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MARS via LASSO
March 18, 2024, 4:43 a.m. | Dohyeong Ki, Billy Fang, Adityanand Guntuboyina
stat.ML updates on arXiv.org arxiv.org
Abstract: Multivariate adaptive regression splines (MARS) is a popular method for nonparametric regression introduced by Friedman in 1991. MARS fits simple nonlinear and non-additive functions to regression data. We propose and study a natural lasso variant of the MARS method. Our method is based on least squares estimation over a convex class of functions obtained by considering infinite-dimensional linear combinations of functions in the MARS basis and imposing a variation based complexity constraint. Our estimator can …
abstract arxiv class data functions lasso least mars math.st multivariate natural popular regression simple squares stat.ml stat.th study type via
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