Abbreviated Journal Title
J. Comput. Graph. Stat.
BIC; heteroscedasticity; regression trees; weighted least squares; HETEROSCEDASTICITY; Statistics & Probability
This article proposes a data-driven tree method, called "treed variance" (TV), to model heteroscedasticity in linear regression. Specifically, we use a score test statistic to recursively bisect data into heterogenous groups, and then adopt the pruning methodology of CART to determine the best tree size. The proposed method provides not only a piecewise constant modeling of the error variance, but also facilitates a natural check of homoscedasticity. We assess the performance of the TV method via simulation studies and illustrate its use with an empirical example.
Journal of Computational and Graphical Statistics
"Treed variance" (2006). Faculty Bibliography 2000s. 6619.