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Статті в журналах з теми "Processus faiblement dépendant"
Soulier, Philippe. "Estimation adaptative de la densité spectrale d'un processus gaussien faiblement ou fortement dépendant." Comptes Rendus de l'Académie des Sciences - Series I - Mathematics 330, no. 8 (April 2000): 733–36. http://dx.doi.org/10.1016/s0764-4442(00)00252-4.
Повний текст джерелаMarot, Didier, Bikram Oli, Rachel Gelet, and Fateh Bendahmane. "Nouveau dispositif pour l’étude de la suffusion suivant différents états mécaniques." Revue Française de Géotechnique, no. 178 (2024): 3. http://dx.doi.org/10.1051/geotech/2024006.
Повний текст джерелаDejemeppe, Muriel, and Bruno Van der Linden. "Numéro 40 - avril 2006." Regards économiques, October 12, 2018. http://dx.doi.org/10.14428/regardseco.v1i0.15873.
Повний текст джерелаDejemeppe, Muriel, and Bruno Van der Linden. "Numéro 40 - avril 2006." Regards économiques, October 12, 2018. http://dx.doi.org/10.14428/regardseco2006.04.01.
Повний текст джерелаДисертації з теми "Processus faiblement dépendant"
Wade, Modou. "Apprentissage profond pour les processus faiblement dépendants." Electronic Thesis or Diss., CY Cergy Paris Université, 2024. http://www.theses.fr/2024CYUN1299.
Повний текст джерелаThis thesis focuses on deep learning for weakly dependent processes. We consider a class of deep neural network estimators with sparsity regularisation and/or penalty regularisation.Chapter1 is a summary of the work. It presents the deep learning framework and reviews the main results obtained in chapters 2, 3, 4, 5 and 6.Chapter 2 considers deep learning for psi-weakly dependent processes. We have established the convergence rate of the empirical risk minimization (ERM) algorithm on the class of deep neural network (DNN) estimators. For these estimators, we have provided a generalization bound and an asymptotic learning rate of order O(n^{-1/alpha}) for all alpha > 2 is obtained. A bound of the excess risk for a large class of target predictors is also established. Chapter 3 presents the sparse-penalized deep neural networks estimator under weak dependence. We consider nonparametric regression and classification problems for weakly dependent processes. We use a method of regularization by penalization. For nonparametric regression and binary classification, we establish an oracle inequality for the excess risk of the sparse-penalized deep neural networks (SPDNN) estimator. We have also provided a convergence rate for these estimators.Chapter 4 focuses on the penalized deep neural networks estimator with a general loss function under weak dependence. We consider the psi-weak dependence structure and, in the specific case where the observations are bounded, we deal with the theta_{infty}-weak dependence. For learning psi and theta_{infty}-weakly dependent processes, we have established an oracle inequality for the excess risks of the sparse-penalized deep neural networks estimator. We have shown that when the target function is sufficiently smooth, the convergence rate of these excess risks is close to O(n^{-1/3}).Chapter 5 presents robust deep learning from weakly dependent data. We assume that the output variable has finite r moments, with r >= 1. For learning strong mixing and psi-weakly dependent processes, a non-asymptotic bound for the expected excess risk of the deep neural networks estimator is established. We have shown that when the target function belongs to the class of H"older smooth functions, the convergence rate of the expected excess risk for exponentially strongly mixing data is close to or equal to that obtained with an independent and identically distributed sample. Chapter 6 focuses on deep learning for strongly mixing observation with sparse-penalized regularization and minimax optimality. We have provided an oracle inequality and a bound on the class of H"older smooth functions for the expected excess risk of the deep neural network estimator. We have also considered the problem of nonparametric regression from strongly mixing data with sub-exponential noise. When the target function belongs to the class of H"older composition functions, we have established an upper bound for the oracle inequality of the L_2 error. In the specific case of autoregressive regression with standard Laplace or normal error, we have provided a lower bound for the L_2 error in this classe, which matches up to a logarithmic factor the upper bound; thus the deep neural network estimator achieves optimal convergence rate
Boulin, Alexis. "Partitionnement des variables de séries temporelles multivariées selon la dépendance de leurs extrêmes." Electronic Thesis or Diss., Université Côte d'Azur, 2024. http://www.theses.fr/2024COAZ5039.
Повний текст джерелаIn a wide range of applications, from climate science to finance, extreme events with a non-negligible probability can occur, leading to disastrous consequences. Extremes in climatic events such as wind, temperature, and precipitation can profoundly impact humans and ecosystems, resulting in events like floods, landslides, or heatwaves. When the focus is on studying variables measured over time at numerous specific locations, such as the previously mentioned variables, partitioning these variables becomes essential to summarize and visualize spatial trends, which is crucial in the study of extreme events. This thesis explores several models and methods for partitioning the variables of a multivariate stationary process, focusing on extreme dependencies.Chapter 1 introduces the concepts of modeling dependence through copulas, which are fundamental for extreme dependence. The notion of regular variation, essential for studying extremes, is introduced, and weakly dependent processes are discussed. Partitioning is examined through the paradigms of separation-proximity and model-based clustering. Non-asymptotic analysis is also addressed to evaluate our methods in fixed dimensions.Chapter 2 study the dependence between maximum values is crucial for risk analysis. Using the extreme value copula function and the madogram, this chapter focuses on non-parametric estimation with missing data. A functional central limit theorem is established, demonstrating the convergence of the madogram to a tight Gaussian process. Formulas for asymptotic variance are presented, illustrated by a numerical study.Chapter 3 proposes asymptotically independent block (AI-block) models for partitioning variables, defining clusters based on the independence of maxima. An algorithm is introduced to recover clusters without specifying their number in advance. Theoretical efficiency of the algorithm is demonstrated, and a data-driven parameter selection method is proposed. The method is applied to neuroscience and environmental data, showcasing its potential.Chapter 4 adapts partitioning techniques to analyze composite extreme events in European climate data. Sub-regions with dependencies in extreme precipitation and wind speed are identified using ERA5 data from 1979 to 2022. The obtained clusters are spatially concentrated, offering a deep understanding of the regional distribution of extremes. The proposed methods efficiently reduce data size while extracting critical information on extreme events.Chapter 5 proposes a new estimation method for matrices in a latent factor linear model, where each component of a random vector is expressed by a linear equation with factors and noise. Unlike classical approaches based on joint normality, we assume factors are distributed according to standard Fréchet distributions, allowing a better description of extreme dependence. An estimation method is proposed, ensuring a unique solution under certain conditions. An adaptive upper bound for the estimator is provided, adaptable to dimension and the number of factors
Harel, Michel. "Convergence faible de la statistique linéaire de rang pour des variables aléatoires faiblement dépendantes et non stationnaires." Paris 11, 1989. http://www.theses.fr/1989PA112359.
Повний текст джерелаCe travail est composé de trois parties. La première consiste en la convergence faible du processus empirique tronqué corrigé pour des suites de variables aléatoires non stationnaires
Kabui, Ali. "Value at risk et expected shortfall pour des données faiblement dépendantes : estimations non-paramétriques et théorèmes de convergences." Phd thesis, Université du Maine, 2012. http://tel.archives-ouvertes.fr/tel-00743159.
Повний текст джерелаPrieur, Clementine. "APPLICATIONS STATISTIQUES DE SUITES FAIBLEMENT DEPENDANTES ET DE SYSTEMES DYNAMIQUES." Phd thesis, Université de Cergy Pontoise, 2001. http://tel.archives-ouvertes.fr/tel-00001436.
Повний текст джерелаd'applications statistiques de suites dépendantes et
stationnaires. Nous étudions deux classes de suites
dépendantes. Nous nous intéressons d'une part à des suites
faiblement dépendantes, où notre notion de dépendance faible est
une variante de la notion introduite par Doukhan \& Louhichi, d'autre part à certains systèmes dynamiques
présentant une propriété de décroissance des
corrélations. Nous traitons du comportement asymptotique du
processus empirique, fondamental en statistiques. Nous étudions
aussi un estimateur à noyau de la densité dans nos deux cadres de
dépendance. Enfin, nous nous intéressons à un problème de
rupture d'une fonction de régression en dépendance faible. A ces
fins, nous développons des idées de Rio pour montrer un
théorème limite centrale en dépendance faible, ainsi que des
nouvelles inégalités de moments qui étendent celles de Louhichi. Enfin, nous illustrons certains de nos résultats par des
simulations.
Wintenberger, Olivier. "Contributions à la statistique des processus : estimation, prédiction et extrêmes." Habilitation à diriger des recherches, Université Paris Dauphine - Paris IX, 2012. http://tel.archives-ouvertes.fr/tel-00757756.
Повний текст джерелаYahaya, Mohamed. "Extension au cadre spatial de l'estimation non paramétrique par noyaux récursifs." Thesis, Lille 3, 2016. http://www.theses.fr/2016LIL30066/document.
Повний текст джерелаIn this thesis, we are interested in recursive methods that allow to update sequentially estimates in a context of spatial or spatial-temporal data and that do not need a permanent storage of all data. Process and analyze Data Stream, effectively and effciently is an active challenge in statistics. In fact, in many areas, decisions should be taken at a given time at the reception of a certain amount of data and updated once new data are available at another date. We propose and study kernel estimators of the probability density function and the regression function of spatial or spatial-temporal data-stream. Specifically, we adapt the classical kernel estimators of Parzen-Rosenblatt and Nadaraya-Watson. For this, we combine the methodology of recursive estimators of density and regression and that of a distribution of spatial or spatio-temporal data. We provide applications and numerical studies of the proposed estimators. The specifcity of the methods studied resides in the fact that the estimates take into account the spatial dependence structure of the relevant data, which is far from trivial. This thesis is therefore in the context of non-parametric spatial statistics and its applications. This work makes three major contributions. which are based on the study of non-parametric estimators in a recursive spatial/space-time and revolves around the recursive kernel density estimate in a spatial context, the recursive kernel density estimate in a space-time and recursive kernel regression estimate in space
Книги з теми "Processus faiblement dépendant"
Rio, Emmanuel. Théorie asymptotique des processus aléatoires faiblement dépendants (Mathématiques et Applications). Springer, 1999.
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