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Outliers and robustness for ordinal data

Marco Riani, Francesca Torti and Sergio Zani

This chapter tackles the topics of robustness and multivariate outlier detection for ordinal data. We initially review outlier detection methods in regression for continuous data and give an example which shows that graphical tools of data analysis or traditional diagnostic measures based on all the observations are not sufficient to detect multivariate atypical observations. Then we focus on ordinal data and illustrate how to detect atypical measurements in customer satisfaction surveys. Next, we review the generalized linear model of ordinal regression and apply it to the ABC survey. The chapter concludes with an analysis of a set of diagnostics to check the goodness of the suggested model and the presence of anomalous observations.