The supplement includes three data analyses that were excluded from the final version of
the paper: the motivating data set on gasoline consumption from Chen, Lockhart and …

A low-cost air quality station has been developed for real-time monitoring of main
atmospheric pollutants. Sensors for CO, CO2, NO2, O3, VOC, PM2. 5 and PM10 were …

The purpose of this paper is to show in regression clustering how to choose the most
relevant solutions, analyze their stability, and provide information about best combinations of …

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 …

The assessment of agreement in method comparison and observer variability analysis of
quantitative measurements is usually done by the Bland–Altman Limits of Agreement, where …

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

Accurate outlier detection is not only a necessary preprocessing step, but can itself give
important insights into the data. However, especially, for non-linear regression the detection …

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

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