Robust mixture modeling based on scale mixtures of skew-normal distributions

Computational Statistics and Data Analysis

Volumn 54, Issue 12, 2010, Pages 2926-2941

  Basso, Rodrigo M ^a   Lachos, Víctor H ^a   Cabral, Celso Rômulo Barbosa ^b   Ghosh, Pulak ^c  

^a UNIVERSITY OF CAMPINAS   (Brazil)

^b FEDERAL UNIVERSITY OF AMAZONAS   (Brazil)

^c NOVARTIS PHARMA AG   (Switzerland)

Author keywords

[No Author keywords available]

Indexed keywords

MIXTURES; NORMAL DISTRIBUTION; POPULATION STATISTICS;

ASYMPTOTIC PROPERTIES; HETEROGENEOUS DATA; INFORMATION MATRIX; LINEAR COMBINATIONS; MAXIMUM LIKELIHOOD ESTIMATE; POPULATION HETEROGENEITY; SIMULATION STUDIES; SKEW-NORMAL DISTRIBUTION;

MAXIMUM LIKELIHOOD ESTIMATION;

EID: 77955394355     PISSN: 01679473     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.csda.2009.09.031     Document Type: Article

References (36)

1. H. Akaike A new look at the statistical model identification IEEE Transactions on Automatic Control 19 1974 716 723
2. D.F. Andrews, and C.L. Mallows Scale mixtures of normal distributions Journal of the Royal Statistical Society, Series B 36 1974 99 102
3. R.B. Arellano-Valle, M.D. Branco, and M.G. Genton A unified view on skewed distributions arising from selections Canadian Journal of Statistics 34 2006
4. B.C. Arnold, R.J. Beaver, R.A. Groeneveld, and W.Q. Meeker The nontruncated marginal of a truncated bivariate normal distribution Psychometrika 58 1993 471 488
5. A. Azzalini A class of distributions which includes the normal ones Scandinavian Journal of Statistics 12 1985 171 178
6. A. Azzalini The skew-normal distribution and related multivariate families Scandinavian Journal of Statistics 32 2005 159 188
7. A. Azzalini, and A. Capitanio Distributions generated by perturbation of symmetry with emphasis on a multivariate skew t-distribution Journal of the Royal Statistical Society, Series B 65 2003 367 389
8. Z.D. Bai, P.R. Krishnaiah, and L.C. Zhao On rates of convergence of efficient detection criteria in signalprocessing with white noise IEEE Transactions on Information Theory 35 1989 380 388
9. K.E. Basford, D.R. Greenway, G.J. Mclachlan, and D. Peel Standard errors of fitted component means of normal mixtures Computational Statistics 12 1997 1 17 (Pubitemid 127385683)
10. C. Biernacki, G. Celeux, and G. Govaert Assessing a mixture model for clustering with the integratedcompleted likelihood IEEE Transactions on Pattern Analysis and Machine Intelligence 22 2000 719 725
11. C. Biernacki, G. Celeux, and G. Govaer Choosing starting values for the em algorithm for getting the highest likelihood in multivariate gaussian mixture models Computational Statistics & Data Analysis 41 2003 561 575
12. D. Bhning Computer-Assisted Analysis of Mixtures and Applications 2000 Chapman & Hall/CRC
13. M.D. Branco, and D.K. Dey A general class of multivariate skew-elliptical distributions Journal of Multivariate Analysis 79 2001 99 113
14. A.P. Dempster, N.M. Laird, and D.B. Rubin Maximum likelihood from incomplete data via the EM algorithm Journal of the Royal Statistical Society, Series B 39 1977 1 38
15. J.G. Dias, and M. Wedel An empirical comparison of EM, SEM and MCMC performance for problematic gaussian mixture likelihoods Statistics and Computing 14 2004 323 332
16. T.J. DiCiccio, and A.C. Monti Inferential aspects of the skew exponential power distribution Journal of the American Statistical Association 99 2004 439 450
17. B. Efron, and R. Tibshirani Bootstrap methods for standard errors, confidence intervals, and other measures of statistical accuracy Statistical Science 1 1986 54 75
18. S. Frhwirth-Schnatter Finite Mixture and Markov Switching Models 2006 Springer Verlag
19. J.A. Hartigan, and M.A. Wong A k-means clustering algorithm Applied Statistics 28 1979 100 108
20. N. Henze A probabilistic representation of the skew-normal distribution Scandinavian Journal of Statistics 13 1986 271 275
21. V.H. Lachos, H. Bolfarine, R.B. Arellano-Valle, and L.C. Montenegro Likelihood-based inference for multivariate skew-normal regression models Communications in Statistics - Theory and Methods 36 2007 1769 1786
22. Lachos, V.H., Ghosh, P., Arellano-Valle, R.B., 2010. Likelihood based inference for skew normal independent linear mixed models. Statistica Sinica (in press)
23. T.I. Lin Maximum likelihood estimation for multivariate skew normal mixture models Journal of Multivariate Analysis 100 2009 257 265
24. T.I. Lin Robust mixture modeling using multivariate skew t distributions Statistics and Computing 2009 10.1007/s11222-009-9128-9
25. T.I. Lin, J.C. Lee, and W.J. Hsieh Robust mixture modelling using the skew t distribution Statistics and Computing 17 2007 81 92
26. T.I. Lin, J.C. Lee, and S.Y. Yen Finite mixture modelling using the skew normal distribution Statistica Sinica 17 2007 909 927
27. C. Liu, and D.B. Rubin The ECME algorithm: A simple extension of EM and ECM with faster monotone convergence Biometrika 81 1994 633 648
28. G.J. McLachlan, and T. Krishnan The EM Algorithm and Extensions second edition 2008 John Wiley
29. G.J. McLachlan, and D. Peel Robust cluster analysis via mixtures of multivariate t-distributions A. Amin, D. Dori, P. Pudil, H. Freeman, Lecture Notes in Computer Science vol. 1451 1998 658 666
30. G.J. McLachlan, and G.J. Peel Finite Mixture Models 2000 John Wiley and Sons
31. X.L. Meng, and D.B. Rubin Maximum likelihood estimation via the ECM algorithm: A general framework Biometrika 80 1993 267 278
32. D. Nityasuddhi, and D. Bhning Asymptotic properties of the EM algorithm estimate for normal mixture models with component specific variances Computational Statistics & Data Analysis 41 2003 591 601
33. D. Peel, and G.J. McLachlan Robust mixture modelling using the t distribution Statistics and Computing 10 2000 339 348
34. G. Schwarz Estimating the dimension of a model Annals of Statistics 6 1978 461 464
35. G. Verbeke, and E. Lesaffre A linear mixed-effects model with heterogeneity in the random-effects population Journal of the American Statistical Association 91 1996
36. J. Wang, and M.G. Genton The multivariate skew-slash distribution Journal of Statistical Planning and Inference 136 2006 209 220