Improving the convergence rate of the em algorithm for a mixture model fitted to grouped truncated data

References

• Behboodian , J. 1972 . Information matrix for a mixture of two normal distributions . J. Statist. Comput. Simul , 1 : 295 – 314 .
   Google Scholar
• Berndt , E.R. , Hall , B.H. , Hall , R.E. and Hausman , J.A. 1974 . Estimation and inference in nonlinear structural models . Annals of Economic and Social Measurement , 3 : 653 – 665 .
   Google Scholar
• Bock , R.D. and Aitkin , M. 1981 . Marginal maximum likelihood estimation of item parameters: application of an EM algorithm . Psychometrika , 46 : 443 – 459 .
   Web of Science ®Google Scholar
• Davenport , J.W. , Pierce , M.A. and Hathaway , R.J. 1988 . A numerical comparison of EM and quasi-Newton type algorithms for computing MLE's for a mixture of normal distributions . Computer Science and Statistics: Proceedings of the 20th Symposium on the Interface . 1988 . pp. 410 – 415 .
   Google Scholar
• Dempster , A.P. , Laird , N.M. and Rubin , D.B. 1977 . Maximum likelihood from incomplete data via the EM algorithm . J. R. Statist. Soc. B , 39 : 1 – 38 .
   Google Scholar
• Efron , B. and Hinkley , D.V. 1978 . Assessing the accuracy of the maximum likelihood estimator: observed versus expected Fisher information . Biometrika , 65 : 457 – 487 .
   Web of Science ®Google Scholar
• Everitt , B.S. 1984 . Maximum likelihood estimation of the parameters in a mixture of two univariate normal distributions; a comparison of different algorithms . The Statistician , 33 : 205 – 215 .
   Google Scholar
• Griffiths , W.E. , Hill , R.C. and pope , P.J. 1987 . Small sample properties of pro bit model estimators . J. Amer. Statist. Assoc , 82 : 929 – 937 .
   Web of Science ®Google Scholar
• Hartley , H.O. and Hocking , R.R. 1971 . The analysis of incomplete data . Biometrics , 27 : 783 – 823 .
   Web of Science ®Google Scholar
• Jones , P.N. and McLachlan , G.J. 1990 . Algorithm AS 254: Maximum likelihood estimation from grouped and truncated data with finite normal mixture models . Appl. Statist , 39 : 273 – 312 .
   Web of Science ®Google Scholar
• Louis , T.A. 1982 . Finding the observed information matrix when using the EM algorithm . J. R. Statist. Soc. B , 44 : 226 – 233 .
   Web of Science ®Google Scholar
• McLachlan , G.J. and Basford , K.E. 1988 . Mixture Models: Inference and Applications to Clustering , New York : Marcel Dekker .
   Google Scholar
• McLachlan , G.J. and Jones , P.N. 1988 . Fitting mixture models to grouped and truncated data via the EM algorithm . Biometrics , 44 : 517 – 578 .
   Google Scholar
• Meilijson , J. 1989 . A fast improvement to the EM algorithm on its own terms . J. R. Statist. Soc. B , 51 : 127 – 138 .
   Google Scholar
• Rao , C.R. 1973 . Linear Statistical Inference and its Applications , New York : Wiley .
   Google Scholar
• Redner , R.A. and Walker , H.F. 1984 . Mixture densities, maximum likelihood and the EM algorithm . Siam Review , 26 : 195 – 239 .
   Web of Science ®Google Scholar
• Sundberg , R. 1976 . An iterative method for solution of the likelihood equations for incomplete data from exponential families . Commun. Statist.-Simula. Computa , B5 : 55 – 64 .
   Web of Science ®Google Scholar