Title: EM estimation of a structural equation model
Authors: Myriam Tami - University of Montpellier (France) [presenting]
Abstract: A new estimation method of a Structural Equation Model (SEM) is proposed. Contrasting with the classical SEM approach, our method is not based on the constrained estimation of the covariance structure of the data. It consists in viewing the Latent Variables (LV's) as missing data and using the EM algorithm to maximize the whole model's likelihood, which simultaneously provides estimators not only of the model's coefficients, but also of the values of LV's. Through a simulation study, we investigate how fast and accurate the method is, and eventually apply it to real environmental data.