Benoit Liquet

Responsabilities

2015 - 2017 Professor at Pau et Pays de l’Adour (Anglet Campus)

• Coordinator/Instructor of the course Biostatistics 1. L2 (undergraduate second year students).
• Coordinator/Instructor of the course Data Analysis. L2
• Coordinator/Instructor of the course Biostatistics 2. L3 (undergraduate third year students).
• Coordinator/Instructor of the course Probability and Statistics. L1 (undergraduate first year students).
• Coordinator/Instructor Advanced Data Mining for MSID Master Students.
• Instructor of the course Time Series Analysis for DynEA (Aquatic Ecosystem Dynamics) Master Students.
• Instructor of the course Generalized Linear Model for DynEA Master Students.

2013 - 2015 Senior Lecturer at The university of Queensland (Brisbane).

• Co-designer with Dirk Kroese of the new course STAT1301 Advanced Analysis of Scientific Data.
• Coordinator of the course STAT1201 Analysis of Scientific Data (900 undergraduate students).
• Coordinator of the course STAT3003 Experimental Design.
• Instructor of the course STAT4401 Advanced Statistics.
• Instructor of the course STAT1201 Analysis of Scientific Data.
• Supervision of honours and Masters students.

Research interests

High dimensional data, Big data, Omics data, Model selection, Dimension reduction and semi-parametric model, Multi-state model and survival model, Multiple testing

Projects

July - 2017 R Workshop: SIAME at Pau et Pays de l’Adour (Anglet Campus)

Please find documents for the workshop here Workshop material

Publications

Journal Publications

[A45] Liquet B., K. Mengersen, A. N. Pettitt, and M. Sutton. Bayesian Variable Selection Regression Of Multivariate Responses For Group Data. Bayesian Analysis 12, 4 (2017).

[A44] H. P. Chong, Froen F., Richardson S., Liquet B., S. Charnock-Jones and Smith G. C, Age at menarche and the risk of operative delivery. The Journal of Maternal-Fetal \& Neonatal Medicine 28:1-8 (2017).

[A43] Picat M. Q., Pellegrin I., Bitard J., Wittkop L., Proust-Lima C., Liquet B., Moreau J. F., Bonnet F., Blanco P., Thiebaut R., Integrative analysis of immunological data to explore chronic immune activation in successfully treated HIV patients. Plos One. 12(1): e0169164 (2017).

[A42] Proust-Lima C., Philipps V., Liquet B., Estimation of Extended Mixed Models Using Latent Classes and Latent Processes: the R package lcmm. Journal of Statistical Software 78 (2017).

[A41] Benham T., Duan Q., Kroese D.P., Liquet B., CEoptim: Cross-Entropy R package for optimization. Journal of Statistical Software 76, 8 (2017).

[A40] Liquet B., and Saracco J., BIG-SIR a Sliced Inverse Regression Approach for Massive Data. Statistics and Its Interface. 9, 4 (2016).

[A39] Liquet B., Lafaye de Micheaux P, Hejblum B., Thiebaut R., Group and Sparse Group Partial Least Square Approaches Applied in Genomics Context. Bioinformatics 32:1, 35-42 (2016).

[A38] Chong H., Cordeaux Y., Froen F., Richardson S., Liquet B., Charnock-Jones S. and Smith G. C., Age-related changes in murine myometrial transcript profile are mediated by exposure to the female sex hormones. Aging Cell 15, 1 (2016).

[A37] Delorme P., Lafaye de Micheaux P., Liquet B. and Riou J., Type-II Generalized Family-Wise Error Rate Formulas with Application to Sample Size Determination. Statistics in Medicine 35,1 (2016).

[A36] Liquet B., Bottolo L., Campanella G., Richardson S. and Chadeau-Hyam M., R2GUESS: GPU-based R package for Bayesian variable selection regression of multivariate responses. Journal of Statistical Software Vol 69, Issue 2, (2016).

[A35] Liquet B., and Nazarathy Y., A dynamic view to moment matching of truncated distributions. Statistics and Probability Letters 104 87-93 (2015).

[A34] Commenges D., Proust-Lima C., Samieri C., and Liquet B., A universal approximate cross-validation criterion and its asymptotic distribution. The International Journal of Biostatistics 11:1, 51-67 (2015).

[A33] Truong T., Liquet B., Menegaux F., Plancoulaine S., Laurent-Puig P., Mulot C., Cordina-Duverger E., Sanchez M., Arveux P., Kerbrat P., Richardson S., Guénel P., Breast cancer risk, nightwork, and circadian clock gene polymorphisms. Endocr Relat Cancer 21:4, 629-38 (2014).

[A32] Chavent M., Girard S., Kuentz V., Liquet B., Nguyen T. M. N., Saracco J., A sliced inverse regression approach for data stream. Computational Statistics 29:5, 1129-1152 (2014)

[A31] Coudret R., Liquet B., Saracco J., Comparison of sliced inverse regression approaches for underde- termined cases. Journal de la Société Française de Statistique Vol 155:2 (2014).

[A30] Lafaye de Micheaux P., Liquet B., Marques S. and Riou J., Power and sample size determination in clinical trials with multiple primary continuous correlated end points. Journal of Biopharmaceutical Statistics 24:2, 378-97, (2014).

[A29] Bottolo L., Chadeau-Hyam M., Hastie D. I., Zeller T., Liquet B., Castagne R., Wild P. S., Schillert A., Munzel T., Tregouet D., Cambien F., Petretto E., Blankenberg S., Tiret L. and Richardson S., GUESS- ing polygenic associations with multiple phenotypes using a GPU-based Evolutionary Stochastic Search algorithm. Plos Genetics 9:8, (2013).

[A28] Chadeau-Hyam M., Jombart T., Campanella G., Bottolo L., Vineis P., Liquet B., Vermeulen R., Deci- phering the complex: Methodological overview of statistical models to derive OMICS-based biomarkers. Environmental and Molecular Mutagenesis 54:7, 542-557, (2013).

[A27] Liquet B., and Riou J., Correction of the significance level after multiple coding of an explanatory variable in generalized linear model. BMC Medical Research Methodology 13:75, (2013).

[A26] Liquet B., Le Cao K., Hocini H., and Thiebaut R., A novel approach for biomarker selection and the integration of repeated measure experiments from two platforms. BMC, Bioinformatics 13:325, (2012).

[A25] Liquet B., Rondeau V., and Timsit J. F., Investigating hospital heterogeneity with a multi-state frailty model: application to nosocomial pneumonia disease in intensive care units. BMC Medical Research Methodology 12:79, (2012).

[A24] Coeurjolly J.F., Makao M., Timsit J.F. and Liquet B., Attributable risk estimation for adjusted disability multistate models: application to nosocomial infections. Biometrical Journal 54:5, 600-616, (2012).

[A23] Chavent M., Kuentz V., Liquet B., and Saracco J., ClustOfVar: An R Package for the Clustering of Variables. Journal of Statistical Software 50:13, 1-16, (2012).

[A22] Josse J., Chavent M., Liquet B., and Husson F., Handling missing values with Regularized Iterative Multiple Correspondence Analysis. Journal of Classification 29:1, 91-116, (2012).

[A21] Commenges D., Liquet B. and Proust-Lima C., Choice of prognostic estimators by estimating difference of expected conditional Kullback-Leibler risks. Biometrics 68:2, 380-387, (2012).

[A20] Liquet B. and Saracco, J., A graphical tool for selecting the number of slices and the dimension of the model in SIR and SAVE approaches. Computational Statistics 27:1, 103-125, (2012).

[A19] Chavent M., Kuentz V., Liquet B. and Saracco J., A sliced inverse regression approach for a stratified population. Communications in Statistics - Theory and Methods 40:21, (2011).

[A18] Laboute E., Liquet B., Savalli L., Puig P., Trouve P., Influence of the type of knee brace on clinical postoperative evolution after anterior cruciate ligament reconstructions in competitive sportspeople. Journal de Traumatologie du Sport, 28: 3, 103-125, (2011)

[A17] Liquet B. and Commenges D., Choice of estimators based on different observations : Modified AIC and LCV criteria. Scandinavian Journal of Statistics 38, 268-287, (2011).

[A16] Liquet B., Choix d’estimateurs basé sur le risque de Kullback-Leibler. Journal de la Société Française de Statistique 151:1, (2010).

[A15] Chavent M., Liquet B. and Saracco J., A semiparametric approach for a multivariate sample selection model. Statistica Sinica 20:2, 513-536, (2010).

[A14] Kuentz V., Liquet B. and Saracco J., “Bagging” version of Sliced Inverse Regression. Communications in Statistics- Theory and Methods 39, 1985-1996, (2010).

[A13] Lafaye de Micheaux P. and Liquet B., ConvergenceConcepts: an R package to investigate various modes of convergence. The R Journal 1, 18-25, (2009).

[A12] Lafaye de Micheaux P. and Liquet B., Understanding Convergence Concepts: A Visual-Minded and Graphical Simulation-Based Approach. The American Statistician 63:2, 173-178, (2009).

[A11] Commenges, D. and Liquet B., Asymptotic distribution of score statistics for spatial cluster detection with censored data. Biometrics 64, 1-8, (2008).

[A10] Cenier T., Amat C., Litaudon P., Garcia S.StatLearn 2013, Lafaye de Micheaux P., Liquet B., Roux S. and Buonviso N., Odor vapor pressure and quality modulate local field potential oscillatory patterns in the olfactory bulb of the anesthetized rat. European Journal of Neuroscience 27:6, 1432-1440, (2008).

[A9] Liquet B. and Saracco, J. Application of the bootstrap approach to the choice of dimension and the α parameter in the SIRα method. Communication in Statistics - Simulation and computation 37:6, 1198-1218, (2008).

[A8] Rondeau V., Michiels S., Liquet B. and Pignon J. P., Investigating trial and treatment heterogeneity in an individual patient data meta-analysis of survival data by means of the penalized maximum likelihood approach. Statistics in Medicine 27, 1894-1910, (2008).

[A7] Commenges D., Joly P., Gégout-Petit A. and Liquet B., Choice between semi-parametric estimators of Markov and non-Markov multi-state models from generally coarsened observations. Scandinavian Journal of Statistics 34, 33-52, (2007).

[A6] Liquet B. and Saracco, J., Pooled marginal slicing approach via SIRα with discrete covariables. Com- putational Statistics 22:4, 599-617, (2007).

[A5] Liquet B., Saracco, J. and Commenges D., Selection between proportional and stratified hazards models based on Expected Log-likelihood. Computational Statistics 22:4, 619-634, (2007).

[A4] Liquet B. and Commenges D., Computation of the p-value of the maximum of score tests in the generalized linear model; application to multiple coding. Statistics & Probability Letters 71, 33-38, (2005).

[A3] Liquet B. and Commenges D., Estimating the expectation of the log-likelihood with censored data for estimator selection. Lifetime data analysis 10, 351-367, (2004).

[A2] Liquet B., Sakarovitch C. and Commenges D., Bootstrap choice of estimators in non-parametric families: an extension of EIC. Biometrics 59, 172-178, (2003).

[A1] Liquet B. and Commenges D., Correction of the p-value after multiple coding of an explanatory variable in logistic regression. Statistics in Medicine 20, 2815-2826, (2001).

Publications: scholarly books

[B4] Commenges D., Jacqmin-Gadda H.(eds), Alioum A., Joly P., Leffondre K., Liquet B., Proust-Lima C., Rondeau V., and Thiébaut R. Dynamical Biostatistical models, Series: Chapman & Hall/CRC Biostatistics Series, (2015).

[B3] Commenges D., Jacqmin-Gadda H.(eds), Alioum A., Joly P., Leffondre K., Liquet B., Proust-Lima C., Rondeau V., and Thiébaut R. Modeles Biostatistiques pour l’Epidémiologie. De Boeck Superieur, (2015)..

[B2] Lafaye de Micheaux P., Drouilhet R., and Liquet B., Le logiciel R, Maîtriser le langage, Effectuer desanalyses statistiques. 565 pages. Springer, 2011.

[B1] Lafaye de Micheaux P., Drouilhet R., and Liquet B., The R software. Fundamentals of Programmingand Statistical Analysis, Springer, in press (accepted February 2013). Translation of [1] with additional chapters.

Publications: book sections

[C3] Saracco J., Gannoun A., Guinot C. and Liquet B., A semiparametric approach to estimate reference curves for biophysical properties of the skin. In Statistical Methods for Biostatistics and Related Fields. Berlin: Springer, 181-205, (2007).

[C2] Gannoun A., Liquet B., Saracco J. and Wolfgang U., A kernel method in analysis of replicated micro- array experiments. In Statistical Methods for Biostatistics and Related Fields. Berlin: Springer, 45-61, (2007).

[C1] Liquet B. and Commenges D., Selecting a semi-parametric estimator by the expected log-likelihood. In Probability, Statistics and Modelling in Public Health, Springer, 332-349, (2005).

Other publication: articles submitted or in revision

[S1] Smith G. C, Froen F., Richardson S., Liquet B., Chong H., Charnock-Jones S. and Chong H. P., Age at menarche and the risk of operative delivery. Revision in Paediatric and Perinatal Epidemiology.

[S2] B. Liquet, K. Mengersen, A. N. Pettitt, and M. Sutton. Bayesian Variable Selection Regression Of Multivariate Responses For Group Data. Submitted.

Invited conferences/Lectures

[IV15] 2016 (December), Lafaye de Micheaux P., Liquet B. and Riou J., ``Type-II generalized family-wise error rate formulas with application to sample size determination’’, 9th International Conference of the ERCIM (European Research Consortium for Informatics and Mathematics) Spain.

[IV14] 2016 (December), ``Bayesian Variable Selection Regression Of Multivariate Responses For Group Data’’, Invited to the Big Bayes Session at the Australian Statistical Conference.

[IV13] 2016 (December), ``Statistical Methods For Analysing High-Dimensional Data and Massive Data’’, International Conference on Mathematics : Education, Theory, and Application (ICMETA). Universitas Sebelas Maret (Indonesia).

[IV12] 2016 (December), Two invited lectures: A tutorial for penalized regression models'' andA tutorial for PLS and Bayesian Variable Selection’’. Short course on ``Stat-XP: statistics to analyse OMICs data and characterise the exposome’’, organized Imperial College London, England.

[IV11] 2016 (December) Sutton M., Liquet B., and Thiêbaut R. ``Sparse Group Subgroup PLS for Genomics’’. International Symposium for Big Data Visualisation Analytics (BDVA) Sydney.

[IV10] 2016 (October) Lafaye de Micheaux P., Liquet B. and Sutton M., ``A Unified Regularized Group PLS Algorithm Scalable to Big Data’’, journ'ee STAtistique de Rennes (jSTAR), 13rd edition on Big Data, Rennes, France.

[IV9] 2016 (July) Dimension Reduction approaches for BIG-DATA. Workshop on Big-Data organized by ACEMS at Queensland University of Technology.

[IV8] 2016 (February) Statistical Methods for Analysising High-dimensional Data and Massive Data. Lecture at the University of Melbourne.

[IV7] 2015 (December), Multivariate approaches: Dimension reduction for big data sets. Short course on “Statistical approaches to characterize the exposome: Overview and Perspective’’, organized Imperial College London, England.

[IV6] 2014 (December), Multivariate approaches: Dimension reduction for big data sets. Short course on “Statistical approaches to characterize the exposome: Overview and Perspective”, organized Imperial College London, England.

[IV5] 2014 (November), R2GUESS: A Graphic Processing Unit-Based R Package for Bayesian Variable Selection Regression of Multivariate Responses, Bayes on the Beach conference organized in Gold Coast, Australia.

[IV4] 2013 (October), Multi-state models and nosocomial infections. Workshop on “dynamic predictions for repeated markers and repeated events: models and validation in cancer”, organized in Bordeaux, France.

[IV3] 2013 (April), Strategies to analyse ’omics’ data: From standard T-tests in genomics to a more general Bayesian approach. workshop Statlearn’13. Bordeaux University (France).

[IV2] 2009 (November), Choice of estimators based on different observations: Modified AIC and LCV criteria. Colloque CRM-ISM-GERAD de Statistique. McGill University (Canada).

[IV1] 2009 (October), Choix d’estimateurs basés sur des observations différentes. GDR “Statistique et Santé”, Université Paris-Descartes.

Invited seminar (2011-2016)

[IS26] 2016 (November) A Unified Regularized Group PLS Algorithm Scalable to Big Data. ISG (Intelligent Systems Group) seminar at UPV/EHU University (Spain).

[IS25] 2016 (November) Type-II Generalized Family-Wise Error Rate Formulas with Application to Sample Size Determination. LMAP seminar, Universit'e de Pau et Pays de l’Adour.

[IS24] 2016 (October) Bayesian Variable Selection Regression Of Multivariate Responses For Group Data. Seminar at the OMMAS team from the IMB (Institute Mathe'emetics de Bordeaux) University of Bordeaux.

[IS23] 2016 (August) BIG-SIR a Sliced Inverse Regression Approach for Massive Data. Seminar at the Centre for Statistics from The University of Queensland.

[IS22] 2016 (May) BIG-SIR a Sliced Inverse Regression Approach for Massive Data. LMAP, Universit'e de Pau et Pays de l’Adour.

[IS21]2016 (February) Type-II Generalized Family-Wise Error Rate Formulas with Application to Sample Size Determination. Victorian Centre For Biostatistics Seminar, Melbourne.

[IS20] 2016 (February) Semi-parametric regression approach for massive data. Maths seminar series at Queensland University of Technology.

[IS19] 2016 (February) Group and Sparse Group Partial Least Square Approaches Applied in Genomics Context. SSAI Queensland Branch Event.

[IS18] 2015 (November) Statistical Methods for the Analysis of High-dimensional data Applied in Biostatistics Context. MIRA, Milieux et ressources aquatiques. Universit'e de Pau et Pays de l’Adour.

[IS17] 2015 (October) Group and Sparse Group Partial Least Square Approaches Applied in Genomics Context. ISPED, University of Bordeaux.

[IS16] 2015 (October) Group and Sparse Group Partial Least Square Approaches Applied in Genomics Context. LMAP, Universit'e de Pau et Pays de l’Adour.

[IS15] 2015 (August) BIG-SIR a Sliced Inverse Regression Approach for Massive Data. ACEMS seminar, Queensland University of Technology.

[IS14] 2015 (August) Group and Sparse Group Partial Least Square Approaches Applied in Genomics Context. Queensland Brain Institute, Australia.

[IS13] 2015 (June) Group and Sparse Group Partial Least Square Approaches Applied in Genomics Context. Imperial College, London.

[IS12] 2015 (June) Group and Sparse Group Partial Least Square Approaches Applied in Genomics Context. ACEMS seminar, University of Queensland.

[IS11] 2015 (January) R2GUESS: A Graphic Processing Unit-Based R Package for Bayesian Variable Selection Regression of Multivariate Responses. Queensland Brain Institute, Australia.

[IS10] 2014 (December) Strategies to analyse ’omics’ data. Grenoble University, France.

[IS9] 2013 (November) Type-II Generalized Family-Wise Error Rate Formulas with Application to sample Size Determination. Universitas Sebelas Maret, Indonesia.

[IS8] 2012 (April) Multi-state approaches to model nosocomial infection in intensive care units. Universitas Sebelas Maret, Indonesia.

[IS7] 2012 (March) On Akaike and likelihood cross-validation criteria for model selection. Queensland Uni- versity of Technology (QUT), Brisbane.

[IS6] 2012 (March) A novel approach for the integration of repeated measures experiments and biomarker selection . University Queensland Seminar Series - Computational Biology.

[IS5] 2012 (March) Choice of Estimators by Estimating Differences of Expected Kullback-Leibler Risks, Fac- ulty of Science Macquarie University New South Wales.

[IS4] 2012 (March) Multistate approaches to model nosocomial pneumonia disease in intensive care units. The George Institute for Global Health, Sydney.

[IS3] 2012 (March) On Akaike and likelihood cross-validation criteria for model selection. School of Mathe- matics and Statistics. The University of New South Wales, Sydney.

[IS2] 2012 (February) On Akaike and likelihood cross-validation criteria for model selection. Department of Statistics. The University of Auckland.

[IS1] 2011 (September) On Akaike and likelihood cross-validation criteria for model selection. Department of Biostatistics. University of Copenhagen.

International and national communications, proceeding (2007-2016)

[CO30] Liquet B., Bayesian Variable selection regression of multivariate responses for group data, ISBA 2016 World Meeting International Society for Bayesian Analysis, Sardinia (Italy) June 2016.

[CO29] Liquet B., Statistical Methods for the Analysis of High-dimensional Data and Massive Data, Workshop on Computational and Mathematical Foundations for Big Data Analytics, QUT, Australia (July 2016).

[CO28] Liquet B., Lafaye de Micheaux P, Hejblum B., Thiebaut R., Group and Sparse Group Partial Least Square Approaches Applied in Genomics Context., The R User Conference 2016, Stanford, California (US), June 2016.

[CO27] Liquet B., Lafaye de Micheaux P, Hejblum B., Thiebaut R., Group and Sparse Group Partial Least Square Approaches Applied in Genomics Context., Les cinqui`emes Rencontres R, Toulouse, France, June 2016.

[CO26] Liquet B., and Saracco J., BIG-SIR a Sliced Inverse Regression Approach for Massive Data, Quatri`emes rencontres R, Grenoble (France), June 2015.

[CO25] Lafaye de Micheaux P., Liquet B., Perminder S., Anbupalam T. and Wei W., New Statistical tools to study heritabiliy of the brain, Australian Statistical Conference in conjunction with the Institute of Mathematical Statistics Annual Meeting, Sydney (Australia), July 2014.

[CO24] Delorme, P., Lafaye de Micheaux P., Liquet, B. and Riou, J., Type-II Generalized Family-Wise Error Rate Formulas with Application to Sample Size Determination, 8th International Conference on Multiple Comparison Procedures, Southampton (England), July 2013.

[CO23] Delorme, P., Lafaye de Micheaux P., Liquet, B. and Riou, J., Package SSDDA : Sample Size Determination and Data Analysis in the context of continuous co-primary endpoints in clinical trials, Deuxièmes rencontres R, Lyon (France), June 2013.

[CO22] Delorme, P., Lafaye de Micheaux P., Liquet, B. and Riou, J., Calcul de taille d’échantillon dans le cadre de critères de jugements multiples avec un contrôle de la r-power et du gFWER, 44 Journées de Statistique, Toulouse (France) Mai 2013.

[CO21] Liquet B., Chadeau-Hyam M., Bottolo L., and Richardson S. R2GUESS: a GPU-based R package for a Bayesian variable selection model accommodating multivariate responses, Statistical Methods for (Post)-Genomics Data 2013, Amsterdam, January 2013.

[CO20] Chavent M., Genuer R., Liquet B., and Saracco J., ClustOfVar: an R package for dimension reduction via clustering of variable. Application to supervised classification and variables selection for omics data (Poster presentation)„Statistical Methods for (Post)-Genomics Data 2013, Amsterdam, January 2013.

[CO19] Liquet B., Chadeau-Hyam M., Bottolo L., and Richardson S., GUESS: GPU-based C++ software for Bayesian variable selection regression of multiple responses (Poster presentation), Australasian Applied Statistics Conference 2012, Queenstown (New Zeland) December 2012.

[CO18] Liquet B., Chadeau-Hyam M., Bottolo L., and Richardson S., GUESS: GPU-based C++ software for Bayesian variable selection regression of multiple responses (Poster presentation), Australasian Applied Statistics Conference 2012, Queenstown (New Zeland) December 2012.

[CO18] Liquet B., Le Cao K., Hocini H., and Thiebaut R., A novel approach for biomarker selection and the integration of repeated measure experiments from two platforms, NZSA 2012 Conference, Dunedin (New Zealand), December 2012.

[CO17] Delorme P., Lafaye de Micheaux P., Liquet B. and Riou J., Power and sample size computation for a control of the “r-power” (Poster presentation), NZSA 2012 Conference, Dunedin (New Zealand), December 2012.

[CO16] Delorme P., Lafaye de Micheaux P., Liquet B. and Riou J., Power and sample size computation for a control of the “r-power” (Poster presentation), NZSA 2012 Conference, Dunedin (New Zealand), December 2012.

[CO15] Liquet B., Le Cao K., Hocini H., and Thiebaut R., A novel approach for biomarker selection and the integration of repeated measure experiments from two platforms, XXVIèmes International Biometric Conference, Kobe (Japan), August 2012.

[CO14] Liquet B., Commenges D. and Proust-lima C., Choice of prognostic estimators in joint models by esti- mating differences of expected conditional Kullback-Leibler risks, Biometrics by the Blowholes, Kiama, New South Wales, December 2011, Australia.

[CO13] Riou J., Liquet B., Correction of the significance level after multiple coding of an explanatory variable in generalized linear model., 7th International Conference on Multiple Comparison Procedures, Washington D.C., USA, August 2011.

[CO12] Liquet B., Proust-Lima C., lcmm: an R package for estimation of latent class mixed models and joint latent class models. The R User Conference 2011. Coventry, UK.

[CO11] Chavent M., Girard S., Kuentz V., Liquet B., Nguyen T. M. N., Saracco J., An adaptive SIR method for block-wise evolving data streams. ASMDA -XIV International Conference, Rome Italy, 2011.

[CO10] Riou J., Liquet B., Méthodes de correction du degré de signification pour une recherche de codage optimal dans un modèle linéaire généralisé 43èmes Journées de Statistique (SFdS), Tunis, Mai 2011.

[CO9] Chavent M., Kuentz V., Liquet B., Saracco J., Classification de variables: le package ClustOfVar. 43èmes Journées de Statistique (SFdS), Tunis, Mai 2011.

[CO8] Liquet B., Choice of estimators based on different observations: Modified AIC and LCV criteria. XXVèmes International Biometric Conference, Florianopolis (Brazil), december 2010.

[CO7] Chavent, M., Kuentz, V., Liquet, B., Saracco, J., Regression inverse par tranches pour une population stratifiee. 42èmes Journées de Statistique (SFdS), Marseille, Mai 2010.

[CO6] Lafaye de Micheaux, P. and Liquet, B., Le Package R ConvergenceConcepts : un nouvel outil graphique pour l’étude de quelques modes de convergence de variables aléatoires, Marseille, Mai 2010.

[CO5] Chavent M., Kuentz V. and Liquet B., Données manquantes en ACM : l’algorithme NIPALS. XVIèmes Rencontres de la Société Francophone de Classification, Grenoble 2009.

[CO4] Liquet B. and Saracco J., A criterion for selecting the number of slices and the dimension of the model in SIR and SAVE approaches. ASMDA -XIII International Conference, Vilnius Lithuania, 2009.

[CO3] Nguile Makao M., Timsit J.F., Coeurjolly J.F., and Liquet B., Prédiction de la pneumonie nosocomiale à l’aide d’un modèle multi-états., XXXXIe Journées de Statistique, Bordeaux, 2009.

[CO2] Chavent M., Liquet B. and Saracco J., Two steps estimation in a multivariate semiparametric sample selection model XXXXe Journées de Statistique, Montréal, 2008.

[CO1] Desjardins S., Desgagné A., Lafaye de Micheaux P., Liquet B., Generalization of the Paired T-Test for the Missing Values Case, Joint Statistical Meeting, Salt Lake City, Poster, 2007.