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Salim BOUZEBDA Enseignant-chercheur

Professeur des Universités | GI - Génie Informatique | LMAC - Laboratoire de Mathématiques Appliquées de Compiègne

Some recent papers :

http://lmac.utc.fr/auteur/salim-bouzebda

  • S. Bouzebda et N. Taachouche. Limit theorems for conditional U-statistics analysis on hyperspheres for missing at random data in the presence of measurement error. Journal of Computational and Applied Mathematics. 472(2026) 1—29. https://doi.org/10.1016/j.cam.2025.116811
  • N. Berrahou, S. Bouzebda et L. Douge. A nonparametric distribution-free test of independence among continuous random vectors based on L1-norm. Bernoulli. 31(2025). 31(2) : 1325—1350. http://dx.doi.org/10.3150/24-BEJ1772
  • S. Bouzebda et N. Taachouche. Nonparametric conditional U-statistics on Lie groups with measurement errors. Journal of Complexity. 89C(2025), no. 101944, 1—86. https://doi.org/10.1016/j.jco.2025.101944
  • S. Bouzebda et N. Taachouche. Multivariate spatial conditional U-quantiles : a Bahadur-Kiefer representation. Results in Applied Mathematics. (2025) 1—30. https://doi.org/10.1016/j.rinam.2025.100593
  • S. Bouzebda et N. Taachouche. Oracle inequalities and upper bounds for kernel conditional U-statistics estimators on manifolds and more general metric spaces. Stochastics. 96 (2024). no. 8, pp. 2135—2198. http://dx.doi.org/10.1080/17442508.2024.2391898
  • S. Bouzebda et I. Soukarieh. Limit theorems for a class of processes generalizing the U-empirical process. Stochastics. 96(2024). no. 1, pp. 799—845. http://dx.doi.org/10.1080/17442508.2024.2320402
  • I. Soukarieh et S. Bouzebda. Conditional U-statistics for Locally Stationary Functional Time Series. Statistical Inference for Stochastic Processes. 27(2024). no. 2, 227—304. https://link.springer.com/article/10.1007/s11203-023-09305-y
  • S. Bouzebda et A. Keziou. Empirical likelihood based confidence intervals for functional of copulas. J. Nonparametr. Stat. 36(2024). no. 4, pp. 1192—1224
    https://doi.org/10.1080/10485252.2024.2312396
  • N. Berrahou, S. Bouzebda et L. Douge. The Bahadur representation for empirical and smooth quantile estimators under association. Methodol. Comput. Appl. Probab. 26(2024), no. 2., pp. 1—37. https://doi.org/10.1007/s11009-024-10086-x
  • S. Bouzebda et A.A. Ferfache. Asymptotic Properties of Semiparametric M-Estimators with Multiple Change Points. Physica A. Statistical Mechanics and its Applications. 609 (2023). 128363. pp. 1—29. https://doi.org/10.1016/j.physa.2022.128363
  • S. Bouzebda et I. Soukarieh. Renewal type Bootstrap for increasing degree U-process of a Markov chain. J. Multivariate Anal. 195C (2023). 25 pp. https://doi.org/10.1016/j.jmva.2022.105143
  • S. Bouzebda et N. Taachouche. On the variable bandwidth kernel estimation of conditional U-statistics at optimal rates in sup-norm. Physica A. Statistical Mechanics and its Applications. (2023). pp. 1-72. https://doi.org/10.1016/j.physa.2023.129000
  • S. Bouzebda, I. Elhattab et A.A. Ferfache. General M-Estimator Processes and their m out of n Bootstrap with Functional Nuisance Parameters. Methodol. Comput. Appl. Probab. 24(2022), no. 4, 2961–3005. https://doi.org/10.1007/s11009-022-09965-y
  • S. Bouzebda et N. Taachouche. On the variable bandwidth kernel estimation of conditional U-statistics at optimal rates in sup-norm. Physica A. Statistical Mechanics and its Applications. 625(2023). Paper No. 129000. pp. 1—72. https://doi.org/10.1016/j.physa.2023.129000
  • S. Bouzebda et M. Chaouch. Uniform limit theorems for a class of conditional Z-estimators when covariates are functions. J. Multivariate Anal. 189 (2022). Paper No. 104872, 21 pp. (Full paper 44pp). https://doi.org/10.1016/j.jmva.2021.104872
  • M. Mohamedi, S. Bouzebda et A. Laksaci. The Consistency and Asymptotic Normality of the Kernel type Expectile Regression Estimator for Functional Data. J. Multivariate Anal. (2021). Volume 181, 104673. 24 pp. https://doi.org/10.1016/j.jmva.2020.104673
  • S. Bouzebda et A.A. Ferfache. Asymptotic properties of M-estimators based on estimating equations and censored data in models with multiple change points. Journal of Mathematical Analysis and Applications. 497(2021). no. 2, 124883. 44 pp. https://doi.org/10.1016/j.jmaa.2020.124883
  • S. Bouzebda et T. El-hadjali. Uniform Convergence Rate of the Kernel Regression Estimator Adaptive to Intrinsic Dimension in Presence of Censored Data. J. Nonparametr. Stat. 32(2020) no. 04, pp. 864—914. https://doi.org/10.1080/10485252.2020.1834107
  • S. Bouzebda et B. Nemouchi. Uniform consistency and uniform in bandwidth consistency for nonparametric regression estimates and conditional U-statistics involving functional data. J. Nonparametr. Stat. 32(2020) no. 02, pp. 452—509. https://doi.org/10.1080/10485252.2020.1759597
Salim Bouzebda

Contacts

Salim BOUZEBDA
Professeur des Universités
Directeur du Laboratoire LMAC EA 2222

Université de Technologie de Compiègne,
Laboratoire de Mathématiques Appliquées de Compiègne (L.M.A.C.)
Département Génie Informatique

Bâtiment Blaise Pascal
57 Avenue de Landshut
CS 60319
60203 COMPIEGNE CEDEX

Bureau : GI 126
E-mail : salim.bouzebda@utc.fr
Tél :  (+33) 3 44 23 44 69
Fax: (+33) 3 44 23 44 77
Publications
Data Science
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