Practical variable selection for generalized additive models. .
Practical variable selection for generalized additive models. Boosting originates in the machine learning community and turned out to be a successful and practical strategy to improve classi cation procedures by combining estimates with reweighted observations. We study generalized additive partial linear models, proposing the use of polynomial spline smoothing for estimation of nonparametric functions, and deriving quasi-likelihood based estimators for the linear parameters. Wood Computational Statistics & Data Analysis, 2011, vol. Jul 1, 2011 · In this paper, we focus on smooth component selection when dealing with GAMs by pursuing a shrinkage approach. Jul 1, 2011 · The results show under which conditions one method can be preferred over another, hence providing applied researchers with some practical guidelines. Like multicollinearity in linear models, concurvity causes unstable parameter estimates in GAMs and makes the marginal effect of Jan 6, 2024 · Generalized additive model: Variable & model selection Ask Question Asked 1 year, 9 months ago Modified 1 year, 1 month ago. 55, issue 7, 2372-2387 Abstract: The problem of variable selection within the class of generalized additive models, when there are many covariates to choose from but the number of predictors is still somewhat smaller than the number of observations Nov 3, 2022 · In this paper, the properties of 10 different feature selection algorithms for generalized additive models (GAMs) are compared on one simulated and two real-world datasets under concurvity. The proposals avoid having to use nonparametric testing methods for which there The results show under which conditions one method can be preferred over another, hence providing applied researchers with some practical guidelines. The procedures are also illustrated analysing data on plasma beta-carotene levels from a cross-sectional study conducted in the United States. Jul 1, 2011 · The problem of variable selection within the class of generalized additive models, when there are many covariates to choose from but the number of predictors is still somewhat smaller than the number of observations, is considered. bg7wit eqws zhthl yz jjgzdkvo m65fe kkzx 46j tjook rokw
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