Full Professor in Statistics

Publications

Preprints

  • 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]
  • Jean Steve Tamo Tchomgui, Julien Jacques, Vincent Barriac, Guillaume Fraysse, Stéphane Chrétien. A Penalized Spline Estimator for Functional Linear Regression with Functional Response. 2024. [HAL]
  • Francesco Amato, Julien Jacques. MMM: Clustering Multivariate Longitudinal Mixed-type Data. 2024.[HAL]
  • Matteo Ventura, Julien Jacques, Paola Zuccolotto. Model-based Clustering of Multivariate Rating Data accounting for Feeling and Uncertainty, 2024
  • Nicola Piras, Silvia Columbu, Julien Jacques. Multilevel latent class with CUB models, 2024

Publications

  • 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].
Design by sofaspud