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- W2261782076 abstract "AbstractThe challenge in regression problems with categorical covariates is the high number of parameters involved. Common regularization methods like the Lasso, which allow for selection of predictors, are typically designed for metric predictors. If independent variables are categorical, selection strategies should be based on modified penalties. For categorical predictor variables with many categories a useful strategy is to search for clusters of categories with similar effects. We focus on generalized linear models and present L 1-penalty approaches for factor selection and clustering of categories. The methods proposed are investigated in simulation studies and applied to a real world classification problem.KeywordsCategorical PredictorAdaptive VersionClassical Linear ModelGeneralize RidgeDummy CoefficientThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves." @default.
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- W2261782076 date "2013-01-01" @default.
- W2261782076 modified "2023-09-27" @default.
- W2261782076 title "Regularization and Model Selection with Categorical Covariates" @default.
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- W2261782076 doi "https://doi.org/10.1007/978-3-319-00035-0_21" @default.
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