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CONSISTENCY OF RIDGE FUNCTION FIELDS FOR VARYING NONPARAMETRIC REGRESSION

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  • معلومة اضافية
    • Contributors:
      Scripps Institution of Oceanography (SIO - UC San Diego); University of California San Diego (UC San Diego); University of California (UC)-University of California (UC); Institut de Recherche Mathématique de Rennes (IRMAR); Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes); Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-École normale supérieure - Rennes (ENS Rennes)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-INSTITUT AGRO Agrocampus Ouest; Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)
    • بيانات النشر:
      HAL CCSD
      Taylor & Francis
    • الموضوع:
      2009
    • Collection:
      Université de Rennes 1: Publications scientifiques (HAL)
    • نبذة مختصرة :
      International audience ; A nonparametric regression model proposed in [Pelletier and Frouin, Ap- plied Optics, 2006] as a solution to the geophysical problem of ocean color remote sensing is studied. The model, called ridge function field, com- bines a regression estimate in the form of a superposition of ridge func- tions, or equivalently a neural network, with the idea pertaining to varying- coefficients models, where the parameters of a parametric family are allowed to vary with other variables. Under mild assumptions on the underlying dis- tribution of the data, the strong universal consistency of the least-squares ridge function fields estimate is established.
    • Relation:
      hal-00460632; https://hal.science/hal-00460632; https://hal.science/hal-00460632/document; https://hal.science/hal-00460632/file/rfbp-cstm2008.pdf
    • الرقم المعرف:
      10.1080/03610920802395702
    • Rights:
      info:eu-repo/semantics/OpenAccess
    • الرقم المعرف:
      edsbas.3E4302F3