title: A Composite Likelihood Inference in Latent Variable Models for Ordinal Longitudinal Responses.
abstract: The paper proposes a composite likelihood estimation approach that uses bivariate instead of multivariate marginal probabilities for ordinal longitudinal responses using a latent variable model. 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 composite likelihood 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.
authors: Department of Statistics, Athens University of Economics and Business, 76 Patission Street, 10434, Athens, Greece. vasdekis@aueb.gr.
authors: Vassilis G S
authors: VG
authors: Vasdekis
authors: Department of Statistics, University of Bologna, Via Belle Arti 41, 40126, Bologna, Italy.
authors: Silvia
authors: S
authors: Cagnone
authors: Department of Statistics, London School of Economics and Political Science, Houghton Street, London, WC2A 2AE, UK.
authors: Irini
authors: I
authors: Moustaki
keywords: composite likelihood; goodness-of-fit measures; latent variables; longitudinal; ordinal data