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Local Modeling in a Regression Framework

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  • Oshan, Taylor M.

Abstract

This chapter introduces the concept of local versus global models and describes one type of local model, Geographically Weighted Regression, and its recent successor, Multiscale Geographically Weighted Regression. The conceptual basis for this type of model is explained in terms of data-borrowing. An empirical example is given to demonstrate both the value of local regression models and freely available software for their calibration.

Suggested Citation

  • Oshan, Taylor M., 2022. "Local Modeling in a Regression Framework," OSF Preprints hpbd8, Center for Open Science.
    • Handle: RePEc:osf:osfxxx:hpbd8
    DOI: 10.31219/osf.io/hpbd8
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    References listed on IDEAS

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    1. Kelley Pace, R. & Barry, Ronald, 1997. "Sparse spatial autoregressions," Statistics & Probability Letters, Elsevier, vol. 33(3), pages 291-297, May.
    2. Gollini, Isabella & Lu, Binbin & Charlton, Martin & Brunsdon, Christopher & Harris, Paul, 2015. "GWmodel: An R Package for Exploring Spatial Heterogeneity Using Geographically Weighted Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i17).
    3. Gelfand A.E. & Kim H-J. & Sirmans C.F. & Banerjee S., 2003. "Spatial Modeling With Spatially Varying Coefficient Processes," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 387-396, January.
    4. Ziqi Li & A. Stewart Fotheringham & Taylor M. Oshan & Levi John Wolf, 2020. "Measuring Bandwidth Uncertainty in Multiscale Geographically Weighted Regression Using Akaike Weights," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 110(5), pages 1500-1520, September.
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