2020
DOI: 10.1007/s11749-020-00711-5
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Inference and computation with generalized additive models and their extensions

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 f… Show more

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Cited by 74 publications
(48 citation statements)
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“…Results using a zero-inflated negative binomial model were virtually indistinguishable from the results using a negative binomial distribution. Due to the prevalence of the negative binomial distribution in the use of modeling fisheries count data with overdispersion and moderate zero inflation (Barry and Welsh 2002;Drexler and Ainsworth 2013;Dance and Rooker 2019) and the potential overutilization of zero-inflated models (Wood 2020), we opted to model the LA Creel estimates using the negative binomial distribution. All statistical analyses were performed using R (R Core Team 2020) and the package mgcv (Wood 2006).…”
Section: Methodsmentioning
confidence: 99%
“…Results using a zero-inflated negative binomial model were virtually indistinguishable from the results using a negative binomial distribution. Due to the prevalence of the negative binomial distribution in the use of modeling fisheries count data with overdispersion and moderate zero inflation (Barry and Welsh 2002;Drexler and Ainsworth 2013;Dance and Rooker 2019) and the potential overutilization of zero-inflated models (Wood 2020), we opted to model the LA Creel estimates using the negative binomial distribution. All statistical analyses were performed using R (R Core Team 2020) and the package mgcv (Wood 2006).…”
Section: Methodsmentioning
confidence: 99%
“…Furthermore, as discussed in the next section, the REML estimates of regression coefficients have an asymptotically MAP Bayesian interpretation that is very useful for obtaining simulated credible intervals for predictions. For a recent review on inference and computation in GAMs see Wood ( 2020 ).…”
Section: Statistical Model For the Test Positive Ratementioning
confidence: 99%
“…The prior for α 1 was already stated in (4). Through a restricted maximum likelihood approach, we estimate the corresponding hyperparameter γ 1 such that it maximizes the marginal likelihood of z and E given γ 1 (for additional information on this estimation procedure and general empirical Bayes theory for penalized splines see Wood, 2011Wood, , 2020. Regarding the linear coefficients θ 1 , we assume flat priors, i.e.…”
Section: Imputation-stepmentioning
confidence: 99%