Full Professor in Statistics

Publications

Preprints

  • Youba Abed , Julien Jacques , Victor Costa , Benoît Celse , Denis Guillaume. Heterogeneous transfer learning for highly non-linear regression tasks with application to the hydrotreatment of tire pyrolysis feedstocks. 2025 [HAL]
  • Eliz Peyraud, Julien Jacques, Guillaume Metzler. A Stochastic Approximation of EM Algorithm for Handling Missing Data in Cox Regression Models. 2025 [HAL]
  • Selman Sezgin, Julien Jacques, Kahina Mokrani, Sylvain Allio. FunCast: a forecasting model for functional data using covariates. 2025 [HAL]
  • Eliz Peyraud, Julien Jacques, Guillaume Metzler, Ines Faivre, Mathis Dousse. Mixture of Cox regression models with L 1 -penalization for modeling patients survival time after liver transplantation. 2024. [HAL]
  • Francesco Amato, Julien Jacques. MMM: Clustering Multivariate Longitudinal Mixed-type Data. 2024.[HAL]

Publications

  • Matteo Ventura, Paola Zuccolotto, Julien Jacques (2025).M ultivariate Latent Class Modeling of Rating Data for Investigating the Sustainability Perception and the Economic Behavior in the Made in Italy Sector. Statistical Papers, 2026.
  • Jean Steve Tamo Tchomgui, Julien Jacques, Vincent Barriac, Guillaume Fraysse, Stéphane Chrétien. A Penalized Spline Estimator for Functional Linear Regression with Functional Response. Advances in Data Analysis and Classification, 2026. [HAL]
  • Noé Lebreton, Julien Ah-Pine, Julien Jacques, Matthieu Neveu. Pattern matching for multivariate time series forecasting. Statistical Analysis and Data Mining, 2026 [HAL]
  • Nicola Piras, Silvia Columbu, Julien Jacques (2025). Multilevel latent class with CUB models. Journal of Classification.[web]
  • Matteo Ventura, Julien Jacques, Paola Zuccolotto (2025). Model-based Clustering of Multivariate Rating Data accounting for Feeling and Uncertainty. Journal of Classification.[web]
  • J. Jacques, T.B. Murphy. Model-Based Clustering and Variable Selection for Multivariate Count Data, Computo, 2025.[web]
  • N. Hernandez, J. Cugliari, J. Jacques (2024). Simultaneous predictive bands for functional time series using minimum entropy sets, Communications in Statistics - Simulation and Computation, 1-25. [Arxiv]
  • J.S. Tamo Tchomgui, J. Jacques, V. Barriac, G. Fraysse, S. Chrétien (2024). A mixture of experts regression model for functional response with functional covariates. Statistics and Computing, 34[124]. [HAL]
  • F. Amato, J. Jacques, I. Prim-Allaz (2024). Clustering Longitudinal Ordinal Data via Finite Mixture of Matrix-Variate Distributions. Statistics and Computing, 34[81] [HAL]
  • C. Biernacki, J. Jacques, C. Keribin (2023). A Survey on Model-Based Co-Clustering: High Dimension and Estimation Challenges. Journal of Classification, 40[2], 332-381 [HAL]
  • L. Iapteff, J. Jacques, B. Celse and V. Costa (2023). Reducing the number of experimental points to fit kinetic models: a Bayesian approach. Industrial & Engineering Chemistry Research, 2023, 62, 28, 10903-10914. [HAL]
  • M. Amovin, I. Gannaz, J. Jacques (2022). Outlier detection in multivariate functional data through a contaminated mixture model. Computational Statistics and Data Analysis, 174, 107496. [HAL]
  • J. Jacques, S. Samardzic (2022). Analyzing cycling sensors data through ordinal logistic regression with functional covariates. Journal of the Royal Statistical Society, Series C, 71[4], 969-986. [HAL]
  • A. Gourru, J. Velcin, C. Gravier, J. Jacques (2022). Dynamic Gaussian Embedding of Authors. TheWebConf 2022, Lyon, France.
  • C. Bouveyron, J. Jacques, A. Schmutz, F. Simoes, S. Bottini (2022). Co-Clustering of Multivariate Functional Data for the Analysis of Air Pollution in the South of France. Annals of Applied Statistics, 16, 1400-1422. [HAL]
  • Y. Ben Slimen, S. Allio, J. Jacques (2022). Co-clustering for binary and functional data, Communications in Statistics - Simulation and Computation, 51[9], 4845-4866. [HAL]
  • L. Iapteff, J. Jacques, M. Rolland, B. Celse (2021). Reducing the number of experiments required for modeling the hydrocracking process with kriging through Bayesian transfer learning. Journal of the Royal Statistical Society, Series C, 70, 1344-1364. [HAL]
  • M. Selosse, I. Gormley, J. Jacques and C. Biernacki (2020). A bumpy journey: exploring deep Gaussian mixture models, ICBINB@NeurIPS 2020. [HAL]
  • M. Selosse, J. Jacques and C. Biernacki (2020). ordinalClust: a package for analyzing ordinal data, R journal, 12[2], 173-188. [HAL]
  • A. Gourru, J. Velcin, J. Jacques (2020). Gaussian Embedding of Linked Documents from a Pretrained Semantic Space. IJCAI 2020, Yokohama, Japan. [paper and video]
  • M. Selosse, J. Jacques and C. Biernacki (2020). Self-Organized Co-Clustering for textual data synthesis, Pattern Recognition, 103, 2020. [HAL]
  • A. Schmutz, L. Chèze, P. Martin and J. Jacques (2020). A method to estimate horse speed per stride from one IMU with Machine Learning method, Sensors, 20[2], 518. [web] [HAL]
  • A. Gourru, J. Velcin, J. Jacques and A. Guille (2020). Document Network Projection in Pretrained Word Embedding Space. ECIR 2020, Lisbon, Portugal. [paper] and [talk]
  • J. Dupuy, A. Guille, J. Jacques (2020). Document Network Embedding: Coping for Missing Links and Missing Content. SAC 2020, Brno, Czech Republic. [HAL]
  • A. Schmutz, J. Jacques, C. Bouveyron, L. Chèze and P. Martin (2020). Clustering multivariate functional data in group-specific functional subspaces, Computational Statistics, 35, 1101-1131. [HAL]
  • M. Selosse, J. Jacques and C. Biernacki (2020). Model-based co-clustering for mixed type data, Computational Statistics and Data Analysis, 144, 106866. [HAL]
  • M. Selosse, J. Jacques, C. Biernacki and F. Cousson-Gélie (2019). Analyzing health quality survey using constrained co-clustering model for ordinal data and some dynamic implication, Journal of the Royal Statistical Society, Series C, 68 [5], 1327-1349. [HAL]
  • F. Martínez-Álvarez, A. Schmutz, G. Asencio-Cortés, J. Jacques (2019). A novel hybrid algorithm to forecast functional time series based on pattern sequence similarity with application to electricity demand, Energies, 12[1], 94.
  • J. Jacques and C. Biernacki (2018), Model-based co-clustering for ordinal data, Computational Statistics and Data Analysis, 123, 101-115. [HAL]
  • C. Bouveyron, L. Bozzi L., J. Jacques J. and F-X. Jollois (2018). The Functional Latent Block Model for the Co-Clustering of Electricity Consumption Curves, Journal of the Royal Statistical Society, Series C, 67 [4], 897-915. [HAL]
  • Y. Ben Slimen, S. Allio, J. Jacques (2018). Model-Based Co-clustering for Functional Data, Neurocomputing, 291, 97-108. [HAL]
  • K. Nagbe, J. Cugliari and J. Jacques (2018). Electricity Demand Forecasting Using a Functional State Space Model, Energies, 11 [5], 1120. [HAL]
  • Md A. Hasnat, J. Velcin, S. Bonnevay and J. Jacques (2017). Evolutionary clustering for categorical data using parametric links among multinomial mixture models, Econometrics and Statistics, 3, 141-159. [HAL]
  • Y. Ben Slimen, S. Allio, J. Jacques (2017). Anomaly Prevision in Radio Access Networks Using Functional Data Analysis. IEEE Globecom 2017, Singapore.
  • J. Jacques and C. Ruckebusch (2016). Model-based co-clustering for hyperspectral images, Journal of Spectral Imaging, 5 [1], 1-6. [HAL]
  • E. Bacou, K. Haurogné, G. Mignot, M. Allard, L. De Beaurepaire, J. Marchand, E. Terenina, Y. Billon, J. Jacques, J. Bach, P. Mormède, J. Hervé, and B. Lieubeau (2017). Acute social stress-induced immunomodulation in pigs high and low responders to ACTH, Physiology and Behavior, 169, 1-8.
  • C. Biernacki and J. Jacques (2016), Model-based clustering of multivariate ordinal data relying on a stochastic binary search algorithm, Statistics and Computing, 26 [5], 929-943. [HAL]
  • L. Yengo, J.Jacques, C.Biernacki and M.Canouil (2016), Variable Clustering in High-Dimensional Linear Regression: The R Package clere, The R Journal, 8[1], 92-106. [HAL] [R package]
  • Md A. Hasnat, J. Velcin, S. Bonnevay and J. Jacques. A Comparative Study of Clustering Methods with Multinomial Distribution. IDA 2015, Saint-Etienne, France.
  • C. Bouveyron, E. Côme and J. Jacques (2015), The discriminative functional mixture model for the analysis of bike sharing systems, Annals of Applied Statistics, 9[4], 1726-1760. [HAL] [R package]
  • F. Herbert, N. Tchitchek, D. Bansal, J. Jacques, S. Pathak, C. Bécavin, C. Fesel, E. Dalko, P-A. Cazenave, C. Preda, B. Ravindran, S. Sharma, B. Das and S. Pied (2015), Evidence of IL-17, IP-10, and IL-10 involvement in multiple-organ dysfunction and IL-17 pathway in acute renal failure associated to Plasmodium falciparum malaria, Journal of Translational Medicine, 13[1], 1-11.
  • J.Jacques and C.Biernacki (2014), Model-based clustering for multivariate partial ranking data, Journal of Statistical Planning and Inference, 149, 201-217. [web] [HAL] [R package]
  • J.Jacques, Q.Grimonprez and C.Biernacki (2014), Rankcluster: An R package for clustering multivariate partial rankings, The R Journal, 6[1], 101-110. [web] [HAL] [R package]
  • J.Jacques and C.Preda (2014), Functional data clustering: a survey, Advances in Data Analysis and Classification, 8[3], 231-255. [HAL] [R package]
  • L. Yengo, J.Jacques and C.Biernacki (2014), Variable clustering in high dimensional linear regression, Journal de la Société Française de Statistique, 155[2], 38-56. [HAL]
  • J.Jacques and C.Preda (2014), Model-based clustering of multivariate functional data, Computational Statistics and Data Analysis, 71, 92-106. [HAL] [R package]
  • C.Bouveyron and J.Jacques (2013), Adaptive mixtures of regressions: Improving predictive inference when population has changed, Communication in Statistics - Simulation and Computation, 43[10], 2570-2592. [HAL] [R package]
  • J.Jacques and C.Preda (2013), Funclust: a curves clustering method using functional random variable density approximation, Neurocomputing, 112, 164-171. [HAL] [R package]
  • C.Biernacki and J.Jacques (2013), A generative model for rank data based on sorting algorithm, Computational Statistics and Data Analysis, 58, 162-176. [HAL] [HAL] [R package]
  • C.Bouveyron and J.Jacques (2011), Model-based Clustering of Time Series in Group-specific Functional Subspaces, Advances in Data Analysis and Classification, 5[4], 281-300. [HAL] [R package]
  • C.Bouveyron, P.Gaubert and J.Jacques (2011), Adaptive models in regression for modeling and understanding evolving populations, Case Studies in Business, Industry and Government Statistics, 4[2], 83-92. [HAL]
  • Jacques J., Bouveyron C., Girard S., Devos O., Duponchel L., Ruckebusch C. (2010). Gaussian mixture models for the classification of high-dimensional vibrational spectroscopy data, Journal of Chemometrics 24 [11-12], 719-727. [HAL]
  • C.Bouveyron and J.Jacques (2010). Adaptive linear models for regression: improving prediction when population has changed, Pattern Recognition Letters, 31[14], 2237-2247. [web] [HAL]
  • J.Jacques and C.Biernacki (2010). Extension of model-based classification for binary data when training and test populations differ, Journal of Applied Statistics, 37[5], 749-766. [HAL]
  • C. Langlois-Jacques and J. Jacques (2009). Détection d'hétérogénéitéé au sein de mesures de qualité de l'environnement, La Revue de Modulad, 40, 41-52. [web]
  • J.Jacques and C.Biernacki (2007). Classement de données binaires lorsque les populations d'apprentissage et de test sont différentes. Revue des Nouvelles Technologies de l'Information, Data Mining et apprentissage statistique : application en assurance, banque et marketing, A1, 109-130. [paper]
  • J.Jacques, C.Lavergne and N.Devictor (2006). Sensitivity Analysis in presence of Model Uncertainty and Correlated Inputs, Reliability Engineering and System Safety 91, 1126-1134. [paper]

Book chapter

  • J.Jacques and D.Fraix-Burnet (2015), Linear Regression in High Dimension and/or for Correlated Inputs, in Statistics for Astrophysics, EAS Publications Series, Vol. 66, ISBN: 978-2-7598-1729-0.
  • F.Beninel, C.Biernacki, C.Bouveyron, J.Jacques and A.Lourme (2012), Parametric link models for knowledge transfer in statistical learning, in Knowledge Transfer: Practices, Types and Challenges, Nova Publishers, ISBN: 978-1-62081-579-3. [ebooks]

Others

  • J.Jacques (2012), Contribution à l’apprentissage statistique à base de modèles génératifs pour données complexes, Habilitation thesis, University Lille 1 [pdf].
  • J.Jacques (2011), Pratique de l'analyse de sensibilité : comment évaluer l'impact des entrées aléatoires sur la sortie d'un modèle mathématique, Pub. IRMA Lille Vol. 71-III (2011) : [pdf]. Package R correspondant : [ici]
  • J.Jacques (2005). Contributions à l'analyse de sensibilité et à l'analyse discriminante, PhD thesis, University Joseph Fourier [pdf].
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