Academic literature on the topic 'Modèle de covariance'
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Journal articles on the topic "Modèle de covariance":
LALOË, D. "La genèse et le développement des concepts de l’évaluation génétique classique." INRAE Productions Animales 24, no. 4 (September 8, 2011): 323–30. http://dx.doi.org/10.20870/productions-animales.2011.24.4.3264.
El Aabaribaoune, Mohammad, Emanuele Emili, and Vincent Guidard. "Estimation of the error covariance matrix for IASI radiances and its impact on the assimilation of ozone in a chemistry transport model." Atmospheric Measurement Techniques 14, no. 4 (April 13, 2021): 2841–56. http://dx.doi.org/10.5194/amt-14-2841-2021.
Valette-Florence, Rita, and Pierre Valette-Florence. "Effets des émotions et de la personnalité de la marque sur l’engagement du consommateur via les effets médiateurs de la confiance et de l’attachement à la marque." Recherche et Applications en Marketing (French Edition) 35, no. 1 (June 26, 2019): 87–116. http://dx.doi.org/10.1177/0767370119846075.
Autin, Claude, Jacques Fearnley, and Ronald Rioux. "Effets des erreurs dans les coefficients structuraux d’un modèle intersectoriel « rectangulaire ». Une approche de type Monte-Carlo." L'Actualité économique 51, no. 1 (July 14, 2009): 86–95. http://dx.doi.org/10.7202/800607ar.
Dao, Elizabeth, Cindy K. Barha, John R. Best, Ging-Yuek Hsiung, Roger Tam, and Teresa Liu-Ambrose. "The Effect of Aerobic Exercise on White Matter Hyperintensity Progression May Vary by Sex." Canadian Journal on Aging / La Revue canadienne du vieillissement 38, no. 02 (March 14, 2019): 236–44. http://dx.doi.org/10.1017/s0714980818000582.
Varga, Štefan. "Estimations of covariance components in mixed linear models." Mathematica Bohemica 121, no. 1 (1996): 29–33. http://dx.doi.org/10.21136/mb.1996.125947.
Quesada-Ruiz, Samuel, Jean-Luc Attié, William A. Lahoz, Rachid Abida, Philippe Ricaud, Laaziz El Amraoui, Régina Zbinden, et al. "Benefit of ozone observations from Sentinel-5P and future Sentinel-4 missions on tropospheric composition." Atmospheric Measurement Techniques 13, no. 1 (January 14, 2020): 131–52. http://dx.doi.org/10.5194/amt-13-131-2020.
Fonseca, Wéverton José Lima, Wéverson Lima Fonseca, Laylson da Silva Borges, Amauri Felipe Evangelista, Paulo Henrique Amaral Araújo de Sousa, Genilson Sousa do Nascimento, Carlandia Pacheco de Figueiredo, et al. "ESTIMATES COVARIANCE FUNCTIONS TO GOATS MILK PRODUCTION USING REGRESSION MODELS RANDOM." Nucleus Animalium 7, no. 2 (November 30, 2015): 83–92. http://dx.doi.org/10.3738/1982.2278.1498.
Adlouni, Salaheddine El, and Taha B. M. J. Ouarda. "Comparaison des méthodes d’estimation des paramètres du modèle GEV non stationnaire." Revue des sciences de l'eau 21, no. 1 (April 29, 2008): 35–50. http://dx.doi.org/10.7202/017929ar.
Fariña, Bibiana, Jonay Toledo, Jose Ignacio Estevez, and Leopoldo Acosta. "Improving Robot Localization Using Doppler-Based Variable Sensor Covariance Calculation." Sensors 20, no. 8 (April 17, 2020): 2287. http://dx.doi.org/10.3390/s20082287.
Dissertations / Theses on the topic "Modèle de covariance":
Perrin, Olivier. "Modèle de covariance d'un processus non-stationnaire par déformation de l'espace et statistique." Paris 1, 1997. http://www.theses.fr/1997PA010099.
Valeyre, Sébastien. "Modélisation fine de la matrice de covariance/corrélation des actions." Thesis, Sorbonne Paris Cité, 2019. https://tel.archives-ouvertes.fr/tel-03180258.
A new methodology has been introduced to clean the correlation matrix of single stocks returns based on a constrained principal component analysis using financial data. Portfolios were introduced, namely "Fundamental Maximum Variance Portfolios", to capture in an optimal way the risks defined by financial criteria ("Book", "Capitalization", etc.). The constrained eigenvectors of the correlation matrix, which are the linear combination of these portfolios, are then analyzed. Thanks to this methodology, several stylized patterns of the matrix were identified: i) the increase of the first eigenvalue with a time scale from 1 minute to several months seems to follow the same law for all the significant eigenvalues with 2 regimes; ii) a universal law seems to govern the weights of all the "Maximum variance" portfolios, so according to that law, the optimal weights should be proportional to the ranking based on the financial studied criteria; iii) the volatility of the volatility of the "Maximum Variance" portfolios, which are not orthogonal, could be enough to explain a large part of the diffusion of the correlation matrix; iv) the leverage effect (increase of the first eigenvalue with the decline of the stock market) occurs only for the first mode and cannot be generalized for other factors of risk. The leverage effect on the beta, which is the sensitivity of stocks with the market mode, makes variable theweights of the first eigenvector
Muñiz, Alvarez Lilian. "Estimation nonparamétrique de la structure de covariance des processus stochastiques." Toulouse 3, 2010. http://thesesups.ups-tlse.fr/1089/.
The main objective of this thesis is the development of nonparametric methods for estimating the covariance of a stochastic process. Assuming different conditions on the process, estimators of the covariance function are introduced, having the property of being non-negative definite functions. In addition, a method for estimating the covariance matrix of a stochastic process in a high dimensional setting is proposed. Our work is organized as follows: in Chapter 1 we give a general introduction, where we present briefly the concepts and definitions underlying our work. Then come three chapters, detailing the proposed new estimation methods. More precisely, in each chapter we have developed different nonparametric estimation techniques: function approximation by wavelet thresholding in Chapter 2, model selection in Chapter 3, and estimation by Group-Lasso penalization in Chapter 4. The theoretical behavior of the estimators is studied in all cases and its good practical performances are shown in some numerical examples
Spinnato, Juliette. "Modèles de covariance pour l'analyse et la classification de signaux électroencéphalogrammes." Thesis, Aix-Marseille, 2015. http://www.theses.fr/2015AIXM4727/document.
The present thesis finds itself within the framework of analyzing and classifying electroencephalogram signals (EEG) using discriminant analysis. Those multi-sensor signals which are, by nature, highly correlated spatially and temporally are considered, in this work, in the timefrequency domain. In particular, we focus on low-frequency evoked-related potential-type signals (ERPs) that are well described in the wavelet domain. Thereafter, we will consider signals represented by multi-scale coefficients and that have a matrix structure electrodes × coefficients. Moreover, EEG signals are seen as a mixture between the signal of interest that we want to extract and spontaneous activity (also called "background noise") which is overriding. The main problematic is here to distinguish signals from different experimental conditions (class). In the binary case, we focus on the probabilistic approach of the discriminant analysis and Gaussian mixtures are used, describing in each class the signals in terms of fixed (mean) and random components. The latter, characterized by its covariance matrix, allow to model different variability sources. The estimation of this matrix (and of its inverse) is essential for the implementation of the discriminant analysis and can be deteriorated by high-dimensional data and/or by small learning samples, which is the application framework of this thesis. We are interested in alternatives that are based on specific covariance model(s) and that allow to decrease the number of parameters to estimate
Ricci, Sophie. "Assimilation variationnelle océanique : modélisation multivariée de la matrice de covariance d'erreur d'ébauche." Toulouse 3, 2004. http://www.theses.fr/2004TOU30048.
Lai, Chantal. "Les déterminants de la performance sur le marché des extensions de marque : modèle explicatif et validation empirique sur des produits de grande consommation." Paris 1, 2000. http://www.theses.fr/2000PA010003.
Passemier, Damien. "Inférence statistique dans un modèle à variances isolées de grande dimension." Phd thesis, Université Rennes 1, 2012. http://tel.archives-ouvertes.fr/tel-00780492.
Bachoc, François. "Estimation paramétrique de la fonction de covariance dans le modèle de Krigeage par processus Gaussiens : application à la quantification des incertitudes en simulation numérique." Phd thesis, Université Paris-Diderot - Paris VII, 2013. http://tel.archives-ouvertes.fr/tel-00881002.
Bouayed, Mohamed Amine. "Modélisation stochastique par éléments finis en géomécanique." Vandoeuvre-les-Nancy, INPL, 1997. http://www.theses.fr/1997INPL087N.
Geraets, David. "Modélisation stochastique de champs de vitesse géophysique en exploration pétrolière." Phd thesis, École Nationale Supérieure des Mines de Paris, 2002. http://pastel.archives-ouvertes.fr/pastel-00001236.
Books on the topic "Modèle de covariance":
Anderson, Gordon. Alternative error covariance assumptions in dynamic panel data models. Toronto: Dept. of Economics and Institute for Policy Analysis, University of Toronto, 1988.
Michael, Baker. Growth rate heterogeneity and the covariance structure of life cycle earnings. Toronto: Dept. of Economics and Institute for Policy Analysis, University of Toronto, 1992.
Brandt, Michael W. A no-arbitrage approach to range-based estimation of return covariances and correlations. Cambridge, Mass: National Bureau of Economic Research, 2003.
Woodruff, David J. Linear models for item scores: Reliability, covariance structure, and psychometric inference. Iowa City, Iowa: American College Testing Program, 1993.
Gaver, Donald Paul. Bayesian prediction of mean square errors with covariates. Monterey, Calif: Naval Postgraduate School, 1992.
Polites, Michael E. The estimation error covariance matrix for the ideal state reconstructor with measurement noise. [Washington, D.C.]: National Aeronautics and Space Administration, Scientific and Technical Information Division, 1988.
Daniel, Kent. Covariance risk, mispricing, and the cross section of security returns. Cambridge, MA: National Bureau of Economic Research, 2000.
Khalaf, Lynda. Structural change in covariance and exchange rate pass-through: The case of Canada. Ottawa: Bank of Canada, 2006.
Lewis, Karen K. Should the holding period matter for the intertemporal consumption-based CAPM? Cambridge, MA: National Bureau of Economic Research, 1991.
Sengupta, Debasis. Linear models: An integrated approach. River Edge, N.J: World Scientific, 2003.
Book chapters on the topic "Modèle de covariance":
Shekhar, Shashi, and Hui Xiong. "Cross-Covariance Models." In Encyclopedia of GIS, 193. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_230.
Wan, Thomas T. H. "Covariance Structure Models." In Evidence-Based Health Care Management, 155–75. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4615-0795-6_9.
Brown, Jonathon D. "Analysis of Covariance." In Linear Models in Matrix Form, 443–67. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11734-8_13.
Westland, J. Christopher. "Full-Information Covariance SEM." In Structural Equation Models, 39–49. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-12508-0_3.
Deutsch, Hans-Peter. "The Variance-Covariance Method." In Derivatives and Internal Models, 391–418. London: Palgrave Macmillan UK, 2002. http://dx.doi.org/10.1057/9780230502109_22.
Deutsch, Hans-Peter. "The Variance-Covariance Method." In Derivatives and Internal Models, 397–425. London: Palgrave Macmillan UK, 2004. http://dx.doi.org/10.1057/9781403946089_22.
Deutsch, Hans-Peter. "The Variance — Covariance Method." In Derivatives and Internal Models, 430–61. London: Palgrave Macmillan UK, 2009. http://dx.doi.org/10.1057/9780230234758_20.
Deutsch, Hans-Peter, and Mark W. Beinker. "The Variance-Covariance Method." In Derivatives and Internal Models, 521–57. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-22899-6_22.
Madhyastha, N. R. Mohan, S. Ravi, and A. S. Praveena. "Analysis of Covariance." In A First Course in Linear Models and Design of Experiments, 165–81. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-8659-0_6.
Zimmerman, Dale L. "Inference for Variance–Covariance Parameters." In Linear Model Theory, 451–86. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-52063-2_16.
Conference papers on the topic "Modèle de covariance":
Caveney, Derek S., Yeonsik Kang, and J. Karl Hedrick. "Probabilistic Mapping for Unmanned Rotorcraft Using Point-Mass Targets and Quadtree Structures." In ASME 2005 International Mechanical Engineering Congress and Exposition. ASMEDC, 2005. http://dx.doi.org/10.1115/imece2005-82889.
Olsen, Peder A., Vaibhava Goel, and Steven J. Rennie. "Discriminative training for full covariance models." In ICASSP 2011 - 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2011. http://dx.doi.org/10.1109/icassp.2011.5947557.
KONING, A. J. "GENERATING COVARIANCE DATA WITH NUCLEAR MODELS." In Proceedings of the International Workshop. WORLD SCIENTIFIC, 2006. http://dx.doi.org/10.1142/9789812773401_0017.
Wiesel, Ami. "Regularized covariance estimation in scaled Gaussian models." In 2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP). IEEE, 2011. http://dx.doi.org/10.1109/camsap.2011.6136012.
Wiesel, Ami, and Alfred O. Hero. "Distributed covariance estimation in Gaussian graphical models." In 2010 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM). IEEE, 2010. http://dx.doi.org/10.1109/sam.2010.5606735.
Rolfs, Benjamin T., and Bala Rajaratnam. "Natural order recovery for banded covariance models." In 2012 IEEE 7th Sensor Array and Multichannel Signal Processing Workshop (SAM). IEEE, 2012. http://dx.doi.org/10.1109/sam.2012.6250512.
Yang, Liyan, and Jason Rife. "Estimating Covariance Models for Collaborative Integrity Monitoring." In 29th International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS+ 2016). Institute of Navigation, 2016. http://dx.doi.org/10.33012/2016.14757.
Smith, Scott F., and Kay C. Wiese. "Improved covariance model parameter estimation using RNA thermodynamic properties." In 2007 2nd Bio-Inspired Models of Network, Information and Computing Systems (BIONETICS). IEEE, 2007. http://dx.doi.org/10.1109/bimnics.2007.4610108.
Kontar, Raed, Shiyu Zhou, and John Horst. "Simulation Optimization for Computer Models With Multivariate Output." In ASME 2017 12th International Manufacturing Science and Engineering Conference collocated with the JSME/ASME 2017 6th International Conference on Materials and Processing. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/msec2017-2907.
Jin, Kaizhong, Xiang Cheng, Jiaxi Yang, and Kaiyuan Shen. "Differentially Private Correlation Alignment for Domain Adaptation." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/502.
Reports on the topic "Modèle de covariance":
Carroll, Raymond J. Covariance Analysis in Generalized Linear Measurement Error Models. Fort Belvoir, VA: Defense Technical Information Center, August 1988. http://dx.doi.org/10.21236/ada197661.
De Iaco, Sandra, Sabrina Maggio, Monica Palma, and Donato Posa. Space-time multivariate analysis based on anisotropic covariance models. Cogeo@oeaw-giscience, September 2011. http://dx.doi.org/10.5242/iamg.2011.0278.
Zhang, Yongping, Wen Cheng, and Xudong Jia. Enhancement of Multimodal Traffic Safety in High-Quality Transit Areas. Mineta Transportation Institute, February 2021. http://dx.doi.org/10.31979/mti.2021.1920.
Tanny, Josef, Gabriel Katul, Shabtai Cohen, and Meir Teitel. Application of Turbulent Transport Techniques for Quantifying Whole Canopy Evapotranspiration in Large Agricultural Structures: Measurement and Theory. United States Department of Agriculture, January 2011. http://dx.doi.org/10.32747/2011.7592121.bard.
Das, Rita, and Bimal K. Sinha. Robust Optimium Invariant Tests of Covariance Structures Useful in Linear Models. Fort Belvoir, VA: Defense Technical Information Center, August 1986. http://dx.doi.org/10.21236/ada174659.
Ito, Hiroshi, W. W. Buck, and F. Gross. Covariant quark model of pion structure. Office of Scientific and Technical Information (OSTI), January 1990. http://dx.doi.org/10.2172/6332887.
Smith, D. L. Covariance matrices for nuclear cross sections derived from nuclear model calculations. Office of Scientific and Technical Information (OSTI), January 2005. http://dx.doi.org/10.2172/838257.
Chan, Louis K. C., Jason Karceski, and Josef Lakonishok. On Portfolio Optimization: Forecasting Covariances and Choosing the Risk Model. Cambridge, MA: National Bureau of Economic Research, March 1999. http://dx.doi.org/10.3386/w7039.
Kelly, Bryan, Seth Pruitt, and Yinan Su. Characteristics Are Covariances: A Unified Model of Risk and Return. Cambridge, MA: National Bureau of Economic Research, April 2018. http://dx.doi.org/10.3386/w24540.
Boggs, Paul T., and Janet R. Donaldson. The computation and use of the asymptotic covariance matrix for measurement error models. Gaithersburg, MD: National Institute of Standards and Technology, 1989. http://dx.doi.org/10.6028/nist.ir.89-4102.