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 …

Statisticians often face the choice between using probability models or a paradigm defined
by minimising a loss function. Both approaches are useful and, if the loss can be re-cast into …

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 …

In this paper a robust approach to modeling electricity spot prices is introduced. Differently
from what has been recently done in the literature on electricity price forecasting, where the …

The linear regression model requires robust estimation of parameters, if the measured data
are contaminated by outlying measurements (outliers). While a number of robust estimators …

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 a powerful general method for detecting anomalies in
structured data, whose diagnostic power has been shown in many statistical contexts …

Minimum density power divergence estimation provides a general framework for robust
statistics, depending on a parameter α, which determines the robustness properties of the …

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 …

In this work, we deal with a robust fitting of a wrapped normal model to multivariate circular
data. Robust estimation is supposed to mitigate the adverse effects of outliers on inference …