Title

Treed variance

Authors

Authors

X. G. Su; C. L. Tsai;X. Yan

Comments

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Abbreviated Journal Title

J. Comput. Graph. Stat.

Keywords

BIC; heteroscedasticity; regression trees; weighted least squares; HETEROSCEDASTICITY; Statistics & Probability

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.

Journal Title

Journal of Computational and Graphical Statistics

Volume

15

Issue/Number

2

Publication Date

1-1-2006

Document Type

Article

DOI Link

http://dx.doi.org/10.1198/106186006x113575

Language

English

First Page

356

Last Page

371

WOS Identifier

WOS:000238044400005

ISSN

1061-8600

Recommended Citation

"Treed variance" (2006). Faculty Bibliography 2000s. 6619.
https://stars.library.ucf.edu/facultybib2000/6619