A new point of view of robust statistics based on a geometrical approach is tackled in this paper. Estimation procedures are carried out from a new robust cost function based on a chaining of elementary convex norms. ...A new point of view of robust statistics based on a geometrical approach is tackled in this paper. Estimation procedures are carried out from a new robust cost function based on a chaining of elementary convex norms. This chain is randomly articulated in order to treat more efficiently natural outliers in data-set. Estimated parameters are considered as random fields and each of them, named articulated estimator random field （AERF） is a manifold or stratum of a stratified space with Riemannian geometry properties, From a high level excursion set, a probability distribution model Mata is presented and a system model validation geometric criterion （SYMOVAGEC） for system model structures Msys based on Rieeian scalar curvatures is proposed. Numerical results are drawn in a context of system identification.展开更多
文摘A new point of view of robust statistics based on a geometrical approach is tackled in this paper. Estimation procedures are carried out from a new robust cost function based on a chaining of elementary convex norms. This chain is randomly articulated in order to treat more efficiently natural outliers in data-set. Estimated parameters are considered as random fields and each of them, named articulated estimator random field （AERF） is a manifold or stratum of a stratified space with Riemannian geometry properties, From a high level excursion set, a probability distribution model Mata is presented and a system model validation geometric criterion （SYMOVAGEC） for system model structures Msys based on Rieeian scalar curvatures is proposed. Numerical results are drawn in a context of system identification.
