|
Título:
|
Distributed multivariate regression with unknown noise covariance in the presence of outliers: an MDL approach
|
|
Autor/a:
|
López Valcarce, Roberto; Romero Gonzalez, Daniel; Sala Álvarez, José; Pagès Zamora, Alba Maria
|
|
Otros autores:
|
Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions; Universitat Politècnica de Catalunya. SPCOM - Grup de Recerca de Processament del Senyal i Comunicacions |
|
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. |
|
Abstract:
|
Peer Reviewed |
|
Materia(s):
|
-Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal
-Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Telemàtica i xarxes d'ordinadors
-Signal processing
-Wireless LANs
-Covariance matrices
-Iterative methods
-Maximum likelihood estimation
-Regression analysis
-Wireless sensor networks
-Distributed multivariate regression
-Unknown noise covariance matrices
-MDL approach
-Wireless sensor network
-Maximum likelihood estimate
-Minimum description length principle
-Iterative consensus techniques
-Tractament del senyal
-Xarxes locals sense fil Wi-Fi |
|
Derechos:
|
|
|
Tipo de documento:
|
Artículo - Versión presentada
Objeto de conferencia |
|
Editor:
|
Institute of Electrical and Electronics Engineers (IEEE)
|
|
Compartir:
|
|
