Quantile Regression for Clustering and Modeling Data

Cristina Davino, Domenico Vistocco
(2015) Advances in Statistical Models for Data Analysis, (eds. Morlini I, Minverva T, Vichi M), pp 83-93. Studies in Classification, Data Analysis, and Knowledge Organization, Springer

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This paper aims to propose an innovative approach to identify a typology in a quantile regression model. Quantile regression is a regression technique that allows to focus on the effects that a set of explanatory variables has on the entire conditional distribution of a dependent variable. The proposal concerns the use of multivariate techniques to simultaneously cluster and model data and it is illustrated using an empirical analysis. This analysis regards the impact of student features on the university outcome, measured by the degree mark. The analysis is based on the idea that the dependence structure could be different for units belonging to different groups.