Treed variance

    Authors

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

    Comments

    Authors: contact us about adding a copy of your work at STARS@ucf.edu

    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

    WOS:000238044400005

    ISSN

    1061-8600

    Share

    COinS