title: Convex Regression with Interpretable Sharp Partitions.
abstract: We consider the problem of predicting an outcome variable on the basis of a small number of covariates, using an interpretable yet non-additive model. We proposeconvex regression with interpretable sharp partitions(CRISP) for this task. CRISP partitions the covariate space into blocks in a data-adaptive way, and fits a mean model within each block. Unlike other partitioning methods, CRISP is fit using a non-greedy approach by solving a convex optimization problem, resulting in low-variance fits. We explore the properties of CRISP, and evaluate its performance in a simulation study and on a housing price data set.
authors.affiliation: Department of Biostatistics, University of Washington, Seattle, WA 98195.
authors.forename: Ashley
authors.initials: A
authors.lastname: Petersen
authors.affiliation: Department of Biostatistics, University of Washington, Seattle, WA 98195.
authors.forename: Noah
authors.initials: N
authors.lastname: Simon
authors.affiliation: Departments of Biostatistics and Statistics, University of Washington, Seattle, WA 98195.
authors.forename: Daniela
authors.initials: D
authors.lastname: Witten
keywords: convex optimization; interpretability; non-additivity; non-parametric regression; prediction