Big Chemical Encyclopedia

Chemical substances, components, reactions, process design ...

Articles Figures Tables About

Regression methods, assumptions multivariate

The prediction of Y-data of unknown samples is based on a regression method where the X-data are correlated to the Y-data. The multivariate methods, usually used for such a calibration, are principal component regression (PCR) and partial least squares regression (PLS). Both methods are based on the assumption of linearity and can deal with co-linear data. The problem of co-linearity is solved in the same way as the formation of a PCA plot. The X-variables are added together into latent variables, score vectors. These vectors are independent since they are orthogonal to each other and they can therefore be used to create a calibration model. [Pg.7]

The problem of over-optimistic estimates of model quality is a general one for all multivariate regression methods, and a number of model quality diagnostics have been developed that do not rely on parametric assumptions, to both limit the model fitting process and to assess the ability of the model to generalise beyond the training set. [Pg.248]

The statistical methods most often employed for developing ADMET in silico structure-property relationships are linear multivariate methods, such as multiple linear regression (MLR) orpartial least squares (PLS). Although aimed at the same end point, namely, to derive a statistically sound and predictive structure-property relationship, the underlying assumptions regarding the information contained in the independent variables, that is, the chemical... [Pg.1011]

Parametru/non-parametric techniques This first distinction can be made between techniques that take account of the information on the population distribution. Non parametric techniques such as KNN, ANN, CAIMAN and SVM make no assumption on the population distribution while parametric methods (LDA, SIMCA, UNEQ, PLS-DA) are based on the information of the distribution functions. LDA and UNEQ are based on the assumption that the population distributions are multivariate normally distributed. SIMCA is a parametric method that constructs a PCA model for each class separately and it assumes that the residuals are normally distributed. PLS-DA is also a parametric technique because the prediction of class memberships is performed by means of model that can be formulated as a regression equation of Y matrix (class membership codes) against X matrix (Gonzalez-Arjona et al., 1999). [Pg.31]


See other pages where Regression methods, assumptions multivariate is mentioned: [Pg.4]    [Pg.143]    [Pg.408]    [Pg.133]    [Pg.16]    [Pg.50]    [Pg.155]    [Pg.58]    [Pg.739]    [Pg.158]    [Pg.96]    [Pg.348]   
See also in sourсe #XX -- [ Pg.229 , Pg.230 , Pg.231 , Pg.232 , Pg.233 , Pg.234 , Pg.235 ]




SEARCH



Multivariate methods

Multivariate regression

Regression assumptions

Regression methods

© 2024 chempedia.info