Title

Tree-Structured Model Diagnostics For Linear Regression

Keywords

AIC; CART; Heteroscedasticity; Linear models; Regression trees

Abstract

This paper studies model diagnostics for linear regression models. We propose two tree-based procedures to check the adequacy of linear functional form and the appropriateness of homoscedasticity, respectively. The proposed tree methods not only facilitate a natural assessment of the linear model, but also automatically provide clues for amending deficiencies. We explore and illustrate their uses via both Monte Carlo studies and real data examples. © 2008 Springer Science+Business Media, LLC.

Publication Date

2-1-2009

Publication Title

Machine Learning

Volume

74

Issue

2

Number of Pages

111-131

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1007/s10994-008-5080-8

Socpus ID

59449089201 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/59449089201

This document is currently not available here.

Share

COinS