# Geographically Weighted Regression Analysis for Spatial Economics Data: A Bayesian Recourse

@article{Ma2020GeographicallyWR, title={Geographically Weighted Regression Analysis for Spatial Economics Data: A Bayesian Recourse}, author={Zhihua Ma and Yishu Xue and Guanyu Hu}, journal={International Regional Science Review}, year={2020}, volume={44}, pages={582 - 604} }

The geographically weighted regression (GWR) is a well-known statistical approach to explore spatial non-stationarity of the regression relationship in spatial data analysis. In this paper, we discuss a Bayesian recourse of GWR. Bayesian variable selection based on spike-and-slab prior, bandwidth selection based on range prior, and model assessment using a modified deviance information criterion and a modified logarithm of pseudo-marginal likelihood are fully discussed in this paper. Usage of…

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