Full and Limited information estimation methods for latent variable models with longitudinal categorical data
Irini Moustaki (London School of Economics)
Friday 14th October, 2011 15:00-16:00 Maths 203
The talk will discuss composite likelihood estimation and more specifically methods that use bivariate instead of multivariate marginal probabilities for latent variables models with ordinal longitudinal responses. The model considers time-dependent latent variables and item-specific random effects to be accountable for the interdependencies of the multivariate ordinal items. Time-dependent latent variables are linked with an autoregressive model. Simulation results have shown estimators to have a small amount of bias and mean square error and as such they are feasible alternatives to full maximum likelihood. Model selection criteria developed for composite likelihood estimation are used in the applications. Furthermore, lower-order residuals are used as measures-of-fit for the selected models. Simulated and real examples will be presented.