Robust estimation in structural equation models using Bregman and other divergences with t-centre approach to estimate the covariance matrix

S. Penev, T. Prvan

    Research output: Contribution to journalArticle

    Abstract

    Structural equation models seek to find causal relationships between latent variables by analysing the mean and the covariance matrix of some observable indicators of the latent variables. Under a multivariate normality assumption on the distribution of the latent variables and of the errors, maximum likelihood estimators are asymptotically efficient. The estimators are significantly influenced by violation of the normality assumption and hence there is a need to robustify the inference procedures. Previous work minimized the von Neuman divergence or its variant the total von Neumann divergence to estimate the parameters, with the minimum covariance determinant used as a robust estimator of the covariance matrix. We extend this approach by considering other divergences and by developing a robust estimate of the covariance matrix. The robust estimator of the covariance matrix developed is a t-centre like estimator based on several minimum covariance determinant estimators ranging from 0% contamination to 50% contamination. The simulation results are promising. The results can be used for robustifying the fit of structural equation models.
    Original languageEnglish
    Pages (from-to)C339-C354
    Number of pages16
    JournalANZIAM Journal
    Volume56 (CTAC2014)
    DOIs
    Publication statusPublished - 2016

    Fingerprint Dive into the research topics of 'Robust estimation in structural equation models using Bregman and other divergences with t-centre approach to estimate the covariance matrix'. Together they form a unique fingerprint.

    Cite this