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Talks and Poster Presentations (with Proceedings-Entry):

A. Jung, S. Schmutzhard, F. Hlawatsch, A. Hero:
"Performance bounds for sparse parametric covariance estimation in Gaussian models";
Poster: IEEE Int. Conference on Acoustics, Speech, and Signal Processing (ICASSP), Prag, Tschechische Republik; 05-22-2011 - 05-27-2011; in: "Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2011)", IEEE, (2011), 4156 - 4159.



English abstract:
We consider estimation of a sparse parameter vector that determines
the covariance matrix of a Gaussian random vector via a sparse expansion
into known "basis matrices." Using the theory of reproducing
kernel Hilbert spaces, we derive lower bounds on the variance
of estimators with a given mean function. This includes unbiased
estimation as a special case. We also present a numerical comparison
of our lower bounds with the variance of two standard estimators
(hard-thresholding estimator and maximum likelihood estimator).

German abstract:
We consider estimation of a sparse parameter vector that determines
the covariance matrix of a Gaussian random vector via a sparse expansion
into known "basis matrices." Using the theory of reproducing
kernel Hilbert spaces, we derive lower bounds on the variance
of estimators with a given mean function. This includes unbiased
estimation as a special case. We also present a numerical comparison
of our lower bounds with the variance of two standard estimators
(hard-thresholding estimator and maximum likelihood estimator).

Keywords:
Sparsity, sparse covariance estimation, variance bound, reproducing kernel Hilbert space, RKHS.


Electronic version of the publication:
http://publik.tuwien.ac.at/files/PubDat_198278.pdf


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