Simple powerful robust tests based on sign depth

Kevin Leckey (), Dennis Malcherczyk (), Melanie Horn () and Christine H. Müller ()
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Kevin Leckey: TU Dortmund University
Dennis Malcherczyk: TU Dortmund University
Melanie Horn: TU Dortmund University
Christine H. Müller: TU Dortmund University

Statistical Papers, 2023, vol. 64, issue 3, No 6, 857-882

Abstract: Abstract Up to now, powerful outlier robust tests for linear models are based on M-estimators and are quite complicated. On the other hand, the simple robust classical sign test usually provides very bad power for certain alternatives. We present a generalization of the sign test which is similarly easy to comprehend but much more powerful. It is based on K-sign depth, shortly denoted by K-depth. These so-called K-depth tests are motivated by simplicial regression depth, but are not restricted to regression problems. They can be applied as soon as the true model leads to independent residuals with median equal to zero. Moreover, general hypotheses on the unknown parameter vector can be tested. While the 2-depth test, i.e. the K-depth test for $$K = 2$$ K = 2 , is equivalent to the classical sign test, K-depth test with $$K\ge 3$$ K ≥ 3 turn out to be much more powerful in many applications. A drawback of the K-depth test is its fairly high computational effort when implemented naively. However, we show how this inherent computational complexity can be reduced. In order to see why K-depth tests with $$K\ge 3$$ K ≥ 3 are more powerful than the classical sign test, we discuss the asymptotic behavior of its test statistic for residual vectors with only few sign changes, which is in particular the case for some alternatives the classical sign test cannot reject. In contrast, we also consider residual vectors with alternating signs, representing models that fit the data very well. Finally, we demonstrate the good power of the K-depth tests for some examples including high-dimensional multiple regression.

Keywords: K-sign depth; Sign test; Runs test; Outlier robust; Distribution free; Regression; Autoregressive models; Generalized linear model (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s00362-022-01337-5

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