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Maximum likelihood estimation of multinomial probit factor analysis models for multivariate t-distribution

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  • Jie Jiang
  • Xinsheng Liu
  • Keming Yu

Abstract

We propose a model for multinomial probit factor analysis by assuming t-distribution error in probit factor analysis. To obtain maximum likelihood estimation, we use the Monte Carlo expectation maximization algorithm with its M-step greatly simplified under conditional maximization and its E-step made feasible by Monte Carlo simulation. Standard errors are calculated by using Louis’s method. The methodology is illustrated with numerical simulations. Copyright Springer-Verlag 2013

Suggested Citation

  • Jie Jiang & Xinsheng Liu & Keming Yu, 2013. "Maximum likelihood estimation of multinomial probit factor analysis models for multivariate t-distribution," Computational Statistics, Springer, vol. 28(4), pages 1485-1500, August.
    • Handle: RePEc:spr:compst:v:28:y:2013:i:4:p:1485-1500
    DOI: 10.1007/s00180-012-0363-8
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    References listed on IDEAS

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