Bibliography

Major publications by the team in recent years

• 1A. Amarioarei, C. Preda.
  Approximation for the Distribution of Three-dimensional Discrete Scan Statistic, in: Methodology and Computing in Applied Probability, September 2013, 14 p. [ DOI : 10.1007/s11009-013-9382-3 ]
  https://hal.inria.fr/hal-01092992
• 2S. Arlot, A. Celisse.
  Segmentation of the mean of heteroscedastic data via cross-validation, in: Statistics and Computing, 2010, vol. 21, pp. 613–632.
• 3C. Biernacki, G. Celeux, G. Govaert.
  Exact and Monte Carlo Calculations of Integrated Likelihoods for the Latent Class Model, in: Journal of Statistical Planning and Inference, 2010, vol. 140, pp. 2991-3002.
  https://hal.archives-ouvertes.fr/hal-00554344
• 4C. Biernacki, J. Jacques.
  A generative model for rank data based on an insertion sorting algorithm, in: Computational Statistics and Data Analysis, 2013, vol. 58, pp. 162-176. [ DOI : 10.1016/j.csda.2012.08.008 ]
  https://hal.archives-ouvertes.fr/hal-00441209
• 5A. Celisse, J.-J. Daudin, L. Pierre.
  Consistency of maximum likelihood and variational estimators in stochastic block model, in: Electronic Journal of Statistics, 2012, pp. 1847–1899.
  http://projecteuclid.org/handle/euclid.ejs
• 6M. Giacofci, S. Lambert-Lacroix, G. Marot, F. Picard.
  Wavelet-based clustering for mixed-effects functional models in high dimension, in: Biometrics, March 2013, vol. 69, n^o 1, pp. 31-40. [ DOI : 10.1111/j.1541-0420.2012.01828.x ]
  http://hal.inria.fr/hal-00782458
• 7J. Jacques, C. Biernacki.
  Extension of model-based classification for binary data when training and test populations differ, in: Journal of Applied Statistics, 2010, vol. 37, n^o 5, pp. 749-766.
  https://hal.archives-ouvertes.fr/hal-00316080
• 8J. Jacques, C. Preda.
  Funclust: a curves clustering method using functional random variables density approximation, in: Neurocomputing, 2013, vol. 112, pp. 164-171.
  https://hal.archives-ouvertes.fr/hal-00628247
• 9A. Lourme, C. Biernacki.
  Simultaneous Gaussian Model-Based Clustering for Samples of Multiple Origins, in: Computational Statistics, December 2013, vol. 152, n^o 3, pp. 371-391.
  https://hal.inria.fr/hal-00921041
• 10V. Vandewalle, C. Biernacki, G. Celeux, G. Govaert.
  A predictive deviance criterion for selecting a generative model in semi-supervised classification, in: Computational Statistics and Data Analysis, 2013, vol. 64, pp. 220-236.
  https://hal.inria.fr/inria-00516991

Publications of the year

Doctoral Dissertations and Habilitation Theses

• 11A. Amarioarei.
  Approximations for Multidimensional Discrete Scan Statistics, Université de Lille 1, September 2014.
  https://hal.inria.fr/tel-01105214
• 12M. Marbac-Lourdelle.
  Model-based clustering for categorical and mixed data sets, université lille 1, September 2014.
  https://tel.archives-ouvertes.fr/tel-01076418

Articles in International Peer-Reviewed Journals

• 13A. Amarioarei, C. Preda.
  Approximations for two-dimensional discrete scan statistics in some block-factor type dependent models, in: Journal of Statistical Planning and Inference, January 2014, vol. 151-152, 14 p. [ DOI : 10.1016/j.jspi.2014.05.002 ]
  https://hal.inria.fr/hal-01092993
• 14S. Dabo-Niang, S. Ali Ould Abdi, A. Ould Abdi, A. Diop.
  Consistency of a nonparametric conditional mode estimator for random fields, in: Statistical Methods and Applications, 2014, 21 p. [ DOI : 10.1007/s10260-013-0239-2 ]
  https://hal.inria.fr/hal-00921178
• 15S. Dabo-Niang, L. Hamdad, C. Ternynck, A.-F. Yao.
  A kernel spatial density estimation with applications to spatial clustering and Monsoon Asia Drought Atlas analysis, in: Stochastic Environmental Research and Risk Assessment, June 2014, pp. 2075-2099.
  http://hal.univ-lille3.fr/hal-01094743
• 16E. Eirola, A. Lendasse, V. Vandewalle, C. Biernacki.
  Mixture of Gaussians for Distance Estimation with Missing Data, in: Neurocomputing, May 2014, vol. 131, pp. 32-42.
  https://hal.inria.fr/hal-00921023
• 17Q. Grimonprez, A. Celisse, S. Blanck, M. Cheok, M. Figeac, G. Marot.
  MPAgenomics : An R package for multi-patients analysis of genomic markers, in: BMC Bioinformatics, December 2014, vol. 15, 4 p. [ DOI : 10.1186/s12859-014-0394-y ]
  https://hal.inria.fr/hal-00933614
• 18S. Iovleff.
  Probabilistic Auto-Associative Models and Semi-Linear PCA, in: Advances in Data Analysis and Classification, September 2014, 20 p.
  https://hal.archives-ouvertes.fr/hal-00734070
• 19J. Jacques, C. Biernacki.
  Model-based clustering for multivariate partial ranking data, in: Journal of Statistical Planning and Inference, June 2014, vol. 149, pp. 201-217.
  https://hal.inria.fr/hal-00743384
• 20J. Jacques, Q. Grimonprez, C. Biernacki.
  Rankcluster: An R package for clustering multivariate partial rankings, in: The R Journal, June 2014, vol. 6, n^o 1, 10 p.
  https://hal.archives-ouvertes.fr/hal-00840692
• 21J. Jacques, C. Preda.
  Functional data clustering: a survey, in: Advances in Data Analysis and Classification, January 2014, vol. 8, n^o 3, 24 p. [ DOI : 10.1007/s11634-013-0158-y ]
  https://hal.inria.fr/hal-00771030
• 22J. Jacques, C. Preda.
  Model-based clustering for multivariate functional data, in: Computational Statistics and Data Analysis, June 2014, vol. 71, pp. 92-106.
  https://hal.archives-ouvertes.fr/hal-00713334
• 23R. Lebret, S. Iovleff, F. Langrognet, C. Biernacki, G. Celeux, G. Govaert.
  Rmixmod: The R Package of the Model-Based Unsupervised, Supervised and Semi-Supervised Classification Mixmod Library, in: Journal of Statistical Software, December 2014, forthcoming.
  https://hal.archives-ouvertes.fr/hal-00919486
• 24M. Marbac, C. Biernacki, V. Vandewalle.
  Model-based clustering for conditionally correlated categorical data, in: Journal of Classification, 2015, n^o 32, 33 p. [ DOI : 10.1007/s00357 ]
  https://hal.inria.fr/hal-00787757
• 25N. Martin, C. Salazar-Cardozo, C. Vercamer, L. Ott, G. Marot, P. Slijepcevic, C. Abbadie, O. Pluquet.
  Identification of a gene signature of a pre-transformation process by senescence evasion in normal human epidermal keratinocytes, in: Molecular Cancer, 2014, vol. 13, n^o 1, 151 p. [ DOI : 10.1186/1476-4598-13-151 ]
  https://hal.inria.fr/hal-01011012
• 26A. Rau, G. Marot, F. Jaffrézic.
  Differential meta-analysis of RNA-seq data from multiple studies, in: BMC Bioinformatics, 2014, vol. 15, n^o 1, 91 p. [ DOI : 10.1186/1471-2105-15-91 ]
  https://hal.inria.fr/hal-00978902
• 27L. Yengo, J. Jacques, C. Biernacki.
  Variable clustering in high dimensional linear regression models, in: Journal de la Société Française de Statistique, 2014, vol. 155, n^o 2, 19 p.
  https://hal.archives-ouvertes.fr/hal-00764927

Invited Conferences

• 28S. Dabo-Niang.
  Statistical modeling of spatial functional data: application to biomedical data, in: ISNPS conférence, Cadiz, Estonia, June 2014.
  http://hal.univ-lille3.fr/hal-01094757
• 29S. Dabo-Niang, S. Guillas, C. Ternynck.
  Efficiency in functional nonparametric models withautoregressive errors, in: ERCIM, Pise, Italy, December 2014.
  http://hal.univ-lille3.fr/hal-01094762
• 30J. Jacques, C. Biernacki.
  Clustering multivariate ordinal data, in: ERCIM 2014, 7th International Conference of the ERCIM working group on Computational and Methodological Statistics, Pisa, Italy, December 2014.
  https://hal.inria.fr/hal-01100630

International Conferences with Proceedings

• 31C. Biernacki, G. Castellan.
  A Data-Driven Bound on Covariance Matrices for Avoiding Degeneracy in Multivariate Gaussian Mixtures, in: 46° Journées de Statistique, Rennes, France, June 2014.
  https://hal.inria.fr/hal-01099080
• 32C. Théry, C. Biernacki, G. Loridant.
  CorReg : Préselection de variables en régression linéaire avec fortes corrélations, in: 46° journées de statistiques, Rennes, France, SFDS, June 2014.
  https://hal.inria.fr/hal-01092964
• 33L. Yengo, J. Jacques, C. Biernacki.
  Variable Clustering in High Dimensional Probit Regression Models, in: 46èmes Journées de Statistique organisée par la Société Française de Statistique, Rennes, France, 2014.
  https://hal.archives-ouvertes.fr/hal-01100633

National Conferences with Proceedings

• 34Q. Grimonprez, A. Celisse, G. Marot.
  Analyse multi-patients de données génomiques, in: 46e Journées de Statistique, Rennes, France, SFDS, June 2014.
  https://hal.archives-ouvertes.fr/hal-01091476
• 35J. Kellner, A. Celisse.
  High-dimensional test for normality, in: Journées des Statistiques, Rennes, France, June 2014.
  https://hal.inria.fr/hal-01091513
• 36F. Loingeville, J. Jacques, C. Preda, P. Guarini, O. Molinier.
  Analyse de variance à 2 facteurs imbriqués sur données de comptage - Application au contrôle de qualité, in: 46e Journées de Statistique, Rennes, France, June 2014, 6 p.
  https://hal.archives-ouvertes.fr/hal-00986457

Internal Reports

• 37M. Attouch, M. Salem Ahmed, S. Dabo-Niang, A. Diop.
  k-nearest neighbors method estimation of regression function for spatial dependent data, 2014.
  https://hal.inria.fr/hal-00943647
• 38C. Biernacki, J. Jacques.
  Model-Based Clustering of Multivariate Ordinal Data Relying on a Stochastic Binary Search Algorithm, July 2014.
  https://hal.inria.fr/hal-01052447
• 39S. Bouka, S. Dabo-Niang, G. Gayraud, G.-M. Nkiet.
  Minimax testing in a spatial discrete regression scheme, 2014.
  https://hal.inria.fr/hal-00943645
• 40S. Dabo-Niang, C. Ternynck, A.-F. Yao.
  A new spatial regression estimator in the multivariate context, 2014.
  https://hal.inria.fr/hal-00943646
• 41J. Kellner, A. Celisse.
  New goodness-of-fit tes for normality in RKHS, Inria, 2014.
  https://hal.inria.fr/hal-00943669

Other Publications

• 42P. Bhatia, S. Iovleff, G. Govaert.
  blockcluster: An R Package for Model Based Co-Clustering, December 2014.
  https://hal.inria.fr/hal-01093554
• 43C. Bouveyron, E. Côme, J. Jacques.
  The Discriminative Functional Mixture Model for the Analysis of Bike Sharing Systems, July 2014.
  https://hal.archives-ouvertes.fr/hal-01024186
• 44S. Dabo-Niang, A. Laksaci, Z. Kaid.
  On spatial conditional quantile estimation for a functional regressor, July 2014.
  http://hal.univ-lille3.fr/hal-01094745
• 45Q. Grimonprez, A. Celisse, G. Marot.
  Analysis of genomic markers: Make it easy with the R package MPAgenomics, January 2014, SMPGD 2014.
  https://hal.archives-ouvertes.fr/hal-01091543
• 46J. Hamon, G. Even, R. Dassonneville, J. Jacques, C. Dhaenens.
  Use of a novel evolutionary algorithm for genomic selection, January 2015.
  https://hal.inria.fr/hal-01100660
• 47J. Kellner, A. Celisse.
  New normality test in high dimension with kernel methods, April 2014.
  https://hal.archives-ouvertes.fr/hal-00977839
• 48M. Marbac, C. Biernacki, V. Vandewalle.
  Classification de données mixtes par un modèle de mélange de copules gaussiennes, February 2014, 46e Journées de Statistique (Rennes, du 2 au 6 juin 2014 ).
  https://hal.archives-ouvertes.fr/hal-00940613
• 49M. Marbac, C. Biernacki, V. Vandewalle.
  Finite mixture model of conditional dependencies modes to cluster categorical data, February 2014.
  https://hal.archives-ouvertes.fr/hal-00950112
• 50M. Marbac, C. Biernacki, V. Vandewalle.
  Model-based clustering of Gaussian copulas for mixed data, May 2014.
  https://hal.archives-ouvertes.fr/hal-00987760
• 51C. Théry, C. Biernacki, G. Loridant.
  Model-Based Variable Decorrelation in Linear Regression, August 2014.
  https://hal.archives-ouvertes.fr/hal-01099133
• 52L. Yengo, J. Jacques, C. Biernacki, M. Canouil.
  Variable Clustering in High-Dimensional Linear Regression: The R Package clere, February 2014.
  https://hal.archives-ouvertes.fr/hal-00940929