A General Framework for Estimation and Inference of Geographically Weighted Regression Models: 1. Location-Specific Kernel Bandwidths and a Test for Locational Heterogeneity

By Antonio Páez, T. Uchida, K. Miyamoto. 2002

210 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/d997c6e2e73bf8f605b412fc82d9a180dacc2f64

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In Economics and Econometrics

Citations in the corpus

Geographically weighted regression with a non-Euclidean distance metric: a case study using hedonic house price data (2014, background): “For example, different kernel functions have been suggested (e.g. Brunsdon et al. 1996, Fotheringham et al. 1998, Yrigoyen et al. 2007) and different rules to select an optimum bandwidth have also been proposed (e.g. Fotheringham et al. 2002, Páez et al. 2002, Farber and Páez 2007).”
Geographically weighted regression bandwidth selection and spatial autocorrelation: an empirical example using Chinese agriculture data (2010, background): “…not always model residual spatial dependence (e.g. spatial error processes) or spatial lag dependence (Leung et al., 2000a, b; Fotheringham et al., 2002; Páez et al., 2002a, b), although spatial error dependence can be substantially reduced by the procedure in some cases *Corresponding author.”
Simultaneous Coefficient Penalization and Model Selection in Geographically Weighted Regression: The Geographically Weighted Lasso (2009, methodology): “This is evidenced by the spatial expansion method (Casetti, 1992), geographically weighted regression (GWR) designed to model spatial parametric nonstationarity (Brunsdon et al, 1996; Fotheringham et al, 2002), and GWR designed to model variance heterogeneity (Päez et al, 2002).”
Local Linear Estimation of Spatially Varying Coefficient Models: An Improvement on the Geographically Weighted Regression Technique (2008, background): “Päez et al (2002a; 2002b) proposed a maximum-likelihood-based framework of estimation and inference for GWR from the perspective of error-variance heterogeneity.”
Modeling spatial variations of urban growth patterns in Chinese cities: The case of Nanjing (2009, unknown): “One can see that the distance to highway has a more negative effect on land development in the northern part of the study area than the southern part (Fig.”
Multicollinearity and correlation among local regression coefficients in geographically weighted regression (2005, background): “…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 among the residuals (Páez et al. 2002b), generalized linear model specifications (Fotheringham et al.…”
Geographical weighting as a further refinement to regression modelling: An example focused on the NDVI–rainfall relationship (2003, methodology): “A fuller description of geographically weighted regression is provided by Fotheringham et al. (2002) and examples of its application include those presented by Brunsdon, McClatchey, and Unwin (2001) and Paez et al. (2002a).”
Geographically and temporally weighted regression for modeling spatio-temporal variation in house prices (2010, methodology): “To avoid (1) exaggerating the degree of nonstationarity present in the areas where data are sparse or (2) mask subtle spatial nonstationarity where the data are dense (Paez et al. 2002), adaptive weighting functions are used to change the kernel size to suit localized observation patterns.”
Diagnostic Tools and a Remedial Method for Collinearity in Geographically Weighted Regression (2007, methodology): “Päez et al (2002) present an alternative GWR model that can estimate local kernel bandwidths at each model calibration location by using maximum likelihood estimation and calculating the spatial weights as part of a model for variance.”
Testing the Importance of the Explanatory Variables in a Mixed Geographically Weighted Regression Model (2006, background): “…and environmental science, and since its inception the applications and also some statistical inference problems have been extensively studied (for example, see Brunsdon et al, 1996; 1998; 1999; Fotheringham et al, 1997a; 1997b; 1998; 2002; Leung et al, 2000a; 2000b; Päez et al, 2002a; 2002b).”
Modeling Spatial Dimensions of Housing Prices in Milwaukee, WI (2007, unknown): “However, as pointed out by Päez et al (2002a; 2002b) and Fortheringham et al (2002), the fixed kernel approach can produce large local estimation variance in areas where data are sparse, and may mask subtle local variations in areas where data are dense.”