A Family of Geographically Weighted Regression Models

James LeSage

Chapter 11 in Advances in Spatial Econometrics, 2004, pp 241-264 from Springer

Abstract: 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 (search for similar items in EconPapers)
Date: 2004
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DOI: 10.1007/978-3-662-05617-2_11

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