Core argument of the Ph.D. Thesis is Partial Least Squares (PLS), a class of techniques for modelling relations between sets of observed quantities through latent variables in presence of collinearity. Aim of the thesis is to describe PLS, starting from an overview of the discipline where PLS takes place up to the application of PLS to a real dataset, moving through a critical comparison with alternative techniques. Conclusions are made and future perspectives are highlighted
Partial least square regression (PLSR) is a statistical modeling technique that extracts latent fact...
Two multivariable problems of general interest, are factor analysis and regression. This paper exami...
A simple objective function in terms of undeflated X is derived for the latent variables of multivar...
Ž. ŽPLS-regression PLSR is the PLS approach in its simplest, and in chemistry and technology, most u...
Pls regression is a recent technique that generalizes and combines features from principal component...
Partial Least Squares (PLS) is a wide class of methods for modeling relations between sets of observ...
The Chapter deals with the Partial Least Squares (PLS) estimation algorithm and its use in the conte...
The Chapter deals with the Partial Least Squares (PLS) estimation algorithm and its use in the conte...
New Perspectives in Partial Least Squares and Related Methods shares original, peer-reviewed researc...
New Perspectives in Partial Least Squares and Related Methods shares original, peer-reviewed researc...
Small datasets, missing values and the presence of multicollinearity often plague samples used in ma...
In many areas of research and industrial situations, including many data analytic problems in chemis...
Small datasets, missing values and the presence of multicollinearity often plague samples used in ma...
Small datasets, missing values and the presence of multicollinearity often plague samples used in ma...
Partial least squares path modeling (PLS-PM)is an estimator that has found widespread application fo...
Partial least square regression (PLSR) is a statistical modeling technique that extracts latent fact...
Two multivariable problems of general interest, are factor analysis and regression. This paper exami...
A simple objective function in terms of undeflated X is derived for the latent variables of multivar...
Ž. ŽPLS-regression PLSR is the PLS approach in its simplest, and in chemistry and technology, most u...
Pls regression is a recent technique that generalizes and combines features from principal component...
Partial Least Squares (PLS) is a wide class of methods for modeling relations between sets of observ...
The Chapter deals with the Partial Least Squares (PLS) estimation algorithm and its use in the conte...
The Chapter deals with the Partial Least Squares (PLS) estimation algorithm and its use in the conte...
New Perspectives in Partial Least Squares and Related Methods shares original, peer-reviewed researc...
New Perspectives in Partial Least Squares and Related Methods shares original, peer-reviewed researc...
Small datasets, missing values and the presence of multicollinearity often plague samples used in ma...
In many areas of research and industrial situations, including many data analytic problems in chemis...
Small datasets, missing values and the presence of multicollinearity often plague samples used in ma...
Small datasets, missing values and the presence of multicollinearity often plague samples used in ma...
Partial least squares path modeling (PLS-PM)is an estimator that has found widespread application fo...
Partial least square regression (PLSR) is a statistical modeling technique that extracts latent fact...
Two multivariable problems of general interest, are factor analysis and regression. This paper exami...
A simple objective function in terms of undeflated X is derived for the latent variables of multivar...