Prof. Dr. Salim Bouzebda
University of Technology of Compiègne, Compiègne, Hauts-de-France, France
Head of laboratory
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Academic Career Length
19Years (2008-Present)
(Calculated once Scilit publication data is synced: current year − year of first publication + 1)
Representative Publications
View All Publications2 April 2020 in Journal of Nonparametric Statistics
Journal Article
Salim BouzebdaBNBoutheina Nemouchi
Boutheina Nemouchi
W. Stute [(1991), Annals of Probability, 19, 812–825] introduced a class of so-called conditional U-statistics, which may be viewed as a generalisation of the Nadaraya–Watson estimates of a regression function. Stute proved their strong pointwise consistency to We apply the methods developed in Dony and Mason [(2008), Bernoulli, 14(4), 1108–1133] to establish uniformity in and in bandwidth consistency (i.e. , where at some specific rate) to of the estimator proposed by Stute when Y and covariates X are functional taking value in some abstract spaces. In addition, uniform consistency is also established over for a suitably restricted class . The theoretical uniform consistency results, established in this paper, are (or will be) key tools for many further developments in functional data analysis. Applications include the Nadaraya–Watson kernel estimators and the conditional distribution function. Our theorems allow data-driven local bandwidths for these statistics.
1 January 2021 in Journal of Multivariate Analysis
Journal Article
25 July 2022 in Statistical Inference for Stochastic Processes
Journal Article
Salim BouzebdaBNBoutheina Nemouchi
Boutheina Nemouchi2 January 2022 in Journal of Nonparametric Statistics
Journal Article
IAIbrahim M. Almanjahie
Ibrahim M. Almanjahie
Salim BouzebdaZKZoulikha Kaid
Zoulikha Kaid
ALAli Laksaci
Ali Laksaci
In this paper, the problem of the nonparametric estimation of the expectile regression model for strong mixing functional time series data is investigated. To be more precise, we establish the almost complete consistency and the asymptotic normality of the kernel-type expectile regression estimator under some mild conditions. The usefulness of our theoretical results in the financial time series analysis is discussed. Further, we provide some practical algorithms to select the smoothing parameter or to construct the confidence intervals using the bootstrap techniques. In addition, a simulation study is carried out to verify the small sample behaviour of the proposed approach. Finally, we give an empirical example using the daily returns of the stock index SP500.
28 May 2022 in Japanese Journal of Statistics and Data Science
Journal Article
Salim BouzebdaANAmel NEZZAL
Amel NEZZALAbout Me
Professor Bouzebda received his Ph.D. in Statistics from Pierre and Marie Curie University (Paris VI) in 2007. His research interests lie at the intersection of theoretical and applied statistics, encompassing a diverse range of topics including censored data, functional data analysis, multivariate methods, extreme value theory, bootstrap techniques, and the application of artificial intelligence algorithms to high-dimensional and large-scale data.
Professor Bouzebda is the author of more than 170 peer-reviewed articles published in leading international journals, attesting to the breadth and impact of his scholarly contributions. His work is characterized by methodological rigor and a strong commitment to advancing statistical science across disciplinary boundaries.
He has served, and continues to serve, as an Associate Editor for several prominent statistical journals, including Dependence Modeling and Communications in Statistics. He currently holds the rank of Full Professor of Mathematical Statistics and serves as Director of the Research Laboratory in Applied Mathematics (LMAC) at the Université de Technologie de Compiègne, France.
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