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
Copyright Status
Unknown
Socpus ID
59449089201 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/59449089201
STARS Citation
Su, Xiaogang; Tsai, Chih Ling; and Wang, Morgan C., "Tree-Structured Model Diagnostics For Linear Regression" (2009). Scopus Export 2000s. 12243.
https://stars.library.ucf.edu/scopus2000/12243