A new computational algorithm for estimating the smoothing parameters of a multidimensional penalized spline generalized model with anisotropic penalty is presented. This new proposal is based on the mixed model representation of a multidimensional P-spline, in which the smoothing parameter for each covariate is expressed in terms of variance components. On the basis of penalized quasi-likelihood methods (PQL), closed-form expressions for the estimates of the variance components are obtained. This formulation leads to an efficient implementation that can considerably reduce the computational load. The proposed algorithm can be seen as a generalization of the algorithm by Schall (1991) - for variance components estimation - to deal wi...
A fast and stable algorithm for estimating multidimensional adaptive P-spline models is presented. W...
P-splines were introduced by Eilers and Marx (1996). We consider semiparametric models where the smo...
P-splines were introduced by Eilers and Marx (1996). We consider semiparametric models where the smo...
A new computational algorithm for estimating the smoothing parameters of a multidimensional penalize...
A new computational algorithm for estimating the smoothing parameters of a multidimensional penalize...
A new computational algorithm for estimating the smoothing parameters of a multidimensional penalize...
A new computational algorithm for estimating the smoothing parameters of a multidimensional penalize...
A new computational algorithm for estimating the smoothing parameters of a multidimensional penalize...
A new computational algorithm for estimating the smoothing parameters of a multidimensional penalize...
A new computational algorithm for estimating the smoothing parameters of a multidimensional penalize...
A new computational algorithm for estimating the smoothing parameters of a multidimensional penalize...
A new computational algorithm for estimating the smoothing parameters of a multidimensional penalize...
A new computational algorithm for estimating the smoothing parameters of a multidimensional penalize...
A new computational algorithm for estimating the smoothing parameters of a multidimensional penalize...
A fast and stable algorithm for estimating multidimensional adaptive P-spline models is presented. W...
A fast and stable algorithm for estimating multidimensional adaptive P-spline models is presented. W...
P-splines were introduced by Eilers and Marx (1996). We consider semiparametric models where the smo...
P-splines were introduced by Eilers and Marx (1996). We consider semiparametric models where the smo...
A new computational algorithm for estimating the smoothing parameters of a multidimensional penalize...
A new computational algorithm for estimating the smoothing parameters of a multidimensional penalize...
A new computational algorithm for estimating the smoothing parameters of a multidimensional penalize...
A new computational algorithm for estimating the smoothing parameters of a multidimensional penalize...
A new computational algorithm for estimating the smoothing parameters of a multidimensional penalize...
A new computational algorithm for estimating the smoothing parameters of a multidimensional penalize...
A new computational algorithm for estimating the smoothing parameters of a multidimensional penalize...
A new computational algorithm for estimating the smoothing parameters of a multidimensional penalize...
A new computational algorithm for estimating the smoothing parameters of a multidimensional penalize...
A new computational algorithm for estimating the smoothing parameters of a multidimensional penalize...
A new computational algorithm for estimating the smoothing parameters of a multidimensional penalize...
A fast and stable algorithm for estimating multidimensional adaptive P-spline models is presented. W...
A fast and stable algorithm for estimating multidimensional adaptive P-spline models is presented. W...
P-splines were introduced by Eilers and Marx (1996). We consider semiparametric models where the smo...
P-splines were introduced by Eilers and Marx (1996). We consider semiparametric models where the smo...