Generalized additive models (GAMs) can model the nonlinear relationship between a response variable and a set of explanatory variables through smooth functions. GAM is used to assess the direct, ...

This case study demonstrated the potential of Generalized Additive Models (GAMs) in modeling soil pH, a critical factor influencing agricultural production. By capturing non-linear relationships, GAMs ...

Partially linear additive models provide an attractive middle ground between the simplicity of generalized linear model and the flexibility of generalized additive model, and are important models ...

Stasinopoulos, D.M. and Rigby, R.A. (2007) Generalized Additive Models for Location Scale and Shape (GAMLSS) in R. Journal of Statistical Software, 23, 1-46.

Generalized additive models (GAMs) are different; they build upon the generalized linear model (GLM) by allowing the shapes of the relationships between response and covariates to be learned from the ...

In this study, we systematically evaluated six parametric models (exponential, exponential quadratic, monomolecular, logistic, Gompertz, and von Bertalanffy) and the semiparametric generalized ...

solving nonlinear scoring problems where linear regression doesn't fit well using techniques like Generalized Additive Models (GAM) and Support Vector Regression (SVR). Computationally efficient ...

Deployment of machine learning models in real high-risk settings (e.g., healthcare) often depends not only on model’s accuracy but also on its fairness, robustness and interpretability. Generalized ...