Letteratura scientifica selezionata sul tema "Estimation multiple de moyennes"
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Articoli di riviste sul tema "Estimation multiple de moyennes"
Galéa, G., e 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, n. 3 (12 aprile 2005): 331–52. http://dx.doi.org/10.7202/705562ar.
Testo completoTurbillon, Céline, Denis Bosq, Jean-Marie Marion e Besnik Pumo. "Estimation du paramètre des moyennes mobiles hilbertiennes". Comptes Rendus Mathematique 346, n. 5-6 (marzo 2008): 347–50. http://dx.doi.org/10.1016/j.crma.2008.01.008.
Testo completoFerrieux, Dominique. "Estimation à noyau de densités moyennes de mesures aléatoires associées". Comptes Rendus de l'Académie des Sciences - Series I - Mathematics 326, n. 9 (maggio 1998): 1131–34. http://dx.doi.org/10.1016/s0764-4442(98)80075-x.
Testo completoBENALAYA, A., H. SEBEI, F. DE TROCH, P. TROCH e N. ENNABLI. "Estimation des périodicités et de la tendance des températures moyennes mensuelles en Tunisie". Hydrological Sciences Journal 39, n. 6 (dicembre 1994): 593–603. http://dx.doi.org/10.1080/02626669409492782.
Testo completoAssayag, Jackie. "En quête de classe moyenne en inde. Grandeur, recomposition, forfaiture". Annales. Histoire, Sciences Sociales 55, n. 6 (dicembre 2000): 1229–53. http://dx.doi.org/10.3406/ahess.2000.279913.
Testo completoLantri, Fodhil, Nour El Islam Bachari e Ahmed Hafid Belbachir. "Estimation et cartographie des différentes composantes de rayonnement solaire au sol à partir des données météorologiques". Journal of Renewable Energies 20, n. 1 (12 ottobre 2023): 111–30. http://dx.doi.org/10.54966/jreen.v20i1.614.
Testo completoCherkassky, V., e Y. Ma. "Multiple Model Regression Estimation". IEEE Transactions on Neural Networks 16, n. 4 (luglio 2005): 785–98. http://dx.doi.org/10.1109/tnn.2005.849836.
Testo completoGeorge, Edward I. "Minimax Multiple Shrinkage Estimation". Annals of Statistics 14, n. 1 (marzo 1986): 188–205. http://dx.doi.org/10.1214/aos/1176349849.
Testo completoBich, Walter. "Estimation in multiple measurements". Accreditation and Quality Assurance 14, n. 7 (26 maggio 2009): 389–92. http://dx.doi.org/10.1007/s00769-009-0537-4.
Testo completoGarba, Issa, Zakari Seybou Abdourahamane, Abdou Amadou Sanoussi e 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, n. 33 (30 novembre 2023): 52. http://dx.doi.org/10.19044/esj.2023.v19n33p52.
Testo completoTesi sul tema "Estimation multiple de moyennes"
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.
Testo completoIn 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
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.
Testo completoFerrieux, Dominique. "Estimation de densités de mesures moyennes de processus ponctuels associés". Montpellier 2, 1996. http://www.theses.fr/1996MON20245.
Testo completoWiklund, 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.
Testo completoSensor 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.
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.
Testo completoHow 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.
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.
Testo completoThis 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.
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.
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.
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.
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.
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.
Testo completoPLAKSIENKO, ANNA. "Joint estimation of multiple graphical models". Doctoral thesis, Gran Sasso Science Institute, 2021. http://hdl.handle.net/20.500.12571/21632.
Testo completoLee, Joonsung. "Acoustic signal estimation using multiple blind observations". Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/35603.
Testo completoIncludes bibliographical references (p. 109-111).
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.
(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.
by Joonsung Lee.
S.M.
De, Melo F. E. "Multiple-object estimation techniques for challenging scenarios". Thesis, University of Liverpool, 2017. http://livrepository.liverpool.ac.uk/3013627/.
Testo completoLibri sul tema "Estimation multiple de moyennes"
Weinstein, Ehud. Multiple source location estimation using the EM algorithm. Woods Hole, Mass: Woods Hole Oceanographic Institution, 1986.
Cerca il testo completoFeder, Meir. Optimal multiple source location via the EM algorithm. Woods Hole, Mass: Woods Hole Oceanographic Institution, 1986.
Cerca il testo completoPalaszewski, Bo. On multiple test procedures for finding deviating parameters. Göteborg: University of Göteborg, 1993.
Cerca il testo completoMajor, Péter. On the Estimation of Multiple Random Integrals and U-Statistics. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-37617-7.
Testo completoBlech, Richard A. Parallel Gaussian estimation of a block tridiagonal matrix using multiple microcomputers. Cleveland, Ohio: Lewis Research Center, 1989.
Cerca il testo completoSchreuder, Hans T. Data estimation and prediction for natural resources public data. [Fort Collins, Colo.?]: U.S. Dept. of Agriculture, Forest Service, Rocky Mountain Research Station, 1998.
Cerca il testo completoM, Reich Robin, e Rocky Mountain Research Station (Fort Collins, Colo.), a cura di. Data estimation and prediction for natural resources public data. [Fort Collins, Colo.?]: U.S. Dept. of Agriculture, Forest Service, Rocky Mountain Research Station, 1998.
Cerca il testo completoW, Cooper Russell. Estimation and identification of structural parameters in the presence of multiple equilibria. Cambridge, MA: National Bureau of Economic Research, 2002.
Cerca il testo completoPrins, Robert Dean. Effective dose estimation for U.S. Army soldiers undergoing multiple computed tomography scans. [New York, N.Y.?]: [publisher not identified], 2011.
Cerca il testo completoOhya, Jun. Analyzing video sequences of multiple humans: Tracking, posture estimation and behavior recognition. Boston, MA: Springer, 2002.
Cerca il testo completoCapitoli di libri sul tema "Estimation multiple de moyennes"
Jia, Bin, e Ming Xin. "Multiple Sensor Estimation". In Grid-based Nonlinear Estimation and Its Applications, 133–66. Boca Raton, FL : CRC Press, Taylor & Francis Group, 2019. | “A science publishers book.”: CRC Press, 2019. http://dx.doi.org/10.1201/9781315193212-6.
Testo completoRosenblatt, Jonathan D. "Prevalence Estimation". In Handbook of Multiple Comparisons, 183–210. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9780429030888-8.
Testo completoChadli, Mohammed, Pierre Borne e Bernard Dubuisson. "Multiple Model State Estimation". In Multiple Models Approach in Automation, 65–98. Hoboken, NJ USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118577325.ch3.
Testo completoPillai, S. Uṇṇikrishṇa, e C. S. Burrus. "Estimation of Multiple Signals". In Signal Processing and Digital Filtering, 183–218. New York, NY: Springer New York, 1989. http://dx.doi.org/10.1007/978-1-4612-3632-0_4.
Testo completoBuckley, James J. "Estimation in Multiple Regression". In Fuzzy Statistics, 123–28. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-39919-3_25.
Testo completoOhya, Jun. "Posture Estimation". In Analyzing Video Sequences of Multiple Humans, 43–98. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4615-1003-1_3.
Testo completoDeBlasio, Dan, e John Kececioglu. "Alignment Accuracy Estimation". In Parameter Advising for Multiple Sequence Alignment, 19–27. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-64918-4_2.
Testo completoLütkepohl, Helmut. "Estimation of VARMA Models". In Introduction to Multiple Time Series Analysis, 241–83. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-662-02691-5_7.
Testo completoLütkepohl, Helmut. "Estimation of VARMA Models". In Introduction to Multiple Time Series Analysis, 241–83. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-642-61695-2_7.
Testo completoLütkepohl, Helmut. "Estimation of VARMA Models". In New Introduction to Multiple Time Series Analysis, 447–92. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-27752-1_12.
Testo completoAtti di convegni sul tema "Estimation multiple de moyennes"
Bucy, R. S., e 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.
Testo completoVershuur, Dirk J., e 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.
Testo completoHafezi, Sina, Alastair H. Moore e 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.
Testo completoRagab, M. E., e 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.
Testo completoBaar, Tamas, Bence Beke, Peter Bauer, Balint Vanek e Jozsef Bokor. "Smoothed multiple model adaptive estimation". In 2016 European Control Conference (ECC). IEEE, 2016. http://dx.doi.org/10.1109/ecc.2016.7810442.
Testo completoHui Chen e 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.
Testo completoLiu, Pu, Donald R. Brown, Edward A. Clancy, Francois Martel e 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.
Testo completoMarcos 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.
Testo completoThompson, Peter, e Frederick Anderson. "Rotational rate estimation using multiple accelerometers". In Atmospheric Flight Mechanics Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2000. http://dx.doi.org/10.2514/6.2000-4192.
Testo completoCui, Shuguang, Jinjun Xiao, Andrea Goldsmith, Zhi-quan Luo e 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.
Testo completoRapporti di organizzazioni sul tema "Estimation multiple de moyennes"
Li, Ta-Hsin, e Benjamin Kedem. Estimation of Multiple Sinusoids by Parametric Filtering. Fort Belvoir, VA: Defense Technical Information Center, gennaio 1992. http://dx.doi.org/10.21236/ada454954.
Testo completoRabi, Maben, John S. Baras e George Moustakides. Multiple Sampling for Estimation on a Finite Horizon. Fort Belvoir, VA: Defense Technical Information Center, gennaio 2006. http://dx.doi.org/10.21236/ada446968.
Testo completoLake, Douglas. Efficient Maximum Likelihood Estimation for Multiple and Coupled Harmonics. Fort Belvoir, VA: Defense Technical Information Center, dicembre 1999. http://dx.doi.org/10.21236/ada372834.
Testo completoShumway, Robert H., e Sung-Eun Kim. Signal Detection and Estimation of Directional Parameters for Multiple Arrays. Fort Belvoir, VA: Defense Technical Information Center, agosto 2001. http://dx.doi.org/10.21236/ada400949.
Testo completoDELAURENTIS, JOHN M., e ARMIN W. DOERRY. Stereoscopic Height Estimation from Multiple Aspect Synthetic Aperture Radar Images. Office of Scientific and Technical Information (OSTI), agosto 2001. http://dx.doi.org/10.2172/786639.
Testo completoWeinstein, Ehud, e Meir Feder. Multiple Source Location Estimation Using the EM (Estimate-Maximize) Algorithm. Fort Belvoir, VA: Defense Technical Information Center, luglio 1986. http://dx.doi.org/10.21236/ada208762.
Testo completoRao, M. M. Spectral Analysis, Estimation, and Prediction of Multiple Harmonizable Time Series. Fort Belvoir, VA: Defense Technical Information Center, agosto 1990. http://dx.doi.org/10.21236/ada266758.
Testo completoBose, N. K. Multiple Target Tracking: Fast Algorithm for Data Association and State Estimation. Fort Belvoir, VA: Defense Technical Information Center, febbraio 1995. http://dx.doi.org/10.21236/ada300870.
Testo completoEngberg, John, Dennis Epple, Jason Imbrogno, Holger Sieg e Ron Zimmer. Estimation of Causal Effects in Experiments with Multiple Sources of Noncompliance. Cambridge, MA: National Bureau of Economic Research, aprile 2009. http://dx.doi.org/10.3386/w14842.
Testo completoCooper, Russell. Estimation and Identification of Structural Parameters in the Presence of Multiple Equilibria. Cambridge, MA: National Bureau of Economic Research, maggio 2002. http://dx.doi.org/10.3386/w8941.
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