LASSO Isotone for High-Dimensional Additive Isotonic Regression

Abstract

Additive isotonic regression attempts to determine the relationship between a multidimensional observation variable and a response, under the constraint that the estimate is the additive sum of univariate component effects that are monotonically increasing. In this article, we present a new method for such regression called LASSO Isotone (LISO). LISO adapts ideas from sparse linear modeling to additive isotonic regression. Thus, it is viable in many situations with high-dimensional predictor variables, where selection of significant versus insignificant variables is required. We suggest an algorithm involving a modification of the backfitting algorithm CPAV. We give a numerical convergence result, and finally examine some of its properties through simulations. We also suggest some possible extensions that improve performance, and allow calculation to be carried out when the direction of the monotonicity is unknown. Supplemental materials are available online for this article.