The minimum covariance determinant (MCD) method is a highly robust estimator of
multivariate location and scatter, for which a fast algorithm is available. Since estimating the …

The minimum covariance determinant (MCD) estimator is a highly robust estimator of
multivariate location and scatter. It can be computed efficiently with the FAST‐MCD …

In this paper we propose a family of robust estimators for generalized linear models. The
basic idea is to use an M-estimator after applying a variance stabilizing transformation to the …

Differential Evolution (DE) has become one of the leading metaheuristics in the class of
Evolutionary Algorithms, which consists of methods that operate off of survival-of-the-fittest …

The panel-data regression models are frequently applied to micro-level data, which often
suffer from data contamination, erroneous observations, or unobserved heterogeneity …

This chapter suggests a robust Hausman and Taylor (1981), hereafter HT, estimator that
deals with the possible presence of outliers. This entails two modifications of the classical …

We introduce a new class of robust estimators for generalized linear models which is an
extension of the class of projection estimators for linear regression. These projection …

This paper is devoted to highly robust methods for information extraction from data, with a
special attention paid to methods suitable for management applications. The sensitivity of …

The panel data regression models have gained increasing attention in different areas of
research including econometrics, environmental sciences, epidemiology, behavioural and …

High breakdown-point regression estimators protect against large errors and data
contamination. We adapt and generalize the concept of trimming used by many of these …