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Quantile Regression for Clustering and Modeling Data

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Advances in Statistical Models for Data Analysis

Abstract

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.

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Author information

Authors and Affiliations

  1. Department of Political Sciences, University of Macerata, Communications and Intern. Relations, 62100, Macerata, Italy

    Cristina Davino

  2. Department of Economics and Law, University of Cassino, 03043, Cassino, (FR), Italy

    Domenico Vistocco

Authors
  1. Cristina Davino
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  2. Domenico Vistocco
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Corresponding author

Correspondence to Cristina Davino .

Editor information

Editors and Affiliations

  1. Department of Economics "Marco Biagi", University of Modena & Reggio Emilia, Modena, Italy

    Isabella Morlini

  2. Department of Communication and Economics, University of Modena & Reggio Emilia, Reggio Emilia, Italy

    Tommaso Minerva

  3. Department of Statistics, University of Rome “La Sapienza”, Roma, Italy

    Maurizio Vichi

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Davino, C., Vistocco, D. (2015). Quantile Regression for Clustering and Modeling Data. In: Morlini, I., Minerva, T., Vichi, M. (eds) Advances in Statistical Models for Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham. https://doi.org/10.1007/978-3-319-17377-1_10

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