Missouri Journal of Mathematical Sciences

Identifying Outlying Observations in Regression Trees

Nicholas Granered and Samantha C. Bates Prins

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Regression trees are an alternative to classical linear regression models that seek to fit a piecewise linear model to data. The structure of regression trees makes them well-suited to the modeling of data containing outliers. We propose an algorithm that takes advantage of this feature in order to automatically detect outliers. This new algorithm performs well on the four test datasets [7] that are considered to be necessary for a valid outlier detection algorithm in a linear regression context, even though regression trees lack the global linearity assumption. We also show the practical use of this approach in detecting outliers in an ecological dataset collected in the Shenandoah Valley.

Article information

Missouri J. Math. Sci., Volume 28, Issue 1 (2016), 76-87.

First available in Project Euclid: 19 September 2016

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Mathematical Reviews number (MathSciNet)

Zentralblatt MATH identifier

Primary: 62G08: Nonparametric regression

outlier detection influential observations backward-stepping robust models outlier CART


Granered, Nicholas; Bates Prins, Samantha C. Identifying Outlying Observations in Regression Trees. Missouri J. Math. Sci. 28 (2016), no. 1, 76--87. doi:10.35834/mjms/1474295357. https://projecteuclid.org/euclid.mjms/1474295357

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