Better subset regression using the nonnegative garrote
Technometrics
Computational Statistics & Data Analysis
Computational Statistics & Data Analysis
A comparison of methods for the fitting of generalized additive models
Statistics and Computing
Computational Statistics & Data Analysis
Shrinkage and model selection with correlated variables via weighted fusion
Computational Statistics & Data Analysis
Boosting nonlinear additive autoregressive time series
Computational Statistics & Data Analysis
Bayesian projection approaches to variable selection in generalized linear models
Computational Statistics & Data Analysis
Computational Statistics & Data Analysis
Fast Bayesian model assessment for nonparametric additive regression
Computational Statistics & Data Analysis
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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. Two very simple but effective shrinkage methods and an extension of the nonnegative garrote estimator are introduced. The proposals avoid having to use nonparametric testing methods for which there is no general reliable distributional theory. Moreover, component selection is carried out in one single step as opposed to many selection procedures which involve an exhaustive search of all possible models. The empirical performance of the proposed methods is compared to that of some available techniques via an extensive simulation study. 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.