References
- Cruz-Medina, I. R., Hettmansperger, T. P., & Thomas, H. (2004). Semiparametric mixture models and repeated measures: The multinomial cut point model. Journal of the Royal Statistical Society: Series C (Applied Statistics), 53, 463–474. doi: 10.1111/j.1467-9876.2004.05203.x
- Hall, P., & Zhou, X. H. (2003). Nonparametric estimation of component distributions in a multivariate mixture. The Annals of Statistics, 31, 201–224. doi: 10.1214/aos/1046294462
- Hettmansperger, T. P., & Thomas, H. (2000). Almost nonparametric inference for repeated measures in mixture models. Journal of the Royal Statistical Society. Series B, 62, 811–825. doi: 10.1111/1467-9868.00266
- Kasahara, H., & Shimotsu, K. (2014). Nonparametric identification and estimation of the number of components in multivariate mixtures. Journal of the Royal Statistical Society. Series B, 76(1), 97–111. doi: 10.1111/rssb.12022
- Lindsay, B. G. (1995). Mixture models: Theory, geometry and applications. Hayward: Institute for Mathematical Statistics.
- McLachlan, G. J., & Peel, D. (2000). Finite mixture models. New York: Wiley.
- Owen, A. B. (1988). Empirical likelihood ratio confidence intervals for a single functional. Biometrika, 75, 237–249. doi: 10.1093/biomet/75.2.237
- Owen, A. B. (2001). Empirical likelihood. New York: Chapman & Hall/CRC.
- Qin, J., & Lawless, J. (1994). Empirical likelihood and general estimating equations. The Annals of Statistics, 22, 300–325. doi: 10.1214/aos/1176325370
- Thomas, H., & Horton, J. J. (1997). Competency criteria and the class inclusion task: Modeling judgments and justifications. Developmental Psychology, 33, 1060–1073. doi: 10.1037/0012-1649.33.6.1060
- Titterington, D. M., Smith, A. F. M., & Makov, U. E. (1985). Statistical analysis of finite mixture distributions. New York: Wiley.