Svante Wold

Summary recommendations for standardization and reporting of metabolic analyses

Cross-Validatory Estimation of the Number of Components in Factor and Principal Components Models

By means of factor analysis (FA) or principal components analysis (PCA) a matrix Y with the elements y ik is approximated by the model Here the parameters α, β and θ express the...

A completely automatic french curve: fitting spline functions by cross validation

The cross validation mean square error technique is used to determine the correct degree of smoothing, in fitting smoothing solines to discrete, noisy observations from some unk...

PLS-regression: a basic tool of chemometrics

The Collinearity Problem in Linear Regression. The Partial Least Squares (PLS) Approach to Generalized Inverses

The use of partial least squares (PLS) for handling collinearities among the independent variables X in multiple regression is discussed. Consecutive estimates $({\text{rank }}1...

New Chemical Descriptors Relevant for the Design of Biologically Active Peptides. A Multivariate Characterization of 87 Amino Acids

In this study 87 amino acids (AA.s) have been characterized by 26 physicochemical descriptor variables. These descriptor variables include experimentally determined retention va...

The Prediction of Bradykinin Potentiating Potency of Pentapeptides. An Example of a Peptide Quantitative Structure-activity Relationship.

The variation in amino acid sequence, in a set of bradykinin potentiating pentapeptides, is described by three variables per amino acid position. The variables were derived from...

The kernel algorithm for PLS

Abstract A fast and memory‐saving PLS regression algorithm for matrices with large numbers of objects is presented. It is called the kernel algorithm for PLS. Long (meaning havi...

A PLS kernel algorithm for data sets with many variables and fewer objects. Part 1: Theory and algorithm

Abstract A fast PLS regression algorithm dealing with large data matrices with many variables ( K ) and fewer objects ( N ) is presented For such data matrices the classical alg...

INLR, implicit non-linear latent variable regression

A simple way to develop non-linear PLS models is presented, INLR (implicit non-linear latent variable regression). The paper shows that by simply added squared x-variables x2a, ...

The GIFI approach to non‐linear PLS modeling

Abstract The GIFI approach to non‐linear modeling involves the transformation of quantitative variables to a set of 1/0 dummies in a similar manner to the way qualitative variab...

Multivariate design of process experiments (M-DOPE)