Sparse Partially Linear Additive Models
نویسندگان
چکیده
منابع مشابه
Sparse Partially Linear Additive Models
The generalized partially linear additive model (GPLAM) is a flexible and interpretable approach to building predictive models. It combines features in an additive manner, allowing them to have either a linear or nonlinear effect on the response. However, the assignment of features to the linear and nonlinear groups is typically assumed known. Thus, to make a GPLAM a viable approach in situatio...
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We present a new class of methods for high-dimensional nonparametric regression and classification called sparse additive models (SpAM). Our methods combine ideas from sparse linear modeling and additive nonparametric 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. SpAM is essentially a ...
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We present a new class of models for high-dimensional nonparametric regression and classification called sparse additive models (SpAM). Our methods combine ideas from sparse linear modeling and additive nonparametric regression. We derive a method for fitting the models that is effective even when the number of covariates is larger than the sample size. A statistical analysis of the properties ...
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ژورنال
عنوان ژورنال: Journal of Computational and Graphical Statistics
سال: 2016
ISSN: 1061-8600,1537-2715
DOI: 10.1080/10618600.2015.1089775