document: A new method of regression on latent variables . Application to spectral data Several applications are based on the assessment of a linear model linking a set of variables Y to a set of predictors X . In the presence of strong colinearity among predictors , as in the case with spectral data , several alternative procedures to ordinary least squares -LRB- OLS -RRB- are proposed , We discuss a new alternative approach which we refer to as regression models through constrained principal components analysis -LRB- RM-CPCA -RRB- . This method basically shares certain common characteristics with PLS regression as the dependent variables play a central role in determining the latent variables to be used as predictors . Unlike PLS , however , the approach discussed leads to straightforward models . This method also bears some similarity to latent root regression analysis -LRB- LRR -RRB- that was discussed by several authors . Moreover , a tuning parameter that ranges between 0 and 1 is introduced and the family of models thus formed includes several other methods as particular cases
doc_bio_tags: O O O O O O B I O O O B I O O O O O O O O O B I O O O O O O O O O O B O O O O O O B I O B O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O B I I I I I I O O O O O O O O O O O O O O O O B I O O O O O O O B I O O O O B O O O O O O O O O O O O O O O O O O O O O B I I I O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O
extractive_keyphrases: latent variables spectral data linear model predictors strong colinearity regression models through constrained principal components analysis dependent variables latent root regression analysis tuning parameter
abstractive_keyphrases: near-ir spectroscopy