A new look at the statistical model identification, IEEE Transactions on Automatic Control, vol.19, issue.6, pp.716-723, 1974. ,
DOI : 10.1109/TAC.1974.1100705
Assessing a mixture model for clustering with the integrated completed likelihood, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.22, issue.7, pp.719-725, 2000. ,
DOI : 10.1109/34.865189
Using the classification likelihood to choose the number of clusters, Comp. Sci. Stat, vol.29, issue.2, pp.451-457, 1997. ,
Choosing models in model-based clustering and discriminant analysis, Journal of Statistical Computation and Simulation, vol.2, issue.1, pp.49-71, 1999. ,
DOI : doi: 10.1214/aos/1176344136
URL : https://hal.archives-ouvertes.fr/inria-00073175
Choosing the Number of Component Clusters in the Mixture-Model Using a New Informational Complexity Criterion of the Inverse-Fisher Information Matrix, 1992. ,
DOI : 10.1007/978-3-642-50974-2_5
A classification EM algorithm for clustering and two stochastic versions, Computational Statistics & Data Analysis, vol.14, issue.3, pp.315-332, 1992. ,
DOI : 10.1016/0167-9473(92)90042-E
URL : https://hal.archives-ouvertes.fr/inria-00075196
Maximum likelihood from incomplete data via the EM algorithm, J. Roy. Statist. Soc. Ser. B, vol.39, issue.1, pp.1-38, 1977. ,
Percentage Points of a Test for Clusters, Journal of the American Statistical Association, vol.64, issue.328, p.1647, 1969. ,
DOI : 10.1080/01621459.1958.10501479
How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis, The Computer Journal, vol.41, issue.8, pp.578-588, 1998. ,
DOI : 10.1093/comjnl/41.8.578
MCLUST version 3 for R: Normal mixture modeling and model-based clustering, 2006. ,
Bayes Factors, Journal of the American Statistical Association, vol.2, issue.430, pp.773-795, 1995. ,
DOI : 10.1080/01621459.1995.10476572
Recursive estimation of prior probabilities using a mixture, IEEE Transactions on Information Theory, vol.23, issue.2, pp.203-211, 1977. ,
DOI : 10.1109/TIT.1977.1055693
389: Separating Mixtures of Normal Distributions, Biometrics, vol.31, issue.3, pp.767-769, 1975. ,
DOI : 10.2307/2529563
Discriminant Analysis and Statistical Pattern recognition, Wiley Series in Probability and Statistics, 1992. ,
DOI : 10.1002/0471725293
Clustering mutational spectra via classification likelihood and markov chain monte carlo algorithms, Journal of Agricultural, Biological, and Environmental Statistics, vol.8, issue.1, pp.19-37, 2001. ,
DOI : 10.1198/108571101300325111
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.199.41
The Schwarz criterion and related methods for normal linear models, Biometrika, vol.85, issue.1, pp.13-27, 1998. ,
DOI : 10.1093/biomet/85.1.13
R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, 2010. ,
Estimating the integrated likelihood via posterior simulation using the harmonic mean identity, Bayesian Statistics, vol.8, pp.1-45, 2007. ,
The Identification Problem for a Mixture of Observations from Two Normal Populations, Technometrics, vol.12, issue.4, pp.911-918, 1972. ,
DOI : 10.1080/00401706.1972.10488986
Mixture Densities, Maximum Likelihood and the EM Algorithm, SIAM Review, vol.26, issue.2, pp.195-239, 1984. ,
DOI : 10.1137/1026034
Estimating the Dimension of a Model, The Annals of Statistics, vol.6, issue.2, pp.461-464, 1978. ,
DOI : 10.1214/aos/1176344136
Clustering Methods Based on Likelihood Ratio Criteria, Biometrics, vol.27, issue.2, pp.387-397, 1971. ,
DOI : 10.2307/2529003
Clustering Criteria and Multivariate Normal Mixtures, Biometrics, vol.37, issue.1, pp.35-43, 1981. ,
DOI : 10.2307/2530520
Statistical analysis of finite mixture distributions, Wiley series in probability and mathematical statistics, 1985. ,