BayesBinMix: An R package for model based clustering of multivariate binary data

R Journal

Volumn 9, Issue 1, 2017, Pages 403-420

  Papastamoulis, Panagiotis ^a   Rattray, Magnus ^a  

^a UNIVERSITY OF MANCHESTER   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 85021434863     PISSN: None     EISSN: 20734859     Source Type: Journal    
DOI: 10.32614/rj-2017-022     Document Type: Article

References (47)

1. L. Abel, J.-L. Golmard, and A. Mallet. An autologistic model for the genetic analysis of familial binary data. American journal of human genetics, 53(4):894, 1993.
2. Y. Al-Ohali, M. Cheriet, and C. Suen. Databases for recognition of handwritten Arabic cheques. Pattern Recognition, 36(1):111-121, 2003. URL https://doi.org/10.1016/s0031-3203(02)00064-x.
3. E. S. Allman, C. Matias, and J. A. Rhodes. Identifiability of parameters in latent structure models with many observed variables. The Annals of Statistics, pages 3099-3132, 2009. URL https://doi.org/10.1214/09-aos689.
4. G. Altekar, S. Dwarkadas, J. P. Huelsenbeck, and F. Ronquist. Parallel Metropolis coupled Markov chain Monte Carlo for Bayesian phylogenetic inference. Bioinformatics, 20(3):407-415, 2004. URL https://doi.org/10.1093/bioinformatics/btg427.
5. C. Biernacki, G. Celeux, and G. Govaert. Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(7):719-725, 2000. URL https://doi.org/10.1109/34.865189.
6. C. Bishop. Pattern Recognition and Machine Learning. Springer-Verlag, 2006.
7. D. Böhning. Computer-Assisted Analysis of Mixtures and Applications: Meta-Analysis, Disease Mapping and Others, volume 81. CRC press, 2000.
8. M. á. Carreira-Perpiñán and S. Renals. Practical identifiability of finite mixtures of multivariate Bernoulli distributions. Neural Computation, 12(1):141-152, 2000. URL https://doi.org/10.1162/089976600300015925.
9. A. P. Dempster, N. M. Laird, and D. B. Rubin. Maximum likelihood from incomplete data via the EM algorithm (with discussion). Journal of the Royal Statistical Society B, 39:1-38, 1977. URL http://www.jstor.org/stable/2984875.
10. S. Frühwirth-Schnatter. Finite Mixture and Markov Switching Models. Springer-Verlag, New York, 2006. URL https://doi.org/10.1007/978-0-387-35768-3.
11. A. E. Gelfand and A. F. M. Smith. Sampling-based approaches to calculating marginal densities. Journal of American Statistical Association, 85:398-409, 1990. URL https://doi.org/10.2307/2289776.
12. C. J. Geyer. Markov chain Monte Carlo maximum likelihood. In Proceedings of the 23rd Symposium on the Interface, pages 156-163. Interface Foundation, Fairfax Station, Va, 1991.
13. C. J. Geyer and E. A. Thompson. Annealing Markov chain Monte Carlo with applications to ancestral inference. Journal of the American Statistical Association, 90(431):909-920, 1995. URL https://doi.org/10.1080/01621459.1995.10476590.
14. W. R. Gilks, S. Richardson, and D. J. Spiegelhalter. Markov Chain Monte Carlo in Practice. Chapman and Hall, London, 1996.
15. P. J. Green. Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika, 82(4):711-732, 1995.
16. B. Grün and F. Leisch. Fitting finite mixtures of generalized linear regressions in R. Computational Statistics & Data Analysis, 51(11):5247-5252, 2007. URL https://doi.org/10.1016/j.csda.2006.08.014.
17. B. Grün and F. Leisch. FlexMix version 2: Finite mixtures with concomitant variables and varying and constant parameters. Journal of Statistical Software, 28(4):1-35, 2008. URL https://doi.org/10.18637/jss.v028.i04.
18. R. Ilin. Unsupervised learning of categorical data with competing models. IEEE transactions on neural networks and learning systems, 23(11):1726-1737, 2012. URL https://doi.org/10.1109/tnnls.2012.2213266.
19. H. Jeffreys. An invariant form for the prior probability in estimation problems. Proceedings of the Royal Society of London, Series A, Mathematical and Physical Sciences, 186:453-461, 1946. URL https://doi.org/10.1098/rspa.1946.0056.
20. A. Juan and E. Vidal. On the use of Bernoulli mixture models for text classification. Pattern Recognition, 35(12):2705-2710, 2002. URL https://doi.org/10.1016/s0031-3203(01)00242-4.
21. F. Leisch. FlexMix: A general framework for finite mixture models and latent class regression in R. Journal of Statistical Software, 11(8):1-18, 2004. URL https://doi.org/10.18637/jss.v011.i08.
22. F. Leisch. A toolbox for k-centroids cluster analysis. Computational Statistics & Data Analysis, 51(2): 526-544, 2006. URL https://doi.org/10.1016/j.csda.2005.10.006.
23. T. Li. A general model for clustering binary data. In Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, pages 188-197. ACM, 2005. URL https://doi.org/10.1145/1081870.1081894.
24. M. Lichman. UCI machine learning repository, 2013. URL http://archive.ics.uci.edu/ml.
25. B. G. Lindsay. Mixture models: Theory, geometry and applications. In NSF-CBMS Regional Conference Series in Probability and Statistics, pages i-163. JSTOR, 1995.
26. J. S. Liu. The collapsed Gibbs sampler in Bayesian computations with applications to a gene regulation problem. Journal of the American Statistical Association, 89(427):958-966, 1994. URL https://doi.org/10.2307/2290921.
27. J. M. Marin, K. Mengersen, and C. P. Robert. Bayesian modelling and inference on mixtures of distributions. Handbook of Statistics, 25(1):577-590, 2005. URL https://doi.org/10.1016/s0169-7161(05)25016-2.
28. J. G. McLachlan and D. Peel. Finite Mixture Models. John Wiley & Sons, New York, 2000. URL https://doi.org/10.1002/0471721182.
29. A. Nobile and A. T. Fearnside. Bayesian finite mixtures with an unknown number of components: The allocation sampler. Statistics and Computing, 17(2):147-162, 2007. URL https://doi.org/10.1007/s11222-006-9014-7.
30. P. Papastamoulis. Handling the label switching problem in latent class models via the ECR algorithm. Communications in Statistics-Simulation and Computation, 43(4):913-927, 2014. URL https://doi.org/10.1080/03610918.2012.718840.
31. P. Papastamoulis. Label.switching: An R package for dealing with the label switching problem in MCMC outputs. Journal of Statistical Software, 69(1):1-24, 2016. URL https://doi.org/10.18637/jss.v069.c01.
32. P. Papastamoulis and G. Iliopoulos. Reversible jump MCMC in mixtures of normal distributions with the same component means. Computational Statistics & Data Analysis, 53(4):900-911, 2009. URL https://doi.org/10.1016/j.csda.2008.10.022.
33. P. Papastamoulis and G. Iliopoulos. An artificial allocations based solution to the label switching problem in Bayesian analysis of mixtures of distributions. Journal of Computational and Graphical Statistics, 19:313-331, 2010.URL https://doi.org/10.1198/jcgs.2010.09008.
34. P. Papastamoulis and G. Iliopoulos. On the convergence rate of random permutation sampler and ECR algorithm in missing data models. Methodology and Computing in Applied Probability, 15(2): 293-304, 2013. ISSN 1573-7713. URL https://doi.org/10.1007/s11009-011-9238-7.
35. P. Papastamoulis, M.-L. Martin-Magniette, and C. Maugis-Rabusseau. On the estimation of mixtures of Poisson regression models with large number of components. Computational Statistics & Data Analysis, 93:97-106, 2016. URL https://doi.org/10.1016/j.csda.2014.07.005.
36. M. Plummer, N. Best, K. Cowles, and K. Vines. CODA: Convergence diagnosis and output analysis for MCMC. R News, 6(1):7-11, 2006. URL https://journal.r-project.org/archive/.
37. R. A. Redner and H. F. Walker. Mixture densities, maximum likelihood and the EM algorithm. SIAM review, 26(2):195-239, 1984. URL https://doi.org/10.1137/1026034.
38. Revolution Analytics and S.Weston. Foreach: Foreach Looping Construct for R, 2014. URL http://CRAN.Rproject.org/package=foreach. R package version 1.4.2.
39. Revolution Analytics and S.Weston. doParallel: Foreach Parallel Adaptor for the 'parallel' Package, 2015. URL http://CRAN.R-project.org/package=doParallel. R package version 1.0.10.
40. S. Richardson and P. J. Green. On Bayesian analysis of mixtures with an unknown number of components. Journal of the Royal Statistical Society B, 59(4):731-758, 1997. URL https://doi.org/10.1111/1467-9868.00095.
41. C. E. Rodríguez and S. G. Walker. Label switching in Bayesian mixture models: Deterministic relabeling strategies. Journal of Computational and Graphical Statistics, 23(1):25-45, 2014. URL https://doi.org/10.1080/10618600.2012.735624.
42. G. Schwarz. Estimating the dimension of a model. The Annals of Statistics, 6(2):461-464, 1978. URL https://doi.org/10.1214/aos/1176344136.
43. M. Stephens. Bayesian analysis of mixture models with an unknown number of components-an alternative to reversible jump methods. The Annals of Statistics, 28(1):40-74, 2000a.
44. M. Stephens. Dealing with label switching in mixture models. Journal of the Royal Statistical Society B, 62(4):795-809, 2000b. URL https://doi.org/10.1111/1467-9868.00265.
45. Z. Sun, O. Rosen, and A. R. Sampson. Multivariate Bernoulli mixture models with application to postmortem tissue studies in schizophrenia. Biometrics, 63(3):901-909, 2007. ISSN 1541-0420. URL https://doi.org/10.1111/j.1541-0420.2007.00762.x.
46. M. Tanner and W.Wong. The calculation of posterior distributions by data augmentation. Journal of the American Statistical Association, 82(398):528-540, 1987. URL https://doi.org/10.2307/2289457.
47. A. White, J. Wyse, and T. B. Murphy. Bayesian variable selection for latent class analysis using a collapsed Gibbs sampler. Statistics and Computing, 26(1-2):511-527, 2016. URL https://doi.org/10.1007/s11222-014-9542-5.