Contributions to Proceedings:

S. Schmutzhard, A. Jung, F. Hlawatsch, Z. Ben-Haim, Y. Eldar:
"A Lower Bound on the Estimator Variance for the Sparse Linear Model";
in: "Proc. 44th Asilomar Conf. Signals, Systems, Computers", IEEE Conference Proceedings, Pacific Grove, CA, 2010, 1976 - 1980.

English abstract:
We study the performance of estimators of a sparse
nonrandom vector based on an observation which is linearly
transformed and corrupted by white Gaussian noise. Using the
framework of reproducing kernel Hilbert spaces, we derive a new
lower bound on the estimator variance for a given differentiable
bias function (including the unbiased case) and an almost arbitrary
transformation matrix (including the underdetermined case
considered in compressed sensing theory). For the special case of
a sparse vector corrupted by white Gaussian noise-i.e., without a
linear transformation-and unbiased estimation, our lower bound
improves on a previously proposed bound.

Sparsity, parameter estimation, sparse linear model, denoising, variance bound, reproducing kernel Hilbert space, RKHS

Electronic version of the publication:

Created from the Publication Database of the Vienna University of Technology.