Structural equation modeling (SEM) has become a quasi-standard in marketing and management research when it comes to analyzing the cause-effect relations between latent constructs. For most researchers, SEM is equivalent to carrying out covariance-based SEM (CB-SEM). While marketing researchers have a basic understanding of CB-SEM, most of them are only barely familiar with the other useful approach to SEM-partial least squares SEM (PLS-SEM). The current paper reviews PLS-SEM and its algorithm, and provides an overview of when it can be most appropriately applied, indicating its potential and limitations for future research. The authors conclude that PLS-SEM path modeling, if appropriately applied, is indeed a "silver bullet" for estimating causal models in many theoretical models and empirical data situations.

PLS-SEM: Indeed a Silver Bullet

https://libpls.net/publication/PLS_basic_2001.pdf

PLS-regression: a basic tool of chemometrics

1. Introduction and Overview. 2. Detecting Influential Observations and Outliers. 3. Detecting and Assessing Collinearity. 4. Applications and Remedies. 5. Research Issues and Directions for Extensions. Bibliography. Author Index. Subject Index.

Regression Diagnostics: Identifying Influential Data and Sources of Collinearity

The glutathione S-transferases (GST) represent a major group of detoxification enzymes. All eukaryotic species possess multiple cytosolic and membrane-bound GST isoenzymes, each of which displays distinct catalytic as well as noncatalytic binding properties: the cytosolic enzymes are encoded by at least five distantly related gene families (designated class alpha, mu, pi, sigma, and theta GST), whereas the membrane-bound enzymes, microsomal GST and leukotriene C, synthetase, are encoded by single genes and both have arisen separately from the soluble GST. Evidence suggests that the level of expression of GST is a crucial factor in determining the sensitivity of cells to a broad spectrum of toxic chemicals. In this article the biochemical functions of GST are described to show how individual isoenzymes contribute to resistance to carcinogens, antitumor drugs, environmental pollutants, and products of oxidative stress.A description of the mechanisms of transcriptional and posttranscriptional regulat...

The glutathione S-transferase supergene family: regulation of GST and the contribution of the isoenzymes to cancer chemoprotection and drug resistance.

https://www.who.int/foodsafety/chem/2005_WHO_TEFs_ToxSci_2006.pdf

The 2005 World Health Organization reevaluation of human and Mammalian toxic equivalency factors for dioxins and dioxin-like compounds.

This article provides an overview of methods for reliability assessment of quantitative structure-activity relationship (QSAR) models in the context of regulatory acceptance of human health and environmental QSARs. Useful diagnostic tools and data analytical approaches are highlighted and exemplified. Particular emphasis is given to the question of how to define the applicability borders of a QSAR and how to estimate parameter and prediction uncertainty. The article ends with a discussion regarding QSAR acceptability criteria. This discussion contains a list of recommended acceptability criteria, and we give reference values for important QSAR performance statistics. Finally, we emphasize that rigorous and independent validation of QSARs is an essential step toward their regulatory acceptance and implementation.

Methods for reliability and uncertainty assessment and for applicability evaluations of classification- and regression-based QSARs.

This report describes significance testing for PLS and OPLS® (orthogonal PLS) models. The testing is applicable to single-Y cases and is based on ANOVA of the cross-validated residuals (CV-ANOVA). Two variants of the CV-ANOVA are introduced. The first is based on the cross-validated predictive residuals of the PLS or OPLS model while the second works with the cross-validated predictive score values of the OPLS model. The two CV-ANOVA diagnostics are shown to work well in those cases where PLS and OPLS work well, that is, for data with many and correlated variables, missing data, etc. The utility of the CV-ANOVA diagnostic is demonstrated using three datasets related to (i) the monitoring of an industrial de-inking process; (ii) a pharmaceutical QSAR problem and (iii) a multivariate calibration application from a sugar refinery. Copyright © 2008 John Wiley & Sons, Ltd.

CV‐ANOVA for significance testing of PLS and OPLS® models

In this study 87 amino acids (AA.s) have been characterized by 26 physicochemical descriptor variables. These descriptor variables include experimentally determined retention values in seven thin-layer chromatography (TLC) systems, three nuclear magnetic resonance (NMR) shift variables, and 16 calculated variables, namely six semiempirical molecular orbital indices, total, polar, and nonpolar surface area, van der Waals volume of the side chain, log P, molecular weight, and four indicator variables describing hydrogen bond donor and acceptor properties, and side chain charge. In the present study, the data from a previous characterization of 55 AA.s from our laboratory have been extended with data for 32 additional AA.s and 14 new descriptor variables. The new 32 AA.s were selected to represent both intermediate and more extreme physicochemical properties, compared to the 20 coded AA.s. The new extended and updated principal property scales, the z-scales, were calculated and aligned to previously reported...

Lennart Eriksson

Papers

PLS-regression: a basic tool of chemometrics

Methods for reliability and uncertainty assessment and for applicability evaluations of classification- and regression-based QSARs.

CV‐ANOVA for significance testing of PLS and OPLS® models

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

Statistical Validation of QSAR Results