{"id":48,"date":"2012-04-16T11:45:03","date_gmt":"2012-04-16T09:45:03","guid":{"rendered":"http:\/\/pageperso.math.univ-toulouse.fr\/reference\/?page_id=48"},"modified":"2020-10-09T13:45:47","modified_gmt":"2020-10-09T12:45:47","slug":"publications","status":"publish","type":"page","link":"https:\/\/perso.math.univ-toulouse.fr\/maugis\/publications\/","title":{"rendered":"Publications"},"content":{"rendered":"<p><!--  Articles  --><\/p>\n<h3 style=\"text-align: center\"><b><a name=\"articles\"><\/a><span style=\"color: #2c4474;font-family: 'Comic Sans MS'\">Papers :<\/span><\/b><\/h3>\n<ul>\n<li>De Castro, Y., Gadat, S., Marteau, C. and Maugis-Rabusseau, C. (2020)<br \/>\nSupermix : sparse regularization for mixtures.<br \/>\n<em>\u00a0Annals of Statistics<\/em>, to appear.\u00a0<br \/>\n[<a href=\"https:\/\/hal.archives-ouvertes.fr\/hal-02190117v2\/document\" target=\"_blank\">Hal-02190117<\/a>]<\/li>\n<li>Godichon-Baggioni, A., Maugis-Rabusseau, C. and Rau, A. (2020)<br \/>\nMulti-view cluster aggregation and splitting with an application to multi-omic breast cancer data.<br \/>\n<em>Annals of Applied Statistics<\/em>, 14(2), 752-767.\u00a0<br \/>\n[<a href=\"https:\/\/hal.archives-ouvertes.fr\/hal-01916941\/document\" target=\"_blank\">Hal-01916941<\/a>] [<a href=\"https:\/\/github.com\/andreamrau\/maskmeans\" target=\"_blank\">maskmeans<\/a>]<\/li>\n<li><a href=\"http:\/\/perso.math.univ-toulouse.fr\/gadat\/\" target=\"_blank\">Gadat, S. <\/a>, Kahn, J., <a href=\"http:\/\/math.univ-lyon1.fr\/~marteau\/\" target=\"_blank\">Marteau, C.<\/a> and Maugis-Rabusseau, C.\u00a0(2019)<br \/>\nParameter recovery in two-component contamination mixtures: the L2 strategy\u00a0<br \/>\n<em>Annales de l&#8217;IHP<\/em>, 56 (2), 1391-1418.<br \/>\n[<a href=\"https:\/\/arxiv.org\/abs\/1604.00306\" target=\"_blank\">arXiv:1604.00306<\/a>] [<a href=\"https:\/\/hal.archives-ouvertes.fr\/hal-01713035\/\" target=\"_blank\">Hal-01713035<\/a>]<\/li>\n<li><a href=\"http:\/\/godichon.perso.math.cnrs.fr\/\" target=\"_blank\">Godichon-Baggioni, A.<\/a>, Maugis-Rabusseau, C. and\u00a0<a href=\"http:\/\/andrea-rau.com\" target=\"_blank\">Rau, A.<\/a>\u00a0(2019)<br \/>\nClustering transformed compositional data using\u00a0<em>K<\/em>-means, with applications in gene expression and bicycle sharing system data.<br \/>\n<em>Journal of Applied Statistics<\/em>, 46, 47-65.<br \/>\n[<a href=\"http:\/\/arxiv.org\/abs\/1704.06150\" target=\"_blank\">arXiv:1704.06150<\/a>]\u00a0[<a href=\"https:\/\/hal.archives-ouvertes.fr\/hal-01511574v2\/document\" target=\"_blank\">Hal-01511574<\/a>] [<a href=\"https:\/\/bioconductor.org\/packages\/release\/bioc\/html\/coseq.html\" target=\"_blank\">coseq<\/a>]<\/li>\n<li>Celeux, G., Maugis-Rabusseau, C. and Sedki, M. (2019).\u00a0<br \/>\nVariable selection in model-based clustering and discriminant analysis with regularization approach.<br \/>\n<em>Advances in Data Analysis and Classification<\/em>,13 (1), 259-278.\u00a0<br \/>\n<a href=\"https:\/\/tel.archives-ouvertes.fr\/hal-01053784\/document\" target=\"_blank\">[Hal-01053784]<\/a><\/li>\n<li><a href=\"http:\/\/perso.math.univ-toulouse.fr\/laurent\" target=\"_blank\">Laurent, B.\u00a0<\/a>,\u00a0<a href=\"http:\/\/math.univ-lyon1.fr\/~marteau\/index.html\" target=\"_blank\">Marteau, C.<\/a>\u00a0and Maugis-Rabusseau, C. (2018).<br \/>\nMultidimensional two-component Gaussian mixtures detection.<br \/>\n<em>Annales de l&#8217;IHP (s\u00e9rie B)<\/em>, 54(2), 842-865.\u00a0<br \/>\n[<a href=\"http:\/\/arxiv.org\/abs\/1509.09129\" target=\"_blank\">arXiv:1509.09129<\/a>] [<a href=\"https:\/\/hal.archives-ouvertes.fr\/hal-01207072v1\" target=\"_blank\">Hal-01207072<\/a>]<\/li>\n<li><a href=\"http:\/\/andrea-rau.com\" target=\"_blank\">Rau, A<\/a>. &amp; Maugis-Rabusseau, C.(2018).<br \/>\nTransformation and model choice for RNA-seq co-expression analysis.\u00a0<br \/>\n<em>Briefings in Bioinformatics<\/em>, 19(3), 425-436.<br \/>\n[<a href=\"http:\/\/dx.doi.org\/10.1101\/065607\" target=\"_blank\">bioRxiv, doi: http:\/\/dx.doi.org\/10.1101\/065607<\/a>] [<a href=\"https:\/\/bioconductor.org\/packages\/release\/bioc\/html\/coseq.html\" target=\"_blank\">coseq<\/a>]<\/li>\n<li>G. Rigaill, S. Balzergue, V. Brunaud, E. Blondet, A. Rau, O. Rogier, J. Caius, C. Maugis-Rabusseau,\u00a0L. Soubigou-Taconnat, S. Aubourg, C. Lurin, M.-L. Martin-Magniette and E. Delannoy(2018)<br \/>\nSynthetic datasets for the identification of key ingredients for RNA-seq differential analysis.<br \/>\n<em>Briefings in bioinformatics,<\/em> 19(1), 65-76.<\/li>\n<li><a href=\"http:\/\/perso.math.univ-toulouse.fr\/laurent\" target=\"_blank\">Laurent, B.<\/a>,\u00a0<a href=\"http:\/\/math.univ-lyon1.fr\/~marteau\/index.html\" target=\"_blank\">Marteau, C.<\/a>\u00a0and Maugis-Rabusseau, C. (2016).\u00a0<br \/>\nNon asymptotic detection of two component mixtures with unknown means.\u00a0<br \/>\n<em>Bernoulli<\/em>, Volume 22, Number 1, 242-274.<br \/>\n<a href=\"http:\/\/arxiv.org\/pdf\/1304.6924v2.pdf\" target=\"_blank\">[arXiv:1304.6924]<\/a><\/li>\n<li>Papastamoulis, P.,\u00a0<a href=\"http:\/\/www.agroparistech.fr\/mia\/equipes:membres:page:marielaure\" target=\"_blank\">Martin-Magniette, M.-L.<\/a> and Maugis-Rabusseau, C. (2016).<br \/>\nOn the estimation of mixtures of Poisson regression models with large numbers of components.<br \/>\n<em>Computational Statistics and Data Analysis<\/em>, Volume 93, 97\u2013106.<\/li>\n<li><a href=\"http:\/\/www4.jouy.inra.fr\/gabi\/Presentation\/Annuaire-des-pages-personnelles\/RAU-Andrea\" target=\"_blank\">Rau, A.<\/a>,\u00a0Maugis-Rabusseau, C. ,\u00a0<a href=\"https:\/\/www6.inra.fr\/mia-paris\/Equipes\/Membres\/Marie-Laure-Martin-Magniette\" target=\"_blank\">Martin-Magniette, M.-L.<\/a>\u00a0and <a href=\"http:\/\/www.math.u-psud.fr\/select\/people\/celeux\/Welcome.html\" target=\"_blank\">Celeux, G.<\/a>\u00a0(2015).<br \/>\nCo-expression analysis of high-throughput transcriptome sequencing\u00a0data with Poisson mixture models. <em>Bioinformatics,<\/em> 31 (9), 1420-1427.<br \/>\n<span style=\"font-size: 16px\">\u00a0<\/span><a style=\"font-size: 16px\" href=\"http:\/\/hal.inria.fr\/index.php?halsid=rju9i0qaa7n07nhlav76vmrh65&amp;view_this_doc=inria-00638082&amp;version=1\" target=\"_blank\">[Hal-INRIA, RR-7786]<\/a><span style=\"font-size: 16px\">\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 [<\/span><a style=\"font-size: 16px\" href=\"http:\/\/cran.r-project.org\/web\/packages\/HTSCluster\/index.html\" target=\"_blank\">R package HTSCluster<\/a><span style=\"font-size: 16px\">].<\/span><\/li>\n<li><a href=\"http:\/\/www.math.u-psud.fr\/select\/people\/celeux\/Welcome.html\" target=\"_blank\">Celeux, G<\/a>., <a href=\"http:\/\/www.agroparistech.fr\/mia\/equipes:membres:page:marielaure\" target=\"_blank\">Martin-Magniette, M.-L.<\/a>, Maugis-Rabusseau, C. and <a href=\"http:\/\/www.stat.washington.edu\/raftery\/\" target=\"_blank\">Raftery, A. E.<\/a>(2014). Comparing model selection and regularization approaches to variable selection in model-based clustering.\u00a0<br \/>\n<em>Journal de la SFdS<\/em>, vol. 155, n\u00b02, pp 57-71.<br \/>\n<a href=\"http:\/\/journal-sfds.fr\/index.php\/J-SFdS\/article\/view\/277\/257\" target=\"_blank\">[Article]<\/a><\/li>\n<li>Maugis-Rabusseau, C. and\u00a0<a href=\"http:\/\/www.lsta.upmc.fr\/michelb.html\" target=\"_blank\">Michel, B.<\/a> (2013).\u00a0<br \/>\nAdaptive density estimation using finite Gaussian mixtures.\u00a0<br \/>\n<em>ESAIM : P&amp;S,\u00a0<\/em>Volume 17, pp 698 &#8211; 724.<br \/>\n[<a href=\"http:\/\/journals.cambridge.org\/action\/displayAbstract?fromPage=online&amp;aid=9064102&amp;fulltextType=RA&amp;fileId=S1292810012000183\" target=\"_blank\">Article<\/a>] \u00a0 \u00a0\u00a0<a href=\"http:\/\/arxiv.org\/abs\/1103.4253\" target=\"_blank\">[arXiv:1103.4253]<\/a><\/li>\n<li>Maugis-Rabusseau, C.,\u00a0<a href=\"http:\/\/www.agroparistech.fr\/mia\/equipes:membres:page:marielaure\" target=\"_blank\">Martin-Magniette, M.-L.<\/a>\u00a0and Pelletier, S. (2012).\u00a0<br \/>\nSelvarClustMV: Variable selection approach in model-based clustering allowing for missing values.\u00a0<br \/>\n<em>Journal de la SFdS, <\/em>vol. 153, n\u00b02, pp 21-36.<br \/>\n<a href=\"http:\/\/journal-sfds.fr\/index.php\/J-SFdS\/article\/view\/119\" target=\"_blank\"> [Article]<\/a><\/li>\n<li><a href=\"http:\/\/www.lsta.upmc.fr\/Baudry\/\" target=\"_blank\">Baudry, J.-P.<\/a>, Maugis, C. and\u00a0<a href=\"http:\/\/www.lsta.upmc.fr\/michelb.html\" target=\"_blank\">Michel, B.<\/a> (2012).\u00a0<br \/>\nSlope Heuristics: overview and implementation.\u00a0<br \/>\n<em>Statistics and Computing<\/em>, 22(2), p455-470.<br \/>\n<a href=\"http:\/\/www.springerlink.com\/content\/r9511g207904427l\/\" target=\"_blank\">[Article] <\/a>\u00a0 \u00a0 \u00a0<a href=\"http:\/\/hal.archives-ouvertes.fr\/hal-00461639\/fr\/\" target=\"_blank\">[Hal-INRIA, RR-7223]<\/a>\u00a0 \u00a0 \u00a0 [<a href=\"http:\/\/www.math.univ-toulouse.fr\/~maugis\/CAPUSHE.html\" target=\"_blank\">package CAPUSHE<\/a>]<\/li>\n<li>Maugis, C. ,\u00a0<a href=\"http:\/\/www.math.u-psud.fr\/select\/people\/celeux\/Welcome.html\" target=\"_blank\">Celeux, G.<\/a> and\u00a0<a href=\"http:\/\/www.agroparistech.fr\/mia\/equipes:membres:page:marielaure\" target=\"_blank\">Martin-Magniette, M.-L.<\/a> (2011).\u00a0<br \/>\nVariable selection in model-based discriminant analysis.\u00a0<br \/>\n<em>Journal of Multivariate Analysis<\/em>, 102, 1374-1387.<br \/>\n<a href=\"http:\/\/dx.doi.org\/10.1016\/j.jmva.2011.05.004\" target=\"_blank\">[Article]<\/a>\u00a0<a href=\"http:\/\/hal.archives-ouvertes.fr\/inria-00483229\/fr\/\" target=\"_blank\">[Hal -INRIA, RR-7290]<\/a><\/li>\n<li><a href=\"http:\/\/www.math.u-psud.fr\/select\/people\/celeux\/Welcome.html\" target=\"_blank\">Celeux, G.<\/a>,\u00a0<a href=\"http:\/\/www.agroparistech.fr\/mia\/equipes:membres:page:marielaure\" target=\"_blank\">Martin-Magniette, M.-L.<\/a>,\u00a0Maugis, C. and\u00a0<a href=\"http:\/\/www.stat.washington.edu\/raftery\/\" target=\"_blank\">Raftery, A.E.<\/a> (2011).\u00a0<br \/>\n&#8220;Letter to the Editor&#8221;, <em>Journal of the American Statistical Association<\/em>, 106, 383-383.<\/li>\n<li>Maugis, C. and\u00a0<a href=\"http:\/\/www.lsta.upmc.fr\/michelb.html\" target=\"_blank\">Michel, B.<\/a> (2011).\u00a0<br \/>\nData-driven penalty calibration: A case study for Gaussian mixture model selection.\u00a0<br \/>\n<em>ESAIM : P&amp;S,<\/em> 15, p320-339.<br \/>\n[<a href=\"http:\/\/journals.cambridge.org\/action\/displayAbstract?fromPage=online&amp;aid=8474515&amp;fulltextType=RA&amp;fileId=S1292810010000029\" target=\"_blank\">Article<\/a>]<a href=\"http:\/\/hal.inria.fr\/docs\/00\/28\/50\/32\/PDF\/RR-6550.pdf\" target=\"_blank\">[Hal-INRIA, RR-6550]<\/a><\/li>\n<li>Maugis, C. and\u00a0<a href=\"http:\/\/www.lsta.upmc.fr\/michelb.html\" target=\"_blank\">Michel, B.<\/a> (2011)<br \/>\nA non asymptotic penalized criterion for Gaussian mixture model selection.\u00a0<br \/>\n<em>ESAIM : P&amp;S,<\/em> 15, p41-68.<br \/>\n[<a href=\"http:\/\/journals.cambridge.org\/action\/displayAbstract?fromPage=online&amp;aid=8474482&amp;fulltextType=RA&amp;fileId=S1292810009000044\" target=\"_blank\">Article<\/a>]<a href=\"http:\/\/hal.inria.fr\/docs\/00\/28\/50\/31\/PDF\/RR-6549.pdf\" target=\"_blank\">[Hal-INRIA, RR-6549]<\/a>\u00a0<a href=\".\/image\/Erratum.pdf\" target=\"_blank\">[Erratum]<\/a><\/li>\n<li>Maugis, C.,\u00a0<a href=\"http:\/\/www.agroparistech.fr\/mia\/equipes:membres:page:marielaure\" target=\"_blank\">Martin-Magniette, M.-L.<\/a>,\u00a0Tamby, J.-P., Renou, J.-P., Lecharny, A., Aubourg, S. and\u00a0<a href=\"http:\/\/www.math.u-psud.fr\/select\/people\/celeux\/Welcome.html\" target=\"_blank\">Celeux, G.<\/a> (2009).\u00a0<br \/>\nS\u00e9lection de variables pour la classification par m\u00e9langes gaussiens pour pr\u00e9dire la fonction des g\u00e8nes orphelins.\u00a0<br \/>\n<em>MODULAD<\/em>, 40.<br \/>\n<a href=\"http:\/\/www-roc.inria.fr\/axis\/modulad\/archives\/numero-40\/maugis-40\/Maugis-40.pdf\" target=\"_blank\">[Article]<\/a><\/li>\n<li>Maugis, C.,\u00a0<a href=\"http:\/\/www.math.u-psud.fr\/select\/people\/celeux\/Welcome.html\" target=\"_blank\">Celeux, G.<\/a> and\u00a0<a href=\"http:\/\/www.agroparistech.fr\/mia\/equipes:membres:page:marielaure\" target=\"_blank\">Martin-Magniette, M.-L.<\/a> (2009).\u00a0<br \/>\nVariable selection in model-based clustering: A general variable role modeling.\u00a0<br \/>\n<em>Computational Statistics and Data Analysis<\/em>, 53, 3872-3882.<br \/>\n[<a href=\"http:\/\/www.sciencedirect.com\/science\/article\/pii\/S0167947309001686\" target=\"_blank\">Article<\/a>]<a href=\"http:\/\/hal.inria.fr\/docs\/00\/34\/33\/60\/PDF\/RR-6744.pdf\" target=\"_blank\">[hal-INRIA, RR-6744]<\/a><\/li>\n<li>Maugis, C.,\u00a0<a href=\"http:\/\/www.math.u-psud.fr\/select\/people\/celeux\/Welcome.html\" target=\"_blank\">Celeux, G.<\/a> and\u00a0<a href=\"http:\/\/www.agroparistech.fr\/mia\/equipes:membres:page:marielaure\" target=\"_blank\">Martin-Magniette, M.-L.<\/a> (2009).\u00a0<br \/>\nVariable selection for Clustering with Gaussian Mixture Models.\u00a0<br \/>\n<em>Biometrics<\/em>, 65, 701-709.<br \/>\n[<a href=\"http:\/\/onlinelibrary.wiley.com\/doi\/10.1111\/j.1541-0420.2008.01160.x\/abstract\" target=\"_blank\">Article<\/a>]<a href=\"http:\/\/hal.archives-ouvertes.fr\/docs\/00\/15\/34\/31\/PDF\/RR-6211.pdf\" target=\"_blank\">[Hal-INRIA, RR-6211]<\/a><\/li>\n<\/ul>\n<p><!--  Preprints  --><\/p>\n<h3 style=\"text-align: center\"><b><a name=\"preprints\"><\/a><span style=\"color: #2c4474;font-family: 'Comic Sans MS'\">\u00a0Books :<\/span><\/b><\/h3>\n<ul>\n<li><a href=\"http:\/\/math.univ-lille1.fr\/~biernack\/\" target=\"_blank\">C. Biernacki<\/a>\u00a0&amp; C. Maugis-Rabusseau. Chapter 9: &#8220;High-dimensional clustering&#8221;. Model choice and model aggregation, sous la direction de F.BERTRAND, J-J. DROESBEKE, G. SAPORTA, C. THOMAS-AGNAN Edition Technip, septembre 2017.<\/li>\n<li><a href=\"https:\/\/www6.inra.fr\/mia-paris\/Equipes\/Membres\/Marie-Laure-Martin-Magniette\" target=\"_blank\">Martin-Magniette, M.-L.<\/a>, Maugis-Rabusseau, C. and\u00a0<a href=\"http:\/\/www4.jouy.inra.fr\/gabi\/Presentation\/Annuaire-des-pages-personnelles\/RAU-Andrea\" target=\"_blank\">Rau, A<\/a>. Chapter 10 :&#8221;Clustering of co-expressed genes&#8221;. Model choice and Model aggragation, sous la direction de F.BERTRAND, J-J. DROESBEKE, G. SAPORTA, C. THOMAS-AGNAN Edition Technip, septembre 2017.<\/li>\n<\/ul>\n<h3 style=\"text-align: center\"><b><a name=\"preprints\"><\/a><span style=\"color: #2c4474;font-family: 'Comic Sans MS'\"> Preprints :<\/span><\/b><\/h3>\n<ul>\n<li>Arabaciyan, S., Saint-Antoine, M., Maugis-Rabusseau, C., Francois, J.-M., Singh, A., Parrou, J.-L. and Capp, J.-P. (2020)<br \/>\nInsights on the control of yeast single-cell growth variability by members of the Trehalose Phosphate<br \/>\nSynthase (TPS) complex. Submitted.<\/li>\n<li>Meynet, C. and Maugis-Rabusseau, C. (2012).\u00a0<br \/>\nA sparse variable selection procedure in model-based clustering.\u00a0<br \/>\n<a href=\"http:\/\/hal.inria.fr\/index.php?halsid=1i1ceg2c31rchbi7jnjntolpt6&amp;view_this_doc=hal-00734316&amp;version=1\" target=\"_blank\">[Hal-00734316]<\/a>.<\/li>\n<\/ul>\n<h3 style=\"text-align: center\"><b><a name=\"PhD\"><\/a><span style=\"color: #2c4474;font-family: 'Comic Sans MS'\"> PhD-thesis : <\/span><\/b><\/h3>\n<p>I have defended my PhD Thesis on November 21, 2008 at University Paris-Sud 11.\u00a0My PhD manuscript is available here.<\/p>\n<ul>\n<li><b>Title:<\/b> Variable selection for model-based clustering. Application for transcriptome data analysis.<\/li>\n<li><b>Advisors:\u00a0<\/b><a href=\"http:\/\/www.math.u-psud.fr\/select\/people\/celeux\/Welcome.html\" target=\"_blank\">Gilles Celeux<\/a>\u00a0(INRIA Saclay Ile-de-France) and <a href=\"http:\/\/www.agroparistech.fr\/-Marie-Laure-Martin-Magniette-.html\" target=\"_blank\">Marie-Laure Martin-Magniette<\/a>\u00a0(AgroParisTech et URGV, CR1 INRA)<\/li>\n<li><b>Referees:<\/b> Yannick Baraud (University of Nice Sophia-Antipolis, France) and Mark van der Laan (University of California at Berkeley)<\/li>\n<li><b>Committee:<\/b> Christophe Ambroise, S\u00e9bastien Aubourg,Yannick Baraud, Gilles Celeux, Marie-Laure Martin-Magniette and Pascal Massart<\/li>\n<li><b>Abstract:<\/b><br \/>\nWe are interested in variable selection for clustering with Gaussian mixture models. This research is motivated by the clustering of genes described by\u00a0transcriptome datasets in particular. In the two parts, this problem is regarded as a model selection problem in a model-based cluster analysis framework.<br \/>\nIn the first part, the proposed model, generalizing the one of Raftery and Dean (2006), specifies the variable role for the clustering process. The irrelevant clustering variables can be dependent to a relevant variable subset. Models are compared with a BIC-like criterion. The model identifiability is established and the consistency of the criterion is proved under regularity conditions. In practice, the variable role is obtained through an algorithm embedding two backward stepwise algorithms for variable selection for the clustering and the linear regression. The interest of this procedure is highlighted by a transcriptome dataset application especially. An improvement of the variable role modelling, consisting of partitioning the irrelevant variables according to their dependence or independence with some relevant clustering variables, is suggested to avoid an overpenalization of some models. Finally, the DNA microarray technology generating many missing values, an extension of our variable selection procedure taken into account the existence of missing entries is proposed. It avoids the missing entry imputation usually used in preprocessing.<br \/>\nIn the second part, specific Gaussian mixtures are considered and a non asymptotic penalized criterion is proposed to select the number of mixture components and the relevant clustering variable subset. A general model selection theorem for maximum likelihood estimation, proposed by Massart (2007), is used to obtain the penalty function form. This theorem requires to control the bracketing entropy of studied Gaussian mixture families. This criterion depending on unknown constants, the &#8220;slope heuristics&#8221; method is carried out to allow the practical use of this criterion.<\/li>\n<li><b>Keywords:<\/b> Variable Selection, Model-based Clustering, Gaussian Mixtures, Transcriptome Data.<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>Papers : De Castro, Y., Gadat, S., Marteau, C. and Maugis-Rabusseau, C. (2020) Supermix : sparse regularization for mixtures. \u00a0Annals of Statistics, to appear.\u00a0 [Hal-02190117] Godichon-Baggioni, A., Maugis-Rabusseau, C. and Rau, A. (2020) Multi-view cluster aggregation and splitting with an application to multi-omic breast cancer data. Annals of Applied Statistics, 14(2), 752-767.\u00a0 [Hal-01916941] [maskmeans] Gadat, [&hellip;]<\/p>\n","protected":false},"author":128,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-48","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/perso.math.univ-toulouse.fr\/maugis\/wp-json\/wp\/v2\/pages\/48","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/perso.math.univ-toulouse.fr\/maugis\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/perso.math.univ-toulouse.fr\/maugis\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/perso.math.univ-toulouse.fr\/maugis\/wp-json\/wp\/v2\/users\/128"}],"replies":[{"embeddable":true,"href":"https:\/\/perso.math.univ-toulouse.fr\/maugis\/wp-json\/wp\/v2\/comments?post=48"}],"version-history":[{"count":50,"href":"https:\/\/perso.math.univ-toulouse.fr\/maugis\/wp-json\/wp\/v2\/pages\/48\/revisions"}],"predecessor-version":[{"id":309,"href":"https:\/\/perso.math.univ-toulouse.fr\/maugis\/wp-json\/wp\/v2\/pages\/48\/revisions\/309"}],"wp:attachment":[{"href":"https:\/\/perso.math.univ-toulouse.fr\/maugis\/wp-json\/wp\/v2\/media?parent=48"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}