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A composite likelihood inference in latent variable models for ordinal longitudinal responses

Vasdekis, Vassilis G. S., Cagnone, Silvia and Moustaki, Irini (2012) A composite likelihood inference in latent variable models for ordinal longitudinal responses. Psychometrika, 77 (3). pp. 425-441. ISSN 0033-3123

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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.

Item Type: Article
Official URL: http://www.springerlink.com/content/0033-3123/
Additional Information: © 2012 The Psychometric Society
Library of Congress subject classification: H Social Sciences > HA Statistics
Sets: Departments > Statistics
Rights: http://www.lse.ac.uk/library/usingTheLibrary/academicSupport/OA/depositYourResearch.aspx
Date Deposited: 19 Apr 2012 08:27
URL: http://eprints.lse.ac.uk/43217/

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