Talks and Poster Presentations (with Proceedings-Entry):
S. Schmutzhard, A. Jung, F. Hlawatsch:
"Minimum variance estimation for the sparse signal in noise model";
Talk: IEEE International Symposium on Information Theory (ISIT),
St.Petersburg, Russische Föderation;
- 08-05-2011; in: "IEEE Conf. Proceedings",
We consider estimation of a sparse parameter vector from measurements corrupted by white Gaussian noise. Using the framework of reproducing kernel Hilbert spaces, we derive closed- form expressions of the Barankin bound, i.e., of the minimum locally achievable variance of any estimator with a prescribed bias function, including the unbiased case. We also derive the locally minimum variance (LMV) estimator that achieves the minimum variance, and a necessary and sufficient condition on the pre- scribed bias function for the existence of finite-variance estimators and, simultaneously, of the LMV estimator. Finally, we present a numerical comparison of the variance of the hard-thresholding estimator with the corresponding minimum achievable variance.
Sparsity, denoising, reproducing kernel Hilbert space, RKHS, Barankin bound, minimum variance estimation, unbiased estimation
Electronic version of the publication:
Created from the Publication Database of the Vienna University of Technology.