Title

Treed Variance

Keywords

BIC; Heteroscedasticity; Regression trees; Weighted least squares

Abstract

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. ©2006 American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America.

Publication Date

6-1-2006

Publication Title

Journal of Computational and Graphical Statistics

Volume

15

Issue

2

Number of Pages

356-371

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1198/106186006X113575

Socpus ID

33745327854 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/33745327854

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