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The Monte Carlo EM method for estimating multinomial probit latent variable models

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  • Xingcai Zhou
  • Xinsheng Liu

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  • Xingcai Zhou & Xinsheng Liu, 2008. "The Monte Carlo EM method for estimating multinomial probit latent variable models," Computational Statistics, Springer, vol. 23(2), pages 277-289, April.
    • Handle: RePEc:spr:compst:v:23:y:2008:i:2:p:277-289
    DOI: 10.1007/s00180-007-0091-7
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    References listed on IDEAS

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    1. Bunch, David S., 1991. "Estimability in the Multinomial Probit Model," University of California Transportation Center, Working Papers qt1gf1t128, University of California Transportation Center.
    2. Geweke, John F. & Keane, Michael P. & Runkle, David E., 1997. "Statistical inference in the multinomial multiperiod probit model," Journal of Econometrics, Elsevier, vol. 80(1), pages 125-165, September.
    3. Keane, Michael P, 1992. "A Note on Identification in the Multinomial Probit Model," Journal of Business & Economic Statistics, American Statistical Association, vol. 10(2), pages 193-200, April.
    4. Bunch, David S., 1991. "Estimability in the multinomial probit model," Transportation Research Part B: Methodological, Elsevier, vol. 25(1), pages 1-12, February.
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    Cited by:

    1. Meza, Cristian & Jaffrézic, Florence & Foulley, Jean-Louis, 2009. "Estimation in the probit normal model for binary outcomes using the SAEM algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 53(4), pages 1350-1360, February.
    2. 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.

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