Purpose – Partial least squares (PLS) path modeling is a variance-based structural equation modeling (SEM) technique that is widely applied in business and social sciences. Its ability to model composites and factors makes it a formidable statistical tool for new technology research. Recent reviews, discussions, and developments have led to substantial changes in the understanding and use of PL...

BACKGROUND Papillon-Lefevre syndrome (PLS) is a rare autosomal recessive disorder characterized by palmoplantar hyperkeratosis and early development of aggressive periodontitis. Although cathepsin C (CTSC) gene mutations have been established in about 70-80% of PLS patients, it is assumed that the patients may have dysfunctioning of immune defense mechanisms. OBJECTIVE To assess the associati...

When entering foreign markets, software firms need to make a fundamental choice on the distribution arrangements for software and related services. This choice may involve contracting with local partners or entering foreign markets through company-owned channels. This study focuses on analyzing such boundary choices of software product firms in international markets. Taking a knowledge-based pe...

In recent years, many firms have adopted outsourcing as a means to govern their information technology (IT) operations. While outsourcing is associated with several important benefits, it also entails risk. The main purpose of this study was to validate measures of the risk factors associated with outsourcing IT operations. Insights from transaction costs theory suggest that risk factors are in...

Lactoferrin (Lf) is a potential-targeting ligand for hepatocellular carcinoma (HCC) cells because of its specific binding with asialoglycoprotein receptor (ASGPR). In this present work, a doxorubicin (DOX)-loaded, Lf-modified, polyethylene glycol (PEG)ylated liposome (Lf-PLS) system was developed, and its targeting effect and antitumor efficacy to HCC was also explored. The DOX-loaded Lf-PLS sy...

Genomic selection involves computing a prediction equation from the estimated effects of a large number of DNA markers based on a limited number of genotyped animals with phenotypes. The number of observations is much smaller than the number of independent variables, and the challenge is to find methods that perform well in this context. Partial least squares regression (PLS) and sparse PLS wer...

In this paper I review covariance-based Partial Least Squares (PLS) methods, focusing on common features of their respective algorithms and optimization criteria. I then show how these algorithms can be adjusted for use as optimal scaling tools. Three new PLS-type algorithms are proposed for the analysis of one, two or several blocks of variables: the NonMetric NIPALS, the Non-Metric PLS Regres...

Partial least squares (PLS) methods have recently been used for many pattern recognition problems in computer vision. Here, PLS is primarily used as a supervised dimensionality reduction tool to obtain effective feature combinations for better learning. However, application of PLS to large datasets is hindered by its higher computational cost. We propose an approach to accelerate the classical ...

PLS Generalised Linear Regression: foundations and recent advances with variable selection and validation procedures Vincenzo Esposito Vinzi Universita degli Studi di Napoli; [email protected] PLS (Partial Least Squares) univariate regression (PLS1) is a model linking a numerical dependent variable to a set of numerical (or dummy) explanatory variables especially feasible in situa...