Relevant bibliographies by topics / Estimation multiple de moyennes

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Estimation multiple de moyennes'

Author: Grafiati
Published: 26 October 2024
Last updated: 31 July 2025

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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 français
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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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Amiot, Louis, Vincent Dubreuil, and Valérie Bonnardot. "Le changement climatique en Bretagne : cartographie sur la période observée (1951-2020) et future (2031-2100)." Norois 266, no. 1 (2023): 7–25. https://doi.org/10.3917/nor.266.0007.

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Des cartes climatiques sont réalisées sur la Bretagne pour différents horizons temporels à une résolution spatiale de 1 km à partir d’une méthode de spatialisation géostatistique par régression multiple. Ces cartes montrent l’évolution du climat à plusieurs horizons temporels futurs selon différents scénarios d’émission de gaz à effet de serre. Avec une hausse de 0,9 à 1,1 °C, les températures moyennes annuelles actuelles du nord de la Bretagne correspondent à celles enregistrées dans le sud de la Bretagne dans les années 1950. En fin de siècle, avec une hausse de 1,1 à 3,3 °C (entre les pério
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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 o
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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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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 dimen
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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 clo
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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 me
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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 t
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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 quantitat
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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 o
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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. Net
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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 algo
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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 pro
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Books on the topic "Estimation multiple de moyennes"

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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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Lei, Ming. "Multiple Model Estimation." In Optimal Estimation and Information Fusion: Theory and Algorithms. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-3173-5_10.

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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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Conference papers on the topic "Estimation multiple de moyennes"

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Salaün, Corentin, Xingchang Huang, Iliyan Georgiev, Niloy Mitra, and Gurprit Singh. "Multiple Importance Sampling for Stochastic Gradient Estimation." In 14th International Conference on Pattern Recognition Applications and Methods. SCITEPRESS - Science and Technology Publications, 2025. https://doi.org/10.5220/0013311200003905.

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Cui, Tao, Peng Dong, Zhongliang Jing, and Kai Shen. "Multiple-vehicle distributed weighted consensus state estimation." In 2025 Joint International Conference on Automation-Intelligence-Safety (ICAIS) & International Symposium on Autonomous Systems (ISAS). IEEE, 2025. https://doi.org/10.1109/icaisisas64483.2025.11051817.

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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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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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