Advances in Mixture Models

Computational Statistics and Data Analysis

Volumn 51, Issue 11, 2007, Pages 5205-5210

  Böhning, Dankmar ^a   Seidel, Wilfried ^b   Alfó, Macro ^c   Garel, Bernard ^d   Patilea, Valentin ^e   Walther, Günther ^f  

^a UNIVERSITY OF READING   (United Kingdom)

^b HELMUT SCHMIDT UNIVERSITY   (Germany)

^c SAPIENZA UNIVERSITY OF ROME   (Italy)

^d UNIVERSITÉ DE TOULOUSE   (France)

^e Centre for Research in Economics and Statistics   (France)

^f STANFORD UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 34248221457     PISSN: 01679473     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.csda.2006.10.025     Document Type: Editorial

References (73)

1. Agresti A., Caffo B., and Ohman-Strickland P. Examples in which misspecification of a random effects distribution reduces efficiency, and possible remedies. Comput. Statist. Data Anal. 47 (2006) 639-653
2. Alfò, M., Farcomeni, A., Tardella, L., 2007. Robust semiparametric mixing for detecting differentially expressed genes in microarray experiments. Comput. Statist. Data Anal. (this issue), 10.1016/j.csda.2006.08.009.
3. Bartolucci F., and Scaccia L. The use of mixtures for dealing with non-normal regression errors. Comput. Statist. Data Anal. 48 (2006) 821-834
4. Benton D., and Krishnamoorthy D. Computing discrete mixtures of continuous distributions: noncentral chisquare, noncentral t and the distribution of the square of the sample multiple correlation coefficient. Comput. Statist. Data Anal. 43 (2003) 249-267
5. Besbeas P., and Morgan B.T.J. Integrated squared error estimation of normal mixtures. Comput. Statist. Data Anal. 44 (2004) 517-526
6. Bocci L., Vicari D., and Vichi M. A mixture model for the classification of three-way proximity data. Comput. Statist. Data Anal. 50 (2006) 1625-1654
7. Bohacek S., and Rozovskii B. A diffusion model of roundtrip time. Comput. Statist. Data Anal. 45 (2004) 25-50
8. Böhning D. Computer-Assisted Analysis of Mixtures and Applications. Meta-Analysis, Disease Mapping and Others (2000), Chapman & Hall/CRC, Boca Raton
9. Böhning, D., Kuhnert, R., 2006. The equivalence of truncated count mixture distributions and mixtures of truncated count distributions. Biometrics. 62, 1207-1215.
10. Böhning D., and Patilea V. Asymptotic normality in mixtures of discrete distributions. Scand. J. Statist. 32 (2005) 115-132
11. Böhning D., and Schön D. Nonparametric maximum likelihood estimation of the population size based upon the counting distribution. J. Roy. Statist. Soc., Ser. C, Appl. Statist. 54 (2005) 721-737
12. Böhing D., and Seidel W. Recent developments in mixture models. Comput. Statist. Data Anal. 41 (2003) 349-357
13. Böhning D., Dietz E., and Schlattmann P. Space-time mixture modelling of public health data. Statist. Med. 9 (2000) 2333-2344
14. Böhning D., Dietz E., Kuhnert R., and Schön D. Mixture models for capture-recapture count data. Statist. Methods Appl. 4 (2005) 29-43
15. Bordes, L., Chauveau, D., Vandekerkhove, P., 2007. A stochastic EM algorithm for a semiparametric mixture model. Comput. Statist. Data Anal. (this issue), 10.1016/j.csda.2006.08.015.
16. Caffo, B., An, M.-W., Rohde, C., 2007. Flexible random intercept models for binary outcomes using mixtures of normals. Comput. Statist. Data Anal. (this issue), 10.1016/j.csda.2006.09.031.
17. Carvalho, A.X., Tanner, M.A., 2007. Modelling nonlinear count time series with local mixtures of Poisson autoregressions. Comput. Statist. Data Anal. (this issue), 10.1016/j.csda.2006.09.032.
18. Celeux, G., Forbes, F., Robert, C., Titterington, D.M., 2006. Deviance Information Criteria for missing data models with discussion. Bayesian Anal., in print, published online.
19. Chao A. Estimating the population size for capture-recapture data with unequal capture probabilities. Biometrics 43 (1987) 783-791
20. Congdon P. A model for non-parametric spatially varying regression effects. Comput. Statist. Data Anal. 50 (2006) 422-445
21. Corduneanu A., and Bishop C.M. Variational Bayesian model selection for mixture distributions. In: Jaakkola T., and Richardson T. (Eds). Artificial Intelligence and Statistics (2001), Morgan Kaufmann, Los Altos, CA 27-34
22. D'Elia A., and Piccolo D. A mixture model for preferences data analysis. Comput. Statist. Data Anal. 49 (2005) 917-934
23. Di Zio, M., Guarnera, U., Luzi, O., 2007. Imputation through finite Gaussian mixture models. Comput. Statist. Data Anal. (this issue), 10.1016/j.csda.2006.10.002.
24. Eilers, P.H.C., Borgdorff, M.W., 2007. Non-parametric log-concave mixtures. Comput. Statist. Data Anal. (this issue), 10.1016/j.csda.2006.08.027.
25. Formann, A.K., 2007. Mixture analysis of multivariate categorical data with covariates and missing entries. Comput. Statist. Data Anal. (this issue), 10.1016/j.csda.2006.08.020.
26. Frühwirth-Schnatter S. Finite Mixture and Markov Switching Models (2006), Springer, Heidelberg
27. Garel, B., 2007. Recent asymptotic results in testing for mixtures. Comput. Statist. Data Anal. (this issue), 10.1016/j.csda.2006.09.033.
28. Grün, B., Leisch, F., 2007. Fitting finite mixtures of generalized linear regressions in R. Comput. Statist. Data Anal. (this issue), 10.1016/j.csda.2006.08.014.
29. Hathaway R.J. A constrained formulation of maximum-likelihood estimation for normal mixture distributions. Ann. Statist. 13 (1985) 795-800
30. Hazan A., Landsmanm Z., and Makov U.E. Robustness via a mixture of exponential power distributions. Comput. Statist. Data Anal. 42 (2003) 111-121
31. Hu T., and Sung S.Y. A hybrid EM approach to spatial clustering. Comput. Statist. Data Anal. 50 (2006) 1188-1205
32. Hunt L.A., and Basford K.E. Fitting a mixture model to three-mode three-way data with categorical and continuous missing information. J. Classification 18 (1999) 283-296
33. Hunt L.A., and Basford K.E. Fitting a mixture model to three-mode three-way data with missing information. J. Classification 18 (2001) 209-226
34. Hunt L., and Jorgensen M. Mixture model clustering for mixed data with missing information. Comput. Statist. Data Anal. 41 (2003) 429-440
35. Hürlimann W. Fitting bivariate cumulative returns with copulas. Comput. Statistist. Data Anal. 45 (2004) 355-372
36. Ingrassia, S., Rocci, R., 2007. Constrained monotone EM algorithms for finite mixture of multivariate Gaussians. Comput. Statist. Data Anal. (this issue), 10.1016/j.csda.2006.10.011.
37. Jacobs R., Jordan M., Nowlan S., and Hinton G. Adaptive mixtures of local experts. Neural Comput. 3 (1991) 79-87
38. Jamshidian M. On algorithms for restricted maximum likelihood estimation. Comput. Statist. Data Anal. 45 (2004) 137-157
39. Jara, A., Garcia-Zattera, M.-J., Lesaffre, E., 2007. A Dirichlet process mixture model for the analysis correlated binary responses. Comput. Statist. Data Anal. (this issue), 10.1016/j.csda.2006.09.010.
40. Jordan M., and Jacobs R. Hierarchical mixtures-of-experts and the EM algorithm. Neural Comput. 6 (1994) 181-214
41. Jordan M.I., Ghahramani Z., Jaakkola T.S., and Saul L.K. An introduction to variational methods for graphical models. Mach. Learning 37 (1999) 183-233
42. Karlis, D., Patilea, V., 2007. Confidence intervals of the hazard rate function for discrete distributions using mixtures. Statist. Data Anal. (this issue), 10.1016/j.csda.2006.07.037.
43. Kheiri, S., Kimber, A., Meshkani, M.R., 2007. Bayesian analysis of an inverse Gaussian correlated frailty model. Comput. Statist. Data Anal. (this issue), 10.1016/j.csda.2006.07.026.
44. Klar B., and Meintanis S.G. Tests for normal mixtures based on the empirical characteristic function. Comput. Statist. Data Anal. 49 (2005) 227-242
45. Knorr-Held L., and Raßer S. Bayesian detection of clusters and discontinuities in disease maps. Biometrics 56 (2000) 13-21
46. Li J., and Zha H. Two-way Poisson mixture models for simultaneous document classification and word clustering. Comput. Statist. Data Anal. 50 (2006) 163-180
47. Lindsay, B.G., 1995. Mixture models: theory geometry and applications. NSF-CBMS Regional Conference Series in Probability and Statistics, vol. 5. Institute of Mathematical Statistics, Hayward.
48. Link, W.A., 2003. Nonidentifiability of population size from capture-recapture data with heterogenous detection populations. Biometrics 59, 1123-1130.
49. Mao, C.X., 2007. Estimating population sizes for capture-recapture sampling with binomial mixtures. Comput. Statist. Data Anal. (this issue), 10.1016/j.csda.2006.09.025.
50. McGrory, C.A., Titterington, D.M., 2007. Variational approximations in Bayesian model selection for finite mixture distributions. Comput. Statist. Data Anal. (this issue), 10.1016/j.csda.2006.07.020.
51. McLachlan G.J., and Basford K.E. Mixture Models. Inference and Applications to Clustering (1988), Marcel Dekker, New York
52. McLachlan G.J., and Peel D. Finite Mixture Models (2000), Wiley, New York
53. McLachlan G.J., Peel D., and Bean R.W. Modelling high-dimensional data by mixtures of factor analyzers. Comput. Statist. Data Anal. 41 (2003) 379-388
54. McLachlan, G.J., Bean, R.W., Ben-Tovim Jones, L., 2007 Extension of the mixture of factor analyzers model to incorporate the multivariate t-Distribution. Comput. Statist. Data Anal. (this issue), doi:10.1016/j.csda.2006.09.015.
55. Meulders M., DeBoeck P., Kuppens P., and Van Mechelen I. Constrained latent class analysis of three-way three-mode data. J. Classification 19 (2002) 277-302
56. Nadarajah S. Information matrices for Laplace and Pareto mixtures. Comput. Statist. Data Anal. 50 (2006) 950-966
57. Neerchal N.K., and Morel J.G. An improved method for the computation of maximum likelihood estimates for multinomial overdispersion models. Comput. Statist. Data Anal. 49 (2005) 33-43
58. Priebe C.E., Miller M.I., and Ratnanather T. Segmenting magnetic resonance images via hierarchical mixture modelling. Comput. Statist. Data Anal. 50 (2006) 551-567
59. Rufo, M.J., Pérez, C.J., Martín, J., 2007. Bayesian analysis of finite mixtures of multinomial and negative-multinomial distributions. Comput. Statist. Data Anal. (this issue), 10.1016/j.csda.2006.09.026.
60. Seidel W., Sever K., and Ševčiková H. Efficient calculation of the NPMLE of a mixing distribution for mixtures of exponentials. Comput. Statist. Data Anal. 50 (2006) 1248-1271
61. Spiegelhalter D.J., Best N.G., Carlin B.P., and Van der Linde A. Bayesian measures of model complexity and fit (with discussion). J. Roy. Statist. Soc., Ser. B 64 (2002) 583-639
62. Steiner, P.M., Hudec, M., 2007. Classification of large data sets with mixture models via sufficient EM. Comput. Statist. Data Anal. (this issue), 10.1016/j.csda.2006.09.014.
63. Sultan, K.S., Ismail, M.A., Al-Moisheer, A.S., 2007. Mixture of two inverse Weibull distributions: properties and estimation. Comput. Statist. Data Anal. (this issue), 10.1016/j.csda.2006.09.016.
64. Tao J., Shi N.-Z., and Lee S.Y. Drug risk assessment with determining the number of sub-populations under finite mixture normal models. Comput. Statist. Data Anal. 46 (2004) 661-676
65. Titterington D.M., Smith A.F.M., and Makov U.E. Statistical Analysis of Finite Mixture Distributions (1985), Wiley, New York
66. Ueda N., and Ghahramani Z. Bayesian model search for mixture models based on optimizing variational bounds. Neural Networks 15 (2002) 1223-1241
67. van der Heijden P.G.M., Bustami R., Cruyff M., Engbersen G., and van Houwelingen H.C. Point and interval estimation of the population size using the truncated Poisson regression model. Statist. Model.-An Internat. J. 3 (2003) 305-322
68. Vaz de Melo Mendes B., and Lopes F.H. Data driven estimates for mixtures. Comput. Statist. Data Anal. 47 (2006) 583-598
69. Vermunt J.K. Multilevel latent class models. Sociological Methodology 33 (2003) 213-239
70. Vermunt, J.K., 2007. A hierarchical mixture model for clustering three-way data sets. Comput. Statist. Data Anal. (this issue), 10.1016/j.csda.2006.08.005.
71. Woodward W.A., and Sain S.R. Testing for outliers from a mixture distribution when some data are missing. Comput. Statist. Data Anal. 44 (2003) 193-210
72. Yashin A.I., Vaupel J.W., and Iachine I. Correlated individual frailty: an advantageous approach to survival analysis of bivariate data. Math. Popul. Studies 5 (1995) 1-15
73. Zheng J., and Frey H.C. Quantification of variability and uncertainty using mixture distributions: evaluation of sample size, mixing weights, and separation between components. Risk Anal. 24 (2004) 553-571