The design of regression models that are not affected by outliers is an important task which
has been subject of numerous papers within the statistics community for the last decades …

High breakdown estimation (HBE) addresses the problem of getting reliable parameter
estimates in the face of outliers that may be numerous and badly placed. In multiple …

Abstract Least Trimmed Squares (LTS) is a robust regression method with respect to
outliers. It is based on performing Ordinary Least Squares (OLS) estimates on sub-datasets …

Usually, in the regression models, the data are contaminated with unusually observations
(outliers). For that reason the last 30 years have developed robust regression estimators …

The least squares of depth trimmed (LST) residuals regression, proposed in Zuo and Zuo
(2023)\cite {ZZ23}, serves as a robust alternative to the classic least squares (LS) regression …

The presence of groups containing high leverage outliers makes linear regression a difficult
problem due to the masking effect. The available high breakdown estimators based on Least …

Robust linear regression is one of the most popular problems in the robust statistics
community. It is often conducted via least trimmed squares, which minimizes the sum of the k …

Multiple outliers are frequently encountered in regression models used in business,
economics, engineers and applied studies. The ordinary least squares (OLS) estimator fails …

Least trimmed squares (LTS), as a robust method, is widely used in linear regression
models. However, the ordinary LTS of simple linear regression treats the response and …

The estimation of regression models is often based on the least squares method, which is
very sensitive to misspecification and data errors. An alternative estimation approach is …