Practical variable selection for generalized additive models

By Giampiero Marra, Simon N. Wood. Computational Statistics & Data Analysis, 2011

786 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/63ea9bb822d233dec7c4f580d626d695a085f150

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Citations in the corpus

Smoothing Parameter and Model Selection for General Smooth Models (2015, background): “tive and tensor product smooths of height and width were tried as well as a smooth of the product of height and width, but there was no evidence to justify their inclusion - term selection penalties (Marra and Wood, 2011) remove them, p-values indicate they are not signiﬁcant and AIC suggests that t”
CAM: Causal Additive Models, high-dimensional order search and penalized regression (2013, methodology): “For additive model fitting, we can either use hypothesis testing for additive models [15] or the Group Lasso [28], or its improved version with a sparsity-smoothness penalty proposed in [16].”
Generalized Additive Model Selection (2015, methodology): “While the mgcv package is most often used for automated smoothness selection rather than variable selection proper, the package also supports variable selection using an approach that augments the objective function with an additional penalty term (Marra and Wood, 2011).”
Predicting cetacean abundance and distribution in a changing climate (2018, methodology): “We used restricted maximum likeli‐ hood (REML) to optimize the parameter estimates and a variable selection process that uses a shrinkage approach to modify the smoothing penalty, allowing the smooth to be identically zero and removed from the model (Marra & Wood, 2011).”
Hierarchical generalized additive models in ecology: an introduction with mgcv (2019, methodology): “All models were ﬁ tted using mgcv version 1.8 – 26 ( Wood, 2011 ).”