aws
(Package: aws) :
AWS for local constant models on a grid
The function implements the propagation separation approach to nonparametric smoothing (formerly introduced as Adaptive weights smoothing) for varying coefficient likelihood models on a 1D, 2D or 3D grid. For "Gaussian" models, i.e. regression with additive "Gaussian" errors, a homoskedastic or heteroskedastic model is used depending on the content of sigma2
The package contains R-functions implementing the Propagation-Separation Approach to adaptive smoothing as described in J. Polzehl and V. Spokoiny (2006) Propagation-Separation Approach for Local Likelihood Estimation, Prob. Theory and Rel. Fields 135(3):335-362. and J. Polzehl and V. Spokoiny (2004) Spatially adaptive regression estimation: Propagation-separation approach, WIAS-Preprint 998. Additionally it contains an implementation of selected LPA-ICI pointwise adaptive smoothing algorithms from the book V. Katkovnik, K. Egiazarian and J. Astola (2006). Local Approximation Techniques in Signal and Image Processing, SPIE Press Monograph Vol. PM 157.
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