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We introduce GAMSEL (Generalized Additive Model Selection), a penalized likelihood approach for fitting sparse generalized additive models in high dimension.
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[author] Hastie, TrevorT., Tibshirani, RobertR., Friedman, JeromeJ., Hastie, TT., Friedman, JJ. and Tibshirani, RR. (2009). The elements of statistical learning 2. Springer
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[author] Marra, GiampieroG. and Wood, Simon NS. N. (2011). Practical variable selection for generalized additive models. Computational Statistics & Data Analysis 55 2372–2387
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[author] Wood, Simon NS. N. (2011). Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 73 3–36
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[author] Tibshirani, RobertR., Bien, JacobJ., Friedman, JeromeJ., Hastie, TrevorT., Simon, NoahN., Taylor, JonathanJ. and Tibshirani, Ryan JR. J. (2012). Strong rules for discarding predictors in lasso-type problems. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 74 245–266
2012
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[author] Meier, LukasL., Van de Geer, SaraS., Bühlmann, PeterP. et al. (2009). High-dimensional additive modeling. The Annals of Statistics 37 3779–3821
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[author] Friedman, JeromeJ., Hastie, TrevorT. and Tibshirani, RobertR. (2010). Regularization Paths for Generalized Linear Models via Coordinate Descent. Journal of Statistical Software 33 1–22
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2014
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Hastie, T
2015
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[author] Hastie, T.T., Tibshirani, R.R. and Wainwright, M.M. (2015). Statistical Learning with Sparsity: the Lasso and Generalizations. Chapman and Hall, CRC Press
2015
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