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Prism uses a combination of statistical methods to conduct spline-based multiple regression. Prism conducts this regression using regularization, dimensionality reduction, and feature selection, through a combination of smoothing spline regression, PCA, and RVR/LASSO. Smoothing splines can be used to model non-parametric relationships using piece-wise cubic functions (Wahba and Wold 1975; Fox 2000). Relevance vector regression (RVR) refers to application of a relevance vector machine (RVM) to a regression problem; broadly, RVM is similar to multiple linear regression with regularization, using automatic relevance determination for feature selection (Tipping 2000). RVM shares many commonalities with SVM, and is implemented as a special case of a Sparse Bayesian framework (Tipping 2001; Tipping and Faul 2003).

Prism: Multiple spline regression with regularization, dimensionality reduction, and feature selection