TY - CONF T1 - Distributed multivariate regression with unknown noise covariance in the presence of outliers: a minimum description length approach T2 - IEEE Workshop on Statistical Signal Processing (SSP) Y1 - 2016 A1 - R. López-Valcarce A1 - Daniel Romero A1 - Josep Sala A1 - Alba Pagès-Zamora KW - compass KW - wsn AB -
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.
JF - IEEE Workshop on Statistical Signal Processing (SSP) CY - Palma de Mallorca, Spain ER -