We develop scalable methods for fitting penalized regression spline based generalized additive models with of the order of 10 4 coefficients to up to 10 8 data. Computational feasi
We propose an objective Bayesian approach to the selection of covariates and their penalised splines transformations in generalised additive models. The methodology is based on a combination of contin
Correlations for fitted values of the generalized additive model terms are in the upper triangle while those for the means traces are in the lower triangle. Significance codes: *p<0.05, **p<0.01, ***p
Stars indicate significance of each term according to a Wald test against the null hypothesis that the term is zero (*** p<0.001, ** p<0.01, * p<0.05).
