On the self-similar nature of Ethernet traffic (extended version)
IEEE/ACM Transactions on Networking (TON)
A well-conditioned estimator for large-dimensional covariance matrices
Journal of Multivariate Analysis
Properties of the singular, inverse and generalized inverse partitioned Wishart distributions
Journal of Multivariate Analysis
On Spatial Power Spectrum and Signal Estimation Using the Pisarenko Framework
IEEE Transactions on Signal Processing - Part II
On Using a priori Knowledge in Space-Time Adaptive Processing
IEEE Transactions on Signal Processing
GLRT-Based Detection-Estimation for Undersampled Training Conditions
IEEE Transactions on Signal Processing - Part I
Capacity of a mobile multiple-antenna communication link in Rayleigh flat fading
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
The pseudo-Wishart distribution and its application to MIMO systems
IEEE Transactions on Information Theory
A covariance shaping framework for linear multiuser detection
IEEE Transactions on Information Theory
On the robustness of MIMO LMMSE channel estimation
IEEE Transactions on Wireless Communications
Connectivity-informed fMRI activation detection
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part II
A novel sparse graphical approach for multimodal brain connectivity inference
MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Robust Capon beamforming against large DOA mismatch
Signal Processing
Direct data-driven portfolio optimization with guaranteed shortfall probability
Automatica (Journal of IFAC)
An iterative stochastic ensemble method for parameter estimation of subsurface flow models
Journal of Computational Physics
A novel sparse group Gaussian graphical model for functional connectivity estimation
IPMI'13 Proceedings of the 23rd international conference on Information Processing in Medical Imaging
A revised inference for correlated topic model
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part II
Wireless Personal Communications: An International Journal
Adaptive evolutionary clustering
Data Mining and Knowledge Discovery
Volume-based method for spectrum sensing
Digital Signal Processing
Hi-index | 35.69 |
We address covariance estimation in the sense of minimum mean-squared error (MMSE) when the samples are Gaussian distributed. Specifically, we consider shrinkage methods which are suitable for high dimensional problems with a small number of samples (large p small n). First, we improve on the Ledoit-Wolf (LW) method by conditioning on a sufficient statistic. By the Rao-Blackwell theorem, this yields a new estimator called RBLW, whose mean-squared error dominates that of LW for Gaussian variables. Second, to further reduce the estimation error, we propose an iterative approach which approximates the clairvoyant shrinkage estimator. Convergence of this iterative method is established and a closed form expression for the limit is determined, which is referred to as the oracle approximating shrinkage (OAS) estimator. Both RBLW and OAS estimators have simple expressions and are easily implemented. Although the two methods are developed from different perspectives, their structure is identical up to specified constants. The RBLW estimator provably dominates the LW method for Gaussian samples. Numerical simulations demonstrate that the OAS approach can perform even better than RBLW, especially when n is much less than p. We also demonstrate the performance of these techniques in the context of adaptive beamforming.