Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/286858 
Year of Publication: 
2021
Citation: 
[Journal:] OR Spectrum [ISSN:] 1436-6304 [Volume:] 43 [Issue:] 3 [Publisher:] Springer [Place:] Berlin, Heidelberg [Year:] 2021 [Pages:] 809-834
Publisher: 
Springer, Berlin, Heidelberg
Abstract: 
The design of regression models that are not affected by outliers is an important task which has been subject of numerous papers within the statistics community for the last decades. Prominent examples of robust regression models are least trimmed squares (LTS), where the k largest squared deviations are ignored, and least trimmed absolute deviations (LTA) which ignores the k largest absolute deviations. The numerical complexity of both models is driven by the number of binary variables and by the value k of ignored deviations. We introduce leveraged least trimmed absolute deviations (LLTA) which exploits that LTA is already immune against y-outliers. Therefore, LLTA has only to be guarded against outlying values in x, so-called leverage points, which can be computed beforehand, in contrast to y-outliers. Thus, while the mixed-integer formulations of LTS and LTA have as many binary variables as data points, LLTA only needs one binary variable per leverage point, resulting in a significant reduction of binary variables. Based on 11 data sets from the literature, we demonstrate that (1) LLTA’s prediction quality improves much faster than LTS and as fast as LTA for increasing values of k and (2) that LLTA solves the benchmark problems about 80 times faster than LTS and about five times faster than LTA, in median.
Subjects: 
Trimmed absolute deviations
Least absolute deviations
Least trimmed squares
Robust statistics
Combinatorial machine learning
JEL: 
J05
C11
C30
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

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