Geographically Weighted Sparse Group Lasso: Local and Global Variable Selections for GWR
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-2969-6_16
Reference22 articles.
1. Bivand, R., Yu, D.: spgwr: Geographically Weighted Regression (2022). https://CRAN.R-project.org/package=spgwr. R package version 0.6-35
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3. Brunsdon, C., Fotheringham, S., Charlton, M.: Geographically weighted regression: a method for exploring spatial nonstationarity. Geogr. Anal. 28, 281–298 (1996). https://doi.org/10.1111/j.1538-4632.1996.tb00936.x
4. Craven, P., Wahba, G.: Smoothing noisy data with spline functions: estimating the correct degree of smoothing by the method of generalized cross-validation. Numer. Math. 31, 377–403 (1979). https://doi.org/10.1007/BF01404567
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