Jerome Saracco

Jérôme SARACCO

Statisticien (Professeur des Universités, Institut Polytechnique de Bordeaux)

Ecole Nationale Supérieure de Cognitique (ENSC Bordeaux INP)

Institut de Mathématiques de Bordeaux, UMR CNRS 5251, équipe OptimAl

Inria Bordeaux Sud Ouest, équipe CQFD

Email : jerome.saracco« @ »ensc.fr / jerome.saracco« @ »inria.fr

Introduction

La liste des publications est présentée de la manière suivante :

  • Articles dans des revues internationales de Statistique à comité de lecture,
  • Articles dans des revues nationales de Statistique à comité de lecture,
  • Chapitres d’ouvrage (avec comité de lecture),
  • Publications dans des revues internationales d’autres spécialités scientifiques,
  • Publications dans des revues de transfert,
  • Articles en révision ou soumis pour publication,
  • Actes de conférences internationales avec publications des actes,
  • Rapports de recherche ou rapports techniques.

Articles dans des revues internationales de Statistique à comité de lecture

  1. Charlier, I., Paindaveine, D., Saracco, J. (2019). Multiple-ouput quantile regression through optimal quantization. To appear in Scandinavian Journal of Statistics. DOI: 10.1111/sjos.12426
  2. Chavent, M., Genuer, R., Saracco, J. (2019). Combining clustering of variables and feature selection using random forests. Communications in Statistics – Simulation and computation, 1-20. DOI : 10.1080/03610918.2018.1563145
  3. Chavent, M., Kuentz-Simonet, V., Labenne, A., Saracco, J. (2018). ClustGeo: an R package for hierarchical clustering with spatial constraints. Computational Statistics, 33, 1799-1822. DOI : 10.1007/s00180-018-0791-1
  4. Jlassi, I., Saracco, J. (2017). Variable importance assessment in sliced inverse regression for variable selection. Communications in Statistics – Simulation and computation, 1-31. DOI : 10.1080/03610918.2017.1375522
  5. Liquet, B., Saracco, J. (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
  6. Charlier, I., Paindaveine, D., Saracco, J. (2015). QuantifQuantile : an R package for performing quantile régression through optimal l quantization. The R journal, Vol. 7/2, 65-80. DOI :  10.32614/RJ-2015-021
  7. Charlier, I., Paindaveine, D., Saracco, J. (2015). Conditional quantile estimation based on optimal quantization : from theory to practice. Computational Statistics and Data Analysis, 91, 20–39. DOI : 10.1016/j.csda.2015.05.008
  8. Charlier, I., Paindaveine, D., Saracco, J. (2015). Conditional quantile estimation through optimal quantization. Journal of Statistical Planning and Inference, 156,14–30. DOI : 10.1016/j.jspi.2014.08.003
  9. Coudret, R., Durrieu, G., Saracco, J. (2015). Comparison of kernel density estimators with assumption on number of modes. Communications in Statistics – Simulation and Computation, 44(1), 196-216. DOI: 10.1080/03610918.2013.770530
  10. Bercu, B., Nguyen, T.M.N., Saracco, J. (2015). On the asymptotic behavior of the recursive Nadaraya-Watson estimator associated with the recursive SIR method. Statistics, 49(3), 660-679. DOI : 10.1080/02331888.2014.884097
  11. Coudret, R., Girard, S., Saracco, J. (2014). A new sliced inverse regression method for multivariate response variable. Computational Statistics and Data Analysis, 77, 285–299. DOI : 10.1016/j.csda.2014.03.006
  12. Chavent, M., Girard, S., Kuentz-Simonet, V., Liquet, B., Nguyen, T.M.N., Saracco, J. (2014). A sliced inverse regression approach for data stream. Computational Statistics, 29(5), 1129-1152. DOI : 10.1007/s00180-014-0483-4
  13. Coudret, R., Liquet, B., Saracco, J. (2014). Comparison of sliced inverse regression approaches for underdetermined cases. Journal de la Société Française de Statistique, 155(2), 72-96. Link
  14. Baysse, C., Bihannic, D., Gégout-Petit, A., Prenat, M. and Saracco, J. (2014). Hidden Markov Model for the detection of a degraded operating mode of optronic equipment. Journal de la Société Française de Statistique, 155(3), 48-61. Link
  15. Chavent, M., Kuentz-Simonet, V., Liquet, B., Saracco, J. (2012). ClustOfVar: An R Package for the Clustering of Variables. Journal of Statistical Software, 50, 1-16. DOI : 10.18637/jss.v050.i13
  16. Chavent, M., Kuentz-Simonet, V., Saracco, J. (2012). Orthogonal rotation in PCAMIX. Advances in Data Analysis and Classification, 6 (2), 131-146. DOI : 10.1007/s11634-012-0105-3
  17. Azaïs, R., Gégout-Petit, A., Saracco, J. (2012). Optimal quantization applied to Sliced Inverse Regression. Journal of Statistical Planning and Inference, 142, 481-492. DOI : 10.1016/j.jspi.2011.08.006
  18. Bercu, B., Nguyen, T.M.N., Saracco, J. (2012). A new approach of recursive and non recursive SIR methods. Journal of the Korean Statistical Society, 41, 16-36. DOI : 10.1016/j.jkss.2011.05.005
  19. Liquet, B., Saracco, J. (2012). A criterion for selecting the number of slices and the dimension of the model in SIR and SAVE approaches. Computational Statistics, 27, 103-125. DOI : 10.1007/s00180-011-0241-9
  20. Chavent, M., Kuentz, V., Liquet, B., Saracco, J. (2011). Sliced Inverse Regression for stratified population. Communications in Statistics – Theory and Methods, 40, 1-22. DOI : 10.1080/03610926.2010.501940
  21. Kuentz,, Saracco, J. (2010). Cluster-Based Sliced Inverse Regression. Journal of the Korean Statistical Society, 39(2), 251-267. DOI : 10.1016/j.jkss.2009.08.004
  22. Kuentz, Liquet B., Saracco, J. (2010). Bagging versions of Sliced Inverse Regression. Communications in Statistics – Theory and Methods, 39(11), 1985-1996. DOI : 10.1080/03610920902948251
  23. Gannoun, A., Saracco, J., Yu, K. (2010). On semiparametric mode regression estimation. Communications in Statistics – Theory and methods, 39(7), 1141-1157. DOI : 10.1080/03610920902859581
  24. Chavent, M., Liquet, B., Saracco, J. (2010). A semiparametric approach for multivariate sample selection model. Statistica Sinica, 20(2), 513-536. Link
  25. Chavent, M., Guégan, H., Kuentz, V., Patouille, B., Saracco, J. (2009). PCA- and PMF-based methodology for air pollution sources identification and apportionment. Environmetrics, 20(8), 928-942. DOI : 10.1002/env.963
  26. Liquet, B., Saracco, J. (2008). Application of the bootstrap approach to the choice of dimension and the α parameter in the SIRα Communications in Statistics – Simulation and Computation, 37(6), 1198-1218. DOI : 10.1080/03610910801889011
  27. Chavent, M., Saracco, J. (2008). On central tendency and dispersion measures for intervals and hypercubes. Communications in Statistics – Theory and Methods, 37(8-10), 1471-1482. DOI : 10.1080/03610920701678984
  28. Ferrigno, S., Gannoun, A., Saracco, J. (2008) Inverse regression methods based on fuzzy partitions. International Journal of Pure and Applied Mathematics, 43, 43-62. Link
  29. Gannoun, A., Saracco, J., Yu, K. (2007). Comparison of kernel estimators of conditional distribution function and quantile regression under censoring. Statistical Modelling, 7(4), 329-344. DOI : 10.1177/1471082X0700700404
  30. Chavent, M., Guégan, H., Kuentz, V., Patouille, B., Saracco, J. (2007). Apportionment of air pollution by source at a French urban site. Case Studies in Business, Industry and Government Statistics (CSBIGS), 1-2, 119-129. Link
  31. Barreda, L., Gannoun, A., Saracco, J. (2007). Some extensions of multivariate SIR. Journal of Statistical Computation and Simulation, 77(1-2), 1-17. DOI : 10.1080/10629360600687840
  32. Liquet, B., Saracco, J. (2007). Pooled marginal slicing approach via SIRα with discrete covariables. Computational Statistics, 22(4), 599-617. DOI : 10.1007/s00180-007-0078-4
  33. Liquet, B., Saracco, J., Commenges, D. (2007). Selection between proportional and stratified hazards models based on Expected Log-likelihood. Computational Statistics, 22(4), 619-634. DOI : 10.1007/s00180-007-0079-3
  34. Gannoun, A., Saracco, J., Yuan, A., Bonney, G.E. (2005). Nonparametric quantile regression with censored data. Scandinavian Journal of Statistics, 32, 527-550. DOI : 10.1111/j.1467-9469.2005.00456.x
  35. Saracco, J. (2005). Asymptotics for pooled marginal slicing estimator based on SIRα Journal of Multivariate Analysis, 96, 117-135. DOI : 10.1016/j.jmva.2004.10.003
  36. Gannoun, A., Saracco, J., Urfer, W., Bonney, G.E. (2004). Nonparametric analysis of replicated microarray experiments. Statistical Modelling, 4 (3), 195-209. DOI : 10.1191/1471082X04st073oa
  37. Gannoun, A., Girard, S., Guinot, C., Saracco, J. (2004). Sliced Inverse Regression In Reference Curves Estimation. Computational Statistics and Data Analysis, 46 (1), 103-122. DOI : 10.1016/S0167-9473(03)00141-5
  38. Gannoun, A., Guinot, C., Saracco, J. (2004). Reference curves estimation via alternating sliced inverse regression. Environmetrics, 15, 81-99. DOI : 10.1002/env.630
  39. Gannoun, A, Saracco, J. (2003). A cross validation criteria for SIRα and PSIRα methods in view of prediction. Computational Statistics, 18, 585-603. DOI :  10.1007/BF03354618
  40. Gannoun, A., Saracco, J., Yu, K. (2003). Nonparametric time series prediction by conditional median and quantiles. Journal of Statistical Planning and Inference, 117, 207-223. DOI : 10.1016/S0378-3758(02)00384-1
  41. Gannoun, A., Saracco, J., Yuan, A., Bonney, G.E. (2003). On adaptive transformation-retransformation estimate of conditional spatial median. Communications in Statistics – Theory and methods, 32 (10), 1981-2011. DOI : 10.1081/STA-120023262
  42. Gannoun, A., Saracco, J., Bonney, G.E. (2003). A note on partitioning estimate of conditional distribution under censoring. International Journal of Pure and Applied Mathematics, 5, 95-103. Link
  43. Gannoun, A, Saracco, J. (2003). An asymptotic theory for SIRα Statistica Sinica, 13 (2), 297- 310. Link
  44. Gannoun, A., Saracco, J. (2002). A new proof of strong consistency of kernel estimation of density function and mode under random censorship. Statistics and Probability Letters, 59, 61-66. DOI : 10.1016/S0167-7152(02)00166-9
  45. Gannoun, A., Girard, S., Guinot, C., Saracco, J. (2002). Reference curves based on nonparametric quantile regression. Statistics in Medicine, 21, 3119-3155. DOI : 10.1002/sim.1226
  46. Saracco, J. (2001). Pooled Slicing methods versus Slicing methods. Communications in Statistics – Simulation and Computation, 30(3), 489-511. DOI : 10.1081/SAC-100105075
  47. Saracco, J. (1999). Sliced Inverse Regression under linear constraints. Communications in Statistics – Theory and Methods, 28(10), 2367-2393. DOI : 10.1080/03610929908832426
  48. Saracco, J. (1997). An asymptotic theory for Sliced Inverse Regression. Communications in Statistics – Theory and Methods, 26(9), 2141-2171. DOI : 10.1080/03610929708832039
  49. Aragon, Y., Saracco, J. (1997). Sliced Inverse Regression (SIR): an appraisal of small sample alternatives to slicing. Computational Statistics, 12, 109-130. Link

Articles dans des revues nationales de Statistique à comité de lecture

  1. Claudio, K., Couallier, V., Le Gat, Y., Saracco, J. (2014) Estimation de la consommation d’eau d’un secteur hydraulique à partir d’un échantillon d’usagers télérelevés. Journal de la Société Française de Statistique, 155(4), 160-177. Lien
  2. Kuentz-Simonet, V., Lyser, S., Candau, J., Deuffic, P., Chavent, M., Saracco, J. (2013). Une approche par classification de variables de la typologie d’observations : le cas dune enquête agriculture et environnement, Journal de la Société Française de Statistique, 154(2), 37-63. Lien
  3. Nguyen, T.M.N., Saracco, J. (2010). Estimation récursive en régression inverse par tranches (sliced inverse regression). Journal de la Société Française de Statistique, 151(2), 19-46. Lien
  4. Chaouch, M., Gannoun, A., Saracco, J. (2009). Quantile spatial conditionnel et non conditionnel : une méthode d’estimation et implémentation en R des estimateurs. Journal de la Société Française de Statistique, 150(2), 1-27. Lien
  5. Chavent, M., Kuentz, V., Saracco, J. (2007). Analyse en Facteurs : Présentation et comparaison des logiciels SAS, SPAD et SPSS. La Revue Modulad, 37, 1-30. Lien
  6. Gannoun, A., Girard, S., Guinot, C., Saracco, J. (2004). Implémentation en C d’estimateurs non paramétriques de quantiles conditionnels. Application au tracé de courbes de référence. La Revue Modulad, 31, 59-70. Lien
  7. Gannoun, A., Guinot, C., Saracco, J. (2002). Méthodes de régression semi-paramétrique de type « slicing » ou « pooled slicing » : mises en œuvre sous le logiciel SAS et application sur un jeu de données. La Revue Modulad, 29, 1-38. Lien
  8. Gannoun, A., Girard, S., Guinot, C., Saracco, J. (2002). Trois méthodes non paramétriques pour l’estimation de courbes de référence. Application à l’analyse des propriétés biophysiques de la peau. Revue de Statistique Appliquée, 1, 65-89. Lien
  9. Saracco, J. (1999). Implémentation en Splus des méthodes SIR univariées et multivariées. La Revue Modulad, 22, 78-100. Lien
  10. Saracco, J., Larramendy, I., Aragon, Y. (1999). La régression inverse par tranches ou méthodes SIR : présentation générale. La Revue Modulad, 22, 21-39. Lien

Chapitres d’ouvrage (avec comité de lecture)

  1. Saracco, J., Chavent, M. (2016). Clustering of Variables for Mixed Data, in Statistics for Astrophysics: Clustering and Classification, EAS Publications Series, vol. 77, EDP Sciences,  91-119. DOI : 10.1051/eas/1677007
  2. Girard, S. and Saracco, J. (2016). Supervised and unsupervised classification using mixture models, in Statistics for Astrophysics: Clustering and Classification, EAS Publications Series, vol. 77, EDP Sciences,  69-90. DOI : 10.1051/eas/1677005
  3. Girard, S. and Saracco, J. (2014). An introduction to dimension reduction in nonparametric kernel regression. Chapter book in Statistics for astrophysics : Methods and Applications of Regression, D. Fraix-Burnet and D. Valls-Gabaud (eds), EAS Publications series, vol. 66, 167-196. DOI : 10.1051/eas/1466012
  4. Saracco, J. (2010). Construction de courbes de référence (Chapitre 12, pp 325-356) du livre Approches non-paramétriques en régression, Eds. J.-J. Droesbeke et G. Saporta, Editions TECHNIP. Lien
  5. Gannoun, A., Liquet, B., Saracco, J., Urfer, W. (2007). A kernel method in analysis of replicated micro-array experiments. Chapter book in Statistical Methods for Biostatistics and Related Fields, Eds. W. Haerdle, Y. Mori, P. Vieu. Springer Berlin Heidelberg, pp 45-61. DOI : 10.1007/978-3-540-32691-5_3
  6. Saracco, J., Gannoun, A., Guinot, C., Liquet, B. (2007). A semiparametric approach to estimate reference curves for biophysical properties of the skin. Chapter book in Statistical Methods for Biostatistics and Related Fields, Eds. W. Haerdle, Y. Mori, P. Vieu, Springer Berlin Heidelberg, pp 181-205. DOI : 10.1007/978-3-540-32691-5_10
  7. Saracco, J., Gannoun, A., Guinot, C. (2003). Estimation de courbes de référence pour l’analyse de propriétés biophysiques. Annexe au rapport sur la Statistique et les Probabilités, Commission de Réflexion sur l’Enseignement des Mathématiques (CREM), 34-39. Lien

Articles dans des revues internationales (à comité de lecture) d’autres spécialités scientifiques

  1. Ellies-Oury, M.P., Chavent, M., Conanec, A., Bonnet, M., Picard, B., Saracco, J. (2019). Statistical model choice including variable selection based on variable importance: A relevant way for biomarkers selection to predict meat tenderness. Scientific Reports, Vol. 9 (1):10014. DOI: https://doi.org/10.1038/s41598-019-46202-y
  2. Ellies-Oury, M.-P., Lorenzo, H., Denoyelle, C., Conanec, A., Saracco, J., Picard B. (2019). An original methodology for the selection of biomarkers of tenderness in 5 different muscles. Foods, 8(6), 206. DOI: https://doi.org/10.3390/foods8060206
  3. Conanec ,A., Picard, B., Cantalapiedra-Hijar, G., Chavent, M., Denoyelle, C., Gruffat, D., Normand, J., Saracco, J., Ellies-Oury M.P. (2019). Interaction and trade-off among animal and slaughtering performances, nutritional and organoleptic quality of the meat of 30 Blonde d’Aquitaine heifers.  Foods, 8(6), 197. DOI: https://doi.org/10.3390/foods8060197
  4. Ron-Angevin, R., García, L., Fernández-Rodríguez, A., Saracco, J., André, J.-M.,  Lespinet-Najib, V. (2019). Impact of speller size in a visual P300-brain-computer interface (BCI) system under two conditions of constraint for eyes movement. Computational Intelligence and Neuroscience, Vol. 2019, Article ID 7876248, 16 pages. DOI: https://doi.org/10.1155/2019/7876248
  5. Lorenzo, H., Razzaq, M., Odeberg, J., Saracco, J., Tregouet, D.-A., Thiébaut, R. (2019). High-dimensional multi-block analysis of factors associated with thrombin generation potential. IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS) 2019. DOI: 10.1109/CBMS.2019.00094
  6. Carrion-Castillo, A., Van der Haegen, L., Tzourio-Mazoyer, N., Kavaklioglu, T., Badillo, S., Chavent, M., Saracco, J., Brysbaert, M., Fisher, S.E., Mazoyer, B., Francks, C. (2019). Genome sequencing for rightward hemispheric language dominance.  Genes, Brain and Behavior.
    DOI : 10.1111/gbb.12572
  7. Labache, L., Joliot M., Saracco, J., Jobard, G., Hesling, I., Zago, L., Mellet, E., Petit, L., Crivello, F., Mazoyer, B., Tzourio‐Mazoyer, N. (2018). A SENtence Supramodal Areas AtlaS (SENSAAS) based on multiple task-induced activation mapping and graph analysis of intrinsic connectivity in 144 healthy right-handers. Brain Structure and Function, p. 1-24. DOI : 07. 10.1007/s00429-018-1810-2.
  8. Ellies-Oury, M.P., Gagaoua, M., Chavent, M, Saracco ,J., Picard, B. (2017). Biomarker abundance in two beef muscles depending on animal breeding practices and carcass characteristics. JSM Bioinformatics, Genomics and Proteomics, 2(1) : 1013. Link
  9. Ellies-Oury, M.-P, Cantalapiedra-Hijar, G.,Durand, D., Gruffat, D., Listrat, A., Micol, D., Ortigues-Marty, I., Hocquette, -F., Chavent, M., Saracco, J., Picard, B. (2016). An innovative approach combining Animal Performances, nutritional value and sensory quality of meat. Meat Science, 122, 163-172. DOI : 10.1016/j.meatsci.2016.08.004
  10. Claudio, K., Couallier, V., Leclerc, C., Le Gat, Y., Litrico, X., Saracco, J. (2015). Detecting leaks through AMR data analysis. Water Science and Technology: Water Supply, 15 (6) 1368-1372. DOI : 10.2166/ws.2015.071
  11. Claudio, C., Couallier, V., Leclerc, C., Le Gat, Y., Saracco, J. (2015). Consumption estimation with a partial automatic meter reading deployment. Water Science and Technology: Water Supply, 15(1), 50-58. DOI : 10.2166/ws.2014.082
  12. Baillon, L., Pierron, F., Coudret, R., Normendeau, E., Caron, A., Peluhet, L., Labadie, P., Budzinski, H., Durrieu, G., Saracco, J., Elie, P., Couture, P., Baudrimont, M., Bernatchez, L. (2015) Transcriptome profile analysis reveals specific signatures of pollutants in Atlantic eels. Ecotoxicology, 24(1), 71-84. DOI : 10.1007/s10646-014-1356-x
  13. Andrianoelisoa, S., Menut, C., Collas de Chatelperron, P., Danthu, P., Saracco, J. (2006). Intraspecific chemical variability and highlighting of chemotypes of leaf essential oils from Ravensara aromatica Sonnerat, an endemic tree to Madagascar, Flavour and Flagrance Journal, 21(5), 833-838. DOI : 10.1002/ffj.1735

Publications dans des revues de transfert

  1. Saracco, J., Chavent, M., Audin-Garcia, L., Lespinet-Najib, V., Ron-Angevin, R. (2018). Classification de variables et analyse multivariée de données mixtes issues d’une étude BCI. Ingénierie cognitique, 1(2), 1-26. DOI : 10.21494/ISTE.OP.2018.0311
  2. Ellies-Oury, M.P., Cantalapiedra-Hijar, G., Durand, D., Gruffat, D., Listrat, A., Micol, D., Ortigues-Marty, I., Hocquette, J.F., Chavent, M., Saracco, J., Picard, B. (2017). Une nouvelle approche méthodologique pour piloter la conduite en élevage. Viandes et Produits Carnés, Juillet 2017, 33, 3, 8p. (VPC-2017-33-3-1). Lien

Articles en révision ou soumis pour publication

  1. Lorenzo, H., Saracco, J., Thiébaut, R. (2019). ddsPLS: A Package to Deal with Multiblock Supervised Problems with Missing Samples in High Dimension. Submitted for publication.
  2. Lorenzo, H., Saracco, J., Thiébaut, R. (2019). A parsimonious PLS with missing values for heterogeneous data. Submitted for publication.
  3. Jlassi, I., Saracco, J. (2018). A Smooth Nonparametric Estimator of a Conditional Quantile. Submitted for publication.

Actes de conférences internationales avec publications des actes

  1. Genuer, R., Chavent, M., Saracco, J. (2019). Combinaison de la classification de variables et de la sélection de variables par forêts aléatoires. Journées de Statistique 2019 – Société Française de Statistique, Juin 2019, Nancy, France. Lien
  2. Labache, L., Joliot, M., Saracco, J., Tzourio-Mazoyer, N. (2019). Étude de la variabilité inter-individuelle de données de connectivités intrinsèques : détection de réseaux instables et de sous-populations dans un tableau tridimensionnel. Journées de Statistique 2019 – Société Française de Statistique, Juin 2019, Nancy, France. Lien
  3. Conanec, A., Picard, B., Cantalapiedra-Hijar, G., Chavent, M., Denoyelle, C., Gruffat, D., Normald, J., Saracco, J., Ellies-Oury, M.P. (2018). Etude des interactions et du compromis entre les performances de 30 génisses Blonde d’Aquitaine et les qualités nutritionnelle et sensorielle de la viande, 2018. Rencontres Recherches Ruminants (3R), Paris. Lien
  4. Lorenzo, H. Thiébaut, R., Saracco, J. (2018). Multi-block high-dimensional lasso-penalized analysis with imputation of missing data applied to postgenomic data in an Ebola vaccine trial. Annual workshop on Statistical Methods for Post Genomic Data – SMPGD 2018, Jan 2018, Montpellier, France.
  5. Charlier, I., Paindaveine, D., Saracco, J. (2014). Conditional quantile estimation using optimal quantization : a numerical study. Proceedings in Computational Statistics (COMPSTAT) 21th Symposium on Computational Statistics, 361-368.
  6. Chavent, M., Kuentz, V., Saracco, J. (2009). A partioning method for the clustering of categorical variables. In Classification as a tool for research (Proceedings of IFCS’2009), Herman Locarek-Junge, Claus Weihs Eds., Springer.
  7. Kuentz, V., Saracco, J. (2008). A cluster-based approach for sliced inverse regression. Proceedings in Computational Statistics (COMPSTAT 2008), 499-507, CD-ROM, Physica-Verlag/Springer, Heidelberg.
  8. Gannoun, A., Saracco, J. (2001). Cross validation criteria for SIRα and PSIRα methods in view of prediction. Proceedings of the 10th International Symposium on Applied Stochastic Models and Data Analysis (ASMDA), G. Govaert, J. Janssen, N. Limnios, 1, 443-448.
  9. Saracco, J. (1996). Sliced Inverse Regression: asymptotics properties and small sample behaviour of some new estimation methods ». Proceedings in Computational Statistics (COMPSTAT) 12th Symposium, Ed. A. Prat E. Ripoll, 2, 105-106.

Brevets industriels

  1. Didier Bihannic, Camille Baysse, Benoîte de Saporta, François Dufour, Anne Gégout-Petit, Jérome Saracco et al. Procédé de maintenance d’un équipement. France, Patent n° : 068689 FR MPH/ MAG.
  2. Claudio, K., Couallier, V., Le Gat, Y., Saracco, J. et al. Procédé pour estimer en temps réel la consommation totale d’un fluide distribué à des usagers, et réseau de distribution mettant en œuvre ce procédé. PCT/FR2012/052355.

Rapports de recherche ou rapports techniques

  1. Baysse, C., Gégout-Petit, A., Saracco, J. (2011). Modèle de Markov caché pour la détection d’un mode de fonctionnement dégradé d’un équipement optronique. Tech. Report, Equipe Projet CQFD, INRIA Bordeaux Sud Ouest, Thales Optronique.
  2. Chavent, M., Kuentz, V., Saracco, J. (2010). Rotation in Multiple Correspondence Analysis: a planar rotation iterative procedure. Cahiers du GREThA 2010-04.
  3. Chavent, M., Kuentz, V., Saracco, J. (2010). Clustering of categorical variables around latent variables. Cahiers du GREThA 2010-02.
  4. Chaouch, M., Gannoun, A., Saracco, J. (2008). Conditional spatial quantile: characterization and nonparametric estimation. Cahier du GREThA, 2008-10.
  5. Gannoun, A., Saracco, J., Urfer, W., Bonney, G.E., (2002). Nonparametric analysis of replicated microarray experiments. Technical report, SFB 475, Dortmund University, Germany, n° 70/2002.
  6. Gannoun, A., Saracco, J., Yuan, A., Bonney, G.E. (2002). Nonparametric quantile regression with censored data. Rapport de Recherche, Unit_e de Biom_etrie, ENSAM-INRA-UM 2, n° 02-09.
  7. Gannoun, A., Saracco, J., Urfer, W. Bonney, G.E., (2002). Nonparametric modeling approach for discovering differentially expressed genes in replicated microarray experiments. Technical report, SFB 475, Dortmund University, Germany, n° 41/2002.
  8. Gannoun, A., Saracco, J., Yuan, A., Bonney, G.E. (2002). On adaptive transformation-retransformation estimate of conditional spatial median. Rapport de Recherche, Unité de Biométrie, ENSAM-INRA-UM2, n° 02-08.
  9. Gannoun, A., Girard, S., Guinot, C., Saracco, J. (2001). Dimension-reduction in reference curves estimation. Rapport de Recherche, Unité de Biométrie, ENSAM-INRA-UM 2, n° 01-06.
  10. Gannoun, A., Saracco, J. (2000). Méthodes SIR univariées de type Slicing ou Pooled Slicing, méthode SIR multivariée : Présentation théorique et aide à l’utilisation de l’implémentation sous Splus. Rapport de Recherche, Unité de Biométrie, ENSAM-INRA-UM 2, n° 00-05.
  11. Saracco, J. (1997). Distribution-free and link-free fast estimation for sample selection models. Cahier du GREMAQ (Toulouse I), n° 97.10.453.