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Mis-parametrization subsets for a penalized least squares model selection

Xavier Guyon 1, Cécile Hardouin 1, 2

(2011-11)

When identifying a model by a penalized minimum contrast procedure, we give a description of the over and under fitting parametrization subsets for a least squares contrast. This allows to determine an accurate sequence of penalization rates ensuring good identification. We present applications for the identification of the covariance for a general time series, and for the variogram identification of a geostatistical model.

1:	Statistique, Analyse et Modélisation Multidisciplinaire (SAmos-Marin Mersenne) (SAMM)
	Université Paris I - Panthéon-Sorbonne
2:	Modélisation aléatoire de Paris X (MODAL'X)
	Université Paris X - Paris Ouest Nanterre La Défense

Subject

:

Mathematics/Statistics Statistics/Statistics Theory

Keyword(s): least squares contrast – penalized contrast – model selection – misfitting – AIC – BIC – mixture models – geostatistics.

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From: Cécile Hardouin <>

Submitted on: Monday, 5 December 2011 11:22:55

Updated on: Monday, 5 December 2011 11:33:48