Relevant bibliographies by topics / Estimation multiple de moyennes

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Academic literature on the topic 'Estimation multiple de moyennes'

Author: Grafiati
Published: 26 October 2024

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Journal articles on the topic "Estimation multiple de moyennes"

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Galéa, G., and S. Canali. "Régionalisation des modules annuels et des régimes d'étiage du bassin hydrographique de la Moselle française : lien entre modèles régionaux." Revue des sciences de l'eau 18, no. 3 (2005): 331–52. http://dx.doi.org/10.7202/705562ar.

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Les modélisations régionales proposées sur les modules annuels et les débits moyens d'étiage des sous-bassins de la Moselle française devraient contribuer à l'amélioration des connaissances sur le fonctionnement physique actuel des hydrosystèmes. Elles s'inscrivent dans le contexte des directives de la loi Pêche et de la loi sur l'Eau et plus récemment de la Directive Cadre Européenne. Quarante neuf sous-bassins répartis en trois jeux ont permis de caler et valider un modèle régional des modules annuels et un modèle régional d'étiage. Dans le cas du bassin hydrographique de la Moselle française une certaine dépendance existe entre modèles régionaux dont la loi statistique choisie est la loi de Weibull à 2 paramètres. Une pseudo-dépendance est observée entre la loi régionale des modules annuels et la loi régionale des débits moyens d'étiage pour les années moyennes à sèches. Cette propriété va permettre en particulier l'usage d'une procédure simplifiée commune, établie à partir de la connaissance de jaugeages épisodiques d'étiage, pour l'estimation des descripteurs de débit d'un sous-bassin non observé : le module médian /qa et le débit quotidien minimal médian /vcnd=1. Pour le modèle régional d'étiage un deuxième descripteur local est nécessaire. Il s'agit d'un temps caractéristique d'étiage du sous-bassin ∆e (j) permettant de généraliser le modèle à toute durée d. Le concept débit-durée-fréquence QdF appliqué aux étiages exploite la convergence observée des distributions de différentes durées d et est indépendant de la loi fréquentielle choisie. Le caractère opérationnel de ces modélisations régionales dépend essentiellement de la précision d'estimation des descripteurs de débit du sous-bassin étudié /qa, pour les modules annuels et /vcnd=1 pour les étiages. Ces descripteurs de débit ont été estimés selon deux approches : l'approche classique par régression multiple et selon une approche simple de recherche d'un coefficient de tendance k entre jaugeages épisodiques d'étiage concomitants au sous-bassin étudié (pas ou peu d'observations) et au sous-bassin de référence (chronique de débit continue). Pour cela, un choix de cinq jaugeages d'étiage par an sur les douze dernières années en moyenne a été fait. Le descripteur de débit du sous-bassin étudié est ensuite déduit du produit de k par le descripteur de débit du sous-bassin de référence. Pour /vcnd=1, sb. étudié nous observons dans la majorité des cas une nette amélioration de l'estimation obtenue par régression, notamment une forte réduction des écarts les plus importants. Une similitude des classes de superficie entre sous-bassin étudié et sous-bassin de référence n'est pas exigée. La proximité géographique des sous-bassins semble donner de meilleurs résultats. En ce qui concerne le module médian /qasb. étudié, son estimation par régression multiple est assez performante. Parallèlement à cela, le coefficient k de tendance permet, de même que pour, une estimation cohérente de /qasb. étudié. Ce résultat un peu inattendu laisse supposer que la pseudo-dépendance observée entre modèles régionaux a bien une réalité physique. Nous avons insisté sur cette démarche "de régionalisation" nécessitant un faible investissement en mesures de débit des sous-bassins non observés par rapport au réseau national de suivi hydrométrique. Elle se présente à notre avis comme une alternative (ou complémentarité) intéressante aux méthodes de régionalisation à bases géostatistiques : telles que l'identification du voisinage hydrologique homogène du sous-bassin étudié ou encore la prise en compte de l'effet structurant du réseau hydrographique dans la cartographie du descripteur de débit. L'ensemble des connaissances relative à cette recherche est repris dans un Système d'Information Géographique pour répondre éventuellement à la demande.
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Turbillon, Céline, Denis Bosq, Jean-Marie Marion, and Besnik Pumo. "Estimation du paramètre des moyennes mobiles hilbertiennes." Comptes Rendus Mathematique 346, no. 5-6 (2008): 347–50. http://dx.doi.org/10.1016/j.crma.2008.01.008.

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Ferrieux, Dominique. "Estimation à noyau de densités moyennes de mesures aléatoires associées." Comptes Rendus de l'Académie des Sciences - Series I - Mathematics 326, no. 9 (1998): 1131–34. http://dx.doi.org/10.1016/s0764-4442(98)80075-x.

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BENALAYA, A., H. SEBEI, F. DE TROCH, P. TROCH, and N. ENNABLI. "Estimation des périodicités et de la tendance des températures moyennes mensuelles en Tunisie." Hydrological Sciences Journal 39, no. 6 (1994): 593–603. http://dx.doi.org/10.1080/02626669409492782.

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Assayag, Jackie. "En quête de classe moyenne en inde. Grandeur, recomposition, forfaiture." Annales. Histoire, Sciences Sociales 55, no. 6 (2000): 1229–53. http://dx.doi.org/10.3406/ahess.2000.279913.

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RésuméAprès un siècle d'études, il subsiste une zone en friche dans le champ des sciences sociales de l'Asie du Sud : celle de la ou des classes moyennes. Se posent non seulement les questions de son estimation numérique et de sa recomposition depuis cent cinquante ans, mais aussi celle de sa forfaiture puisque d'aucuns la considèrent comme responsable de la fabrication de la démocratie la plus inégalitaire dans le monde. Ces questions sont abordées à partir de réflexions sociologiques, historiques et épistémologiques.
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Lantri, Fodhil, Nour El Islam Bachari, and Ahmed Hafid Belbachir. "Estimation et cartographie des différentes composantes de rayonnement solaire au sol à partir des données météorologiques." Journal of Renewable Energies 20, no. 1 (2023): 111–30. http://dx.doi.org/10.54966/jreen.v20i1.614.

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L’extraction des différentes composantes de la radiation solaire à partir des données météorologiques dépend étroitement de la connaissance exacte des coordonnées géographiques (latitude, longitude, altitude, etc.) de la position correspondante au pixel donné et les valeurs des variables météorologiques dont (la visibilité, la pression, l’humidité relative…) selon le modèle utilisé. En premier lieu, pour calculer les valeurs de radiation solaire dans une point (x, y), nous affectons les valeurs des variables météorologiques acquises par la plus proche station (latitude, longitude) et ensuite on a coloré le pixel (x, y) selon la valeur calculée. L’étude comparative entre les radiations calculées à partir des données météorologiques et les radiations mesurées au sol pour la station d’Oran (35°24, -0°36) donne les valeurs statistiques moyennes suivantes: ; ; , r=0.98 pour le global, , , , r=0.95 pour le direct et , , , r=0.83 pour le diffus.
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Cherkassky, V., and Y. Ma. "Multiple Model Regression Estimation." IEEE Transactions on Neural Networks 16, no. 4 (2005): 785–98. http://dx.doi.org/10.1109/tnn.2005.849836.

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George, Edward I. "Minimax Multiple Shrinkage Estimation." Annals of Statistics 14, no. 1 (1986): 188–205. http://dx.doi.org/10.1214/aos/1176349849.

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Bich, Walter. "Estimation in multiple measurements." Accreditation and Quality Assurance 14, no. 7 (2009): 389–92. http://dx.doi.org/10.1007/s00769-009-0537-4.

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Garba, Issa, Zakari Seybou Abdourahamane, Abdou Amadou Sanoussi, and Illa Salifou. "Optimisation de l'Evaluation de la Biomasse Fourragère en Zone Sahélienne Grâce à l’Utilisation de la Méthode de Régression Linéaire Multiple en Conjonction Avec la Stratification." European Scientific Journal, ESJ 19, no. 33 (2023): 52. http://dx.doi.org/10.19044/esj.2023.v19n33p52.

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L'objectif de cette étude, conduite dans la zone pastorale du Niger, est d'optimiser l'estimation de la biomasse fourragère à l'échelle des faciès avec la méthode de Régression Linéaire Multiple (RLM). Les données utilisées englobent les mesures in situ de la masse herbacée entre 2001 et 2012, des données pluviométriques de station, les variables agrométéorologiques dérivées des données météorologiques de « l'European Centre for Medium-Range Weather Forecasts » (ECMWF) traitées via AgroMetShell (AMS), les images satellitaires NDVI de SPOT VEGETATION traitées avec le programme « Vegetation Analysis in Space and Time » (VAST) pour obtenir des variables biophysiques à partir des séries annuelles de NDVI décadaires, et les données de pluies estimées RFE provenant du « Famine Early Warning Systems NETwork » (FEWSNET). Les strates ont été identifiées sur la base de la carte des sols de la FAO, la couche des écorégions et les zones bioclimatiques du pays. Le modèle a été développé en utilisant la méthode de la RLM avec une approche ascendante de sélection de variables basée sur le coefficient de détermination (R²) ajusté et la racine de l'erreur quadratique moyenne (RMSE). Pour évaluer la robustesse du modèle, la validation croisée « leave one out – cross validation » (LOO-CV) a été employé pour calculer les R² de validation et effectué un diagnostic systématique des résidus afin de mieux caractériser le modèle. À l'échelle de l'ensemble de la zone d'étude (échelle globale), le RLM a produit un R² ajusté de 0,69 et un RMSE de 282 kg MS.ha-1, avec seulement une légère différence de 2,72 kg MS.ha-1 entre le RMSE de la calibration et celui de la validation. La stratification a amélioré la performance des modèles, avec des résultats prometteurs. Les modèles basés sur les types de sols FAO ont montré des R² élevés pour Ge5-1a, Qc1, Qc7-1a, Ql1-1a et Re35-a. Les écorégions telles que l'Azaouak, le Manga1 et le Manga2 ont également obtenu de bons résultats. Les paramètres des modèles par faciès ont été encore plus prometteurs, avec des R² allant de 0,77 à 0,93. Ces travaux auront un impact significatif en améliorant la qualité des informations utilisées pour planifier les initiatives de développement visant à protéger la société nigérienne contre les crises pastorales.
 
 The aim of this study, conducted in the pastoral zone of Niger, was to optimize the estimation of forage biomass at the scale of the different facies using Multiple Linear Regression (MLR) method. The data used include field measurements of herbaceous mass between 2001 and 2012, station rainfall data, agrometeorological variables derived from meteorological data of the European Centre for Medium-Range Weather Forecasts (ECMWF) processed via AgroMetShell (AMS), SPOT VEGETATION NDVI satellite images processed with the Vegetation Analysis in Space and Time (VAST) program to obtain biophysical variables from annual decadal NDVI series, and estimated RFE rainfall data from the US Famine Early Warning Systems NETwork (FEWSNET) to calculate annual rainfall totals. We identified strata based on the FAO soil map, the ecoregion layer and the country's bioclimatic zones. The model was developed using MLR with a bottom-up variable selection approach based on adjusted R² and root mean square error (RMSE). To assess the model's robustness, we used leave-one-out cross validation (LOO-CV) to calculate the validation R², and carried out systematic residual diagnostics to better characterize the model. At the scale of the entire study area (global scale), the MLR produced an adjusted R² of 0.69 and an RMSE of 282 kg MS.ha-1, with only a slight difference of 2.72 kg MS.ha-1 between the calibration and validation RMSE. Stratification improved model performance, with promising results. Models based on FAO soil types showed high R²s for Ge5-1a, Qc1, Qc7-1a, Ql1-1a and Re35-a. Ecoregions such as Azaouak, Manga1 and Manga2 also performed well. Model parameters by facies were even more promising, with R² ranging from 0.77 to 0.93. This work will have a significant impact in improving the quality of information used to plan development initiatives for protecting Nigerian society from pastoral crises.
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Dissertations / Theses on the topic "Estimation multiple de moyennes"

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Fermanian, Jean-Baptiste. "High dimensional multiple means estimation and testing with applications to machine learning." Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASM035.

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Nous étudions dans cette thèse l'influence de la grande dimension dans des problèmes de test et d'estimation. Notre analyse porte sur la dépendance en la dimension de la vitesse de séparation d'un test de proximité et du risque quadratique de l'estimation multiples de vecteurs. Nous complétons les résultats existants en étudiant ces dépendances dans le cas de distributions non isotropes. Pour de telles distributions, le rôle de la dimension est alors joué par des notions de dimension effective définies à partir de la covariance des distributions. Ce cadre permet d'englober des données de dimension infinie comme le kernel mean embedding, outil de machine learning que nous chercherons à estimer. A l'aide de cette analyse, nous construisons des méthodes d'estimation simultanée de vecteurs moyennes de différentes distributions à partir d'échantillons indépendants de chacune. Ces estimateurs ont de meilleures performances théorique et pratique relativement aux moyennes empiriques, en particulier dans des situations défavorables où la dimension (effective) est grande. Ces méthodes utilisent explicitement ou implicitement la relative facilité du test par rapport à l'estimation. Elles reposent sur la construction d'estimateurs de distances et de moments de la covariance pour lesquels nous fournissons des bornes de concentration non asymptotiques. Un intérêt particulier est porté à l'étude de données bornées pour lesquels une analyse spécifique est nécessaire. Nos méthodes sont accompagnées d'une analyse minimax justifiant leur optimalité. Dans une dernière partie, nous proposons une interprétation du mécanisme d'attention utilisé dans les réseaux de neurones Transformers comme un problème d'estimation multiple de vecteurs. Dans un cadre simplifié, ce mécanisme partage des idées similaires avec nos approches et nous mettons en évidence son effet de débruitage en grande dimension<br>In this thesis, we study the influence of high dimension in testing and estimation problems. We analyze the dimension dependence of the separation rate of a closeness test and of the quadratic risk of multiple vector estimation. We complement existing results by studying these dependencies in the case of non-isotropic distributions. For such distributions, the role of dimension is played by notions of effective dimension defined from the covariance of the distributions. This framework covers infinite-dimensional data such as kernel mean embedding, a machine learning tool we will be seeking to estimate. Using this analysis, we construct methods for simultaneously estimating mean vectors of different distributions from independent samples of each. These estimators perform better theoretically and practically than the empirical mean in unfavorable situations where the (effective) dimension is large. These methods make explicit or implicit use of the relative ease of testing compared with estimation. They are based on the construction of estimators of distances and moments of covariance, for which we provide non-asymptotic concentration bounds. Particular interest is given to the study of bounded data, for which a specific analysis is required. Our methods are accompanied by a minimax analysis justifying their optimality. In a final section, we propose an interpretation of the attention mechanism used in Transformer neural networks as a multiple vector estimation problem. In a simplified framework, this mechanism shares similar ideas with our approaches, and we highlight its denoising effect in high dimension
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Tran, Nguyen Duy. "Performance bounds in terms of estimation and resolution and applications in array processing." Phd thesis, École normale supérieure de Cachan - ENS Cachan, 2012. http://tel.archives-ouvertes.fr/tel-00777503.

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This manuscript concerns the performance analysis in signal processing and consists into two parts : First, we study the lower bounds in characterizing and predicting the estimation performance in terms of mean square error (MSE). The lower bounds on the MSE give the minimum variance that an estimator can expect to achieve and it can be divided into two categories depending on the parameter assumption: the so-called deterministic bounds dealing with the deterministic unknown parameters, and the so-called Bayesian bounds dealing with the random unknown parameter. Particularly, we derive the closed-form expressions of the lower bounds for two applications in two different fields: (i) The first one is the target localization using the multiple-input multiple-output (MIMO) radar in which we derive the lower bounds in the contexts with and without modeling errors, respectively. (ii) The other one is the pulse phase estimation of X-ray pulsars which is a potential solution for autonomous deep space navigation. In this application, we show the potential universality of lower bounds to tackle problems with parameterized probability density function (pdf) different from classical Gaussian pdf since in X-ray pulse phase estimation, observations are modeled with a Poisson distribution. Second, we study the statistical resolution limit (SRL) which is the minimal distance in terms of the parameter of interest between two signals allowing to correctly separate/estimate the parameters of interest. More precisely, we derive the SRL in two contexts: array processing and MIMO radar by using two approaches based on the estimation theory and information theory. We also present in this thesis the usefulness of SRL in optimizing the array system.
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Ferrieux, Dominique. "Estimation de densités de mesures moyennes de processus ponctuels associés." Montpellier 2, 1996. http://www.theses.fr/1996MON20245.

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L'objet principal de ce travail est l'estimation par la methode du noyau de la densite de la mesure moyenne d'une mesure aleatoire discrete, ou d'un processus ponctuel, sous hypothese d'association. Le premier chapitre donne les proprietes generales des suites de mesures aleatoires associees et des exemples. Le second chapitre donne les principales proprietes asymptotiques de l'estimateur telles que les convergences en probabilite et presque sure, la loi limite, et le choix optimal de la fenetre. Dans le troisieme chapitre ces resultats sont exploites pour l'estimation de la derivee de deux mesures moyennes. Le quatrieme chapitre etudie un point de vue nouveau sur la statistique des processus ponctuels quand une seule observation est disponible. Le dernier chapitre presente quelques simulations
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Wiklund, Åsa. "Multiple Platform Bias Error Estimation." Thesis, Linköping University, Department of Electrical Engineering, 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-2126.

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<p>Sensor fusion has long been recognized as a mean to improve target tracking. Sensor fusion deals with the merging of several signals into one to get a better and more reliable result. To get an improved and more reliable result you have to trust the incoming data to be correct and not contain unknown systematic errors. This thesis tries to find and estimate the size of the systematic errors that appear when we have a multi platform environment and data is shared among the units. To be more precise, the error estimated within the scope of this thesis appears when platforms cannot determine their positions correctly and share target tracking data with their own corrupted position as a basis for determining the target's position. The algorithms developed in this thesis use the Kalman filter theory, including the extended Kalman filter and the information filter, to estimate the platform location bias error. Three algorithms are developed with satisfying result. Depending on time constraints and computational demands either one of the algorithms could be preferred.</p>
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Helversen, Bettina von. "Quantitative estimation from multiple cues." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät II, 2008. http://dx.doi.org/10.18452/15718.

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Wie schätzen Menschen quantitative Größen wie zum Beispiel den Verkaufspreis eines Autos? Oft benutzen Menschen zur Lösung von Schätzproblemen sogenannte Cues, Informationen, die probabilistisch mit dem zu schätzenden Kriterium verknüpft sind. Um den Verkaufspreis eines Autos zu schätzen, könnte man zum Beispiel Informationen über das Baujahr, die Automarke, oder den Kilometerstand des Autos verwenden. Um menschliche Schätzprozesse zu beschreiben, werden häufig linear additive Modelle herangezogen. In meiner Dissertation schlage ich alternative ein heuristisches Modell zur Schätzung quantitativer Größen vor: das Mapping-Modell. Im ersten Kapitel meiner Dissertation teste ich das Mapping-Modell gegen weitere, in der Literatur etablierte, Schätzmodelle. Es zeigte sich, dass das Mapping-Modell unter unterschiedlichen Bedingungen in der Lage war, die Schätzungen der Untersuchungsteilnehmer akkurat vorherzusagen. Allerdings bestimmte die Struktur der Aufgabe - im Einklang mit dem Ansatz der „adaptiven Werkzeugkiste“ - im großen Maße, welches Modell am besten geeignet war, die Schätzungen zu erfassen. Im zweiten Kapitel meiner Dissertation greife ich diesen Ansatz auf und untersuche, in wie weit die Aufgabenstruktur bestimmt, welches Modell die Schätzprozesse am Besten beschreibt. Meine Ergebnisse zeigten, dass das Mapping-Modell am Besten dazu geeignet war die Schätzungen der Versuchsteilnehmer zu beschreiben, wenn explizites Wissen über die Aufgabe vorhanden war, während ein Exemplar-Modell den Schätzprozess erfasste, wenn die Abstraktion von Wissen schwierig war. Im dritten Kapitel meiner Dissertation, wende ich das Mapping-Modell auf juristische Entscheidungen an. Eine Analyse von Strafakten ergab, dass das Mapping-Modell Strafzumessungsvorschläge von Staatsanwälten besser vorhersagte als eine lineare Regression. Dies zeigt, dass das Mapping-Modell auch außerhalb von Forschungslaboratorien dazu geeignet ist menschliche Schätzprozesse zu beschreiben.<br>How do people make quantitative estimations, such as estimating a car’s selling price? Often people rely on cues, information that is probabilistically related to the quantity they are estimating. For instance, to estimate the selling price of a car they could use information, such as the car’s manufacturer, age, mileage, or general condition. Traditionally, linear regression type models have been employed to capture the estimation process. In my dissertation, I propose an alternative cognitive theory for quantitative estimation: The mapping model which offers a heuristic approach to quantitative estimations. In the first part of my dissertation l test the mapping model against established alternative models of estimation, namely, linear regression, an exemplar model, and a simple estimation heuristic. The mapping model provided a valid account of people’s estimates outperforming the other models in a variety of conditions. Consistent with the “adaptive toolbox” approach on decision, which model was best in predicting participants’ estimations was a function of the task environment. In the second part of my dissertation, I examined further how different task features affect the performance of the models make. My results indicate that explicit knowledge about the cues is decisive. When knowledge about the cues was available, the mapping model was the best model; however, if knowledge about the task was difficult to abstract, participants’ estimations were best described by the exemplar model. In the third part of my dissertation, I applied the mapping model in the field of legal decision making. In an analysis of fining and incarceration decisions, I showed that the prosecutions’ sentence recommendations were better captured by the mapping model than by legal policy modeled with a linear regression. These results indicated that the mapping model is a valid model which can be applied to model actual estimation processes outside of the laboratory.
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Hemmendorff, Magnus. "Single and Multiple Motion Field Estimation." Licentiate thesis, Linköping University, Linköping University, Computer Vision, 1999. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-54343.

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<p>This thesis presents a framework for estimation of motion fields both for single and multiple layers. All the methods have in common that they generate or use constraints on the local motion. Motion constraints are represented by vectors whose directions describe one component of the local motion and whose magnitude indicate confidence.</p><p>Two novel methods for estimating these motion constraints are presented. Both methods take two images as input and apply orientation sensitive quadrature filters. One method is similar to a gradient method applied on the phase from the complex filter outputs. The other method is based on novel results using canonical correlation presented in this thesis.</p><p>Parametric models, e.g. affine or FEM, are used to estimate motion from constraints on local motion. In order to estimate smooth fields for models with many parameters, cost functions on deformations are introduced.</p><p>Motions of transparent multiple layers are estimated by implicit or explicit clustering of motion constraints into groups. General issues and difficulties in analysis of multiple motions are described. An extension of the known EM algorithm is presented together with experimental results on multiple transparent layers with affine motions. Good accuracy in estimation allows reconstruction of layers using a backprojection algorithm. As an alternative to the EM algorithm, this thesis also introduces a method based on higher order tensors.</p><p>A result with potential applicatications in a number of diffeerent research fields is the extension of canonical correlation to handle complex variables. Correlation is maximized using a novel method that can handle singular covariance matrices.</p>
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Burney, S. M. A. "Estimation methods for multiple time series." Thesis, University of Strathclyde, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.382231.

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PLAKSIENKO, ANNA. "Joint estimation of multiple graphical models." Doctoral thesis, Gran Sasso Science Institute, 2021. http://hdl.handle.net/20.500.12571/21632.

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The fast development of high-throughput technologies such as microarray or next-generation sequencing, and the consequent in-depth investigation of the genome in several international large scale projects, have led to the generation of large amounts of high-dimensional omics datasets. Scientists can use such data to acquire a deep understanding of complex cellular mechanisms, the molecular basis of diseases’ development, etc. Among other questions, relationships between different genes or other similar units can reveal regulatory mechanisms whose disruption can be associated with diseases. Network inference methods and, more specifically, graphical models estimation can be used to identify gene relationships and direct interactions not mediated by other factors. Simply speaking, a graphical model is a graph whose vertices correspond to random variables and edges denote conditional dependence relationships between them. There are plenty of methods for carrying out graphical model inference from a given dataset, even in the high-dimensional setting where the number of variables is much larger than the number of samples (a common situation in omics studies for the enormous number of genes involved and a limited number of samples collected). However, nowadays, it is common to collect and analyze more than one dataset. Multiple datasets can be obtained in different laboratories or with different technologies, arise from various studies, or be of different omics types. Their joint analysis can lead to a more accurate characterization of the underlying biological system, but it also requires specific techniques. In this thesis, we propose jewel – a novel method for the joint analysis of multiple datasets under the assumption that they are drawn from Gaussian distributions that share the same network dependency. In this context, the conditional dependence relationships between variables (genes) are encoded by the inverse covariance matrix. Although we assume that the conditional dependence structure is the same between different conditions, we let the covariance matrices be different to account for different sources of data origin. In this setting, combining the individual datasets into a single one and estimating a sole graphical model would mask the covariance matrices’ heterogeneity, while estimating separate models for each case would not take advantage of the common underlying structure. Therefore, a joint analysis of the datasets is preferable, and to this aim in this thesis we present a novel joint estimation method jewel. It extends the Meinshausen and Bühlmann regression-based approach to the case of multiple datasets by the mean of a group lasso penalty which guarantees the symmetry of the solution. We design a fast algorithm for the method’s implementation, incorporating the smart active shooting approach for a fixed regularization parameter and the warm start approach for an entire grid of regularization parameters. We also state a theorem for jewel’s consistency, providing upper and lower bounds for regularization parameter. Moreover, we extend the Bayesian information criterion and cross-validation procedures to the multiple datasets framework to provide a practical tool for real case applications. We explore the behavior of jewel in different simulation settings, analyzing the influence of various input parameters, and comparing the method to other available alternatives for joint estimation, revealing good and competitive performances. Finally, we illustrate the method’s performance in real data example regarding transcriptional regulatory networks based on gene expression data. We implement the proposed method in the novel R package jewel.
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Lee, Joonsung. "Acoustic signal estimation using multiple blind observations." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/35603.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.<br>Includes bibliographical references (p. 109-111).<br>This thesis proposes two algorithms for recovering an acoustic signal from multiple blind measurements made by sensors (microphones) over an acoustic channel. Unlike other algorithms that use a posteriori probabilistic models to fuse the data in this problem, the proposed algorithms use results obtained in the context of data communication theory. This constitutes a new approach to this sensor fusion problem. The proposed algorithms determine inverse channel filters with a predestined support (number of taps). The Coordinated Recovery of Signals From Sensors (CROSS) algorithm is an indirect method, which uses an estimate of the acoustic channel. Using the estimated channel coefficients from a Least-Squares (LS) channel estimation method, we propose an initialization process (zero-forcing estimate) and an iteration process (MMSE estimate) to produce optimal inverse filters accounting for the room characteristics, additive noise and errors in the estimation of the parameters of the room characteristics.<br>(cont.) Using a measured room channel, we analyze the performance of the algorithm through simulations and compare its performance with the theoretical performance. Also, in this thesis, the notion of channel diversity is generalized and the Averaging Row Space Intersection (ARSI) algorithm is proposed. The ARSI algorithm is a direct method, which does not use the channel estimate.<br>by Joonsung Lee.<br>S.M.
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De, Melo F. E. "Multiple-object estimation techniques for challenging scenarios." Thesis, University of Liverpool, 2017. http://livrepository.liverpool.ac.uk/3013627/.

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A series of methods for solving the multi-object estimation problem in the context sequential Bayesian inference is presented. These methods concentrate on dealing with challenging scenarios of multiple target tracking, involving fundamental problems of nonlinearity and non-Gaussianity of processes, high state dimensionality, high number of targets, statistical dependence between target states, and degenerate cases of low signal-to-noise ratio, high uncertainty, lowly observable states or uninformative observations. These difficulties pose obstacles to most practical multi-object inference problems, lying at the heart of the shortcomings reported for state-of-the-art methods, and so elicit novel treatments to enable tackling a broader class of real problems. The novel algorithms offered as solutions in this dissertation address such challenges by acting on the root causes of the associated problems. Often this involves essential dilemmas commonly manifested in Statistics and Decision Theory, such as trading off estimation accuracy with algorithm complexity, soft versus hard decision, generality versus tractability, conciseness versus interpretativeness etc. All proposed algorithms constitute stochastic filters, each of which is formulated to address specific aspects of the challenges at hand while offering tools to achieve judicious compromises in the aforementioned dilemmas. Two of the filters address the weight degeneracy observed in sequential Monte Carlo filters, particularly for nonlinear processes. One of these filters is designed for nonlinear non-Gaussian high-dimensional problems, delivering representativeness of the uncertainty in high-dimensional states while mitigating part of the inaccuracies that arise from the curse of dimensionality. This filter is shown to cope well with scenarios of multimodality, high state uncertainty, uninformative observations and high number of false alarms. A multi-object filter deals with the problem of considering dependencies between target states in a way that is scalable to a large number of targets, by resorting to probabilistic graphical structures. Another multi-object filter treats the problem of reducing the computational complexity of a state-of-the-art cardinalized filter to deal with a large number of targets, without compromising accuracy significantly. Finally, a framework for associating measurements across observation sessions for scenarios of low state observability is proposed, with application to an important Space Surveillance task: cataloging of space debris in the geosynchronous/geostationary belt. The devised methods treat the considered challenges by bringing about rather general questions, and provide not only principled solutions but also analyzes the essence of the investigated problems, extrapolating the implemented techniques to a wider spectrum of similar problems in Signal Processing.
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Books on the topic "Estimation multiple de moyennes"

1

Weinstein, Ehud. Multiple source location estimation using the EM algorithm. Woods Hole Oceanographic Institution, 1986.

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Feder, Meir. Optimal multiple source location via the EM algorithm. Woods Hole Oceanographic Institution, 1986.

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Palaszewski, Bo. On multiple test procedures for finding deviating parameters. University of Göteborg, 1993.

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Major, Péter. On the Estimation of Multiple Random Integrals and U-Statistics. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-37617-7.

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Blech, Richard A. Parallel Gaussian estimation of a block tridiagonal matrix using multiple microcomputers. Lewis Research Center, 1989.

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Schreuder, Hans T. Data estimation and prediction for natural resources public data. U.S. Dept. of Agriculture, Forest Service, Rocky Mountain Research Station, 1998.

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M, Reich Robin, and Rocky Mountain Research Station (Fort Collins, Colo.), eds. Data estimation and prediction for natural resources public data. U.S. Dept. of Agriculture, Forest Service, Rocky Mountain Research Station, 1998.

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W, Cooper Russell. Estimation and identification of structural parameters in the presence of multiple equilibria. National Bureau of Economic Research, 2002.

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Prins, Robert Dean. Effective dose estimation for U.S. Army soldiers undergoing multiple computed tomography scans. [publisher not identified], 2011.

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Ohya, Jun. Analyzing video sequences of multiple humans: Tracking, posture estimation and behavior recognition. Springer, 2002.

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Book chapters on the topic "Estimation multiple de moyennes"

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Jia, Bin, and Ming Xin. "Multiple Sensor Estimation." In Grid-based Nonlinear Estimation and Its Applications. CRC Press, 2019. http://dx.doi.org/10.1201/9781315193212-6.

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Rosenblatt, Jonathan D. "Prevalence Estimation." In Handbook of Multiple Comparisons. Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9780429030888-8.

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Chadli, Mohammed, Pierre Borne, and Bernard Dubuisson. "Multiple Model State Estimation." In Multiple Models Approach in Automation. John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118577325.ch3.

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Pillai, S. Uṇṇikrishṇa, and C. S. Burrus. "Estimation of Multiple Signals." In Signal Processing and Digital Filtering. Springer New York, 1989. http://dx.doi.org/10.1007/978-1-4612-3632-0_4.

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Buckley, James J. "Estimation in Multiple Regression." In Fuzzy Statistics. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-39919-3_25.

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Ohya, Jun. "Posture Estimation." In Analyzing Video Sequences of Multiple Humans. Springer US, 2002. http://dx.doi.org/10.1007/978-1-4615-1003-1_3.

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DeBlasio, Dan, and John Kececioglu. "Alignment Accuracy Estimation." In Parameter Advising for Multiple Sequence Alignment. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-64918-4_2.

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Lütkepohl, Helmut. "Estimation of VARMA Models." In Introduction to Multiple Time Series Analysis. Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-662-02691-5_7.

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Lütkepohl, Helmut. "Estimation of VARMA Models." In Introduction to Multiple Time Series Analysis. Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-642-61695-2_7.

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Lütkepohl, Helmut. "Estimation of VARMA Models." In New Introduction to Multiple Time Series Analysis. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-27752-1_12.

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Conference papers on the topic "Estimation multiple de moyennes"

1

Bucy, R. S., and S. C. Leung. "Estimation of Multiple Directions." In IEEE Military Communications Conference MILCOM 1986. IEEE, 1986. http://dx.doi.org/10.1109/milcom.1986.4805846.

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Vershuur, Dirk J., and A. J. Berkhout. "Multiple technology: Part 1, Estimation of multiple reflections." In SEG Technical Program Expanded Abstracts 1994. Society of Exploration Geophysicists, 1994. http://dx.doi.org/10.1190/1.1822820.

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Hafezi, Sina, Alastair H. Moore, and Patrick A. Naylor. "Multiple DOA estimation based on estimation consistency and spherical harmonic multiple signal classification." In 2017 25th European Signal Processing Conference (EUSIPCO). IEEE, 2017. http://dx.doi.org/10.23919/eusipco.2017.8081406.

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Ragab, M. E., and K. H. Wong. "Multiple nonoverlapping camera pose estimation." In 2010 17th IEEE International Conference on Image Processing (ICIP 2010). IEEE, 2010. http://dx.doi.org/10.1109/icip.2010.5651178.

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Baar, Tamas, Bence Beke, Peter Bauer, Balint Vanek, and Jozsef Bokor. "Smoothed multiple model adaptive estimation." In 2016 European Control Conference (ECC). IEEE, 2016. http://dx.doi.org/10.1109/ecc.2016.7810442.

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Hui Chen and Feng Lian. "Bias estimation for multiple passive sensors." In 2012 International Conference on Measurement, Information and Control (MIC). IEEE, 2012. http://dx.doi.org/10.1109/mic.2012.6273487.

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Liu, Pu, Donald R. Brown, Edward A. Clancy, Francois Martel, and Denis Rancourt. "EMG-force estimation for multiple fingers." In 2013 IEEE Signal Processing in Medicine and Biology Symposium (SPMB). IEEE, 2013. http://dx.doi.org/10.1109/spmb.2013.6736772.

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Marcos de Carvalho, Paulo. "Internal Multiple Attenuation And Wavelet Estimation." In 6th International Congress of the Brazilian Geophysical Society. European Association of Geoscientists & Engineers, 1999. http://dx.doi.org/10.3997/2214-4609-pdb.215.sbgf393.

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Thompson, Peter, and Frederick Anderson. "Rotational rate estimation using multiple accelerometers." In Atmospheric Flight Mechanics Conference. American Institute of Aeronautics and Astronautics, 2000. http://dx.doi.org/10.2514/6.2000-4192.

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Cui, Shuguang, Jinjun Xiao, Andrea Goldsmith, Zhi-quan Luo, and H. Poor. "Estimation Diversity with Multiple Heterogeneous Sensors." In 2006 IEEE International Conference on Communications. IEEE, 2006. http://dx.doi.org/10.1109/icc.2006.255031.

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Reports on the topic "Estimation multiple de moyennes"

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Li, Ta-Hsin, and Benjamin Kedem. Estimation of Multiple Sinusoids by Parametric Filtering. Defense Technical Information Center, 1992. http://dx.doi.org/10.21236/ada454954.

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Rabi, Maben, John S. Baras, and George Moustakides. Multiple Sampling for Estimation on a Finite Horizon. Defense Technical Information Center, 2006. http://dx.doi.org/10.21236/ada446968.

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Lake, Douglas. Efficient Maximum Likelihood Estimation for Multiple and Coupled Harmonics. Defense Technical Information Center, 1999. http://dx.doi.org/10.21236/ada372834.

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Shumway, Robert H., and Sung-Eun Kim. Signal Detection and Estimation of Directional Parameters for Multiple Arrays. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada400949.

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DELAURENTIS, JOHN M., and ARMIN W. DOERRY. Stereoscopic Height Estimation from Multiple Aspect Synthetic Aperture Radar Images. Office of Scientific and Technical Information (OSTI), 2001. http://dx.doi.org/10.2172/786639.

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Weinstein, Ehud, and Meir Feder. Multiple Source Location Estimation Using the EM (Estimate-Maximize) Algorithm. Defense Technical Information Center, 1986. http://dx.doi.org/10.21236/ada208762.

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Rao, M. M. Spectral Analysis, Estimation, and Prediction of Multiple Harmonizable Time Series. Defense Technical Information Center, 1990. http://dx.doi.org/10.21236/ada266758.

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Bose, N. K. Multiple Target Tracking: Fast Algorithm for Data Association and State Estimation. Defense Technical Information Center, 1995. http://dx.doi.org/10.21236/ada300870.

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Engberg, John, Dennis Epple, Jason Imbrogno, Holger Sieg, and Ron Zimmer. Estimation of Causal Effects in Experiments with Multiple Sources of Noncompliance. National Bureau of Economic Research, 2009. http://dx.doi.org/10.3386/w14842.

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Cooper, Russell. Estimation and Identification of Structural Parameters in the Presence of Multiple Equilibria. National Bureau of Economic Research, 2002. http://dx.doi.org/10.3386/w8941.

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