PUBLICATIONS 

Gilles Celeux

Journals 

• M. Sedki, G. Celeux and C. Maugis-Rabusseau "Variable selection in model-based clustering and discriminant analysis with a regularization approach" Advances in Data Analysis and Classification, (2018).
• M.Gallopin, G. Celeux, F. Jaffrezic and A. Rau "A model selection criterion for model-based clustering of annotated gene expression data" Stat Appl Genet Mol Biol, 14, 413-428, (2015).
• J.-P. Baudry and G. Celeux "EM for mixtures-Initialization requires special care" Statistics and Computing, 25, 713-726, (2015).
• A. Rau, C. Maugis-Rabusseau, M.-L. Martin-Magniette and G. Celeux "Co-expression analysis of high-throughput transcriptome sequencing data with Poisson mixture models" Bioinformatics 31, 1420-1427, (2015).
• C. Keribin, V. Brault, G. Celeux and G. Govaert "Estimation and selection for the latent block model on categorical data" Statistics and Computing, 25, 1201-1216, (2015).
• J.-P. Baudry, M. Cardoso, G. Celeux, M. J. Amorim and A. Sousa Ferreira "Enhancing the selection of a model-based clustering with external categorical variables" Advances in Data Analysis and Classification, 9, 177-196, (2015).
• G. Celeux and V. Robert "Towards an objective team efficiency rate in basketball" Journal of the SFdS 156, 2, 51-68, (2015).
• S. Fu, G. Celeux, N. Bousquet and M. Couplet "Bayesian inference for inverse problems occurring in uncertainty analysis" International Journal for Uncertainty Quantification, 5, 73-98, (2015).
• R. Lebret, S. Iovleff, F. Langrognet, C. Biernacki, G. Celeux, and G. Govaert "Rmixmod: The R Package of the Model-Based Unsupervised, Supervised and Semi-Supervised Classification Mixmod Library", Journal of Statistical Software, 68, Issue 6 (2015). 
• R. Fouchereau, G. Celeux and P. Pamphile "Probabilistic modeling of S-N curves" International Journal of Fatigue, 68, 217-223, (2014).
• G. Celeux, M.-L. Martin-Magniette, C. Maugis-Rabusseau and A. E. Raftery "Comparing Model Selection and Regularization Approaches to Variable Selection in Model-Based Clustering" Journal of the SFdS 155, 2, 57-71, (2014).
• A. Rau, M. Gallopin, G. Celeux and F. Jaffrézic "Data-based filtering for replicated high-throughput transcriptome sequencing experiments" Bioinformatics 29, 2146-2152, (2013).
• V. Vandewalle, C. Biernacki, G. Celeux and G. Govaert "A predictive deviance criterion for selecting a generative model in semi-supervised classification", Computational Statistics and Data Analysis 24, 220-236, (2013).
• G. Celeux, M. El Anbari, J.-M. Marin and C. P. Robert "Regularization in regression: comparing Bayesian and frequentist methods in a poorly informative situation", Bayesian Analysis 7, 477-502, (2012). 
• C. Maugis, G. Celeux and M.-L. Martin-Magniette "Variable selection in Model-based Discriminant Analysis", Journal of Multivariate Ananlysis, 102, 1374-1387, (2011). 
• G. Celeux and M.-L. Martin-Magniette, C. Maugis, and A. E. Raftery "Letter to the Editor", Journal of the American Statistical Association, 106, 383-383, (2011). 
• C. Bouveyron, G. Celeux, and S. Girard "Intrinsic dimension estimation by maximum likelihood in isotropic probabilistic PCA", Pattern Recognition Letters, 32 , 1706-1713, (2011). 
• C. Biernacki, G. Celeux, and G. Govaert "Exact and Monte Carlo calculations of integrated likelihoods for the latent class model", Journal of Statistical Planning and Inference, 140 , 2991-3002, (2010). 
• J.-P. Baudry, A. Raftery, G. Celeux, K. Lo, and R. Gottardo "Combining mixture components for clustering", Journal of Computational and Graphical Statistics, 19 , 332-353, (2010). 
• P. Barbillon, G. Celeux, A. Grimaud, Y. Lefebvre and E. de Rocquigny "Non linear methods for inverse statistical problems", Computational Statistics and Data Analysis, 55 , 132-142, (2010). 
• G. Celeux, A. Grimaud, Y. Lefebvre and E. de Rocquigny "Identifying variability in multivariate systems through linearised inverse methods", Inverse Problems in Engineering, 18, 401-415, (2010). 
• C. Maugis, G. Celeux and M.-L. Martin-Magniette "Variable selection for Clustering with Gaussian Mixture Models", Biometrics, 53, 3872-3882, (2009). 
• C. Maugis, G. Celeux and M.-L. Martin-Magniette "Variable selection in model-based clustering: A general variable role modeling", Computational Statistics and Data Analysis, 65, 701-709, (2009). 
• C. Maugis, M.-L. Martin-Magniette, J.-P. Tamby, J.-P. Renou, A. Lecharny, S. Aubourg and G. Celeux "Sélection de variables pour la classification par mélanges gaussiens pour prédire la fonction des gènes orphelins", La Revue Modulad, 40, 69-80, (2009). 
• G. Celeux and J.-B. Durand "Selecting Hidden Markov Model State Number with Cross-Validated Likelihood", Computational Statistics, , 23, 541-564, (2008). 
• G. Celeux "Book review: Data Clustering: Theory Algorithms and Applications by Gan, G., Chaoqun, MA, and Wu, J.", Biometrics, 64, 656-657, (2008). 
• H. Bertholon, N. Bousquet and G. Celeux "An alternative competing risk model to the Weibull distribution for modelling aging in lifetime data analysis", Lifetime Data Analysis, 12, 481-504, (2006). 
• I. Brito, G. Celeux and A. M. Ferreira "Combining methods in supervised classification: a comparative study on discrete and continuous problems", REVSTAT 4, 3, (2006).
• G. Bouchard and G. Celeux "Selection of generative models in Classification", IEEE Trans on PAMI,28, 544-554, (2006). 
• C. Biernacki, G. Celeux, G. Govaert, G. and F. Langrognet "Le logiciel MIXMOD d'analyse de mélange pour la classification et l'analyse discriminante" La Revue de Modulad, 35 , 25-44, (2006). 
• C. Biernacki, G. Celeux, G. Govaert and F. Langrognet "Model-based cluster analysis and discriminant analysis with the MIXMOD software", Computational Statistics and Data Analysis, 51, 587-600. (2006). 
• G. Celeux, F. Forbes, C. P. Robert and D. M. Titterington "Deviance information criteria for missing data models" (with Discussion)  Bayesian Analysis, 1, 4, 651-706. (2006) 
• G. Celeux, F. Corset, A. Lannoy and B. Ricard "Designing a Bayesian network for preventive maintenance from expert opinion in a rapid and reliable way". Reliability Engineering & System Safety, 91, 772-777. (2006).
• G. Celeux G., J.-M. Marin and C. Robert "Sélection bayésienne de variables en régression linéaire". Journal de la Société Française de Statistique, 147, 59-79, (2006).
• G. Celeux, J.-M. Marin and C. P. Robert "Iterated importance sampling in missing data problems". Computational Statistics and Data Analysis, 50, 3386-3404. (2006).
• G. Celeux, O. Martin and Ch. Lavergne "Mixture of linear mixed models - Application to repeated data clustering". Statistical Modelling, 5, 243-267. (2005).
• J.-B. Durand, L. Bozzi, G. Celeux, and Ch. Derquenne "Analyse de courbes de consommation électrique par chaînes de Markov cachées". Revue de Statistique Appliquée 52 (4), 71-91. (2004).
• G. Celeux, J. Nascimento and J. Marques "Learning switching dynamic models for objects tracking". Pattern Recognition, 37, 1841-1853, (2004).
• C. Biernacki, G. Celeux and G. Govaert  "Choosing starting values for the EM algorithm for getting the highest likelihood in multivariate Gaussian mixture models''. Computational Statistics and Data Analysis, 41, 561-575, (2003).
• G. Celeux,  F. Forbes and N. Peyrard "EM Procedures Using Mean Field-Like Approximations for Markov Model-Based Image Segmentation". Pattern Recognition,36, 131-144, (2003).
• G. Celeux, F. Corset, M.-A. Garnero and C. Breuils "Accounting for inspection errors and change in maintenance behaviour".  Journal of Management Mathematics, 13, 51-59, (2002) .
• G. Celeux,  S. Chrétien, F. Forbes and A. Mkhadri "A component-wise EM algorithm for mixtures". Journal of Computational and Graphical Statistics, 10, 699-712, (2001).
• G. Celeux "Situations de maintenance à structure de données incomplètes". Journal de la Société Française de Statistitique, 141, 3,  43-59 (2001).
• G. Celeux, M. Hurn and C. Robert " Computational and inferential difficulties with mixture posterior distributions ". Journal of the American Statistical Association, 95, 957-970, (2000).
• C. Biernacki, G. Celeux and G. Govaert " Assessing a mixture model for clustering with the integrated completed likelihood". IEEE Trans. on PAMI, 22, 719-725, (2000).
• G. Celeux, M. Persoz, J. Ngatchou-Wandji and F. Perrot " Bayesian Modelling of PWR Vessels Flaw Distributions". Reliability Engineering and System Safety, 66, 243-252, (1999).
• C. Biernacki, G. Celeux and G. Govaert " An improvement of the NEC criterion for assessing the number of components arising from a mixture". Pattern Recognition Letters, 20, 267-272, (1999).
• A. Mkhadri, G. Celeux and A. Nasrollah - " Regularization in Discriminant Analysis: a survey". Computational Statistics and Data Analysis, 23, 403-423, (1997).
• Bensmail H., Celeux G., Raftery A. and Robert C. "Inference in Model-Based Cluster Analysis". Statistics and Computing, 7, 1-10, (1997).
• Bensmail H. and Celeux G. "Regularized Gaussian discriminant analysis through eigenvalue decomposition". Journal of the American Statistical Association, 91, 1743-48. (1996).
• G. Celeux and G. Soromenho "An entropy criterion for assessing the number of clusters in a mixture model". Journal of Classification, 13, 195-212, (1996) .
• G. Celeux, D. Chauveau and J. Diebolt "Some Stochastic versions of the EM Algorithm". Journal of Statistical Computation and Simulation, 55, 287-314.(1996).
• G. Celeux and G. Govaert "Parsimonious Gaussian models in cluster analysis". Pattern Recognition, 28, 781-793, (1995).
• C. Robert, G. Celeux and J. Diebolt "Bayesian estimation of hidden Markov chains: A stochastic implementation". Statistics and Probability letters, 16, 77-83, (1993).
• J. Diebolt and G. Celeux "Asymptotic Properties of a Stochastic EM Algorithm for Estimating Mixing Proportions". Stochastics Models, 9, 599-613, (1993).
• G. Celeux and G. Govaert "Comparison of the Mixture and the Classification Maximum likelihood in Cluster Analysis". Journal of Statistical Computation and Simulation, 47, 127-146, (1993).
• G. Celeux and Cl. Robert "Une histoire de discrétisation" (avec commentaires). La Revue de Modulad 11, 7-42, (1993).
• G. Celeux and G. Govaert "A Classification EM Algorithm for Clustering and Two Stochastic versions". Computational Statistics and Data Analysis, 14, 315-332. (1992).
• G. Celeux and J. Diebolt "A Stochastic Approximation type EM algorithm for the mixture problem". Stochastics and Stochastics Reports, 41, 119-134. (1992).
• G. Celeux and A. Mkhadri "Discrete Regularized Discriminant Analysis". Statistics and Computing, 2, 143-151, (1992).
• G. Celeux and G. Govaert "Clustering Criteria for Discrete Data and Latent Class Model". Journal of Classification, 8, 157-176. (1991).
• C. Soubiran, G. Celeux, J. Diebolt and Ch. Robert "Analyse de mélanges gaussiens pour de petits échantillons : application à la cinématique stellaire". Revue de Statistique Appliquée39 (3), 17-36. (1991).
• G. Celeux and J. Clairambault "Analyse Discriminante appliquée à l'étude du rythme cardiaque : développements méthodologiques". La Revue de Modulad8, 73-80, (1991).
• J. Clairambault and G. Celeux "Analyse Discriminante appliquée à l'étude du rythme cardiaque". La Revue de Modulad 8, 61-72, (1991).
• G. Celeux and J. Diebolt "Une version de type recuit simulé de l'algorithme EM". C. R. Acad. Sci. Paris t. 310 Série I, 119-124, (1990).
• G. Celeux and J. Diebolt "The EM and SEM Algorithms for Mixtures : Statistical and Numerical Aspects". Cahiers du Centre d'Etudes de Recherche Opérationnelle32, 135-151, (1990).
• G.Celeux and J.C. Turlot "Estimation de la qualité d'une règle discriminante". La Revue de Modulad 4, 37-46, (1989).
• G. Celeux "Classification and modèles". Rapport de recherche INRIA no810 (1987) and Revue de Statistique Appliquée36 (4), 43-58, (1988).
• G. Celeux and J. Diebolt "L'algorithme SEM : un algorithme d'apprentissage probabiliste pour la reconnaissance de mélanges de densités". Revue de Statistique Appliquée34 (2), 35-52, (1986).
• G. Celeux - "Etude exhaustive de l'algorithme de réallocation-recentrage dans un cas simple". RAIRO Recherche opérationnelle 20, 229-243, (1986).
• G. Celeux and J. Diebolt "The SEM algorithm : a probabilistic teacher algorithm derived from the EM algorithm for the mixture problem". Comp. Statis. Quaterly, 2, 73-82, (1985).
• G. Celeux, N. Lauro and Y. Lechevallier "Contributi dell' analysis multidimensionnale nello studio di gruppi clinici a priori mal definiti". Rivista di Statistica Applicata. 15, 93-117, (1982).
• G. Celeux and Y. Lechevallier "Méthodes de segmentation non paramétriques". Revue de Statistique Appliquée30(4), 39-53, (1982).
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  Conference Proceedings 
• Keribin C., Celeux G. and Robertt V.. "The Latent Block Model: a usefil model for high dimensional data", ISI 2017 - 61st World Statistics Congress. pp 1-6. Marrakesh, Morocco. (2017). 
• Keribin C., Brault V., Celeux G. and Govart G. "Model selection for the binary latent block model", COMPSTAT 2012: Proceedings in Computational Statistics. 
• Baudry, J.-P., Celeux, G. and Marin, J.-M. "Selecting models focussing on the modeller's purpose", COMPSTAT 2008: Proceedings in Computational Statistics (P. Brito, Ed.), Physica-Verlag, Heidelberg, pp. 337-348. (2008). 
• G. Celeux, "Mixture models for classification", Studies in Classification, Data Analysis, and Knowledge Organization, pp. 3-14. (2007). 
• N. Bousquet and G. Celeux, "Measures of Bayesian discrepancy between prior beliefs and data knowledge", ESREL 2006: Safety and reliability for managing risk, pp. 867-872. (2006). 
• F. Billy, N. Bousquet, G. Celeux and E. Remy, "Notions et mesures de cohérence bayésienne entre connaissance a priori et données observées", Lambda Mu 15, Lille. (2006). 
• F. Billy, N. Bousquet, G. Celeux and E. Remy.", "Inférence des paramètres d'une loi de Weibull - Approches classique et bayésienne", Lambda Mu 15, Lille. (2006). 
• F. Josse, F. Billy, N. Bousquet and G. Celeux, "Vraisemblance d'enchainements causaux : validation d'un explication a priori confrontée au retour d'expérience", Lambda Mu 15, Lille. (2006). 
• G. Celeux, F. Forbes and N. Peyrard "Modèlele de Potts avec champ externe et algorithme de type EM pour la segmentation d'image". RFIA 2004, Toulouse.
• G. Bouchard and G. Celeux "Supervised classification with spherical Gaussian mixtures." CLADG 2003, Bologna, pp. 75-78 (2003).
• G. Celeux, F. Corset, A. Lannoy and  B. Ricard "Designing a graphical model for preventive maintenance from expert opinion in a rapid and reliable way." Proceedings of ESREL 2002,  400-405, Lyon (2002).
• G. Celeux and A. Rodionov "A shock model for assessing component aging reliability." Proceedings of 22rd ESREDA Seminar Madrid  (2002).
• O. Martin,  G.Celeux, and C. Lavergne " Classification de données répétées issues de puces à ADN. Application à l'analyse de profils d'expression."  JOBIM 2002,  pp. 81-92 (2002).
• A. Guérin-Dugué and G. Celeux "Discriminant analysis on dissimilarity data : A new fast Gaussian like algorithm". Artificial Intelligence and Statistics 2001, 202-207, Key West (2001).
• I. Brito and G. Celeux "Discriminant analysis by Hierarchical coupling in EDDA context". Data Analysis, Classification and related Methods, 175-180, Namur (2000).
• A. Ferreira, G. Celeux and H. Bacelar "Discrete discriminant analysis: the performance of combining models by a hierarchical coupling approach". Data Analysis, Classification and related Methods, 181-186, Namur (2000).
• I. Brito and G. Celeux "Combining EDDA's models". Applied Stochastic Models and Data Analysis, 136-141, Lisbonne (1999).
• A. Ferreira, G. Celeux and H. Bacelar "Combining models in discriminant analysis - a hierarchical coupling approach". Applied Stochastic Models and Data Analysis, 159-164, Lisbonne (1999).B. Villain, B. Vérité, C. Biernacki, and G. Celeux "A practical approach of expert elicitation for Bayesian reliability analysis of aging". In ESREL 99, pp. 707-712, Munich (1999).
• K. Aubert, P. Bryla, G. Celeux, C. Lavergne and Y. Vernaz "A degradation model integrating prior kinetics parameters and qualitative in-service inspection data". In ESREL 99, pp. 743-748, Munich (1999).
• G. Celeux " Bayesian inference for Mixture: the label switching problem". Proceedings Compstat 98, pp. 227-232, Physica-Verlag (1998).M.-L. Monfort, G. Celeux, A. Mule and S. Muller "Estimation de la fiabilité d'un produit nouveau ou existant à améliorer à partir de retour d'expériences multiples and d'expertises". Actes de ln10, Arcachon (1997).
• A. Bracquemont, G. Celeux, E. Idée, A; Lannoy, S. Muller and M.-H. Ravaux "Effet de données manquantes de retour d'expérience sur l'estimation des paramètres d'une loi de fiabilité". Actes du 2me congrès Qualité and Sûreté de fonctionnement, Angers (1997).
• M. Bacha, G. Celeux, E. Idée, A. Lannoy and D. Vasseur "A dependence model for competing risk failure times". ESREL 96, Crète (1996).M. Bacha, G. Celeux, E. Idée, A. Lannoy and D. Vasseur "Dependency modelling for parallel pumps". ESREDA 96, Chamonix (1996).
• M. Bacha, G. Celeux, E. Idée, A. Lannoy and D. Vasseur "Apport d'un modèle de dépendance and du retour d'expérience à la conceptiond'un système série". X^e colloque national de Fiabilité and de Maintenabilité, Saint-Malo (1996).
• G. Celeux and G. Govaert "Fuzzy Clustering and Mixture Models". Contribued paper, Compstat 94, Springer-Verlag p.154-159 (1994).
• M. Bacha, G. Celeux, E. Idée, A. Lannoy and D. Vasseur "Estimation bayésienne des paramètres d'une loi de Weibull". Actes des rencontres Qualité and Sureté, Compiègne pp. 112-118 (1994).
• M. Bacha, G. Celeux, J. Diebolt and E. Idée "Estimation of the infimum of highly censored Weibull distributions". in New appoaches in Classification and Data Analysis, North-Holland, p. 533-538 (1994).
• G. Celeux and J. Clairambault "Estimations de chaînes de Markov cachées: méthodes and problèmes". in Méthodes Markoviennes en traitement du signal and images, GDR traitement du signal and images, CNRS, pp. 5-19, (1992).
• G. Celeux, G. Hébrail, A. Mkhadri and M. Suchard "Reduction of a Large-Scale and Ill-Conditioned Statistical Problem on Textual Data". Applied Sochastic Models and Data Analysis 5, 129-137, World Scientific (1991).
• G. Celeux and G. Govaert "Stochastic Algorithm for Clustering". Compstat 90, 3-8, Springer-Verlag (1990).
• G. Celeux and J. Diebolt "A probabilistic teacher algorithm for iterative maximum likelihood estimation". Classification and related topics of Data Analysis, éditeur H.H. Bock, 617-623, North Holland (1987).
• G. Celeux "Validity tests in cluster analysis using a probabilistic teacher algorithm". Compstat 86, 163-168, Springer-Verlag (1986).
• G. Celeux "Influence des stratégies d'initialisation de l'algorithme de réallocation-recentrage pour la reconnaissance de deux intervalles disjoints de la droite réelle". Actes des Journées de la SFC, ASU Lille, 56-61, (1986).
• M. Broniatowski, G. Celeux and J. Diebolt "Reconnaissance de mélanges de densités par un algorithme d'apprentissage probabiliste". Data Analysis and Informatics 3, 359-373, North Holland (1983).
• G. Celeux and Y. Lechevallier "Non parametric decision trees by Bayesian approach". Compstat 82, 161-166, Springer-Verlag (1982).
• M. Broniatowski, G. Celeux and J. Diebolt "A Gaussian mixture recognition method". Methods of Operat. Research 43, 299-317, (1981).
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  Books 
• C.  Bouveyron, G. Celeux, B. Murphy and A. Raftery, “Model-based Clustering and Classification for Data Science”. Cambridge University Press (2019).
• S.  Fruhwirth-Schnatter, G. Celeux and C.P. Robert (eds), “Handbook of Mixture Analysis”. Chapman & Hall / CRC (2018).
• M. Bacha, G. Celeux, E. Idée, A. Lannoy and D. Vasseur "Estimation de durées de vie fortement censurées". Editions Eyrolles, Paris (1998).
• G. Celeux and J.-P. Nakache "Analyse Discriminante sur variables qualitatives". Polytechnica, Paris (1994).
• G. Celeux "Analyse Discriminante sur variables continues". Collection Didactique 7, INRIA (1990).
• G. Celeux, E. Diday, G. Govaert, Y. Lechevallier and H. Ralambondrainy "Classification automatique des données - aspects statistiques and informatiques". Dunod (1989).
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  Book Chapters 
• G. Celeux “EM methods for Finite Mixtures” in Handbook of Mixture Analysis. Editors S.  Fruhwirth-Schnatter, G. Celeux and C.P. Robert; Chapman & Hall/CRC (2018), chapter 2, pp. 21-40.
• G. Celeux, K. Kamary, G. Malsiner-Walli, J.-M.M. Marin and C. P.. Robert “Computational Solutions for Bayesian Inference in Mixture Models” in Handbook of Mixture Analysis. Editors S.  Fruhwirth-Schnatter, G. Celeux and C.P. Robert; Chapman & Hall/CRC (2018), chapter 5, pp. 73-96.
• G. Celeux,  S.  Fruhwirth-Schnatter and C. P.. Robert “Model Selection for Mixture Models - Perspectives and Strategies” in Handbook of Mixture Analysis. Editors S.  Fruhwirth-Schnatter, G. Celeux and C.P. Robert; Chapman & Hall/CRC (2018), chapter 7,  pp. 117-154.
• G. Celeux and G. Govaert "Latent Class Models for Categorical Data" in Handbook of Cluster Analysis. Editors C. Hennig, M. Meila, F.Murtagh and R. Rocci; Chapman & Hall/CRC (2015), chapter 9, pp. 173-194.
• G. Celeux "Bayesian Inference and Markov Chain Monte Carlo Methods" in Construction Reliability. Editors J. Baroth, D. Breysse and F. Schefs; John Wiley (2011), chapter 11, pp. 207-226.
• G. Celeux "Discriminant Analysis" in Data Analysis. Editor G. Govaert; John Wiley (2009), chapter 7, pp. 181-214.
• G. Celeux "Analyse Discriminante" dans Analyse des données. Editeur G. Govaert; Hermès (2003), chapitre 7, pp. 201-234.
• G. Celeux "Modèles Probabilistes en Classification". dans Modèles pour l'analyse des Données Multidimensionnelles. Editeurs J.J. Droesbeke, B. Fichet and Ph. Tassi; éditions Economica, pp. 165-214. (1992).
• G. Celeux "Résultats Asymptotiques and Validation en Classification". dans Modèles pour l'analyse des Données Multidimensionnelles. Editeurs J.J. Droesbeke, B. Fichet and Ph. Tassi; éditions Economica, pp. 215-240. (1992).
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