Simultaneous Coefficient Penalization and Model Selection in Geographically Weighted Regression: The Geographically Weighted Lasso

By David C. Wheeler. 2009

168 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/401f51da617cb5bba2cde72f0afc2337cd9dc5aa

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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, methodology): “Methods to address local collinearity issues that are inherent in local regression modelling have also been developed (Wheeler 2007, 2009).”
GWmodel: An R Package for Exploring Spatial Heterogeneity Using Geographically Weighted Models (2013, methodology): “…models Collinearity is a problem in any form of regression modelling and the importance of assessing and taking action has been raised by the many commentaries on local collinearity in GW regression (Wheeler and Tiefelsdorf 2005; Wheeler 2007, 2009, 2010, 2013a; Griffith 2008; Páez et al. 2011).”
A Big Data–Based Geographically Weighted Regression Model for Public Housing Prices: A Case Study in Singapore (2018, background): “In addition, different approaches to address local collinearity issues have been studied (Wheeler 2007, 2009).”
An extension of geographically weighted regression with flexible bandwidths (2014, methodology): “The gwrr package calculates the variance-decomposition proportions and the condition indexes for the weighted design matrix of a GWR model to detect collinearity which, if indicated, can be relieved by geographically weighted ridge regression (Wheeler, 2007) and geographically weighted lasso (Wheele”
Living with Collinearity in Local Regression Models (2012, background): “These remedies build on the geographically weighted models proposed by Wheeler (2007; 2009), following the initial highlighting of this issue presented in Wheeler and Tiefelsdorf (2005).”
Links, comparisons and extensions of the geographically weighted regression model when used as a spatial predictor (2011, background): “For instances where collinearity problems cannot be adequately addressed with basic GWR, the ridge or lasso GWR models of Wheeler (2007, 2009) would be a more formal way of dealing with them.”
A Moran coefficient-based mixed effects approach to investigate spatially varying relationships (2016, methodology): “With regard to the first problem, Wheeler (2007; 2009) proposes 15 regularized GWR, by combining ridge and/or lasso regression with GWR, and its 16 robustness in terms of the multicollinearity problem has been demonstrated.”
Modelling hedonic residential rents for land use and transport simulation while considering spatial effects (2010, methodology): “…statistical tools. erefore, several authors have compared the GWR models with alternatives in this regard, particularly with methodological improvements of the GWR approach (Griffith 2008; Wheeler 2007, 2009) and Bayesian spatially varying coefficient process models (Wheeler and Waller 2009).”
A Simulation-Based Study of Geographically Weighted Regression as a Method for Investigating Spatially Varying Relationships (2011, background): “…coefficients is reduced, but not eliminated (see Wheeler, 2009, figures 1, 3) and, while the geographically weighted lasso can shrink some coefficients, possibly to zero, to stabilize the model, the estimates still tend to be positively correlated with those from GWR (see Wheeler, 2009, figure 4).”
Two-Step Lasso Estimation of the Spatial Weights Matrix (2015, methodology): “The Lasso has also been applied in the GIS literature, where the focus is on estimation of a spatial model where spatial dependence is a function of geographic distance; see, for example, Huang et al. [10] and Wheeler [11].”