Assoc. Professor Benoit Liquet
Dr. Benoit Liquet is a Professor at UPPA (France). In addition he is affiliated with ACEMS as research fellow.
He was previously senior lecturer in Statistics at the University Of Queensland (from 2013-2015), Senior Investigator Statistician at MedicalResearch Council Biostatistics Unit in Cambridge (from 2012-2013), Associate Professor at Bordeaux University (from 2017-2012). Throughout his career he has extensively worked in developing novel statistical models mainly to provide novel tools to analyse clinical, health and biological data arising from epidemiological studies. More recently (since 2011), he moved to the field of computational biology and generalised some of these methods so that they scale to high throughput ("omic") data.
In his new position at UPPA, Benoît Liquet is an active member of the LMAP (Laboratory of Mathematics and Its Applications - UMR CNR 5142) and a member of MIRA ("Milieux et resources aquatiques") where he is developing an interest in how climate change impacts marine ecosystems.
Besides his many overseas collaborations (England, Denmark, Australia, Canada), he is currently working in collaboration with the Basque Country University (UPV/EHU) on novel statistical methods for the analysis of high-dimensional data and Big Data.
To date, he has published 42 articles in international peer-reviewed journals in addition to three book sections and two books on R software. He has also co-authored two books on dynamic biostatistical models. He is the contributor/maintainer of more than 12 R packages available on CRAN(https://cran.r-project.org/). His h-index is 17 (Google Scholar, October 2016) with more than 700 citations.
Benoît Liquet is also an accomplished athlete. After playing in national level in badminton (won the national French University title by team in 2012 and 8th place to the Europeen one), he moved to triathlon competition and more recently to IRONMAN distance. He has qualified three times (2014, 2015 and 2017) for the IRONMAN world championship in Hawaii.
Research Interests:
Applied statistics
Bayesian statistical modelling
Big Data
Bioinformatics
Biostatistics
Variable selection
Projects
Publications
Invited talks, refereed proceedings and other conference outputs
(2018). Bayesian Variable Selection Regression Of Multivariate Responses For Group Data.
Bayesian Statistics in the Big Data Era.
(2018). Statistical Methods for the Analysis of High-Dimensional and Massive Data using R.
Australasian Applied Statistics Conference 2018.
(2018). Type-II Generalized Family-Wise Error Rate Formulas with Application to Sample Size Determination.
Workshop in honor of Daniel Commenges’ 70th birthday.
(2018). A Unified Regularized Group PLS Algorithm Scalable to Big Data.
International Workshop on Perspectives On High-dimensional Data Analysis (HDDA-VIII-2018).
(2018). A Unified Regularized Group PLS Algorithm Scalable to Big Data. Application on genomics data.
“CIBB2018: Computational Intelligence methods for Bioinformatics and Biostatistics”, organized at Caparica (Portugal) .
(2016). Bayesian Variable Selection Regression Of Multivariate Responses For Group Data.
Australian Statistical Conference.
(2016). Statistical Methods For Analysing High-Dimensional Data and Massive Data.
International Conference on Mathematics : Education, Theory, and Application (ICMETA).
Journal Articles
(2019). Age at menarche and the risk of operative delivery.
The Journal of Maternal-Fetal & Neonatal Medicine. 32(3), 411-418. doi: 10.1080/14767058.2017.1381823
(2019). [HDDA] sparse subspace constrained partial least squares.
Journal of Statistical Computation and Simulation. 89(6), 1005-1019. doi: 10.1080/00949655.2018.1555830
(2018). Sparse partial least squares with group and subgroup structure.
Statistics in Medicine. 37(23), 3338 - 3356. doi: 10.1002/sim.7821
(2018). Pre-diagnostic blood immune markers, incidence and progression of B-cell lymphoma and multiple myeloma: Univariate and functionally informed multivariate analyses.
International Journal of Cancer. 143(6), 1335 - 1347. doi: 10.1002/ijc.31536
(2018). A multivariate approach to investigate the combined biological effects of multiple exposures.
Journal of Epidemiology and Community Health. 72(7), 564 - 571. doi: 10.1136/jech-2017-210061
(2018). Investigating Gene- and Pathway-environment Interaction analysis approaches.
Journal de la Société Française de Statistique. 159(2), 56-83.
(2018). Assessment of Flooding Hazards at An Engineered Beach during Extreme Events: Biarritz, SW France.
Journal of Coastal Research. 85, 801 - 805. doi: 10.2112/SI85-161.1
(2017). Estimation of Extended Mixed Models Using Latent Classes and Latent Processes: The R Package lcmm.
Journal of Statistical Software. 78(2), doi: 10.18637/jss.v078.i02
(2017). Integrative Analysis of Immunological Data to Explore Chronic Immune T-Cell Activation in Successfully Treated HIV Patients.
PLOS ONE. 12(1), e0169164. doi: 10.1371/journal.pone.0169164
(2017). CEoptim: Cross-Entropy R Package for Optimization.
Journal of Statistical Software. 76(8), doi: 10.18637/jss.v076.i08
(2017). Bayesian Variable Selection Regression of Multivariate Responses for Group Data.
Bayesian Analysis. 12(4), 1039 - 1067. doi: 10.1214/17-BA1081
(2016). BIG-SIR: A Sliced Inverse Regression approach for massive data.
Statistics and Its Interface. 9(4), 509-520. doi: 10.4310/SII.2016.v9.n4.a10
(2016). Group and sparse group partial least square approaches applied in genomics context.
Bioinformatics. 32(1), 35-42. doi: 10.1093/bioinformatics/btv535
(2016). Type-II generalized family-wise error rate formulas with application to sample size determination.
Statistics in Medicine. 35(16), 2687-2714. doi: 10.1002/sim.6909
(2016). A graphics processing unit-based package for Bayesian variable selection regression of multivariate responses .
Journal of Statistical Software. 69(2), doi: 10.18637/jss.v069.i02
(2016). Age-related changes in murine myometrial transcript profile are mediated by exposure to the female sex hormones.
Aging Cell. 15(1), 177-180. doi: 10.1111/acel.12406
(2015). A dynamic view to moment matching of truncated distributions.
Statistics & Probability Letters. 104, 87-93. doi: 10.1016/j.spl.2015.05.006
(2015). A Universal Approximate Cross-Validation Criterion for Regular Risk Functions.
The International Journal of Biostatistics. 11(1), 51-67. doi: 10.1515/ijb-2015-0004
