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A Family of Geographically Weighted Regression Models

  • James P. LeSage
Chapter
Part of the Advances in Spatial Science book series (ADVSPATIAL)

Abstract

A Bayesian approach to locally linear regression methods introduced in McMillen (1996) and labeled geographically weighted regressions (GWR) in Brunsdon et al. (1996) is set forth in this chapter. The main contribution of the GWR methodology is use of distance weighted sub-samples of the data to produce locally linear regression estimates for every point in space. Each set of parameter estimates is based on a distance-weighted sub-sample of “neighboring observations,” which has a great deal of intuitive appeal in spatial econometrics. While this approach has a definite appeal, it also presents some problems. The Bayesian method introduced here can resolve some difficulties that arise in GWR models when the sample observations contain outliers or non-constant variance.

Keywords

Posterior Probability Gibbs Sampler Parameter Smoothing Geographically Weight Regression Model Spatial Econometric 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • James P. LeSage
    • 1
  1. 1.University of ToledoUSA

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