Tree-structured model diagnostics for linear regression

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.