Distributed multivariate regression with unknown noise covariance in the presence of outliers: An MDL approach

Roberto Lopez-Valcarce; Daniel Romero; Josep Sala; Alba Pages-Zamora

doi:10.1109/SSP.2016.7551769

Distributed multivariate regression with unknown noise covariance in the presence of outliers: An MDL approach

Roberto Lopez-Valcarce, Daniel Romero, Josep Sala, Alba Pages-Zamora

Electrical and Computer Engineering

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

Abstract

We consider the problem of estimating the coefficients in a multivariable linear model by means of a wireless sensor network which may be affected by anomalous measurements. The noise covariance matrices at the different sensors are assumed unknown. Treating outlying samples, and their support, as additional nuisance parameters, the Maximum Likelihood estimate is investigated, with the number of outliers being estimated according to the Minimum Description Length principle. A distributed implementation based on iterative consensus techniques is then proposed, and it is shown effective for managing outliers in the data.

Original language	English (US)
Title of host publication	2016 19th IEEE Statistical Signal Processing Workshop, SSP 2016
Publisher	IEEE Computer Society
ISBN (Electronic)	9781467378024
DOIs	https://doi.org/10.1109/SSP.2016.7551769
State	Published - Aug 24 2016
Event	19th IEEE Statistical Signal Processing Workshop, SSP 2016 - Palma de Mallorca, Spain Duration: Jun 25 2016 → Jun 29 2016

Publication series

Name	IEEE Workshop on Statistical Signal Processing Proceedings
Volume	2016-August

Other

Other	19th IEEE Statistical Signal Processing Workshop, SSP 2016
Country/Territory	Spain
City	Palma de Mallorca
Period	6/25/16 → 6/29/16

Bibliographical note

Publisher Copyright:
© 2016 IEEE.

Keywords

Multivariate regression
distributed estimation
outliers
wireless sensor networks

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10.1109/SSP.2016.7551769

http://hdl.handle.net/2117/97423

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Lopez-Valcarce, R., Romero, D., Sala, J., & Pages-Zamora, A. (2016). Distributed multivariate regression with unknown noise covariance in the presence of outliers: An MDL approach. In 2016 19th IEEE Statistical Signal Processing Workshop, SSP 2016 Article 7551769 (IEEE Workshop on Statistical Signal Processing Proceedings; Vol. 2016-August). IEEE Computer Society. https://doi.org/10.1109/SSP.2016.7551769

Distributed multivariate regression with unknown noise covariance in the presence of outliers: An MDL approach. / Lopez-Valcarce, Roberto; Romero, Daniel; Sala, Josep et al.
2016 19th IEEE Statistical Signal Processing Workshop, SSP 2016. IEEE Computer Society, 2016. 7551769 (IEEE Workshop on Statistical Signal Processing Proceedings; Vol. 2016-August).

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

Lopez-Valcarce, R, Romero, D, Sala, J & Pages-Zamora, A 2016, Distributed multivariate regression with unknown noise covariance in the presence of outliers: An MDL approach. in 2016 19th IEEE Statistical Signal Processing Workshop, SSP 2016., 7551769, IEEE Workshop on Statistical Signal Processing Proceedings, vol. 2016-August, IEEE Computer Society, 19th IEEE Statistical Signal Processing Workshop, SSP 2016, Palma de Mallorca, Spain, 6/25/16. https://doi.org/10.1109/SSP.2016.7551769

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Distributed multivariate regression with unknown noise covariance in the presence of outliers: An MDL approach

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