Comparison of Outlier Detection Methods in Linear Regression: A Multiple-Criteria Decision-Making Approach

This paper focuses on the application of a suite of simulation studies to assess wellknown and contemporary outlier detection methods in linear regression. These simulations vary across different parameters, including the number of observations, parameters, levels, and direction of contamination. The recorded final parameter estimates are used to rank the methods using Multiple-criteria decision-making (MCDM) tools. The study reveals that method success varies based on simulation settings. MCDM analysis results indicate a limited set of applicable methods when the contamination structure and level are unknown. Additionally, the most successful methods demand increased computation time, while some alternatives exhibit applicability within shorter durations with median rankings. These findings offer valuable insights for researchers employing regression analysis in scenarios where the underlying model is known, and the possibility of potential outliers exists.