Work

Convex Regression with Interpretable Sharp Partitions.

Year: 2016

Type: article

Abstract: (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.

Source: PubMed

DOI: 10.5555/2946645.3007047

Authors: Ashley Petersen, Noah Simon, Daniela Witten

Institution: University of Washington

Language: English

Topic: Statistical Methods and Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

Funder: National Institutes of Health

Awards: Data-Driven Statistical Learning with Applications to Genomics, High-dimensional unsupervised learning, with applications to genomics