Salim BOUZEBDA

Professor Salim Bouzebda received his doctorate in 2007 from the Université Pierre et Marie Curie (Paris VI), a lineage that situates him within one of the most venerable traditions of French mathematical statistics. His scholarly oeuvre is distinguished both by its analytical depth and by its remarkable breadth, encompassing the inferential treatment of censored and incomplete observations, the asymptotic theory of functional and infinite-dimensional data, multivariate dependence modelling, extreme-value theory, and resampling methodology, as well as the rigorous deployment of artificial-intelligence and statistical-learning algorithms for the interrogation of large-scale and high-dimensional data. The unifying thread across this corpus is a sustained commitment to establishing the probabilistic foundations—limit theorems, invariance principles, and concentration phenomena—upon which modern statistical practice ultimately rests.

He is the author of more than one hundred and seventy peer-reviewed articles in leading international journals, a body of work that bears witness to an enduring and influential presence at the interface of theoretical and applied statistics. In recognition of the sustained impact of this scholarship, he has been ranked among the top 2% of scientists worldwide in both the 2024 and 2025 editions of the Stanford University–Elsevier global research-impact rankings, an acknowledgement of the breadth and consistency of his citation footprint across the discipline. Beyond his prolific scholarship, Professor Bouzebda has placed his expertise at the service of the wider scientific community through his membership on the editorial boards of distinguished periodicals, including Dependence Modeling and Communications in Statistics. He currently holds the chair of Full Professor of Mathematical Statistics and directs the Laboratory of Applied Mathematics (LMAC) at the Université de Technologie de Compiègne, where he continues to extend the frontiers of contemporary statistical science and to mentor a new generation of researchers.

His principal research interests include:

• the statistical analysis of stochastic processes;
• empirical and U-empirical process theory;
• bootstrap and resampling methodology;
• strong invariance (approximation) principles;
• semiparametric and nonparametric inference;
• functional data analysis;
• multivariate analysis and copula-based dependence modelling;
• high-dimensional probability;
• change-point analysis;
• limit theorems for dependent random variables;
• and the theory and applications of statistical machine learning.

• S. Bouzebda et A. Keziou. A test of independence in some Copula models. Math. Methods. Statist. 17(2008), no. 2, pp. 123–137.

• S. Bouzebda et A. Keziou. Estimation and tests of independence in copula models via divergences. C. R. Math. Acad. Sci. Paris, 347(2009), no. 11–12, pp. 667—672.
• S. Bouzebda et I. Elhattab. Uniform-in-Bandwidth Consistency for a Nonparametric Estimate of the Entropy under Random Censorship. C. R. Math. Acad. Sci. Paris, 347(2009), no. 13–14, pp. 821—826.

• S. Bouzebda et A. Keziou. A new test procedure of independence in Copula models via chi-square-divergence. Comm. Statist. Theory Methods. 39(2010), no. 1, pp. 1—20.
• S. Bouzebda et A. Keziou. New estimates and tests of independence in some copula models via phi-divergences. Kybernetika. 46(2010), no. 1, pp. 178—201.
• S. Bouzebda et I. Elhattab. Uniform in Bandwidth consistency of Kernel-Type Estimators of Shannon’s Entropy. C. R. Math. Acad. Sci. Paris, 348(2010), no. 5–6, pp. 317—321.
• S. Bouzebda et A. Keziou. A semiparametric test of independence in Copula models for censored data. C. R. Acad. Sci. Paris, 348(2010), no.7–8, pp. 449—453.
• S. Bouzebda. Strong Approximation of the Smoothed Q-Q Processes. Far East J. Theor. Stat. 31(2010). no. 2, pp. 169—191.
• S. Bouzebda. Bootstrap de l’Estimateur de Hill : Théorèmes Limites. Ann. I.S.U.P., 54(2010), Fasc. 1-2. pp. 61—72.
• N-E. El Faouzi, R. Billot et S. Bouzebda. Motorway travel time prediction based on to data and weather effect integration. IET intelligent transport systems A. 4(2010). no. 4, pp. 338—345.

• S. Bouzebda, N-E. El Faouzi et T. Zari. On the Multivariate Two-sample Problem using Strong Approximations of Empirical Copula Processes. Comm. Statist. Theory Methods. 40(2011). no. 8, pp. 1490—1509.
• S. Bouzebda, A. Keziou et T. Zari. K-sample Problem using Strong Approximations of Empirical Copula Processes. Math. Methods. Statist. 20(2011), no. 1, pp. 14—29.
• S. Bouzebda et I. Elhattab. Uniform-in-bandwidth consistency for kernel-type estimators of Shannon’s entropy. Electron. J. Stat. 5(2011), pp. 440—459.
• S. Bouzebda. Some New Multivariate Tests of Independence. Math. Methods. Statist. 20(2011). no. 3, pp. 192—205.

• S. Bouzebda et M. Cherfi. Test of Symmetry Based on Copula Function. J. Statist. Plann. Inference. 142(2012), no. 5, pp. 1262—1271.
• S. Bouzebda et M. Cherfi. General bootstrap for dual φ-divergence estimates. J. Probab. Stat. Volume (2012), Article ID 834107, 33 pp.
• S. Bouzebda et N-E. El Faouzi. New Two-Sample Tests Based on the Integrated Empirical Copula Process. Statistics. 46(2012), no. 3, pp. 313—324.
• S. Bouzebda. On the Strong Approximation of General Bootstrap of Empirical Copula Processes with Applications. Math. Methods. Statist. 21(2012), no. 3, pp. 153—188.
• S. Bouzebda et M. Cherfi. Dual Divergences Estimators of the Tail Index. IRSN. Probab. Stat. (2012). Vol 2012, Article ID 746203, 14 pp.

• S. Bouzebda et T. Zari. A Strong Invariance Theorem of the Tail Empirical Copula Processes. Comm. Statist. Theory Methods. 42(2013). no. 1, pp. 11—27.
• S. Alvarez-Andrade et S. Bouzebda. Strong approximations for weighted bootstrap of empirical and quantile processes with applications. Stat. Methodol. 11(2013), pp. 36—52.
• S. Bouzebda et N. Limnios. On general bootstrap of empirical estimator of a semi-Markov kernel with applications. J. Multivariate Anal. 116(2013), pp. 52—62.
• S. Bouzebda, I. Elhattab, A. Keziou et T. Lounis. New Entropy Estimator with an Application to Test of Normality. Comm. Statist. Theory Methods. 42(2013). no. 12, pp. 2245—2270.
• S. Bouzebda et A. Keziou. A semiparametric maximum likelihood ratio test for the change point in copula models. Stat. Methodol. 14(2013), pp. 39—61.
• S. Bouzebda et N. Limnios. Exchangeably weighted bootstraps of empirical estimator for semi-Markov kernel. C. R. Acad. Sci. Paris, Ser. I. 351(2013), no. 13–14, pp. 569—573.
• S. Bouzebda et N. Limnios. Some asymptotic results for exchangeably weighted bootstraps of the empirical estimator of a semi-Markov kernel with applications. (2013), pp. 195—212. Statistical Models and Methods for Reliability and Survival Analysis. A Festschrift in Honor of Misha Nikulin. John Wiley & Sons, Inc.
• S. Bouzebda et T. Zari. Asymptotic Behavior of Weighted Multivariate Cramér-von Mises-Type Statistics Under Contiguous Alternatives. Math. Methods. Statist. 22(2013), no. 3, pp. 226—252.
• S. Bouzebda et T. Zari. Strong approximation of empirical copula processes by Gaussian processes. Statistics. 47(2013), no. 5, pp.1047—1063.

• S. Bouzebda et I. Elhattab. New kernel-type estimator of Shannon’s entropy. C. R. Acad. Sci. Paris, Ser. I. 352(2014). no. 1. pp. 75—80.
• S. Bouzebda et K. Chokri. Statistical tests in the partially linear additive regression model. Stat. Methodol. 19(2014), pp. 4—24.
• S. Bouzebda. Asymptotic properties of pseudo maximum likelihood estimators and test in semi-parametric copula models with multiple change points. Math. Methods. Statist. 23(2014), no. 1, pp. 38—65.
• S. Bouzebda. General Tests of Independence Based on Empirical Processes Indexed by Functions. Stat. Methodol. 21(2014), pp. 59—87.
• S. Alvarez-Andrade et S. Bouzebda. New nonparametric tests for change-point detection based on the P-P and Q-Q plots processes. Sequential Anal. 33(2014), no. 3, 360—399.
• S. Bouzebda et T. Zari. Strong Approximation of Multidimensional P-P Plots Processes by Gaussian Processes with Applications to Statistical Tests. Math. Methods. Statist. 23(2014), no. 3, pp. 210—238.
• S. Alvarez-Andrade et S. Bouzebda. Asymptotic results for hybrids of empirical and partial sums processes. Statist. Papers. 55(2014), no. 4, pp. 1121—1143.

• S. Alvarez-Andrade et S. Bouzebda. On the local time of the weighted bootstrap and compound empirical processes. Stochastic Analysis and Applications. 33(2015), no. 4, pp. 609—629.
• S. Bouzebda, S. Didi et L. Elhajj. Multivariate Wavelet Density and Regression Estimators for Stationary and Ergodic Continuous Time Processes : Asymptotic results. Math. Methods. Statist. 24(2015), no. 3, pp. 163—199.

• S. Bouzebda, K. Chokri et D. Louani. Some uniform consistency results in the partially linear additive model components estimation. Comm. Statist. Theory Methods. 45(2016). no. 05, pp. 1278—1310.
• Chr. Papamichail, S. Bouzebda et N. Limnios. Reliability calculus of a Markov Renewal Process on Crack Propagation Problem. (2016). Computational Methods for Solids and Fluids, Springer. Volume 41 of the series Computational Methods in Applied Sciences, pp. 343—378.
• S. Bouzebda, M. Chaouch et N. Laïb. Nonparametric Mode Regression Estimation for Functional Stationary Ergodic Data. Asymptotic Normality and Application. Math. Methods. Statist. 25(2016), no. 3, pp. 168—195.
• S. Bouzebda. Some Applications of the Strong Approximation of the Integrated Empirical Copula Processes. Math. Methods. Statist. 25(2016), no. 4, 281—303.

• S. Bouzebda et S. Didi. Wavelet Density and Regression Estimators for Stationary and Ergodic Discrete Time Processes : Asymptotic results. Comm. Statist. Theory Methods. 46(2017). no. 03, pp. 1367—1406.
• S. Bouzebda et S. Didi. Asymptotic results in additive regression model for strictly and ergodic continuous times processes. Comm. Statist. Theory Methods. 46(2017). no. 05, pp. 2454—2493.
• S. Alvarez-Andrade, S. Bouzebda et A. Lachal. Some Asymptotic Results for the Integrated Empirical Processes with Application to the Statistical Tests. Comm. Statist. Theory Methods. 46(2017), no. 07, pp. 3365—3392.
• S. Bouzebda. Kac’s representation for empirical copula process from an asymptotic viewpoint. Statist. Probab. Lett. 123(2017), pp. 107—113.
• S. Alvarez-Andrade et S. Bouzebda. On the hybrids of k-spacing empirical and partial sum processes. Rev. Mat. Complut. 30(2017), no. 1, pp 185—216.
• S. Bouzebda et N. Limnios. The uniform CLT for empirical estimator of a general state space semi-Markov kernel indexed by functions. Theory Probab. Math. Statist. No. 96, (2017), pp. 21–32.

• S. Alvarez-Andrade, S. Bouzebda et A. Lachal. Some Asymptotic Results for the p-Fold Integrated Empirical Processes with Application to the Statistical Tests. Test.
  27(2018), no 4, pp. 826—849. https://doi.org/10.1007/s11749-017-0572-0
• S. Bouzebda, Chr. Papamichail et N. Limnios. On general bootstrap for a multidimensional empirical estimator of a continuous-time semi-Markov kernel with applications. 30(2018). J. Nonparametr. Stat. no. 01, pp. 49 -– 86.
• S. Alvarez-Andrade et S. Bouzebda. On universal almost sure central limit theorem for the hybrid of empirical and partial sums processes. Statistics. 52(2018), no. 3, pp. 519—532.
• S. Bouzebda, C. T. Seck, I. Elhattab. Uniform in bandwidth consistency of nonparametric regression based on copula representation. Statist. Probab. Lett. 137(2018), pp. 173—182.
• S. Bouzebda, Y. Slaoui. Nonparametric recursive method for Kernel-Type Function Estimators for Spatial Data. Statist. Probab. Lett. 139(2018), pp. 103—114.

• S. Bouzebda, Y. Slaoui. Large and moderate deviation principles for recursive kernel estimators of a regression function for spatial data defined by stochastic approximation method. Statist. Probab. Lett. 151(2019). pp. 17 —28.
• S. Bouzebda et B. Nemouchi. Central Limit Theorems for Conditional Empirical and Conditional U-Processes of Stationary Mixing Sequences. Math. Methods. Statist. 28(2019), no. 3, pp. 169 — 207.
• S. Bouzebda et T. Lounis. Estimations and Optimal Tests in Some Parametric Models. Mathématiques appliquées : déterministes et stochastiques. 1(2019), pp 1 — 43.
• Alvarez-Andrade, S. Bouzebda. Some Selected Topics for the Bootstrap of the Empirical and Quantile processes. Theory Stoch. Process. 24(2019). no. 1, pp. 19—48.
• Bouzebda et O. El-Dakkak. Strong approximations for the general bootstrap of empirical processes with applications in selected topics of nonparametric statistics. Ann. I.S.U.P., 63(2019), Fasc. 2-3. pp. 221—246.

• S. Bouzebda, Y. Slaoui. Bandwidth selector for nonparametric recursive density estimation for spatial data defined by stochastic approximation method. Comm. Statist. Theory Methods. 49(2020). no. 12, 2942—2963.
• S. Bouzebda et N. Limnios. Bootstraps of martingale difference arrays under the uniformly integrable entropy. (2020). Statistical Topics and Stochastic Models for Dependant Data with Applications. (ISTE, Wiley). pp. 29—46.
• 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
• S. Bouzebda, Y. Slaoui. Large and moderate deviation principles for recursive kernel estimators for spatial data. 1(2020), Journal of Stochastic Analysis. No. 1 , Article 7. 27 pp.
• M. Mohamedi, S. Bouzebda et A. Laksaci. On the nonparametric estimation of the functional expectile regression. C. R. Acad. Sci. Paris, Ser. I. 358(2020). no. 3. pp. 267—272.
• Chr. Papamichail, S. Bouzebda et N. Limnios. Regression analysis of stochastic fatigue crack growth model in a martingale difference framework. Journal of Statistical Theory and Practice. 14(2020). no. 3. Paper No. 44. 43 pp.
• S. Alvarez-Andrade, S. Bouzebda. Cramér’s type results for some bootstrapped U-statistics. Statist. Papers 61 (2020), no. 4. pp. 1685—1699.
• S. Bouzebda et Y. Slaoui. Nonparametric Recursive Method for Kernel-Type Function Estimators for Censored Data. 1(2020), Journal of Stochastic Analysis. No. 3, Article 4. pp. 1—19.
• S. Bouzebda et M. Cherfi. General inference in semiparametric models through divergences and the duality technique with applications. Theory Stoch. Process. 25 (2020). (41), no. 1, pp. 1—24.
• S. Bouzebda et N. Limnios. Exchangeably weighted bootstraps of martingale difference arrays under the uniformly integrable entropy. 1(2020), Journal of Stochastic Analysis. No. 3 , Article 6. pp. 1—13.
• S. Bouzebda et Ch. Chesneau. A note on the nonparametric estimation of the conditional mode by wavelet methods. Stats. 3(2020). no. 4, 475—483.
• 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 S. Didi. Some Asymptotic Properties of Kernel Regression Estimators of the Mode for Stationnary and Ergodic Continuous Time Processes. Rev. Mat. Complut. 34(2021). no. 3, pp. 811–-852. https://doi.org/10.1007/s13163-020-00368-6
• 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. 181(2021). 104673. pp. 1—24 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. pp. 1—44. https://doi.org/10.1016/j.jmaa.2020.124883
• S. Bouzebda, F. Madani et Y. Souddi. Some Characteristics of The Conditional Set-Indexed Empirical ProcessInvolving Functional Ergodic Data. Bulletin of the Institute of Mathematics Academia Sinica. 16(2021). no. 4, pp. 367–-399.
• S. Bouzebda, I. Elhattab et B. Nemouchi. On the uniform-in-bandwidth consistency of the general conditional U-statistics based on the copula representation. J. Nonparametr. Stat. 32(2021), no. 2, pp. 321–-358.
• S. Allaoui, S. Bouzebda, C, Chesneau et J. Liu. Uniform Almost Sure Convergence and Asymptotic Distribution of the Wavelet-based Estimators of Partial Derivatives of Multivariate Density Function Under Weak Dependence. J. Nonparametr. Stat. 32(2021) no. 2, 170–-196.

• I. M. Almanjahie, S. Bouzebda, Z. Kaid et A. Laksaci. Nonparametric functional time series analysis using expectile regression. J. Nonparametr. Stat. 34(2022), no. 1, pp. 250—281.
• I. M. Almanjahie, S. Bouzebda, Z. Chikr-El-mezouar et A. Laksaci. The Functional kNN Estimator of the Conditional Expectile : Uniform Consistency in Number of Neighbors. Stat. Risk Model. 38(2022), no. 3-4, 47–-63.
• S. Bouzebda, Y. Slaoui. Nonparametric Recursive Method for Moment-Generating Function Kernel-Type Estimators. Statist. Probab. Lett. 184(2022), 109422. pp. 1—12.
• 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, pp. 21. (Full paper 44pp). https://doi.org/10.1016/j.jmva.2021.104872
• S. Bouzebda, M. Chaouch et S. Didi. Some results on kernel estimators for function derivatives based on dependent data. Annals of the Institute of Statistical Mathematics 74(2022), no. 4, pp. 737—771. https://doi.org/10.1007/s10463-021-00814-2
• S. Bouzebda et S. Didi. Some Results About Kernel Estimators for Function Derivatives Based on Stationary and Ergodic Continuous Time Processes with Applications. Comm. Statist. Theory Methods. 51(2022). no. 12, 3886–-3933.
• S. Bouzebda, F. Madani et Y. Souddi. Some Asymptotic Properties of the Conditional Set-Indexed Empirical Process Based on Dependent Functional Data. Int. J. Math. Stat. 23 23(2022). no. 2. 1—29.
• I. Soukarieh et S. Bouzebda. Exchangeably weighted bootstraps of the General Markov U-process. 10(2022). Mathematics. no. 20, 3745, pp. 1—42.
• S. Didi, A. Al Harby et S. Bouzebda. Wavelet Density and Regression Estimators for Functional Stationary and Ergodic Data : Discrete Time. 10(2022). Mathematics. no. 19, pp. 1—33.
• S. Didi et S. Bouzebda. Wavelet Density and Regression Estimators for Continuous Time Functional Stationary and Ergodic Processes. Mathematics. 10(2022). no. 10, 4356. pp. 1—37.
• S. Bouzebda et A. Nezzal. Uniform consistency and uniform in number of neighbors consistency for nonparametric regression estimates and conditional U-statistics involving functional data. Japanese Journal of Statistics and Data Science. 5(2022).
  no. 2, 431–-533.
• S. Bouzebda et N. Limnios. The uniform CLT for the empirical estimator of countable state space semi-Markov kernels indexed by functions with applications. J. Nonparametr. Stat. 34(2022). no., 4, pp. 758–-788.
• 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 I. Soukarieh. Renewal type bootstrap for U- process Markov chains. Markov Process. Related Fields. 25(2022). no.5, pp. 673—735.

• O. Litimein, A. Laksaci, B. Mechab, S. Bouzebda. Local linear estimate of the functional expectile regression. Statist. Probab. Lett. 192(2023), 109682, pp. 1—12.
• S. Bouzebda et I. Soukarieh. Nonparametric conditional U-processes for locally stationary functional random fields under stochastic sampling design. 11(2023). Mathematics. no. 1, pp. 1—69.
• S. Bouzebda, A. Nezzal et T. Zari. Uniform consistency for functional conditional U-statistics using delta-sequences. 11(2023). Mathematics. no. 1, 161. pp. 1—39.
• S. Bouzebda., A. Laksaci et M. Mohamedi. Single index regression model for functional quasi-associated times series data. REVSTAT- Statistical Journal. 20(2023). no. 5, pp. 605–-631. https://revstat.ine.pt/index.php/REVSTAT/article/view/391/603
• S. Allaoui, S. Bouzebda et J. Liu. Asymptotic Distribution of the Wavelet-based estimators of multivariate regression functions in Besov spaces under weak dependence. Journal of Mathematical Inequalities. 17(2023).
  no. 2, 481–-515. http://files.ele-math.com/articles/jmi-17-32.pdf
• S. Bouzebda, I. Elhattab, Y. Slaoui et N. Taachouche. Nonparametric Recursive Method for Moment Generating Function Kernel-Type Estimators under Censored Data. Statistics, Optimization et Information Computing. 11(2023). no. 2, pp. 196—215.
• S. Bouzebda, S. Khardani et Y. Slaoui. On the Consistency and Asymptotic Normality of the Modal Regression in the Nonparametric Random Design Model Censored Data. Comm. Statist. Theory Methods. 52(2023). no. 19, 7069–-7093.
• S. Bouzebda, Y. Slaoui. Nonparametric recursive estimation for multivariate derivative functions by stochastic approximation method. Sankhya A. 85(2023). no. 1, 658–-690.
• S. Bouzebda., A. Laksaci et M. Mohamedi. The k-Nearest Neighbors method in single index regression model for functional quasi-associated times series data. Rev. Mat. Complut. 3(2023). no. 2, 361–-391. https://doi.org/10.1007/s13163-022-00436-z
• S. Bouzebda et B. Nemouchi. Weak-convergence of empirical conditional processes and conditional U-processes involving functional mixing data. Statistical Inference for Stochastic Processes. 26(2023). no. 1, 33–-88. https://doi.org/10.1007/s11203-022-09276-6
• S. Bouzebda. General Tests of Conditional Independence Based on Empirical Processes Indexed by Functions. Japanese Journal of Statistics and Data Science. 6(2023). no. 1, 115–-177.
• S. Bouzebda, T. El-hadjali et A.A. Ferfache. Uniform in bandwidth consistency of conditional U-statistics adaptive to intrinsic dimension in presence of censored data. Sankhya A. 85(2023). no. 2, pp. 1548–-1606. https://doi.org/10.1007/s13171-022-00301-7
• S. Bouzebda et N. Taachouche. Rates of the strong uniform consistency for the kernel-type regression function estimators with general kernels on manifolds. Math. Methods. Statist. 32(2023). No. 1, pp. 27–-81. https://doi.org/10.3103/S1066530723010027
• O. Litimein, F. Alshahrani, S. Bouzebda, A.Laksaci et B. Mechab. Kolmogorov entropy for convergence rate in incomplete functional time series : Application to percentile and cumulative estimation in high dimensional data. Entropy. 25(2023). no. 7, Paper No. 1108, pp. 1—33.
• 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). pp. 1—25. 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. 625(2023). Paper No. 129000. pp. 1—72. https://doi.org/10.1016/j.physa.2023.129000
• S. Allaoui, S. Bouzebda et J. Liu. Multivariate wavelet estimators for weakly dependent processes : strong consistency rate. Comm. Statist. Theory Methods. 52(2023). no. 23. pp. 8317—8350.
• S. Alvarez-Andrade, S. Bouzebda et N. Nessigha. On the strong approximation of the non-overlapping k-spacings process with application to the moment convergence rates. Acta Univ. Sapientiae Math. 15(2023). no. 1 pp. 12-–34.
• S. Bouzebda. On the weak convergence and the uniform-in-bandwidth consistency of the general conditional U-processes based on the copula representation : multivariate setting. Hacet. J. Math. Stat. 52(2023). no. 5. pp. 1303—1348.
• A. Laksaci, S. Bouzebda, F. Alshahrani, O. Litimein, B. Mechab. Spatio-functional local linear asymmetric least square regression estimation : Application for spatial prediction of COVID-19 propagation. Symmetry. 15(2023). no. 12, pp. 1—22.

• S. Bouzebda et A. Nezzal. Weak convergence of kNN empirical conditional processes and kNN conditional U-processes involving functional mixing data. AIMS Mathematics. 9(2024). no 2, pp. 4427-4550.
• Z. Chikr Elmezouar, F. AlshahraniI., M. Almanjahie, S. Bouzebda, Z. Kaid et A. Laksaci. Strong consistency rate in functional single index expectile model for spatial data. AIMS Mathematics. 9(2024). no 2, pp. 5550—5581.
• S. Attaoui, B. Bentata, S. Bouzebda, A. Laksaci. The strong consistency and asymptotic normality of the kernel estimator type for functional single index model in presence of censored data. AIMS Mathematics. 9(2024). no 3, pp. 7340—7371.
• S. Bouzebda. Weak convergence of the conditional single index U-statistics for locally stationary functional time series. AIMS Mathematics. 9(2024). no 6, pp. 14807—14898.
• O. Bouanani, S. Bouzebda. Limit theorems for local polynomial estimation of regression for functional dependent data. AIMS Mathematics. (2024), 9, no. 9, pp. 23651—23691.
• S. Bouzebda, A. Nezza, l, Elhattab. Asymptotic properties of the nonparametric conditional U-statistics smoothed by asymmetric kernels. AIMS Mathematics. 9(2024). no. 9, pp. 26195–-26282.
• B. Agua, S. Bouzebda. Single index regression for locally stationary functional time series. AIMS Mathematics. 9(2024). no. 12, pp. 36202—36258.
• S. Bouzebda, F. Madani et Y. Souddi. Weak Convergence of the Conditional Set-Indexed Empirical Process For Missing at Random Functional Ergodic Data. Mathematics. 12(2024). no. 3, pp. 1—23.
• S. Bouzebda. Uniform in number of neighbors consistency and weak convergence of kNN single index conditional processes and kNN single index conditional U-processes involving functional mixing data. Symmetry. 12(2024). no. 13, pp. 1—81.
• S. Bouzebda. Limit theorems in the nonparametric conditional single index U-processes for locally stationary functional random fields under stochastic sampling design.
  Mathematics. 12(2024). no. 13, pp. 1—80.
• S. Bouzebda, T. El-hadjali et A.A. Ferfache. Central limit theorems for functional Z-estimators with Functional Nuisance Parameters. A. Comm. Statist. Theory Methods. 53(2024), no. 7, pp. 2535—2577. https://doi.org/10.1080/03610926.2022.2138439
• M. Mohammedi, S. Bouzebda, A. Laksaci et O. Bouanani. Asymptotic normality of the k-NN single index regression estimator for functional weak dependence data. Comm. Statist. Theory Methods. 53(2024), no. 9, pp. 3143—3168. https://doi.org/10.1080/03610926.2022.2150823
• K. Chokri, S. Bouzebda. Some uniform-in-bandwidth consistency results in the partially linear additive model components estimation. Comm. Statist. Theory Methods. 53(2024), no. 9, pp. 3383—3424. https://doi.org/10.1080/03610926.2022.2153605
• N. Berrahou, S. Bouzebda et L. Douge. Functional uniform-in-bandwidth moderate deviation principle for the local empirical processes involving functional data. Math. Methods. Statist. 33(2024), no. 1. pp. 26—69. https://doi.org/10.3103/S1066530724700030
• S. Bouzebda et N. Taachouche. Rates of the strong uniform consistency with rates for conditional U-statistics estimators with general kernels on manifolds. Math. Methods. Statist. 33(2024), no. 2. pp. 95—152. https://doi.org/10.3103/S1066530724700066
• 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. Nezzal. Asymptotic properties of conditional U-statistics using delta sequences. Comm. Statist. Theory Methods. 53(2024), no. 13, 4602—4657. https://doi.org/10.1080/03610926.2023.2179887
• K. Chokri, S. Bouzebda. Asymptotic normality for the wavelet partially linear additive model components estimation. Comm. Statist. Theory Methods. 53(2024), no. 23, pp. 8376—8411. https://doi.org/10.1080/03610926.2023.2286905
  -* S. Bouzebda et A. Keziou. Empirical likelihood based confidence intervals for functional of copulas. J. Nonparametr. Stat. 36(2024). no. 4, pp. 1192—1224
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• 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
• I. M. Almanjahie, S. Bouzebda, Z. Kaid et A. Laksaci. The Local Linear Functional kNN Estimator of the Conditional Expectile : Uniform Consistency in Number of Neighbors. Metrika. 87(2024) pp. 1007—1035. https://link.springer.com/article/10.1007/s00184-023-00942-0
• 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
• 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

• I. Votsi et S. Bouzebda. Bootstrap of reliability indicators for semi-Markov processes. Methodol. Comput. Appl. Probab. 27(2025), no. 1., pp. 1—18. https://doi.org/10.1007/s11009-024-10125-7
• Y. Souddi et S. Bouzebda. k-nearest neighbour Estimation of the Conditional Set-Indexed Empirical Process for Functional Data : Asymptotic Properties. Axioms. 14(2025), no 2, pp. 1—38.
• F.A. Almulhim, M. B. Alamari, S. Bouzebda, Z. Kaid and A. Laksaci. Robust Estimation of L₁-Modal Regression under Functional Single-Index Models for Practical Applications. Mathematics. 13(2025), no 4, pp. 1—20.
• S. Didi et S. Bouzebda. Linear wavelet-based estimators of partial derivatives of multivariate density function for stationary and ergodic continuous time processes. Entropy. (2025), pp. 1—31.
• S. Didi et S. Bouzebda. Wavelet Estimation of Partial Derivatives in Multivariate Regression Under Discrete-Time Stationary Ergodic Processes. Mathematics. (2025), pp. 1—35.
• S. Didi et S. Bouzebda. Wavelet-based estimators of partial derivatives of multivariate density function for discrete stationary and ergodic processes. AIMS Mathematics. (2025), pp. 1—35.
• S. Attaoui, O. Benouda, S. Bouzebda, A. Laksaci. Limit theorems for kernel regression estimator for quasi-associated functional censored time series within a single index structure. Mathematics. 13(2025), no. 5, pp. 1—40.
• Y. Baghli, O. Bouanani, and S. Bouzebda, Local linear regression for functional ergodic data with missing at random responses. Mathematics, 13(2025), no. 17, pp. 1–39, 2025.
• A. Keddi, S. Bouzebda, F. Madani. Asymptotic analysis of a kernel estimator for parabolic stochastic partial differential equations driven by cylindrical sub-fractional Brownian motion. 13(2025), no. 16, pp. 1—32.,
• K. Chokri et S. Bouzebda. Optimal almost sure rate of convergence for the wavelets estimator in the partially linear additive models. Symmetry. 17(2025). no. 3, pp. 1—35.
• S. Bouzebda, S. Khardani et Y. Slaoui. On Nonparametric Kernel Estimation of the Mode of the Regression Function in the Strong Mixing Random Design Model Censored Data. Comm. Statist. Theory Methods. 54(2025). no. 9., pp. 2623-2656. https://doi.org/10.1080/03610926.2024.2372062
• I. M. Almanjahie, H R. Abood, S. Bouzebda, F. Alshahrani, A. Laksaci. Nonparametric expectile shortfall regression for functional data. Demonstratio Mathematica, 58(2025), no. 1, Paper No. 20250125, 24 pp.
• M. B. Alamari, F. A. Almulhim, I. M. Almanjahie, S. Bouzebda, A. Laksaci. The scalar-on-function mode estimation using entropy and ergodic properties of functional time series data. Entropy, (2025). no. 6., pp. 1-27.
• S. Alvarez-Andrade, S. Bouzebda. Rates in the complete convergence of weighted bootstrap means. Comm. Statist. Theory Methods. 54(2025), no. 18, 5974—5990. https://doi.org/10.1080/03610926.2024.2449096
• S. Bouzebda. Limit theorems for wavelet conditional U-statistics for time series models. Mathematical Methods of Statistics. 34(2025), no. 2, pp. 181–224.
• L. Douge, S. Bouzebda, N. Berrahou et K. El Fassi. Limit theorems for general recursive regression models involving weakly dependent functional data. Mathematical Methods of Statistics. 34(2025), no. 4, pp. 375—401.
• A. Guendouzi, S. Bouzebda. Cost Optimization in a GI/M/2/N Queue with Heterogeneous Servers, Working Vacations, and Impatient Customers via the Bat Algorithm. Mathematics. (2025), pp. 1—34.
• M. Mohamedi, S. Bouzebda et A. Laksaci. Asymptotic results of the randomly censored kernel-type expectile regression estimator for functional dependent data. Statistical Inference for Stochastic Processes. (2025). 28 (2025), no. 2, Paper No. 10, 39 pp.
• S. Ben Hariz et S. Bouzebda, Rates for the SLLN for long-memory and heavy-tailed processes. J. Appl. Probab. 62(2025)., no 4, pp. 1—28. A paraître.
• S. Bouzebda et N. Taachouche. Multivariate spatial conditional U-quantiles : a Bahadur-Kiefer representation. Results in Applied Mathematics. 26(2025), Paper No. 100593, 30 pp. https://doi.org/10.1016/j.rinam.2025.100593
• 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
• 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.
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• 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
• J. N. G. Tinio, M. Z. Alaya, S. Bouzebda. Optimal Transport Guarantees to Nonparametric Regression for Locally Stationary Time Series. (2026). 60 pp. AISTATS https://arxiv.org/pdf/2412.03414
• A. Azzi, S. Bouzebda, A. Laksaci. Functional random field data analysis using nonparametric L1-modal regression. (2026), 30 pp. Spatial Statistics. https://doi.org/10.1016/j.spasta.2026.100963
• H. Bell’s, M. Mohammedi, S. Bouzebda. Asymptotic Theory for Multivariate Nonparametric Quantile Regression with Stationary Ergodic Functional Covariates and Missing-at-Random Responses. (2026), 63 pp. Symmetry.
• N. Taachouche et S. Bouzebda. Multivariate Spatial Conditional Quantiles on Hyperspheres in the Presence of Measurement Error. Results in Applied Mathematics. 29(2026), Paper No.100690, pp. 1—46. https://doi.org/10.1016/j.rinam.2026.100690
• J. N. G. Tinio, M. Z. Alaya, S. Bouzebda. Bounds in Wasserstein Distance for Locally Stationary Functional Time Series. Computational Statistics. (2026). 61 pp.