Institute of Information Theory and Automation

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Estimation and tests under L-moment condition models and applications to radar detection

Date: 
2013-04-08 14:00
Room: 
Name of External Lecturer: 
Alexis Decurninge
Affiliation of External Lecturer: 
Laboratoire de Statistique Théorique et Appliquée, Université Pierre et Marie Curie, Paris

Since their introduction by Hosking in 1990, L-moments methods has become popular in applications dealing with extreme phenomenon whose underlying distribution is heavy-tailed. They constitute a robust alternative to traditional moment in the estimation of the "form" of the distribution because they effectively captures this type of information. It is therefore natural to generalize L-moment method as the generalized moment methods (GMM) for the moments. We introduce an equivalent point of view for the M-estimators based on the minimization of a divergence with moment constraints. This point of view relies on the minimization of a specific transformation energy instead of the minimization of a distance between two measures of probability. Many definitions of this transformation energy could be taken, we choose one that brings a linear minimization problem for L-moment constraints. Applications of such estimators to the radar detection of small targets in heterogeneous clutter will be presented. These clutters are modelized by a heavy-tail distribution traditionnally hard to estimate without strong hypothesis.

kroupa: 2013-03-25 15:13