The aim of the paper is to propose a quantile regression based strategy to assess heterogeneity in a multi-block type data structure. Specifically, the paper deals with a particular data structure where several blocks of variables are observed on the same units and a structure of relations is assumed between the different blocks. The idea is that quantile regression complements the results of the least squares regression by evaluating the impact of regressors on the entire distribution of the dependent variable, and not only exclusively on the expected value. By taking advantage of this, the proposed approach analyses the relationship among a dependent variable block and a set of regressors blocks but highlighting possible similarities among the statistical units. An empirical analysis is provided in the consumer analysis framework with the aim to cluster groups of consumers according to the similarities in the dependence structure among their overall liking and the liking for different drivers.

本文的目的是提出一种基于分位数回归的策略，以评估多块类型数据结构中的异质性。具体来说，本文涉及一种特殊的数据结构，其中在相同的单元上观察到几个变量块，并假设了不同块之间的关系结构。这个想法是，分位数回归通过评估回归变量对因变量的整个分布的影响，而不只是对预期值的影响，对最小二乘回归的结果进行补充。通过利用这一点，所提出的方法分析了因变量块和一组回归块之间的关系，但突出了统计单位之间可能的相似性。