The EMMIX software for the fitting of mixtures of normal and t-components

Journal of Statistical Software

Volumn 4, Issue , 1999, Pages 1-14

  McLachlan, G J ^a   Peel, D ^a   Basford, K E ^a   Adams, P ^a  

^a UNIVERSITY OF QUEENSLAND   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 0001883037     PISSN: 15487660     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (17)

1. Banfield, J.D., and Raftery, A. (1993). Model-based Gaussian and non-Gaussian clustering. Biometrics 49, 803-821.
2. Byers, S. and Raftery, A.E. (1988). Nearest-neighbor clutter removal for estimating features in spatial point processes. Journal of the American Statistical Association 91, 577-584.
3. Campbell, J.G., Fraley, C., Murtagh, F., and Raftery, A.E. (1998). Linear flaw detection in woven textiles using model-based clustering. Pattern Recognition Letters 18, 1539-1548.
4. Cheeseman, P., and Stutz, J. (1996). Bayesian classification (AutoClass): theory and results. In Advances in Knowledge Discovery and Data Mining , U.M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy (Eds.). Menlo Park, California: The AAAI Press, pp. 61-83.
5. DasGupta, A. and Raftery, A.E. (1998). Detecting features in spatial point processes with clutter via model-based clustering. Journal of the American Statistical Association 93, 294-302.
6. Dempster, A.P., Laird, N.M., and Rubin, D.B. (1977). Maximum likelihood from incomplete data via the EM algorithm (with discussion). Journal of the Royal Statistical Society B 39, 1-38.
7. Fraley, C. and Raftery, A.E. (1998). How many clusters? Which clustering method? - Answers via model-based cluster analysis. Technical Report No. 329. Seattle: Department of Statistics, University of Washington.
8. Haughton, D. (1997). Packages for estimating finite mixtures: a review. Applied Statistics 51, 194-205.
9. Hunt, L., and Jorgensen, J. (1997). Mixture model clustering: a brief introduction to the MULTIMIX program. Unpublished manuscript.
10. McLachlan, G.J. (1987). On bootstrapping the likelihood ratio test statistic for the number of components in a normal mixture. Applied Statistics 36, 318-324.
11. McLachlan, G.J. and Basford, K.E. (1988). Mixture Models: Inference and Applications to Clustering. New York: Marcel Dekker.
12. McLachlan, G.J. and Krishnan, T. (1997). The EM Algorithm and Extensions. New York: Wiley.
13. McLachlan, G.J. and Peel, D. (1996). An algorithm for unsupervised learning via normal mixture models. In ISIS: Information, Statistics and Induction in Science, D.L. Dowe, K.B. Korb, and J.J. Oliver (Eds.), pp. 354-363. Singapore: World Scientific.
14. McLachlan, G.J., Peel D., Adams, P., and Basford, K.E. (1995). An algorithm for assessing by resampling the P-value of the likelihood ratio test on the number of components in normal mixture models. Research Report No. 31. Brisbane: Centre for Statistics, The University of Queensland.
15. McLachlan, G.J. and Peel, D. (1998). Robust cluster analysis via mixtures of multivariate t-distributions. In Lecture Notes in Computer Science Vol. 1451, A. Amin, D. Dori, P. Pudil, and H. Freeman (Eds.). Berlin: Springer-Verlag, pp. 658-666.
16. Wallace, C.S and Boulton, D.M. (1968). An information measure for classification. Computer Journal 11, 185-194.
17. Wallace, C.S. and Dowe D.L. (1994). Intrinsic classification by MML - the Snob program. In 7th Australian Joint Conference on Artificial Intelligence, 37-44.