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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