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 …

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 …

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 …

The general problem addressed in this document is the estimation of “fair” import prices from
international trade data. The work is in support to the determination of the customs value at …