Two-step PLS regression for L-structured data: An application in the cosmetic industry

Statistical Methods and Applications

Volumn 16, Issue 2, 2007, Pages 263-278

  Vinzi, Vincenzo Esposito ^a,b   Guinot, Christiane ^d   Squillacciotti, Silvia ^c  

^a UNIVERSITY OF NAPLES FEDERICO II   (Italy)

^b ESSEC BUSINESS SCHOOL   (France)

^c EDF R AND D   (France)

^d CERIES   (France)

Author keywords

External information; L structures; Partial least squares (PLS) regression; Preference data

Indexed keywords

EID: 34547477634     PISSN: 16182510     EISSN: 1613981X     Source Type: Journal    
DOI: 10.1007/s10260-006-0028-2     Document Type: Article

References (19)

1. Amenta P, D'Ambra L (1997) L'analisi in componenti principali in relazione a un sottospazio di riferimento con informazioni esterne, Quaderni di Statistica, 18, DMQTE, Pescara
2. Chatelin YM, Esposito Vinzi V, Tenenhaus V (2002) State of Art on the PLS Path Modelling through the available software. HEC Research Papers, 764/2002
3. Esposito Vinzi V, Lauro C, Amato S (2004) PLS typological regression: algorithmic, classification and validation issues. In: Vichi M, Monari P, Mignani S, Montanari A (eds) New developments in classification and data analysis. Springer, Berlin Heidelberg New York, pp 133-140
4. Giordano G, Scepi G (1999) La progettazione della qualità attraverso l'analisi di strutture informative differenti. In: Proceedings of XV Riunione Scientifica SIS
5. Höskuldsson A (1988) PLS regression methods. J Chemometrics 2:211-288
6. D'Ambra L, Lauro, NC (1992): "Non Symmertical Exploratory Data Analysis", Statistica Applicata, 4(4), pp 511-529
7. Martens H, Anderssen E, Flatberg A, Gidskehaug LH, Høy M, Westad F, Thybo A, Martens M (2005) Regression of a data matrix on descriptors of both its rows and its column descriptors via latent variables: L-PLSR. Comput Stat Data Anal 48(1):103-123
8. Rayens W, Andersen A (2004) Oriented Partial Least Squares, Rivista di Statistica Applicata, Italian Journal of Applied Statistics, RCE Edizioni, Napoli, 15(3), pp 367-388
9. Risvik E, Ueland∅, Westad F (2003) In: Segmentation strategy for a generic food product in Proceedings of the 5th Pangborn Sensory Science Symposium, Boston, MA, USA
10. Stone M, Brooks RJ (1990) Continuum regression: cross-validated sequentially constructed prediction embracing ordinary least squares, partial least squares, and principal component regression. J R Stat Soc Ser B 52:237-269
11. Takane Y, Shibayama T (1991) Principal component analysis with external information on both subjects and variables. Psychometrika 1:97-120
12. Tenenhaus M (1998) La régression PLS: théorie et pratique, Editions Technip, Paris
13. Tenenhaus M, Esposito Vinzi V, Chatelin YM, Lauro C (2005) PLS path modelling. Comput Stat Data Anal 48(1):159-205
14. Trygg J, Wold S (2002) Orthogonal projections to latent structures (O-PLS). J Chemometrics 16:119-128
15. Umetrics (2002) SIMCA-P and SIMCA-P+ 10 User Guide, UMETRICS AB, Ume Sweden
16. Wold H (1975) Modelling in complex situations with soft informations. In: Third World Congress of Econometric Society, Toronto, Canada, 21-26 August 1975
17. Wold H (1982) Soft modelling: the basic design and some extensions. In: Jöreskog KG, Wold H (eds) Systems under indirect observation, vol 2. Wiley, New York, pp 587-599
18. Wold H (1985) Partial least squares. In: Kotz S, Johnson NL (eds) Encyclopedia of statistical sciences, vol 8. Wiley, New York, pp 581-591
19. Wold S, Martens H, Wold H (1983) The multivariate calibration problem in chemistry solved by the PLS method. In: Paper presented at the Matrix Pencils, Heidelberg