Title
Treed variance
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
Language
English
First Page
356
Last Page
371
WOS Identifier
ISSN
1061-8600
Recommended Citation
"Treed variance" (2006). Faculty Bibliography 2000s. 6619.
https://stars.library.ucf.edu/facultybib2000/6619
Comments
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