Least-squares estimator under k-monotony constraint for discrete functions
We propose two least-squares estimators of a discrete probability under the constraint of k-monotony and study their statistical properties. We give a characterization of these estimators based on the decomposition on a spline basis of k-monotone sequences. We develop an algorithm derived from the Support Reduction Algorithm and we finally present a simulation study to illustrate their properties.
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              Développement suivi          Porteur(s)
              Unité
              MaIAGE          Equipe
              StatInfOmics          Auteur(s)
              Giguelay Jade
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              jade.giguelay@inra.fr              
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