This article discusses a modelling framework that links two well-known statistical methods: structural equation modelling (SEM) and latent class or finite mixture modelling. This hybrid approach was proposed independently by Arminger and Stein [1], Dolan and Van der Maas [4], and Jedidi, Jagpal and DeSarbo [5]. Here, we refer to this approach as mixture SEM or latent class SEM. There are two di...

BACKGROUND We evaluate the combined effect of the presence of elevated depressive symptoms and antidepressant medication use with respect to risk of type 2 diabetes among approximately 120,000 women enrolled in the Women's Health Initiative (WHI), and compare several different statistical models appropriate for causal inference in non-randomized settings. METHODS Data were analyzed for 52,326...

classification trees (j48) were induced to predict the habitat requirements of tench (tinca tinca). 306 datasets were used for the given fish during 8 years in the river basins in flanders (belgium). the input variables consisted of the structural-habitat (width, depth, gradient slope and distance from the source) and physic chemical (ph, dissolved oxygen, water temperature and electric conduct...

This paper suggests a procedure for estimating excursion probabilities for linear and nonlinear systems subjected to Gaussian excitation processes. The stochastic excitation process is modeled most generally by the Karhunen-Loève expansion. The approach is based on the so called “averaged excursion probability flow” which allows for a simple solution for the interaction in excursion problems. S...

Structural equation modeling (SEM) is an extremely flexible linear-in-parameters multivariate statistical modeling technique. It has been used in modeling travel behavior and values since about 1980, and its use is rapidly accelerating, partially due to the availability of improved software. The number of published studies, now known to be more than fifty, has approximately doubled in the past ...