Abstract
This paper deals with the variable selection problem in geographically weighted regression (GWR). GWR is a local estimation method that continuously evaluates geographical effects in regression involving spatial data. Specifically, the method estimates regression coefficients for each observed point using a varying coefficient model. With such a model, variable selection has two aspects: local selection, which applies to each observed point, and global selection, which is common for all observed points. We approach both variable selections simultaneously via sparse group Lasso. To illustrate the proposed method, we apply it to apartment rent data in Tokyo.
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Acknowledgments
This work was partially supported by JSPS KAKENHI Grant Numbers JP20H04151 and JP21K13834. The authors thank the associate editor and the two reviewers for their valuable comments.
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Ohishi, M., Kirishima, K., Okamura, K., Itoh, Y., Yanagihara, H. (2023). Geographically Weighted Sparse Group Lasso: Local and Global Variable Selections for GWR. In: Czarnowski, I., Howlett, R., Jain, L.C. (eds) Intelligent Decision Technologies. KESIDT 2023. Smart Innovation, Systems and Technologies, vol 352. Springer, Singapore. https://doi.org/10.1007/978-981-99-2969-6_16
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DOI: https://doi.org/10.1007/978-981-99-2969-6_16
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