The paper aims at introducing a quantile approach in the Partial Least Squares path modeling framework. This is a well known composite-based method for the analysis of complex phenomena measurable through a network of relationships among observed and unobserved variables. The proposal intends to enhance potentialities of the Partial Least Squares path models overcoming the classical exploration of average effects. The introduction of Quantile Regression and Correlation in the estimationphases of the model allows highlighting how and if the relationships among observed and unobserved variables change according to the explored quantile of interest. The proposed method is applied to two real datasets in the customer satisfaction measurement and in the sensory analysis framework but it proves to be useful also in other applicative contexts. Lien vers l'article
DAVINO, C. and ESPOSITO VINZI, V. (2016). Quantile Composite-Based Path Modeling. Advances in Data Analysis and Classification, 10(4), pp. 491-520.