Quantile composite-based path modeling

Cristina Davino () and Vincenzo Esposito Vinzi ()
Additional contact information
Cristina Davino: University of Macerata
Vincenzo Esposito Vinzi: ESSEC Business School

Advances in Data Analysis and Classification, 2016, vol. 10, issue 4, No 5, 520 pages

Abstract: Abstract 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 estimation phases 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.

Keywords: Quantile regression; PLS path modeling; Multi-block data; 62-07; 62H99; 62J99; 62J05; 62P25 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
http://link.springer.com/10.1007/s11634-015-0231-9 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:advdac:v:10:y:2016:i:4:d:10.1007_s11634-015-0231-9

Ordering information: This journal article can be ordered from
http://www.springer. ... ds/journal/11634/PS2

DOI: 10.1007/s11634-015-0231-9

Access Statistics for this article

Advances in Data Analysis and Classification is currently edited by H.-H. Bock, W. Gaul, A. Okada, M. Vichi and C. Weihs

More articles in Advances in Data Analysis and Classification from Springer, German Classification Society - Gesellschaft für Klassifikation (GfKl), Japanese Classification Society (JCS), Classification and Data Analysis Group of the Italian Statistical Society (CLADAG), International Federation of Classification Societies (IFCS)
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().