GeDS - Geometrically Designed Spline Regression
Spline regression, generalized additive models and
component-wise gradient boosting utilizing geometrically
designed (GeD) splines. GeDS regression is a non-parametric
method inspired by geometric principles, for fitting spline
regression models with variable knots. Joint tensor-product
spline components may involve more than two independent
variables in Normal, generalized, additive, and boosting
models. It efficiently estimates the number of knots and their
positions, as well as the spline order, assuming the response
variable follows a distribution from the exponential family.
GeDS models integrate the broader category of generalized
(non-)linear models, offering a flexible approach to model
complex relationships. A description of the method can be found
in Kaishev et al. (2016) <doi:10.1007/s00180-015-0621-7> and
Dimitrova et al. (2023) <doi:10.1016/j.amc.2022.127493>.
Further extending its capabilities, GeDS's implementation
includes generalized additive models (GAM) and functional
gradient boosting (FGB), enabling versatile multivariate
predictor modeling, as discussed in the forthcoming work of
Dimitrova et al. (2026).
