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Federated generalized additive models for location, scale and shape [PDF]
BMC Medical Research MethodologyBackground The generalized additive model for location, scale and shape (GAMLSS) is a flexible regression model with a wide range of applications. In particular, it is the standard method to estimate age-specific percentile curves for clinical parameters
Annika Swenne +3 more
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Hierarchical generalized additive models in ecology: an introduction with mgcv [PDF]
PeerJ, 2019 In this paper, we discuss an extension to two popular approaches to modeling complex structures in ecological data: the generalized additive model (GAM) and the hierarchical model (HGLM).
Eric J. Pedersen +3 more
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Vector Generalized Additive Models
Journal of the Royal Statistical Society Series B: Statistical Methodology, 1996 SUMMARY Vector smoothing is used to extend the class of generalized additive models in a very natural way to include a class of multivariate regression models. The resulting models are called ‘vector generalized additive models‘. The class of models for which the methodology gives generalized additive extensions includes the multiple ...
Chris J Wild
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Linking watershed nutrient loading to estuary water quality with generalized additive models [PDF]
PeerJ, 2023 Evaluating estuary water quality responses to reductions (or increases) in nutrient loading attributed to on the ground management actions can be challenging due to the strong influence of environmental drivers on nutrient loads and non-linear ...
Michael P. Schramm
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Functional Generalized Additive Models. [PDF]
J Comput Graph Stat, 2014 We introduce the functional generalized additive model (FGAM), a novel regression model for association studies between a scalar response and a functional predictor. We model the link-transformed mean response as the integral with respect to t of F{X(t), t} where F(·,·) is an unknown regression function and X(t) is a functional covariate.
McLean MW +4 more
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Meta-analysis of generalized additive models in neuroimaging studies
NeuroImage, 2021 Analyzing data from multiple neuroimaging studies has great potential in terms of increasing statistical power, enabling detection of effects of smaller magnitude than would be possible when analyzing each study separately and also allowing to ...
Øystein Sørensen +9 more
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Semina: Ciências Exatas e Tecnológicas, 2021 Generalized additive models for location, scale and shape (GAMLSS) are a very flexible statistical modeling framework, being an important generalization of the well-known generalized linear models and generalized additive models.
Fernanda V. Roquim +5 more
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Generalized Additive Models [PDF]
Statistical Science, 1984 The classical linear regression model expresses the response vector Y as a function of the predictor variables \(X_ i\) through the model \(Y=\sum_{i}X_ i\beta_ i+e\), where the \(X_ i\) are observed, the \(\beta_ i\) are estimated by least squares or some other technique, e is the vector of errors.
Hastie, Trevor, Tibshirani, Robert
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Mapping spatial patterns of plant species based on machine-learning and regression models [PDF]
Desert, 2022 Various statistical techniques have been used for species distribution modeling that attempt to predict the occurrence of a given species with respect to environmental conditions.
H. Keshtkar, P. Pourmohammad
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Analyzing Temporal Trends of Urban Evaporation Using Generalized Additive Models
Land, 2022 This study aimed to gain new insights into urban hydrological balance (in particular, the evaporation from paved surfaces). Hourly evaporation data were obtained simultaneously from two high-resolution weighable lysimeters.
Basem Aljoumani +4 more
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