Semiparametric mixture models and repeated measures: The multinomial cut point model

Journal of the Royal Statistical Society. Series C: Applied Statistics

Volumn 53, Issue 3, 2004, Pages 463-474

  Cruz Medina, I R ^a   Hettmansperger, T P ^b,c   Thomas, H ^b  

^a INSTITUTO TECNOLÓGICO DE SONORA   (Mexico)

^b PENNSYLVANIA STATE UNIVERSITY   (United States)

^c PENNSYLVANIA STATE UNIVERSITY   (United States)

Author keywords

EM algorithm; Finite mixtures; Multinomial likelihood; Nonparametric estimation

Indexed keywords

EID: 3042813949     PISSN: 00359254     EISSN: None     Source Type: Journal    
DOI: 10.1111/j.1467-9876.2004.05203.x     Document Type: Article

References (16)

1. Blischke, W. R. (1964) Estimating the parameters of mixtures of binomial distributions. J. Am. Statist. Ass., 59, 510-528.
2. Chrysostomou, M. A. (2002) Estimating the number of components in mixtures of multinomial distributions. Master's Thesis. Pennsylvania State University, University Park.
3. Cruz-Medina, I. R. (2001) Almost nonparametric and nonparametric estimation in mixture models. PhD Dissertation. Pennsylvania State University, University Park.
4. Elmore, R. and Wang, S. (2003) Identifiability and estimation in finite mixture models with multinomial components. Technical Report 03-04. Department of Statistics, Pennsylvania State University, University Park.
5. Hall, P. and Zhou, X. (2003) Nonparametric estimation of component distributions in a multivariate mixture. Ann. Statist., 31, 201-224.
6. Hawkins, D. S., Allen, D. M. and Stromberg, A. J. (2001) Determining the number of components in mixtures of linear models. Comput. Statist. Data Anal., 38, 14-48.
7. Hettmansperger, T. P. and Thomas, H. (2000) Almost nonparametric inference for repeated measures in mixture models. J. R. Statist. Soc. B, 62, 811-825.
8. Jensen, A. R. (1987) Process differences and individual differences in some cognitive tasks. Intelligence, 11, 107-136.
9. Kail, R. (1991) Developmental change in speed of processing during childhood and adolescence. Psychol. Bull., 109, 490-501.
10. Kim, B. S. (1984) Studies of multinomial mixture models. PhD Thesis. University of North Carolina, Chapel Hill.
11. McLachlan, G. J. and Krishnan, T. (1997) The EM Algorithm and Extensions. New York: Wiley.
12. McLachlan, G. J. and Peel, D. (2000) Finite Mixture Models. New York: Wiley.
13. Miller, C. A., Kail, R., Leonard, L. B. and Tomblin, J. B. (2001) Speed of processing in children with specific language impairment. J. Spch Long. Hear. Res., 44, 416-433.
14. Schwarz, G. (1978) Estimating the dimension of a model. Ann. Statist., 6, 461-464.
15. Thomas, H. and Lohaus, A. (1993) Modeling growth and individual differences in spatial tasks. Monogr. Soc. Res. Chid Devlpmnt, 58, no. 9, ser. 237.
16. Witkin, H. A., Lewis, H. B., Hertzman, M., Machover, K., Meissner, P. B. and Wapner, S. (1954) Personality through Perception: an Experimental and Clinical Study. New York: Harper and Bros.