Which Robust Regression Technique Is Appropriate Under Violated Assumptions? A Simulation Study
Abstract: Ordinary least squares (OLS) regression is widely employed for statistical prediction and theoretical explanation in psychology studies. However, OLS regression has a critical drawback: it becomes less accurate in the presence of outliers and non-random error distribution. Several robust regression methods have been proposed as alternatives. However, each robust regression has its own strengths and limitations. Consequently, researchers are often at a loss as to which robust regression method to use for their studies. This study uses a Monte Carlo experiment to compare different types of robust regression methods with OLS regression based on relative efficiency (RE), bias, root mean squared error (RMSE), Type 1 error, power, coverage probability of the 95% confidence intervals (CIs), and the width of the CIs. The results show that, with sufficient samples per predictor (n = 100), the robust regression methods are as efficient as OLS regression. When errors follow non-normal distrib.... https://meth.psychopen.eu/index.php/meth/article/view/8285
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Bibliographic citation
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Which Robust Regression Technique Is Appropriate Under Violated Assumptions? A Simulation Study ; volume:19 ; number:4 ; day:22 ; month:12 ; year:2023
Methodology ; 19, Heft 4 (22.12.2023)
- Creator
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Kim, Jaejin
Li, Johnson Ching-Hong
- DOI
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10.5964/meth.8285
- URN
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urn:nbn:de:101:1-2023122304174656213760
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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15.08.2025, 7:33 AM CEST
Data provider
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Kim, Jaejin
- Li, Johnson Ching-Hong
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