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Academic literature on the topic 'Rang et sélection (statistique)'
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Journal articles on the topic "Rang et sélection (statistique)"
Hoem, Jan M. "La standardisation indirecte améliorée et son application à la divortialité en Suède (1971-1989)." Population Vol. 46, no. 6 (June 1, 1991): 1551–68. http://dx.doi.org/10.3917/popu.p1991.46n6.1568.
Full textNiyonsenga, Gaudence, Darius Gishoma, Ruth Sego, Marie Goretti Uwayezu, Bellancille Nikuze, Margaret Fitch, and Pierre Céléstin Igiraneza. "Connaissances, utilisation et obstacles liés au dépistage du cancer du col utérin dans des hôpitaux de district de Kigali, au Rwanda." Canadian Oncology Nursing Journal 31, no. 3 (July 22, 2021): 275–84. http://dx.doi.org/10.5737/23688076313275284.
Full textLunula Onakudu, Francis, Nathan Utshudienyema Nyongombe, Innocent Mwamba Tshibangu, and Ngona Idi Abdallah. "Optimal breeding for a numerical improvement of sheep in the province of Maniema in the Democratic Republic of Congo." Journal of Applied Biosciences 169 (January 31, 2022): 17658–68. http://dx.doi.org/10.35759/jabs.169.10.
Full textThomas, Lionel. "Incitations et information corrélée : conséquences sur l'organisation des marchés publics." Revue économique 47, no. 3 (May 1, 1996): 457–66. http://dx.doi.org/10.3917/reco.p1996.47n3.0457.
Full textCzernichow, Thomas, Bernadette Dorizzi, and Antonio Muñoz San Roque. "Sélection de variables et séries temporelles par analyse statistique des sensibilités." Revue d'intelligence artificielle 15, no. 3-4 (December 1, 2001): 411–27. http://dx.doi.org/10.3166/ria.15.411-427.
Full textGalbraith, John W., and Victoria Zinde-Walsh. "Évaluation de critères d’information pour les modèles de séries chronologiques." Articles 80, no. 2-3 (October 24, 2005): 207–27. http://dx.doi.org/10.7202/011386ar.
Full textFOULLEY, J. L., and E. MANFREDI. "L’évaluation des reproducteurs : L’évaluation génétique des reproducteurs pour des caractères à seuil." INRAE Productions Animales 5, HS (December 2, 1992): 201–4. http://dx.doi.org/10.20870/productions-animales.1992.5.hs.4286.
Full textDelapierre, Michel, Madeuf, Charles-Albert Michalet, and Christian Milelli. "Participation des investisseurs institutionnels étrangers dans les grandes entreprises françaises : quels critères de sélection ?" Économie appliquée 56, no. 1 (2003): 71–92. http://dx.doi.org/10.3406/ecoap.2003.3094.
Full textMischi, Julian. "La fabrique d’une élite municipale." Revue française de science politique Vol. 73, no. 1 (April 22, 2024): 63–84. http://dx.doi.org/10.3917/rfsp.731.0063.
Full textLegoux, Luc, and Jean-Paul Grémy. "L'exploitation statistique des données administratives : l'exemple de la base informatique de l'OFPRA." Sociétés contemporaines 14-15, no. 2-3 (September 1, 1993): 43–57. http://dx.doi.org/10.3917/soco.p1993.14n1.0043.
Full textDissertations / Theses on the topic "Rang et sélection (statistique)"
Meunier, Hervé. "Algorithmes évolutionnaires parallèles pour l'optimisation multi-objectif de réseaux de télécommunications mobiles." Lille 1, 2002. https://pepite-depot.univ-lille.fr/RESTREINT/Th_Num/2002/50376-2002-93.pdf.
Full textChambaz, Antoine. "Segmentation spatiale et sélection de modèle : théorie et applications statistiques." Paris 11, 2003. http://www.theses.fr/2003PA112012.
Full textWe tacke in this thesis the elaboration of an original method that provides refinement of the localization of the mobIle telecommunication traffic in urban area for France Télécom R&D. This work involves both practical and theoretical developments. Our point of view is of statistical nature. The major themes are spatial segmentation and model selection. We first introduce the various datasets from which our approach stems. They cast some light on the original problem. We motivate the choice of an heteroscedastic regression model. We then present a practical nonparametric regression method based on CART regression trees and its Bagging and Boosting extensions by resampling. The latter classical methods are designed for ho- moscedastic models. We propose an adaptation to heteroscedastic ODes, including an original analysis of variable importance. We apply the method to various traffic datasets. The final results are commented. The above practical work motivates the theoretical study of the consistency of a family of estimators of the order of a segmented model and its associated segmentation. We also cope, in a general framework of model select ion in a nested family of models, with the estimation of the order of a model. We are particularly concerned with consistency properties and rates of und er- or overestimation. We tackle the problem at stake with a linear functional approach, i. E. An approach where the events of interest are described as events concerning the empirical measute. This allows to derive general results that gather and enhance earlier ODes. A large range of techniques are involved : classical arguments of M -estimation, concentration, max- imal inequalities for dependent variables, Stein's lemma, penalization, Large and Moderate Deviations Principles for the empirical measure, à la Huber trick
Bi, Duyan. "Segmentation d'images basée sur les statistiques de rangs des niveaux de gris." Tours, 1997. http://www.theses.fr/1997TOUR4005.
Full textSavalle, Pierre-André. "Interactions entre rang et parcimonie en estimation pénalisée, et détection d'objets structurés." Thesis, Châtenay-Malabry, Ecole centrale de Paris, 2014. http://www.theses.fr/2014ECAP0051/document.
Full textThis thesis is organized in two independent parts. The first part focused on convex matrix estimation problems, where both rank and sparsity are taken into account simultaneously. In the context of graphs with community structures, a common assumption is that the underlying adjacency matrices are block-diagonal in an appropriate basis. However, these types of graphs are usually far from complete, and their adjacency representations are thus also inherently sparse. This suggests that combining the sparse hypothesis and the low rank hypothesis may allow to more accurately model such objects. To this end, we propose and analyze a convex penalty to promote both low rank and high sparsity at the same time. Although the low rank hypothesis allows to reduce over-fitting by decreasing the modeling capacity of a matrix model, the opposite may be desirable when enough data is available. We study such an example in the context of localized multiple kernel learning, which extends multiple kernel learning by allowing each of the kernels to select different support vectors. In this framework, multiple kernel learning corresponds to a rank one estimator, while higher-rank estimators have been observed to increase generalization performance. We propose a novel family of large-margin methods for this problem that, unlike previous methods, are both convex and theoretically grounded. The second part of the thesis is about detection of objects or signals which exhibit combinatorial structures, and we present two such problems. First, we consider detection in the statistical hypothesis testing sense, in models where anomalous signals correspond to correlated values at different sensors. In most existing work, detection procedures are provided with a full sample of all the sensors. However, the experimenter may have the capacity to make targeted measurements in an on-line and adaptive manner, and we investigate such adaptive sensing procedures. Finally, we consider the task of identifying and localizing objects in images. This is an important problem in computer vision, where hand-crafted features are usually used. Following recent successes in learning ad-hoc representations for similar problems, we integrate the method of deformable part models with high-dimensional features from convolutional neural networks, and shows that this significantly decreases the error rates of existing part-based models
Challita, Nicole. "Contributions à la sélection des attributs de signaux non stationnaires pour la classification." Thesis, Troyes, 2018. http://www.theses.fr/2018TROY0012.
Full textTo monitor the functioning of a system, the number of measurements and attributes can now be very large. But it is desirable to reduce the size of the problem by keeping only the discriminating features to learn the monitoring rule and to reduce the processing demand. The problem is therefore to select a subset of attributes to obtain the best possible classification performance. This thesis dissertation presents different existing methods for feature selection and proposes two new ones. The first one, named "EN-ReliefF", is a combination of a sequential ReliefF method and a weighted regression approach: Elastic Net. The second one is inspired by neural networks. It is formulated as an optimization problem allowing defining at the same time a non-linear regression that adapts to the learning data and a parsimonious weighting of the features. The weights are then used to select the relevant features. Both methods are tested on synthesis data and data from rotating machines. Experimental results show the effectiveness of both methods. Remarkable characteristics are the stability of selection and ability to manage linearly correlated attributes for "EN-ReliefF" and the sensitivity and ability to manage non-linear dependencies for the second method
Boisbunon, Aurélie. "Sélection de modèle : une approche décisionnelle." Phd thesis, Université de Rouen, 2013. http://tel.archives-ouvertes.fr/tel-00793898.
Full textEstampes, Ludovic d'. "Traitement statistique des processus alpha-stables : mesures de dépendance et identification des AR stables : tests séquentiels tronqués." Toulouse, INPT, 2003. http://www.theses.fr/2003INPT031H.
Full textKalakech, Mariam. "Sélection semi-supervisée d'attributs : application à la classification de textures couleur." Thesis, Lille 1, 2011. http://www.theses.fr/2011LIL10018/document.
Full textWithin the framework of this thesis, we are interested in feature selection methods based on graph theory in different unsupervised, semi-supervised and supervised learning contexts. We are particularly interested in the feature ranking scores based on must-link et cannot-link constraints. Indeed, these constraints are easy to be obtained on real applications. They just require to formalize for two data samples if they are similar and then must be grouped together or not, without detailed information on the classes to be found. Constraint scores have shown good performances for semi-supervised feature selection. However, these scores strongly depend on the given must-link and cannot-link subsets built by the user. We propose then a new semi-supervised constraint scores that uses both pairwise constraints and local properties of the unconstrained data. Experiments on artificial and real databases show that this new score is less sensitive to the given constraints than the previous scores while providing similar performances. Semi supervised feature selection was also successfully applied to the color texture classification. Indeed, among many texture features which can be extracted from the color images, it is necessary to select the most relevant ones to improve the quality of classification
Olteanu, Madalina. "Modèles à changements de régime : applications aux données financières." Phd thesis, Université Panthéon-Sorbonne - Paris I, 2006. http://tel.archives-ouvertes.fr/tel-00133132.
Full textOn propose d'étudier ces questions à travers deux approches. Dans la première, il s'agit de montrer la consistance faible d'un estimateur de maximum de vraisemblance pénalisée sous des conditions de stationnarité et dépendance faible. Les hypothèses introduites sur l'entropie à crochets de la classe des fonctions scores généralisés sont ensuite vérifiées dans un cadre linéaire et gaussien. La deuxième approche, plutôt empirique, est issue des méthodes de classification non-supervisée et combine les cartes de Kohonen avec une classification hiérarchique pour laquelle une nouvelle dispersion basée sur la somme des carrés résiduelle est introduite.
Reynaud-Bouret, Patricia. "Estimation adaptative de l'intensité de certains processus ponctuels par sélection de modèle." Phd thesis, Paris 11, 2002. http://tel.archives-ouvertes.fr/tel-00081412.
Full textde sélection de modèle au cadre particulier de l'estimation d'intensité de
processus ponctuels. Plus précisément, nous voulons montrer que les
estimateurs par projection pénalisés de l'intensité sont adaptatifs soit dans
une famille d'estimateurs par projection, soit pour le risque minimax. Nous
nous sommes restreints à deux cas particuliers : les processus de Poisson
inhomogènes et les processus de comptage à intensité
multiplicative d'Aalen.
Dans les deux cas, nous voulons trouver une inégalité de type
oracle, qui garantit que les estimateurs par projection pénalisés ont un risque
du même ordre de grandeur que le meilleur estimateur par projection pour une
famille de modèles donnés. La clé qui permet de prouver des inégalités de
type oracle est le phénomène de concentration de la mesure ou plus précisément
la connaissance d'inégalités exponentielles, qui permettent de contrôler en
probabilité les déviations de statistiques de type khi-deux au dessus de leur
moyenne. Nous avons prouvé deux types d'inégalités de concentration. La
première n'est valable que pour les processus de Poisson. Elle est comparable
en terme d'ordre de grandeur à l'inégalité de M. Talagrand pour les suprema de
processus empiriques. La deuxième est plus grossière mais elle est valable
pour des processus de comptage beaucoup plus généraux.
Cette dernière inégalité met en oeuvre des techniques de
martingales dont nous nous sommes inspirés pour prouver des inégalités de
concentration pour des U-statistiques dégénérées d'ordre 2 ainsi que pour des
intégrales doubles par rapport à une mesure de Poisson recentrée.
Nous calculons aussi certaines bornes inférieures pour les
risques minimax et montrons que les estimateurs par projection pénalisés
atteignent ces vitesses.
Books on the topic "Rang et sélection (statistique)"
author, Arboretti Rosa, and Bonnini Stefano author, eds. Ranking of multivariate populations: A permutation approach with applications. Boca Raton: CRC Press/Taylor & Francis Group, 2016.
Find full textHarcourt Brace & Company. Educational Measurement., ed. Stanford achievement test series. San Antonio [Tex.]: Harcourt Brace Educational Measurement, 2004.
Find full textW, Dunnett Charles, and Hoppe Fred M. 1949-, eds. Multiple comparisons, selection, and applications in biometry: A festschrift in honor of Charles W. Dunnett. New York: Dekker, 1993.
Find full textCorain, Livio, Rosa Arboretti, and Stefano Bonnini. Ranking of Multivariate Populations: A Permutation Approach with Applications. Taylor & Francis Group, 2017.
Find full textCorain, Livio, Rosa Arboretti, and Stefano Bonnini. Ranking of Multivariate Populations: A Permutation Approach with Applications. Taylor & Francis Group, 2017.
Find full textCorain, Livio, Rosa Arboretti, and Stefano Bonnini. Ranking of Multivariate Populations: A Permutation Approach with Applications. CRC Press LLC, 2022.
Find full textCorain, Livio, Rosa Arboretti, and Stefano Bonnini. Ranking of Multivariate Populations: A Permutation Approach with Applications. Taylor & Francis Group, 2017.
Find full textCorain, Livio, Rosa Arboretti, and Stefano Bonnini. Ranking of Multivariate Populations: A Permutation Approach with Applications. Taylor & Francis Group, 2017.
Find full textCorain, Livio, Rosa Arboretti, and Stefano Bonnini. Ranking of Multivariate Populations: A Permutation Approach with Applications. Taylor & Francis Group, 2017.
Find full textActing appointments and subsequent promotions in the federal public service: A statistical study = Nominations intérimaires et promotions subséquentes à la fonction publique fédérale : étude statistique. [Ottawa, Ont.]: Public Service Commission of Canada = Commission de la fonction publique du Canada, 2007.
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