摘要：Regression models are used in a wide range of applications providing a powerful scientific tool for researchers from different fields. Linear or simple parametric models are often not sufficient to describe complex relationships between input variables and a response. Such relationships can be better described through flexible approaches such as neural networks but this results in less interpretable models and potential overfitting. Alternatively specific parametric nonlinear functions can be used but the specification of such functions is in general complicated. In this paper we introduce a flexible approach for the construction and selection of highly flexible nonlinear parametric regression models. Nonlinear features are generated hierarchically similarly to deep learning but have additional flexibility on the possible types of features to be considered. This flexibility combined with variable selection allows us to find a small set of important features and thereby more interpretable models. Within the space of possible functions a Bayesian approach introducing priors for functions based on their complexity is considered. A genetically modified mode jumping Markov chain Monte Carlo algorithm is adopted to perform Bayesian inference and estimate posterior probabilities for model averaging. In various applications we illustrate how our approach is used to obtain meaningful nonlinear models. Additionally we compare its predictive performance with several machine learning algorithms.
