Empirical Bayes linear regression with unknown model order
Digital Signal Processing
Estimation of semi-sparse radar range profiles
Digital Signal Processing
A plurality of sparse representations is better than the sparsest one alone
IEEE Transactions on Information Theory
An iterative Bayesian algorithm for sparse component analysis in presence of noise
IEEE Transactions on Signal Processing
Sinusoidal order estimation using angles between subspaces
EURASIP Journal on Advances in Signal Processing
IEEE Transactions on Signal Processing
Performance analysis for sparse support recovery
IEEE Transactions on Information Theory
Computationally efficient sparse Bayesian learning via belief propagation
IEEE Transactions on Signal Processing
CFO estimation in OFDM systems under timing and channel length uncertainties with model averaging
IEEE Transactions on Wireless Communications
On MAP and MMSE estimators for the co-sparse analysis model
Digital Signal Processing
| Hi-index | 35.81 |
We consider linear regression under a model where the parameter vector is known to be sparse. Using a Bayesian framework, we derive the minimum mean-square error (MMSE) estimate of the parameter vector and a computationally efficient approximation of it. We also derive an empirical-Bayesian version of the estimator, which does not need any a priori information, nor does it need the selection of any user parameters. As a byproduct, we obtain a powerful model ("basis") selection tool for sparse models. The performance and robustness of our new estimators are illustrated via numerical examples