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- W4225536276 abstract "In this study, we use a generalized lasso to fit spatially varying coefficient models to the case of predictor variables with both numerical and categorical scales. We constructed the generalized lasso model with two 𝐿1 penalties: one was to link some categories within one categorical predictor, and the other was to link a corresponding categorical predictor between adjacent regions. Then, we applied the proposed method to a province-wise analysis of house sales price data on Java Island, Indonesia, to the original data without concatenation between categories for each categorical predictor. An optimal model is obtained in which categories and regions with the same effect are pooled." @default.
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- W4225536276 date "2022-01-01" @default.
- W4225536276 modified "2023-10-18" @default.
- W4225536276 title "SPATIALLY VARYING COEFFICIENT MODELING OF NUMERICAL AND CATEGORICAL PREDICTOR VARIABLES IN THE GENERALIZED LASSO" @default.
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- W4225536276 doi "https://doi.org/10.3107/jesss.11.pp05" @default.
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