The Monte Carlo EM method for estimating multinomial probit latent variable models
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DOI: 10.1007/s00180-007-0091-7
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References listed on IDEAS
- Bunch, David S., 1991. "Estimability in the Multinomial Probit Model," University of California Transportation Center, Working Papers qt1gf1t128, University of California Transportation Center.
- 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.
- John Geweke & Michael P. Keane & David E. Runkle, 1994. "Statistical inference in the multinomial multiperiod probit model," Staff Report 177, Federal Reserve Bank of Minneapolis.
- 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.
- 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:
- 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.
- 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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Keywords
Multinomial probit model; Latent variable; Maximum likelihood estimates; Monte Carlo EM; Factor analysis;All these keywords.
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