A Family of Geographically Weighted Regression Models

By James Paul LeSage. 2004

158 citations recorded by the source corpus.

Subfields: Economics and Econometrics

View Paper: https://www.semanticscholar.org/paper/2fb89088310df66a766a0aea55f46118dc52397d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. Luc Anselin
2. David Wheeler
3. Michael Tiefelsdorf
4. Roger S. Bivand
5. Daniel A. Griffith
6. Cem Ertur
7. Tobias Ruttenauer
8. J. Le Gallo

Citations in the corpus

• Thirty years of spatial econometrics (2010, methodology): “Further advances in computational techniques focus on various algorithms to extend the limits of maximum likelihood estimation and its associated inference, e.g., in Smirnov and Anselin (2001, 2009); Pace and LeSage (2004, 2009); and Smirnov (2005).”
• Constructing the Spatial Weights Matrix Using a Local Statistic (2004, unknown): “Gaussian distance decline (LeSage 2003); and 3.”
• Multicollinearity and correlation among local regression coefficients in geographically weighted regression (2005, background): “The development of GWR started from local regression and smoothing techniques (Brunsdon et al. 1996; Fotheringham et al. 1998) and became increasingly more sophisticated by considering, for example, maximum likelihood estimation of the kernel bandwidths (Páez et al. 2002a), spatial autocorrelation ”