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We propose a type of generalised additive models with of model componentsbased on pair-copula constructions, with prediction as a main aim. The modelcomponents are designed such that our model may capture potentially complexinteraction effects in the relationship between the response covariates. Inaddition, our model does not require discretisation of continuous covariates,and is therefore suitable for problems with many such covariates. Further, wehave designed a fitting algorithm inspired by gradient boosting, as well asefficient procedures for model selection and evaluation of the modelcomponents, through constraints on the model space and approximations, thatspeed up time-costly computations. In addition to being absolutely necessaryfor our model to be a realistic alternative in higher dimensions, thesetechniques may also be useful as a basis for designing efficient modelsselection algorithms for other types of copula regression models. We haveexplored the characteristics of our method in a simulation study, in particularcomparing it to natural alternatives, such as logic regression, classicboosting models and penalised logistic regression. We have also illustrated ourapproach on the Wisconsin breast cancer dataset and on the Boston housingdataset. The results show that our method has a prediction performance that iseither better than or comparable to the other methods, even when the proportionof discrete covariates is high.

Copulaboost: additive modeling with copula-based model components