Mismatched MMSE estimation of multivariate Gaussian sources

Iñaki Esnaola; Antonia M. Tulino; H. Vincent Poor

doi:10.1109/ISIT.2012.6284652

Mismatched MMSE estimation of multivariate Gaussian sources

Iñaki Esnaola, Antonia M. Tulino, H. Vincent Poor

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

5 Scopus citations

Abstract

The distortion increase in minimum mean-square error (MMSE) estimation of multivariate Gaussian sources is analyzed for the situation in which the statistics are mismatched, i.e., the covariance matrix is not perfectly known during the estimation process. First a deterministic mismatch model with an additive perturbation matrix is considered, for which we provide closed form expressions for the distortion excess caused by the mismatch. The mismatch study is then generalized by using random matrix theory tools which allow an asymptotic result for a broad class of perturbation matrices to be proved.

Original language	English (US)
Title of host publication	2012 IEEE International Symposium on Information Theory Proceedings, ISIT 2012
Pages	716-720
Number of pages	5
DOIs	https://doi.org/10.1109/ISIT.2012.6284652
State	Published - 2012
Event	2012 IEEE International Symposium on Information Theory, ISIT 2012 - Cambridge, MA, United States Duration: Jul 1 2012 → Jul 6 2012

Publication series

Name	IEEE International Symposium on Information Theory - Proceedings

Other

Other	2012 IEEE International Symposium on Information Theory, ISIT 2012
Country/Territory	United States
City	Cambridge, MA
Period	7/1/12 → 7/6/12

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
Information Systems
Modeling and Simulation
Applied Mathematics

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10.1109/ISIT.2012.6284652

Cite this

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title = "Mismatched MMSE estimation of multivariate Gaussian sources",

abstract = "The distortion increase in minimum mean-square error (MMSE) estimation of multivariate Gaussian sources is analyzed for the situation in which the statistics are mismatched, i.e., the covariance matrix is not perfectly known during the estimation process. First a deterministic mismatch model with an additive perturbation matrix is considered, for which we provide closed form expressions for the distortion excess caused by the mismatch. The mismatch study is then generalized by using random matrix theory tools which allow an asymptotic result for a broad class of perturbation matrices to be proved.",

author = "I{\~n}aki Esnaola and Tulino, {Antonia M.} and Poor, {H. Vincent}",

year = "2012",

doi = "10.1109/ISIT.2012.6284652",

language = "English (US)",

isbn = "9781467325790",

series = "IEEE International Symposium on Information Theory - Proceedings",

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booktitle = "2012 IEEE International Symposium on Information Theory Proceedings, ISIT 2012",

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Esnaola, I, Tulino, AM & Poor, HV 2012, Mismatched MMSE estimation of multivariate Gaussian sources. in 2012 IEEE International Symposium on Information Theory Proceedings, ISIT 2012., 6284652, IEEE International Symposium on Information Theory - Proceedings, pp. 716-720, 2012 IEEE International Symposium on Information Theory, ISIT 2012, Cambridge, MA, United States, 7/1/12. https://doi.org/10.1109/ISIT.2012.6284652

Mismatched MMSE estimation of multivariate Gaussian sources. / Esnaola, Iñaki; Tulino, Antonia M.; Poor, H. Vincent.
2012 IEEE International Symposium on Information Theory Proceedings, ISIT 2012. 2012. p. 716-720 6284652 (IEEE International Symposium on Information Theory - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AB - The distortion increase in minimum mean-square error (MMSE) estimation of multivariate Gaussian sources is analyzed for the situation in which the statistics are mismatched, i.e., the covariance matrix is not perfectly known during the estimation process. First a deterministic mismatch model with an additive perturbation matrix is considered, for which we provide closed form expressions for the distortion excess caused by the mismatch. The mismatch study is then generalized by using random matrix theory tools which allow an asymptotic result for a broad class of perturbation matrices to be proved.

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Mismatched MMSE estimation of multivariate Gaussian sources

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