Yumou Qiu,et al. Test for Bandedness of High Dimensional Covariance Matrices with Bandwidth Estimation , 2012, 1208.3321.
[32] P. Bickel,et al. Regularized estimation of large covariance matrices , 2008, 0803.1909.
[33] N. Meinshausen,et al. High-dimensional graphs and variable selection with the Lasso , 2006, math/0608017.
[34] Yang Song,et al. Canonical correlation analysis of high-dimensional data with very small sample support , 2016, Signal Process..
[35] Jie Ding,et al. Bridging AIC and BIC: A New Criterion for Autoregression , 2015, IEEE Transactions on Information Theory.
[36] Jianhua Z. Huang,et al. Covariance matrix selection and estimation via penalised normal likelihood , 2006 .
[37] M. Pourahmadi,et al. Nonparametric estimation of large covariance matrices of longitudinal data , 2003 .
[38] Jie Ding,et al. Model Selection Techniques: An Overview , 2018, IEEE Signal Processing Magazine.
[39] H. Akaike. A new look at the statistical model identification , 1974 .
[40] Adam J. Rothman,et al. A new approach to Cholesky-based covariance regularization in high dimensions , 2009, 0903.0645.
[41] Montse Nájar,et al. Asymptotically Optimal Linear Shrinkage of Sample LMMSE and MVDR Filters , 2014, IEEE Transactions on Signal Processing.
[42] Jiangtao Xi,et al. Cross-Validated Bandwidth Selection for Precision Matrix Estimation , 2018, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[43] Ernst Wit,et al. A computationally fast alternative to cross-validation in penalized Gaussian graphical models , 2013, 1309.6216.
[44] Emil Björnson,et al. Low-Complexity Polynomial Channel Estimation in Large-Scale MIMO With Arbitrary Statistics , 2014, IEEE Journal of Selected Topics in Signal Processing.
[45] Joseph M. Kahn,et al. Fading correlation and its effect on the capacity of multielement antenna systems , 2000, IEEE Trans. Commun..
[46] Yufeng Liu,et al. Hypothesis Testing for Band Size Detection of High Dimensional Banded Precision Matrices , 2014, Biometrika.