%0 Conference Paper %B IEEE Workshop on Statistical Signal Processing (SSP) %D 2016 %T Distributed multivariate regression with unknown noise covariance in the presence of outliers: a minimum description length approach %A R. López-Valcarce %A Daniel Romero %A Josep Sala %A Alba Pagès-Zamora %K compass %K wsn %X
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.
%B IEEE Workshop on Statistical Signal Processing (SSP) %C Palma de Mallorca, Spain %G eng