This book gives a non-technical overview of robust data reduction techniques, encouraging
the use of these important and useful methods in practical applications. The main areas …

Diagnostic tools must rely on robust high-breakdown methodologies to avoid distortion in
the presence of contamination by outliers. However, a disadvantage of having a single, even …

Monitoring robust regression Page 1 Electronic Journal of Statistics Vol. 8 (2014) 646–677
ISSN: 1935-7524 DOI: 10.1214/14-EJS897 Monitoring robust regression Marco Riani, Andrea …

Abstract The Forward Search is an iterative algorithm for avoiding outliers in a regression
analysis suggested by Hadi and Simonoff (J. Amer. Statist. Assoc. 88 (1993) 1264–1272) …

An iteratively reweighted approach for robust clustering is presented in this work. The
method is initialized with a very robust clustering partition based on an high trimming level …

Trimming principles play an important role in robust statistics. However, their use for
clustering typically requires some preliminary information about the contamination rate and …

In the last decade, air pollution in the city of Kermanshah has become a major concern. In
this study, adaptive neuro-fuzzy inference system (ANFIS) was developed to predict five …

Heteroskedastic regression data are modelled using a parameterized variance function.
This procedure is robustified using a method with high breakdown point and high efficiency …

In this research, we demonstrate the effectiveness of a convolutional neural network (CNN)
model, integrated with the ERA5-Land dataset, for accurately simulating daily streamflow in …

Abstract Information criteria for model choice are extended to the detection of outliers in
regression models. For deletion of observations (hard trimming) the family of models is …