Structural equation modeling (SEM) encompasses such diverse statistical techniques as path analysis, confirmatory factor analysis, causal modeling with latent variables, and even analysis of variance ...

R is a powerful open source programming environment primarily known for its statistical capabilities. In this course we will cover some advanced applications of R: distributed computing using the ...

Abstract: A structural regression model has been developed to explore the relationship among key factors in the explanation of utilitarian walking. The model examines the relationship between and ...

Developed by one of the world's leading authorities on the subject, Dr. Peter M. Bentler, EQS provides researchers and statisticians with a simple method for conducting the full range of structural ...

Genome-wide expression and protein profiles provide powerful tools for large-scale analyses of gene interaction and identification of pathways underlying cells' response to perturbations. Clustering ...

Statistical model infrastructures at financial institutions are often developed using a piecemeal approach to model building, in which different components of complex interrelated statistical models ...

Regulators need a method that is versatile, is easy to use and can handle complex path models with latent (not directly observable) variables. In a first application of partial least squares ...