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Sparse additive models
Author(s) -
Ravikumar Pradeep,
Lafferty John,
Liu Han,
Wasserman Larry
Publication year - 2009
Publication title -
journal of the royal statistical society: series b (statistical methodology)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.523
H-Index - 137
eISSN - 1467-9868
pISSN - 1369-7412
DOI - 10.1111/j.1467-9868.2009.00718.x
Subject(s) - additive model , lasso (programming language) , parametric statistics , covariate , smoothing , parametric model , generalized additive model , mathematics , computer science , algorithm , statistics , world wide web
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.