Inference and computation with generalized additive models and their extensions

Simon N. Wood ()
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Simon N. Wood: University of Bristol

TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, 2020, vol. 29, issue 2, No 1, 307-339

Abstract: Abstract Regression models in which a response variable is related to smooth functions of some predictor variables are popular as a result of their appealing balance between flexibility and interpretability. Since the original generalized additive models of Hastie and Tibshirani (Generalized additive models. Chapman & Hall, Boca Raton, 1990) numerous model extensions have been proposed, and a variety of practically useful computational strategies have emerged. This paper provides an overview of some widely applicable frameworks for this type of modelling, emphasizing the similarities between the different approaches, and the equivalence of smoothing, Gaussian latent process models and Gaussian random effects. The focus is particularly on Bayes empirical smoother theory, fully Bayesian inference via stochastic simulation or integrated nested Laplace approximation and boosting.

Keywords: Smoothing; Regression; Smoothing parameters; INLA; Boosting; Empirical bayes; Reduced rank; 62J05; 62J07; 62J12 (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (9)

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DOI: 10.1007/s11749-020-00711-5

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