WIAS Preprint No. 818, (2003)

Varying coefficient regression modeling by adaptive weights smoothing



Authors

  • Polzehl, Jörg
    ORCID: 0000-0001-7471-2658
  • Spokoiny, Vladimir
    ORCID: 0000-0002-2040-3427

2010 Mathematics Subject Classification

  • 62G05

Keywords

  • adaptive weights, local structure, local polynomial regression

Abstract

The adaptive weights smoothing (AWS) procedure was introduced in Polzehl and Spokoiny (2000) in the context of image denoising. The procedure has some remarkable properties like preservation of edges and contrast, and (in some sense) optimal reduction of noise. The procedure is also fully adaptive and dimension free. Simulations with artificial images show that AWS is superior to classical smoothing techniques especially when the underlying image function is discontinuous and can be well approximated by a piecewise constant function. However, the latter assumption can be rather restrictive for a number of potential applications. Here the AWS method is generalized to the case of an arbitrary local linear parametric structure. We also establish some important results about properties of the AWS procedure including the so called ``propagation condition'' and spatial adaptivity. The performance of the procedure is illustrated by examples for local polynomial regression in univariate and bivariate situations.

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