We present an extension of PLS—called partial least squares correspondence analysis (PLSCA)—tailored for the analysis of nominal data. As the name indicates, PLSCA combines features of PLS (analyzing the information common to two tables) and correspondence analysis (CA, analyzing nominal data). We also present inferential techniques for PLSCA such as bootstrap, permutation, and χ2 omnibus tests...

A quantitative structure-antioxidant activity relationship (QSAR) study of 36 flavonoids was performed using the partial least squares projection of latent structures (PLS) method. The chemical structures of the flavonoids have been characterized by constitutional descriptors, two-dimensional topological and connectivity indices. Our PLS model gave a proper description and a suitable prediction...

Partial least squares-based structural equation modeling (PLS-SEM) is extensively used in the field of information systems, as well as in many other fields where multivariate statistical methods are employed. One of the most fundamental issues in PLS-SEM is that of minimum sample size estimation. The “10-times rule” has been a favorite due to its simplicity of application, even though it tends ...

Voluminous studies use Partial Least Squares Structural Equation Modeling (PLS-SEM) to analyze data. One of the reasons for using PLS-SEM is when structural model complex. Studies employing complex models with many constructs and indicators lead selection analysis. The purposes assessing measurement are examine basic dimensions construct variables, validate dimensions, determine number each con...

One important feature of the gene expression data is that the number of genes M far exceeds the number of samples N. Standard statistical methods do not work well when N < M . Development of new methodologies or modification of existing methodologies is needed for the analysis of the microarray data. In this paper, we propose a novel analysis procedure for classifying the gene expression data. ...

Composite-based methods like partial least squares (PLS) path modeling have an advantage over factor-based methods (like CB-SEM) because they yield determinate predictions, while factor-based methods’ prediction is constrained in this regard by factor indeterminacy. To maximize practical relevance, research findings should extend beyond the study’s own data. We explain how PLS practices, derivi...

Linear discriminant analysis (LDA) is a classical statistical approach for dimensionality reduction and classification. In many cases, the projection direction of the classical and extended LDA methods is not considered optimal for special applications. Herein we combine the Partial Least Squares (PLS) method with LDA algorithm, and then propose two improved methods, named LDA-PLS and ex-LDA-PL...

A family of regularized least squares regression models in a Reproducing Kernel Hilbert Space is extended by the kernel partial least squares (PLS) regression model. Similar to principal components regression (PCR), PLS is a method based on the projection of input (explanatory) variables to the latent variables (components). However, in contrast to PCR, PLS creates the components by modeling th...