Outliers are one of the main concerns in statistics. Parametric identification results of
ordinary least squares are sensitive to outliers. Many robust estimators have been proposed …

Outlier detection has attracted considerable interest in various areas. Existing outlier
detection methods usually assume independence of the modeling errors among the data …

This study compares the ability of different robust regression estimators to detect and classify
outliers. Well-known estimators with high breakdown points were compared using simulated …

To conduct regression analysis for data contaminated with outliers, many approaches have
been proposed for simultaneous outlier detection and robust regression, so is the approach …

Robust regression is an important iterative procedure that seeks analyzing data sets that are
contaminated with outliers and unusual observations and reducing their impact over …

This paper studies the outlier detection and variable selection problem in linear regression.
A mean shift parameter is added to the linear model to reflect the effect of outliers, where an …

The least-squares method is currently the most popular approach to estimation because of
tradition and ease of computation. However, real data sets frequently contain outliers, which …

In regression analysis, the presence of outliers in the dataset can strongly distort the
classical least-squares estimator and lead to unreliable results. To deal with this, several …

This paper studies the outlier detection and robust variable selection problem in the linear
regression model. The penalized weighted least absolute deviation (PWLAD) regression …

This work studies outlier detection and robust estimation with data that are naturally
distributed into groups and which follow approximately a linear regression model with fixed …