A comparative analysis of multiple outlier detection procedures in the linear regression model

We evaluate several published techniques to detect multiple outliers in linear regression using an extensive Monte Carlo simulation. These procedures include both direct methods from algorithms and indirect methods from robust regression estimators. We evaluate the impact of outlier density and geometry, regressor variable dimension, and outlying distance in both leverage and residual on detection capability and false alarm (swamping) probability. The simulation scenarios focus on outlier configurations likely to be encountered in practice and use a designed experiment approach. The results for each scenario provide insight and limitations to performance for each technique. Finally, we summarize each procedure's performance and make recommendations.