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New Developments in Latent Variable Models: Non-linear and Dynamic Models

In: Compstat 2008

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  • Irini Moustaki

    (London School of Economics and Political Science Department of Statistics
    Athens University of Economics and Business Department of Statistics)

Abstract

The paper reviews recent work on latent variable models for ordinal longitudinal variables and factor models with non-linear terms. The model for longitudinal data has been recently proposed by Cagnone, Moustaki and Vasdekis (2008). The model allows for time-dependent latent variables to explain the associations among ordinal variables within time where the associations among the same items across time are modelled with item-specific random effects. Rizopoulos and Moustaki (2007) extended the generalized latent variable model framework to allow for non-linear terms (interactions and higher order terms). Both models are estimated with full information maximum likelihood. Computational aspects, goodness-of-fit statistics and an application are presented.

Suggested Citation

  • Irini Moustaki, 2008. "New Developments in Latent Variable Models: Non-linear and Dynamic Models," Springer Books, in: Paula Brito (ed.), Compstat 2008, pages 155-164, Springer.
    • Handle: RePEc:spr:sprchp:978-3-7908-2084-3_13
    DOI: 10.1007/978-3-7908-2084-3_13
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