Publications

103 articles in international peer-reviewed journals, 8 books and 4 book chapters. A full list is also available on Google Scholar.

Books

• B8 Liquet B., Moka S. and Nazarathy Y. (2024), "Mathematical Engineering of Deep Learning ”, published in Chapman & Hall/CRC Data Science Series, available at https://deeplearningmath.org/
• B7 Lafaye de Micheaux P., Drouilhet R. and Liquet B. (2016), “Perangkat Lukan R - Dasar-dasar Pemrograman dan Analisis Statistika”, Statistika dan Pemrograman. (In Indonesian.)
• B6 Lafaye de Micheaux P., Drouilhet R., Liquet B., Pan D., Tang N. and Li Q. (2015), “R: (R: the textbook - Master the language & Perform statistical analyses)”, Higher Education Press (largest textbook publisher in China). (In Chinese.)
• B5 Commenges D.; Jacqmin-Gadda H.(eds); Alioum A.; Joly P.; Leffondre K.; Liquet B.; Proust-Lima C.; Rondeau V. & Thiébaut R. (2015) Dynamical Biostatistical models, Series: Chapman & Hall/CRC Biostatistics Series
• B4 Commenges D.; Jacqmin-Gadda H.(eds); Alioum A.; Joly P.; Leffondre K.; Liquet B.; Proust-Lima C.; Rondeau V. & Thiébaut R. (2015) Modèles Biostatistiques pour l'Epidémiologie. De Boeck Superieur
• B3 Lafaye de Micheaux P., Drouilhet R. and Liquet B. (2014), “Le logiciel R, Maitriser le langage - Effectuer des analyses (bio)statistiques”, Springer, Collection: Statistique et probabilités appliquées, Vol. 1, 2nd Edition., 674 p., Broché, ISBN: 978-2-8178-0534-4. (In French.)
• B2 Lafaye de Micheaux P., Drouilhet R. and Liquet B. (2014), “The R software. Fundamentals of Programming and Statistical Analysis”, Springer, Collection: Statistics and Computing, vol. 40, 655 p., ISBN: 978-1-4614-9019-7.
• B1 Lafaye de Micheaux P., Drouilhet R. and Liquet B. (2011), “Le logiciel R, Maitriser le langage - Effectuer des analyses statistiques”, Springer - Collection: Statistique et probabilités appliquées, Vol. 1, 1st Edition., XVI, 527 p., Broché, ISBN: 978-2-8178-0114-8. (In French.) Nominated for the Roberval Prize.

Book chapters

• C4 Liquet B., Moka S. and Nazarathy Y.,“Navigating Mathematical Basics: A Primer for Deep Learning in Science” for the book Computational Neurosurgery in the book series Advances in Experimental Medicine and Biology. Editors: Antonio Di Ieva, Eric Suero Molina, Sidong Liu and Carlo Russo. (2024)
• 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).

Journal articles

Most recent first.

• A103 Nguyen T, Moka S, Mengersen K, Liquet B. Modeling Spatial Compositional Data: Dirichlet Likelihoods and Cross-Entropy Estimation. Spatial Statistics. 2026. https://doi.org/10.1016/j.spasta.2026.100978(2026)
• A102 Sébastien Coube-Sisqueille, Sudipto Banerjee Liquet B., Nonstationary Spatial Process Models with Spatially Varying Covariance Kernels. Journal of Computational and Graphical Statistics. 2026. https://doi.org/10.1080/10618600.2025.2516020 (2026)
• A101 Ullah, I., Mengersen, K., Pettitt, A. and Liquet B., Using a Supervised Principal Components Analysis for Variable Selection in High-Dimensional Datasets Reduces False Discovery Rates Statistics in Medicine (2025).
• A100 Sugier, P. E., Asgari, Y., Sedki, Truong, T. Liquet, B. Meta-Analysis models with group structure for pleiotropy detection at gene and variant level using summary statistics from multiple datasets. Biostatistics, https://hal.science/view/index/docid/5310922 (2025).
• A99 Nguyen, T., Sous D., Liquet B., Meulé S., Mengersen, K., Bouchette F., Pixel-based satellite mapping for coral island seabed classification: Application to the Maupiti island, French Polynesia. Remote Sensing Applications: Society and Environment (2025).
• A98 Kawahara, R., Kautto, L., Bansal, N., Dipta, P., Liquet-Weiland, B., Ahn, S. B., & Thaysen-Andersen, M. HEXB drives raised paucimannosylation in colorectal cancer and stratifies patient risk. Molecular & Cellular Proteomics, 24(3). (2025)
• A97 Liquet B., Moka S., Muller S., Best subset solution path for linear dimension reduction models using continuous optimization. Biometrical Journal (2025)
• A96 Black D., Gill j., Xie A., Liquet B., W Stummer, ES Molina, Deep learning-based hyperspectral image correction and unmixing for brain tumor surgery. iScience (2024)
• A95 Li I., Ma J., Liquet B.., Mixture Cure Semiparametric Accelerated Failure Time Models With Partly Interval-Censored Data. Biometrical Journal (2024)
• A94 Nguyen T, Moka S, Mengersen K, Liquet B. Spatial Autoregressive Model on a Dirichlet Distribution. arXiv:2403.13076. (2024)
• A93 Bhaskaran A, Ma D, Liquet B., Hong A, Lo SN, Heritier S, Ma J. A maximum penalised likelihood approach for semiparametric accelerated failure time models with time-varying covariates and partly interval censoring.arXiv:2403.12332 (2024)
• A92 Mathur, A., Moka, S., Liquet, B., and Botev, Z. Group COMBSS: Group Selection via Continuous Optimization. Proceedings of Winter Simulation Conference (2024).
• A91 Black, D., Liquet B. , Kaneko, S., Stummer, W., Molina, E. S. A Spectral Library and Method for Sparse Unmixing of Hyperspectral Images in Fluorescence Guided Resection of Brain Tumors,Biomedical Optics Express (In 2024)
• A90 Moka S.,Liquet B., Zhu H., Muller S., COMBSS: best subset selection via continuous optimization. Statistics and Computing (2024)
• A89 Vaughan, S. P., van de Sande, J., Fraser-McKelvie, A., Croom, S., McDermid, R., Liquet-Weiland, B., ..., Lawrence, J. The SAMI galaxy survey: predicting kinematic morphology with logistic regression. Monthly Notices of the Royal Astronomical Society (2024)
• A88 Mourguiart, B., Chevalier, M., Marzloff, M., Caill-Milly, N., Mengersen, K., Liquet B. Dealing with area-to-point spatial misalignment in species distribution models. Ecography (2024)
• A87 Lucotte, E. A., Asgari, Y., Sugier, P. E., Karimi, M., Domenighetti, C., Lesueur, F., ...,Liquet B., Truong, T. Investigation of common genetic risk factors between thyroid traits and breast cancer. Human Molecular Genetics (2024)
• A86 Asgari, Y., Sugier, P. E., Baghfalaki, T., Lucotte, E., Karimi, M., Sedki, M., Liquet B., Truong, T. (2023). GCPBayes pipeline: a tool for exploring pleiotropy at the gene level. NAR Genomics and Bioinformatics (2023)
• A85 Mourguiart B, Liquet B., Mengersen, Kerrie, et al. A new method to explicitly estimate the shift of optimum along gradients in multispecies studies. Journal of Biogeography, (2023)
• A84 Kermorvant C, Liquet, B, Litt G, Mengersen K, Peterson EE, Hyndman RJ, Jones Jr JB, Leigh C. Understanding links between water-quality variables and nitrate concentration in freshwater streams using high frequency sensor data. Plos one. (2023)
• A83 Baghfalaki T, Sugier PE, Asgari Y, Truong T, Liquet, B. GCPBayes: An R package for studying Cross-Phenotype Genetic Associations with Group-level Bayesian Meta-Analysis. R Journal. (2023)
• A82 Kuehn J, Abadie S, Liquet, B. , Roeber V. A deep learning super-resolution model to speed up computations of coastal sea states. Applied Ocean Research. (2023)
• A81 Nguyen T, Mengersen K, Sous D, Liquet, B. SMOTE-CD: SMOTE for compositional data. Plos one. (2023)
• A80 Callens, A., Morichon, D., & Liquet, B. Bayesian networks to predict storm impact using data from both monitoring networks and statistical learning methods. Natural Hazards, 1-20. (2023)
• A79 Moka, Sarat, Liquet, B., Zhu, H., Muller, S. (2022), COMBSS: Best Subset Selection via Continuous Optimization, doi:10.48550/ARXIV.2205.02617
• A78 Poncet P.A., Liquet B., Larroque B., Abadie S. (2022) In-situ measurements of energetic depth-limited wave loading. Applied Ocean Research,125.
• A77 Rodrigues S, Huggins R, Liquet B, (2022) Central subspaces review: methods and applications. Statistics Surveys, 16, 210-237
• A76 Coube S, Banerjee S., Liquet B. (2022) Nonstationary Nearest Neighbor Gaussian Process : hierarchical model architecture and MCMC sampling. doi:10.48550/ARXIV.2203.11873
• A75 Sutton, M., Sugier, P. E., Truong, T., Liquet B. Leveraging pleiotropic association using sparse group variable selection in genomics data. BMC Medical Research Methodology.(2022)
• A74 Suman AA, Russo C, Carrigan A, Nalepka P, Liquet-Weiland B, Newport RA, Kumari P, Di Ieva A. Spatial and time domain analysis of eye-tracking data during screening of brain magnetic resonance images. PLoS One. (2021).
• A73 Nguyen T.,Liquet B. , Mengersen K., Sous D. Mapping of coral reefs with multispectral satellites: a review of recent papers. Remote Sensing Image Processing. (2021).
• A72 Kermorvant C., Liquet B., Litt G., Jones J.B., Mengersen K., Peterson E.E., Hyndman R. H., Leigh C. Reconstructing Missing and Anomalous Data Collected from High-Frequency In-Situ Sensors in Fresh Waters. International Journal of Environmental Research and Public Health. (2021).
• A71 Scarpelli M. D. A., Liquet B., Tucker D., Fuller S., Roe P. Multi-index ecoacoustics analysis for terrestrial soundscapes: a new semi-automated approach using time-series motif discovery and random forest classification. Frontiers in Ecology and Evolution. (2021).
• A70 Callens A., Morichon D., Liquet B. Automatic detection of storm impact regimes with video monitoring and convolutional neural networks. Remote Sensing. (2021).
• A69 Koong K., Preda, V., Liquet B., Di leva A. Application of machine learning of MRI radiomics in sellar tumors: a systematic review and meta-analysis . Neuroradiology. (2021).
• A68 Coube S., and Liquet B. Improving performances of MCMC for Nearest Neighbor Gaussian Process models with full data augmentation. Computational Statistics & Data Analysis.(2021).
• A67 Kawahara R.,..., Liquet B., et al. Community Evaluation of Glycoproteomics Informatics Solutions Reveals High-Performance Search Strategies of Glycopeptide Data. Nature Methods (2021)
• A66 Gianluigi Li Bassi, Jacky Suen, Heidi Dalton, Nicole White, Sally Shrapnel, Jonathon P. Fanning, Liquet B., et al. An Appraisal of Respiratory System Compliance In Mechanically Ventilated Covid-19 Patients. Critical Care BMC. (2021).
• A65 Kermorvant C.., Caill-Milly N., Sous D., Paradinas I., Lissardy M., and Liquet B. Detecting the effects of inter-annual and seasonal changes in environmental factors on the striped red mullet population in the Bay of Biscay. Journal Of Sea Research (2021).
• A64 Broc C., Truong T. and Liquet B. Penalized Partial Least Square for investigationg pleiotropy at the gene/pathway level. BMC Bioinformatics (2021).
• A63 Asanjarani A., Nazarathy Y., and Liquet B. Estimation of Semi-Markov Multistate Models: A Comparison of Sojourn Times and Transition Intensities Approaches. The International Journal of Biostatistics. (2021).
• A62 Baghfalaki T., Truong T., Pettitt A.T., Mengersen K. and Liquet B. Bayesian meta-analysis models to Cross Cancer Genomic Investigation of pleiotropic effects using group structure. Statistics in Medicine (2021).
• A61 Bassu G.L.,...., Liquet B, et al.Design and rationale of the COVID-19 Critical Care Consortium international, multicentre, observational study. BMJ Open (2020).
• A60 Callens A, Wang Y, Fu L, and Liquet B. (2020) Robust estimation procedure for autoregressivemodels with heterogeneity. Environmental Modeling & Assessment. (2020).
• A59 Callens, A., Morichon D., Abadie S., Delpey M. and Liquet B. (2020) Improving local wave forecast with machine learning algorithms: a practical guide. Ocean modelling (2020).
• A58 Floren Hugon, Liquet B., Frank D'Amico. (2020). Multi-site and Multi-year Remote Records of Operative Temperatures with Biomimetic Loggers Reveal Spatio-temporal Variability in Mountain Lizard Activity and Persistence Proxy Estimate Remote Sensing (2020)
• A57 Vishwakarma G. K., Bhattacharjee A., and Banerjee S., and Liquet B. Classification Algorithm for High Dimensional Protein Markers in Time-course Data. Statistics in Medicine (2020).
• A56 Rodriguez-Perez J., Leigh C.,Liquet B., Peterson E., Sous D., Mergensen K. Detecting sensor-based anomalies in high-frequency water-quality data using artificial neural networks. Environmental Science and Technology (2020) .
• A55 Vervelloni J., Liquet B. et al. (2020) A new approach to forecast intensifying disturbance effects on coral reefs Global Change Biology
• A54 P. Lafaye de Micheaux, B. Liquet, and M. Sutton. PLS for Big Data: A unified parallel algorithm for regularised group PLS. Statistics. Survey. 13 (2019), 119 - 149.
• A53 B. Liquet and J. Riou. CPMCGLM: An R package for p-value adjustment when looking for an optimal transformation of a single explanatory variable in generalized linear models. BMC Medical Research Methodology In press (2019).
• A52 C. Broc, B. Calvo and B. Liquet . Penalized Partial Least Square applied to structured data. Arabian Journal of Mathematics pp 1-16 (2019).
• A51 M. Sutton, K. Mengersen, and B . Liquet. Sparse Subspace Constrained Partial Least Squares. Statistical Computation and Simulation 1-15, (2018).
• A50 D. Morichon, I. de Santiago, M. Delpey, T. Somdecoste, A. Callens, B. Liquet, P. Liria. Assessment of flooding hazards at an engineered beach during extreme events : Biarritz, SW France. Journal of Coastal Research 85, 801- 805 (2018) .
• A49 C. Broc, M. Evangelou, T. Truing, and B. Liquet. Investigating Gene- and Pathway-environment Interaction analysis approaches. Journal de la Société Française de Statistique Vol. 159, 2 (2018).
• A48 M. Sutton, R. Thiebaut, and B. Liquet. Sparse partial least squares with group and subgroup structure. Statistics in Medicine 37(23), 3338 - 3356 (2018).
• A47 P. Jain, B. Liquet, J. Vlaanderen, B. Bodinier, K. Van Veldhoven, M. Kogevinas, C. Villanueva, R.Vermeulen, P. Vineis, M. Chadeau-Hyam. A multivariate approach to investigate molecular effects of multiple exposures. Journal of Epidemiology & Community Health (2018) doi: 10.1136/jech-2017-210061.
• A46 R. Vermeulen, F. Saberi Hosnijeh, B. Bodinier, L. Portengen, B. Liquet, et al. Prediagnostic blood immune markers, incidence and progression of B-cell lymphoma and multiple myeloma: univariate and functionally-informed multivariate analyses. International Journal of Cancer (2018) doi: 10.1002/ijc.31536.
• 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) (2017). https://doi.org/10.1371/journal.pone.0169164.
• A42 Benham T., Duan Q., Kroese D.P., Liquet B., CEoptim: Cross-Entropy R package for optimization. Journal of Statistical Software 76 (8) (2017).
• A41 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(2) (2017).
• A40 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).
• A39 Liquet B., and Saracco J., BIG-SIR a Sliced Inverse Regression Approach for Massive Data. Statistics and Its Interface 9, 4 (2016).
• A38 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).
• A37 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).
• 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 69, 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 underdetermined 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., Deciphering 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. \textit 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 \alpha parameter in the SIR_\alpha 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 SIR_\alpha with discrete covariables. Computational 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).

Refereed conference papers

• RC1 Thiebaut R., Liquet B., Hocini H., Hue S., Richert L., Raimbault M., Le Cao K. and Levy Y. A new method for integrated analysis applied to gene expression and cytokines secretion in response to LIPO-5 vaccine in HIV-negative volunteers. Retrovirology 9:2, 121 (2012).