Fitting mixtures of linear regressions

References

• Lindsay , B. G. 1995 . Mixture Models: Theory, Geometry and Applications , NSF-CMBS Regional Conference Series in Probability and Statistics Vol. 5 , Hayward : Institute of Mathematical Statistics .
   Google Scholar
• Böhning , D. 2000 . Computer-Assisted Analysis of Mixtures and Applications: Meta-analysis, Disease Mapping and Others , New York : Chapman & Hall .
   Google Scholar
• McLachlan , G. J. and Peel , D. 2000 . Finite Mixture Models , New York : Wiley .
   Google Scholar
• Quandt , R. and Ramsey , J. 1978 . Estimating mixtures of normal distributions and switching regression . J. Amer. Statist. Assoc. , 73 : 730 – 738 .
   Web of Science ®Google Scholar
• De Veaux , R. D. 1989 . Mixtures of linear regressions . Comput. Statist. Data Anal. , 8 : 227 – 245 .
   Web of Science ®Google Scholar
• Jones , P. N. and McLachlan , G. J. 1992 . Fitting finite mixture models in a regression context . Aust. J. Statist. , 34 : 233 – 240 .
   Google Scholar
• Turner , T. R. 2000 . Estimating the propagation rate of a viral infection of potato plants via mixtures of regressions . Appl. Statist. , 49 ( 3 ) : 371 – 384 .
   Web of Science ®Google Scholar
• Hawkins , D. S. , Allen , D. M. and Stomber , A. J. 2001 . Determining the number of components in mixtures of linear models . Comput. Statist. Data Anal. , 38 : 15 – 48 .
   Web of Science ®Google Scholar
• Zhu , H. and Zhang , H. 2004 . Hypothesis testing in mixture regression models . J. Roy. Statist. Soc. , 66 ( 1 ) : 3 – 16 .
   Google Scholar
• Dempster , A. P. , Laird , N. M. and Rubin , D. B. 1977 . Maximum likelihood from incomplete data via the EM algorithm . J. Roy. Statist. Soc. B , 39 : 1 – 38 .
   Google Scholar
• Celeux , G. and Govaert , G. 1992 . A classification EM algorithm and two stochastic versions . Comput. Statist. Data Anal. , 14 : 315 – 332 .
   Web of Science ®Google Scholar
• Celeux , G. and Diebolt , J. 1985 . The SEM algorithm: a probabilistic teacher algorithm derived from the EM algorithm for the mixture problem . Comput. Statist. Quart. , 2 : 73 – 82 .
   Google Scholar
• Ganesalingam , S. 1989 . Classification and mixture approach to clustering via maximum likelihood . Appl. Statist. , 38 : 455 – 466 .
   Web of Science ®Google Scholar
• Celeux , G. and Govaert , G. 1993 . Comparison of the mixture and the classification maximum likelihood in cluster analysis . J. Statist. Comput. Simul. , 47 : 127 – 146 .
   Google Scholar
• Govaert , G. and Nadif , M. 1993 . Comparison of the mixture and the classification maximum likelihood in cluster analysis with binary data . Comput. Statist. Data Anal. , 23 : 65 – 81 .
   Web of Science ®Google Scholar
• Celeux , G. , Chauveau , D. and Diebolt , J. 1996 . Stochastic versions of the EM algorithm: an experimental study in the mixture case . J. Statist. Comput. Simul. , 55 : 287 – 314 .
   Web of Science ®Google Scholar
• Dias , J. and Wedel , M. 2004 . An empirical comparison of EM, SEM and MCMC performance for problematic Gaussian mixture likelihoods . Statist. Comput. , 14 : 323 – 332 .
   Web of Science ®Google Scholar
• R Development Core Team . 2008 . R: A language and environment for statistical computing , Vienna, , Austria : R Foundation for Statistical Computing . software available at http://www.R-project.org
   Google Scholar
• Hathaway , R. J. and Bezdek , J. C. 1993 . Switching regression models and fuzzy clustering . IEEE Trans. Fuzzy Syst. , 1 : 195 – 204 .
   Google Scholar
• Hastie , T. , Tibshirani , R. and Friedman , J. 2001 . Elements of Statistical Learning , New York : Springer .
   Google Scholar