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- W2593996946 abstract "Summary We present a new class of methods for high dimensional non-parametric regression and classification called sparse additive models. Our methods combine ideas from sparse linear modelling and additive non-parametric regression. We derive an algorithm for fitting the models that is practical and effective even when the number of covariates is larger than the sample size. Sparse additive models are essentially a functional version of the grouped lasso of Yuan and Lin. They are also closely related to the COSSO model of Lin and Zhang but decouple smoothing and sparsity, enabling the use of arbitrary non-parametric smoothers. We give an analysis of the theoretical properties of sparse additive models and present empirical results on synthetic and real data, showing that they can be effective in fitting sparse non-parametric models in high dimensional data." @default.
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- W2593996946 date "2009-10-19" @default.
- W2593996946 modified "2023-10-10" @default.
- W2593996946 title "Sparse Additive Models" @default.
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- W2593996946 doi "https://doi.org/10.1111/j.1467-9868.2009.00718.x" @default.
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