aws (version 1.9-3)

aws-package: Adaptive Weights Smoothing

Description

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.

Details

ll{ Package: aws Version: 1.6 Date: 2009-04-07 License: GPL (>=2) Copyright: 2008 Weierstrass Institute for Applied Analysis and Stochastics. URL: http://www.wias-berlin.de/project-areas/stat/ }

Index: aws AWS for local constant models on a grid aws.gaussian Adaptive weights smoothing for Gaussian data with variance depending on the mean. aws.irreg local constant AWS for irregular (1D/2D) design aws.segment Segmentation by adaptive weights for Gaussian models. awsdata Extract information from an object of class aws binning Binning in 1D, 2D or 3D lpaws Local polynomial smoothing by AWS kernsm 1D, 2D, 3D nonparametric kernel smoothing via fft ICIsmooth pointwise adaptive kernel smoothing ICIcombined pointwise adaptive kernel smoothing with fusing

References

J. Polzehl and V. Spokoiny (2006) Propagation-Separation Approach for Local Likelihood Estimation, Prob. Theory and Rel. Fields 135(3), 335-362.

J. Polzehl and V. Spokoiny (2004) Spatially adaptive regression estimation: Propagation-separation approach, WIAS-Preprint 998.

V. Katkovnik, K. Egiazarian and J. Astola (2006) Local Approximation Techniques in Signal and Image Processing, SPIE Press Monograph Vol. PM 157