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

Author
López, R.; Romero, D.; Sala, J.; Pagès-Zamora, A.
Type of activity
Presentation of work at congresses
Name of edition
2016 IEEE Workshop on Statistical Signal Processing
Date of publication
2016
Presentation's date
2016-06
Book of congress proceedings
Proceedings of the 2016 IEEE Statistical Signal Processing Workshop (SSP)
First page
1
Last page
5
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
DOI
https://doi.org/10.1109/SSP.2016.7551769 Open in new window
Project funding
Distributed techniques for the management and operation of wireless cellular networks, sensor networks and the smart energy grid
Red COMONSENS
Repository
http://hdl.handle.net/2117/97423 Open in new window
URL
http://ieeexplore.ieee.org/document/7551769/ Open in new window
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 impleme...
Citation
López, R., Romero, D., Sala, J., Pages, A. Distributed multivariate regression with unknown noise covariance in the presence of outliers: an MDL approach. A: IEEE Statistical Signal Processing Workshop. "2016 IEEE Statistical Signal Processing Workshop (SSP) took place 25-29 June 2016 in Palma de Mallorca, Spain". Palma de Mallorca: Institute of Electrical and Electronics Engineers (IEEE), 2016.
Keywords
Covariance matrices, Distributed multivariate regression, Iterative consensus techniques, Iterative methods, MDL approach, Maximum likelihood estimate, Maximum likelihood estimation, Minimum description length principle, Regression analysis, Unknown noise covariance matrices, Wireless sensor network, Wireless sensor networks
Group of research
SPCOM - Signal Processing and Communications Group

Participants

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