• [32] M. Selosse, J. Jacques and C. Biernacki (2018). Model-based co-clustering for mixed type data, Preprint HAL n°01893457.
  • [31] M. Selosse, J. Jacques and C. Biernacki (2017). ordinalClust: a package for analyzing ordinal data, Preprint HAL n°01678800.
  • [30] A. Schmutz, J. Jacques, C. Bouveyron, L. Chèze and P. Martin (2017). Clustering multivariate functional data in group-specific functional subspaces, Preprint HAL n°01652467.
  • [29] M. Selosse, J. Jacques, C. Biernacki and F. Cousson-Gélie (2017). Analyzing health quality survey using constrained co-clustering model for ordinal data and some dynamic implication, Preprint HAL n°01643910.


  • [28] F. Martínez-Álvarez, A. Schmutz, G. Asencio-Cortés, J. Jacques (2018). A novel hybrid algorithm to forecast functional time series based on pattern sequence similarity with application to electricity demand, to appear in Energies.
  • [27] J. Jacques and C. Biernacki (2018), Model-based co-clustering for ordinal data, Computational Statistics and Data Analysis, 123, 101-115. [paper]
  • [26] 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, to appear in Journal of the Royal Statistical Society, Series C. [paper]
  • [25] Y. Ben Slimen, S. Allio, J. Jacques (2018). Model-Based Co-clustering for Functional Data, Neurocomputing, 291, 97-108. [paper]
  • [24] K. Nagbe, J. Cugliari and J. Jacques (2018). Electricity Demand Forecasting Using a Functional State Space Model, Energies, 11 [5], 1120. [paper]
  • [23] 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. [paper]
  • [22] J. Jacques and C. Ruckebusch (2016). Model-based co-clustering for hyperspectral images, Journal of Spectral Imaging, 5 [1], 1-6. [paper]
  • [21] 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, to appear in Physiology and Behavior, 169, 1-8.
  • [20] 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. [paper]
  • [19] 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. [paper] [R package]
  • [18] 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. [paper] [R package]
  • [17] 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.
  • [16] J.Jacques and C.Biernacki (2014), Model-based clustering for multivariate partial ranking data, Journal of Statistical Planning and Inference, 149, 201-217. [web] [paper] [R package]
  • [15] 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] [paper] [R package]
  • [14] J.Jacques and C.Preda (2014), Functional data clustering: a survey, Advances in Data Analysis and Classification, 8[3], 231-255. [paper] [R package]
  • [13] 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. [paper]
  • [12] J.Jacques and C.Preda (2014), Model-based clustering of multivariate functional data, Computational Statistics and Data Analysis, 71, 92-106. [paper] [R package]
  • [11] 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. [paper] [R package]
  • [10] J.Jacques and C.Preda (2013), Funclust: a curves clustering method using functional random variable density approximation, Neurocomputing, 112, 164-171. [paper] [R package]
  • [9] C.Biernacki and J.Jacques (2013), A generative model for rank data based on sorting algorithm, Computational Statistics and Data Analysis, 58, 162-176. [paper] [paper] [R package]
  • [8] 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. [paper] [R package]
  • [7] 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. [paper]
  • [6] 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. [paper]
  • [5] 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] [paper]
  • [4] 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. [paper]
  • [3] 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]
  • [2] 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]
  • [1] 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]

Chapitre de livre

  • 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]

Autres documents

  • J.Jacques (2012), Contribution à l’apprentissage statistique à base de modèles génératifs pour données complexes, Habilitation à Diriger des Recherches, 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, thèse de doctorat de l'Université Joseph Fourier [pdf].