Clustering for multivariate continuous and discrete longitudinal data

Annals of Applied Statistics

Volumn 7, Issue 1, 2013, Pages 177-200

  Komárek, Arnošt ^a   Komárková, Lenka ^a  

^a CHARLES UNIVERSITY   (Czech Republic)

Author keywords

Classification; Functional data; Generalized linear mixed model; Multivariate longitudinal data; Repeated observations

Indexed keywords

EID: 84876025228     PISSN: 19326157     EISSN: 19417330     Source Type: Journal    
DOI: 10.1214/12-AOAS580     Document Type: Article

References (45)

1. Benaglia, T., Chauveau, D., Hunter, D. R. and Young, D. (2009). Mixtools: An R package for analyzing finite mixture models. Journal of Statistical Software 32 1-29.
2. Booth, J. G., Casella, G. and Hobert, J. P. (2008). Clustering using objective functions and stochastic search. J. R. Stat. Soc. Ser. B Stat. Methodol. 70 119-139.
3. Cabral, C. R. B., Lachos, V. H. and Madruga, M. R. (2012). Bayesian analysis of skew-normal independent linear mixed models with heterogeneity in the random-effects population. J. Statist. Plann. Inference 142 181-200.
4. Celeux, G., Martin, O. and Lavergne, C. (2005). Mixture of linear mixed models for clustering gene expression profiles from repeated microarray experiments. Stat. Model. 5 243-267.
5. Celeux, G., Forbes, F., Robert, C. P. and Titterington, D. M. (2006). Deviance information criteria for missing data models. Bayesian Anal. 1 651-673 (electronic).
6. Dasgupta, A. and Raftery, A. E. (1998). Detecting features in spatial point processes with clutter via model-based clustering. J. Amer. Statist. Assoc. 93 294-302.
7. De la Cruz-Mesía, R., Quintana, F. A. and Marshall, G. (2008). Model-based clustering for longitudinal data. Comput. Statist. Data Anal. 52 1441-1457.
8. De la Fé Rodríguez, P. Y., Coddens, A., Del Fava, E., Abrahantes, J. C., Shkedy, Z., Martin, L. O. M., Muñoz, E. C., Duchateau, L., Cox, E. and Goddeeris, B. M. (2011). High prevalence of F4+ and F18+Escherichia coli in Cuban piggeries as determined by serological survey. Tropical Animal Health and Production 43 937-946.
9. Dickson, E. R., Grambsch, P. M., Fleming, T. R., Fisher, L. D. and Langworthy, A. (1989). Prognosis in primary biliary cirrhosis: Model for decision making. Hepatology 10 1-7.
10. Fleming, T. R. and Harrington, D. P. (1991). Counting Processes and Survival Analysis. Wiley, New York.
11. Fong, Y., Rue, H. and Wakefield, J. (2010). Bayesian inference for generalized linear mixed models. Biostatistics 11 397-412.
12. Fraley, C. and Raftery, A. E. (2002). Model-based clustering, discriminant analysis, and density estimation. J. Amer. Statist. Assoc. 97 611-631.
13. Fraley, C. and Raftery, A. E. (2006). MCLUST version 3 for R: Normal mixture modeling and model-based clustering. Technical Report 504, Dept. Statistics, Univ. Washington.
14. Gelfand, A. E., Sahu, S. K. and Carlin, B. P. (1995). Efficient parameterisations for normal linear mixed models. Biometrika 82 479-488.
15. Grün, B. and Leisch, F. (2007). Fitting finite mixtures of generalized linear regressions in R. Comput. Statist. Data Anal. 51 5247-5252.
16. Hastie, T., Tibshirani, R. and Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd ed. Springer, New York.
17. Hennig, C. (2004). Breakdown points for maximum likelihood estimators of location-scale mixtures. Ann. Statist. 32 1313-1340.
18. James, G. M. and Sugar, C. A. (2003). Clustering for sparsely sampled functional data. J. Amer. Statist. Assoc. 98 397-408.
19. Johnson, R. A. and Wichern, D. W. (2007). Applied Multivariate Statistical Analysis, 6th ed. Pearson Prentice Hall, Upper Saddle River, NJ.
20. Kass, R. E. and Raftery, A. E. (1995). Bayes factors. J. Amer. Statist. Assoc. 90 773-795.
21. Komárek, A. (2009). A new R package for Bayesian estimation of multivariate normal mixtures allowing for selection of the number of components and interval-censored data. Comput. Statist. Data Anal. 53 3932-3947.
22. Komárek, A. and Komárková, L. (2013). Supplement to "Clustering for multivariate continuous and discrete longitudinal data." DOI:10.1214/12-AOAS580SUPP.
23. Komárek, A., Hansen, B. E., Kuiper, E. M. M., van Buuren, H. R. and Lesaffre, E. (2010). Discriminant analysis using a multivariate linear mixed model with a normal mixture in the random effects distribution. Stat. Med. 29 3267-3283.
24. Laird, N. M. and Ware, J. H. (1982). Random-effects models for longitudinal data. Biometrics 38 963-974.
25. Liu, X. and Yang, M. C. K. (2009). Simultaneous curve registration and clustering for functional data. Comput. Statist. Data Anal. 53 1361-1376.
26. Ma, P., Castillo-Davis, C. I., Zhong, W. and Liu, J. S. (2006). A data-driven clustering method for time course gene expression data. Nucleic Acids Res. 34 1261-1269.
27. McLachlan, G. J. and Basford, K. E. (1988). Mixture Models: Inference and Applications to Clustering. Statistics: Textbooks and Monographs 84. Dekker, New York.
28. McLachlan, G. and Peel, D. (2000). Finite Mixture Models. Wiley, New York.
29. Molenberghs, G. and Verbeke, G. (2005). Models for Discrete Longitudinal Data. Springer, New York.
30. Newton, M. A. and Chung, L. M. (2010). Gamma-based clustering via ordered means with application to gene-expression analysis. Ann. Statist. 38 3217-3244.
31. Peng, J. and Müller, H.-G. (2008). Distance-based clustering of sparsely observed stochastic processes, with applications to online auctions. Ann. Appl. Stat. 2 1056-1077.
32. Plummer, M. (2008). Penalized loss functions for Bayesian model comparison. Biostatistics 9 523-539.
33. Qin, L.-X. and Self, S. G. (2006). The clustering of regression models method with applications in gene expression data. Biometrics 62 526-533.
34. Quandt, R. E. and Ramsey, J. B. (1978). Estimating mixtures of normal distributions and switching regressions. J. Amer. Statist. Assoc. 73 730-738.
35. R Development Core Team. (2012). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0. Available at http://www.R-project.org.
36. Ramoni, M. F., Sebastiani, P. and Kohane, I. S. (2002). Cluster analysis of gene expression dynamics. Proc. Natl. Acad. Sci. USA 99 9121-9126 (electronic).
37. Richardson, S. and Green, P. J. (1997). On Bayesian analysis of mixtures with an unknown number of components. J. Roy. Statist. Soc. Ser. B 59 731-792.
38. Schwarz, G. (1978). Estimating the dimension of a model. Ann. Statist. 6 461-464.
39. Spiegelhalter, D. J., Best, N. G., Carlin, B. P. and van der Linde, A. (2002). Bayesian measures of model complexity and fit. J. R. Stat. Soc. Ser. B Stat. Methodol. 64 583-639.
40. Spiessens, B., Verbeke, G. and Komárek, A. (2002). A SAS-macro for the classification of longitudinal profiles using mixtures of normal distributions in nonlinear and generalised linear mixed models. Technical Report, Biostatistical Center, Catholic Univ. Leuven, Leuven.
41. Stephens, M. (2000). Dealing with label switching in mixture models. J. R. Stat. Soc. Ser. B Stat. Methodol. 62 795-809.
42. Titterington, D. M., Smith, A. F. M. and Makov, U. E. (1985). Statistical Analysis of Finite Mixture Distributions. Wiley, Chichester.
43. Verbeke, G. and Lesaffre, E. (1996). A linear mixed-effects model with heterogeneity in the random-effects population. J. Amer. Statist. Assoc. 91 217-221.
44. Villarroel, L., Marshall, G. and Barón, A. E. (2009). Cluster analysis using multivariate mixed effects models. Stat. Med. 28 2552-2565.
45. Witten, D. M. (2011). Classification and clustering of sequencing data using a Poisson model. Ann. Appl. Stat. 5 2493-2518.