𝔖 Bobbio Scriptorium
✦   LIBER   ✦

Regression coefficients in multilinear PLS

✍ Scribed by Sijmen de Jong


Publisher
John Wiley and Sons
Year
1998
Tongue
English
Weight
108 KB
Volume
12
Category
Article
ISSN
0886-9383

No coin nor oath required. For personal study only.

✦ Synopsis


Three alternative approaches are discussed for finding the final calibration model (regression coefficients) in PLS regression of k-way Y on N-way X. The simplest approach is to skip the deflation of the X-data. From the observation that the specific deflation used in multiway PLS is inconsequential, it also follows that Bro's tri-PLS is equivalent to StΓ₯hle's linear three-way decomposition (LTD).


πŸ“œ SIMILAR VOLUMES


Preventing over-fitting in PLS calibrati
✍ A. A. Gowen; G. Downey; C. Esquerre; C. P. O'Donnell πŸ“‚ Article πŸ“… 2010 πŸ› John Wiley and Sons 🌐 English βš– 867 KB

## Abstract Selection of the number of latent variables (LVs) to include in a partial least squares (PLS) model is an important step in the data analysis. Inclusion of too few or too many LVs may lead to, respectively, under or over‐fitting of the data and subsequently result in poor future model p

Evolutionary variable selection in regre
✍ Hugo Kubinyi πŸ“‚ Article πŸ“… 1996 πŸ› John Wiley and Sons 🌐 English βš– 929 KB

Evolutionary and genetic algorithms are powerful tools for searching global optima of complex functions. An evolutionary approach, the MUSEUM (mutation and selection uncover models) programme, is applied to various QSAR data sets to prove the general applicability of this approach for variable selec

Prediction in Random Coefficient Regress
✍ Dr. Jan Bondeson πŸ“‚ Article πŸ“… 2007 πŸ› John Wiley and Sons 🌐 English βš– 781 KB

Much attention has been given to the problem ofpredicting future obeervatiomfor some individual within a random coefficient regreasion (RCR) model, using the previous observations on that individual aa well es the information from the re& of the data material. In thia paper, the literature on this s

Do not adjust coefficients in Shapley va
✍ Ulrike GrΓΆmping; Sabine Landau πŸ“‚ Article πŸ“… 2010 πŸ› John Wiley and Sons 🌐 English βš– 115 KB

## Abstract Shapley value regression consists of assessing relative importance and accordingly adjusting regression coefficients. It is argued that adjustment of coefficients is unnecessary and even misleading for practically relevant situations. Examples are given, and an alternative procedure is