Littérature scientifique sur le sujet « Covariance matrice »
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Articles de revues sur le sujet "Covariance matrice"
Cappuccio, Nunzio, et Diego Lubian. « Ordering of Covariance Matrice ». Econometric Theory 12, no 4 (octobre 1996) : 746–48. http://dx.doi.org/10.1017/s0266466600007106.
Texte intégralSigaud, Olivier, et Freek Stulp. « Adaptation de la matrice de covariance pour l’apprentissage par renforcement direct ». Revue d'intelligence artificielle 27, no 2 (30 avril 2013) : 243–63. http://dx.doi.org/10.3166/ria.27.243-263.
Texte intégralFourdrinier, Dominique, William E. Strawderman et Martin T. Wells. « Estimation robuste pour des lois à symétrie elliptique à matrice de covariance inconnue ». Comptes Rendus de l'Académie des Sciences - Series I - Mathematics 326, no 9 (mai 1998) : 1135–40. http://dx.doi.org/10.1016/s0764-4442(98)80076-1.
Texte intégralAppourchaux, T., et L. Gizon. « The Art of Fitting P-Mode Spectra ». Symposium - International Astronomical Union 185 (1998) : 43–44. http://dx.doi.org/10.1017/s0074180900238230.
Texte intégralKhoder, Wassim. « Recalage de la navigation inertielle hybride par le filtrage de Kalman sans parfum paramétré à quaternions ». MATEC Web of Conferences 261 (2019) : 06003. http://dx.doi.org/10.1051/matecconf/201926106003.
Texte intégralMeyer, Karin, et Mark Kirkpatrick. « Up hill, down dale : quantitative genetics of curvaceous traits ». Philosophical Transactions of the Royal Society B : Biological Sciences 360, no 1459 (7 juillet 2005) : 1443–55. http://dx.doi.org/10.1098/rstb.2005.1681.
Texte intégralAlekseychik, Pavel, Gabriel Katul, Ilkka Korpela et Samuli Launiainen. « Eddies in motion : visualizing boundary-layer turbulence above an open boreal peatland using UAS thermal videos ». Atmospheric Measurement Techniques 14, no 5 (18 mai 2021) : 3501–21. http://dx.doi.org/10.5194/amt-14-3501-2021.
Texte intégralGonzalez-Ondina, Jose M., Lewis Sampson et Georgy I. Shapiro. « A Projection Method for the Estimation of Error Covariance Matrices for Variational Data Assimilation in Ocean Modelling ». Journal of Marine Science and Engineering 9, no 12 (20 décembre 2021) : 1461. http://dx.doi.org/10.3390/jmse9121461.
Texte intégralZhang, Peng, Wen Juan Qi et Zi Li Deng. « Covariance Intersection Fusion Kalman Estimator for Multi-Sensor System with Measurements Delays ». Applied Mechanics and Materials 475-476 (décembre 2013) : 460–65. http://dx.doi.org/10.4028/www.scientific.net/amm.475-476.460.
Texte intégralFang (方啸), Xiao, Tim Eifler et Elisabeth Krause. « 2D-FFTLog : efficient computation of real-space covariance matrices for galaxy clustering and weak lensing ». Monthly Notices of the Royal Astronomical Society 497, no 3 (17 juin 2020) : 2699–714. http://dx.doi.org/10.1093/mnras/staa1726.
Texte intégralThèses sur le sujet "Covariance matrice"
Valeyre, Sébastien. « Modélisation fine de la matrice de covariance/corrélation des actions ». Thesis, Sorbonne Paris Cité, 2019. https://tel.archives-ouvertes.fr/tel-03180258.
Texte intégralA 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
Zgheib, Rania. « Tests non paramétriques minimax pour de grandes matrices de covariance ». Thesis, Paris Est, 2016. http://www.theses.fr/2016PESC1078/document.
Texte intégralOur work contributes to the theory of non-parametric minimax tests for high dimensional covariance matrices. More precisely, we observe $n$ independent, identically distributed vectors of dimension $p$, $X_1,ldots, X_n$ having Gaussian distribution $mathcal{N}_p(0,Sigma)$, where $Sigma$ is the unknown covariance matrix. We test the null hypothesis $H_0 : Sigma =I$, where $I$ is the identity matrix. The alternative hypothesis is given by an ellipsoid from which a ball of radius $varphi$ centered in $I$ is removed. Asymptotically, $n$ and $p$ tend to infinity. The minimax test theory, other approaches considered for testing covariance matrices and a summary of our results are given in the introduction.The second chapter is devoted to the case of Toeplitz covariance matrices $Sigma$. The connection with the spectral density model is discussed. We consider two types of ellipsoids, describe by polynomial weights and exponential weights, respectively. We find the minimax separation rate in both cases. We establish the sharp asymptotic equivalents of the minimax type II error probability and the minimax total error probability. The asymptotically minimax test procedure is a U-statistic of order 2 weighted by an optimal way.The third chapter considers alternative hypothesis containing covariance matrices not necessarily Toeplitz, that belong to an ellipsoid of parameter $alpha$. We obtain the minimax separation rate and give sharp asymptotic equivalents of the minimax type II error probability and the minimax total error probability. We propose an adaptive test procedure free of $alpha$, for $alpha$ belonging to a compact of $(1/2, + infty)$.We implement the tests procedures given in the previous two chapters. The results show their good behavior for large values of $p$ and that, in particular, they gain significantly over existing methods for large $p$ and small $n$.The fourth chapter is dedicated to adaptive tests in the model of covariance matrices where the observations are incomplete. That is, each value of the observed vector is missing with probability $1-a$, $a in (0,1)$ and $a$ may tend to 0. We treat this problem as an inverse problem. We establish the minimax separation rates and introduce new adaptive test procedures. Here, the tests statistics are weighted by constant weights. We consider ellipsoids of Sobolev type, for both cases : Toeplitz and non Toeplitz matrices
Mahot, Mélanie. « Estimation robuste de la matrice de covariance en traitement du signal ». Phd thesis, École normale supérieure de Cachan - ENS Cachan, 2012. http://tel.archives-ouvertes.fr/tel-00906143.
Texte intégralRicci, Sophie. « Assimilation variationnelle océanique : modélisation multivariée de la matrice de covariance d'erreur d'ébauche ». Toulouse 3, 2004. http://www.theses.fr/2004TOU30048.
Texte intégralHaddouche, Mohamed Anis. « Estimation d'une matrice d'échelle ». Thesis, Normandie, 2019. http://www.theses.fr/2019NORMR058/document.
Texte intégralNumerous results on the estimation of a scale matrix in multivariate analysis are obtained under Gaussian assumption (condition under which it is the covariance matrix). However in such areas as Portfolio management in finance, this assumption is not well adapted. Thus, the family of elliptical symmetric distribution, which contains the Gaussian distribution, is an interesting alternative. In this thesis, we consider the problem of estimating the scale matrix _ of the additif model Yp_m = M + E, under theoretical decision point of view. Here, p is the number of variables, m is the number of observations, M is a matrix of unknown parameters with rank q < p and E is a random noise, whose distribution is elliptically symmetric with covariance matrix proportional to Im x Σ. It is more convenient to deal with the canonical forme of this model where Y is decomposed in two matrices, namely, Zq_p which summarizes the information contained in M, and Un_p, where n = m - q which summarizes the information sufficient to estimate Σ. As the natural estimators of the form ^Σ a = a S (where S = UT U and a is a positive constant) perform poorly when the dimension of variables p and the ratio p=n are large, we propose estimators of the form ^Σa;G = a(S + S S+G(Z; S)) where S+ is the Moore-Penrose inverse of S (which coincides with S-1 when S is invertible). We provide conditions on the correction matrix SS+G(Z; S) such that ^Σa;G improves over ^Σa under the quadratic loss L(Σ; ^Σ) = tr(^ΣΣ‾1 - Ip)² and under the data based loss LS (Σ; ^Σ) = tr(S+Σ(^ΣΣ‾1 - Ip)²).. We adopt a unified approach of the two cases where S is invertible and S is non-invertible. To this end, a new Stein-Haff type identity and calculus on eigenstructure for S are developed. Our theory is illustrated with the large class of orthogonally invariant estimators and with simulations
Ilea, Ioana. « Robust classifcation methods on the space of covariance matrices. : application to texture and polarimetric synthetic aperture radar image classification ». Thesis, Bordeaux, 2017. http://www.theses.fr/2017BORD0006/document.
Texte intégralIn the recent years, covariance matrices have demonstrated their interestin a wide variety of applications in signal and image processing. The workpresented in this thesis focuses on the use of covariance matrices as signatures forrobust classification. In this context, a robust classification workflow is proposed,resulting in the following contributions.First, robust covariance matrix estimators are used to reduce the impact of outlierobservations, during the estimation process. Second, the Riemannian Gaussianand Laplace distributions as well as their mixture model are considered to representthe observed covariance matrices. The k-means and expectation maximization algorithmsare then extended to the Riemannian case to estimate their parameters, thatare the mixture's weight, the central covariance matrix and the dispersion. Next,a new centroid estimator, called the Huber's centroid, is introduced based on thetheory of M-estimators. Further on, a new local descriptor named the RiemannianFisher vector is introduced to model non-stationary images. Moreover, a statisticalhypothesis test is introduced based on the geodesic distance to regulate the classification false alarm rate. In the end, the proposed methods are evaluated in thecontext of texture image classification, brain decoding, simulated and real PolSARimage classification
Hadded, Aouchiche Linda. « Localisation à haute résolution de cibles lentes et de petite taille à l’aide de radars de sol hautement ambigus ». Thesis, Rennes 1, 2018. http://www.theses.fr/2018REN1S008/document.
Texte intégralThe aim of this thesis is to enhance the detection of slow-moving targets with low reflectivity in case of ground-based pulse Doppler radars operating in intermediate pulse repetition frequency. These radars are highly ambiguous in range and Doppler. To resolve ambiguities, they transmit successively short pulse trains with different pulse repetition intervals. The transmission of short pulse trains results in a poor Doppler resolution. As consequence, slow-moving targets with low reflectivity, such as unmanned aerial vehicles, are buried into clutter returns. One of the main drawbacks of the classical Doppler processing of intermediate pulse repetition frequency pulse Doppler radars is the low detection performance of small and slowly-moving targets after ground clutter rejection. In order to address this problem, a two-dimensional range / Dopper processing chain including new techniques is proposed in this thesis. First, an iterative algorithm allows to exploit transmitted pulse trains temporal diversity to resolve range and Doppler ambiguities and detect fast, exo-clutter, targets. The detection of slow, endo-clutter, targets is then performed by an adaptive detection scheme. It uses a new covariance matrix estimation approach allowing the association of pulse trains with different characteristics in order to enhance detection performance. The different tests performed on simulated and real data to evaluate the proposed techniques and the new processing chain have shown their effectiveness
Balvay, Daniel. « Qualité de la modélisation en imagerie dynamique de la microcirculation avec injection d'un agent de contraste : nouveaux critères et applications en multimodalité ». Paris 11, 2005. http://www.theses.fr/2005PA112147.
Texte intégralThe microcirculation dynamic imaging could be a relevant imaging when used in addition with more conventional medical imaging. The dynamic data are modeled, pixel by pixel, to provide microcirculation parameters maps. However there is no efficient tool to assess the modeling quality. The relevance of the parametric maps provided by the dynamic imaging is then limited. Here, we show that a qualitative and quantitative study of the modeling quality needs first to distinguish two questions : the quality of the data fits and the robusness for the random noise. To separate the questions, we designed a new autocorrelation based method which is able to estimate the amplitude of both the correlated and not correlated component of a signal. This method allowed us to correct the correlation coefficient R² and the covariance matrix estimation. It allowed us to define new reliability criteria and a corrected covariance matrix to replace the more conventional indicators. It was shown, on simulated data and in MR data, that new reliabily criteria are obviously better than the R² to assess fit quality. The corrected covariance matrix which assess the robustness and the redoundancy can be calculated in addition to the reliability criteria unlike conventional one which is limited to good data fits. Thus the modeling quality is obviously improved by the new indicators. It should improve the clinical use of microcirculation dynamic imaging where guaranties are needed against artefact. The interest of the new criteria is showed on many different dynamic data. More generaly the new indicators appear as new efficient tools for signal analysis
Yanou, Ghislain. « Une étude théorique et empirique des estimateurs de la matrice de variance-covariance pour le choix de portefeuilles ». Paris 1, 2010. http://www.theses.fr/2010PA010044.
Texte intégralScotta, Juan Pablo. « Amélioration des données neutroniques de diffusion thermique et épithermique pour l'interprétation des mesures intégrales ». Thesis, Aix-Marseille, 2017. http://www.theses.fr/2017AIXM0213.
Texte intégralIn the present report it was studied the neutron thermal scattering of light water for reactors application. The thermal scattering law model of hydrogen bounded to the water molecule of the JEFF-3.1.1 nuclear data library is based on experimental measures performed in the sixties. The scattering physics of this latter was compared with a model based on molecular dynamics calculations developed at the Atomic Center in Bariloche (Argentina), namely the CAB model. The impact of these models was evaluated as well on reactor calculations at cold conditions. The selected benchmark was the MISTRAL program (UOX and MOX configurations), carried out in the zero power reactor EOLE of CEA Cadarache (France). The contribution of the neutron thermal scattering of hydrogen in water was quantified in terms of the difference in the calculated reactivity and the calculation error on the isothermal reactivity temperature coefficient (RTC). For the UOX lattice, the calculated reactivity with the CAB model at 20 °C is +90 pcm larger than JEFF-3.1.1, while for the MOX lattice is +170 pcm because of the high sensitivity of thermal scattering to this type of fuels. In the temperature range from 10 °C to 80 °C, the calculation error on the RTC is -0.27 ± 0.3 pcm/°C and +0.05 ± 0.3 pcm/°C obtained with JEFF-3.1.1 and the CAB model respectively (UOX lattice). For the MOX lattice, is -0.98 ± 0.3 pcm/°C and -0.72 ± 0.3 pcm/°C obtained with the JEFF-3.1.1 library and with the CAB model respectively. The results illustrate the improvement of the CAB model in the calculation of this safety parameter
Livres sur le sujet "Covariance matrice"
Tsukuma, Hisayuki, et Tatsuya Kubokawa. Shrinkage Estimation for Mean and Covariance Matrices. Singapore : Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1596-5.
Texte intégralJong, Robert M. de. Consistency of kernel estimators of heteroscedastic and autocorrelated covariance matrices. Cardiff : Cardiff Business School, 1996.
Trouver le texte intégralSrivastava, M. S. Classification with a preassigned error rate when two covariance matrices are equal. Toronto : University of Toronto, Dept. of Statistics, 1998.
Trouver le texte intégralWoodruff, David. A note on a relationship between covariance matrices and consistently estimated variance components. Iowa City, Iowa : American College Testing Program, 1995.
Trouver le texte intégralU.S. Nuclear Regulatory Commission. Division of Systems Analysis and Regulatory Effectiveness. et Oak Ridge National Laboratory, dir. PUFF-III : A code for processing ENDF uncertainty data into multigroup covariance matrices. Washington, DC : U.S. Nuclear Regulatory Commission, Office of Nuclear Regulatory Research, 2000.
Trouver le texte intégralU.S. Nuclear Regulatory Commission. Division of Systems Analysis and Regulatory Effectiveness. et Oak Ridge National Laboratory, dir. PUFF-III : A code for processing ENDF uncertainty data into multigroup covariance matrices. Washington, DC : U.S. Nuclear Regulatory Commission, Office of Nuclear Regulatory Research, 2000.
Trouver le texte intégralKubokawa, T. Robust improvements in estimation of mean and covariance matrices in elliptically contoured distribution. Toronto : University of Toronto, Dept. of Statistics, 1997.
Trouver le texte intégralPynnönen, Seppo. Testing for additional information in variables in multivariate normal classification with unequal covariance matrices. Vaasa : Universitas Wasaensis, 1988.
Trouver le texte intégralBose, Arup, et Monika Bhattacharjee. Large Covariance and Autocovariance Matrices. Taylor & Francis Group, 2018.
Trouver le texte intégralBose, Arup. Large Covariance and Autocovariance Matrices. Chapman and Hall/CRC, 2018. http://dx.doi.org/10.1201/9780203730652.
Texte intégralChapitres de livres sur le sujet "Covariance matrice"
Wackernagel, Hans. « Covariance Function Matrices ». Dans Multivariate Geostatistics, 151–53. Berlin, Heidelberg : Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-662-05294-5_21.
Texte intégralWackernagel, Hans. « Covariance Function Matrices ». Dans Multivariate Geostatistics, 152–54. Berlin, Heidelberg : Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-662-03550-4_22.
Texte intégralWackernagel, Hans. « Covariance Function Matrices ». Dans Multivariate Geostatistics, 137–39. Berlin, Heidelberg : Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/978-3-662-03098-1_21.
Texte intégralShultz, Thomas R., Scott E. Fahlman, Susan Craw, Periklis Andritsos, Panayiotis Tsaparas, Ricardo Silva, Chris Drummond et al. « Covariance Matrix ». Dans Encyclopedia of Machine Learning, 235–38. Boston, MA : Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_183.
Texte intégralZhang, Xinhua. « Covariance Matrix ». Dans Encyclopedia of Machine Learning and Data Mining, 1–4. Boston, MA : Springer US, 2016. http://dx.doi.org/10.1007/978-1-4899-7502-7_57-1.
Texte intégralZhang, Xinhua. « Covariance Matrix ». Dans Encyclopedia of Machine Learning and Data Mining, 290–93. Boston, MA : Springer US, 2017. http://dx.doi.org/10.1007/978-1-4899-7687-1_57.
Texte intégralKramer, Oliver. « Covariance Matrix Estimation ». Dans Studies in Big Data, 23–32. Cham : Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-33383-0_3.
Texte intégralBrown, Jonathon D. « Analysis of Covariance ». Dans Linear Models in Matrix Form, 443–67. Cham : Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11734-8_13.
Texte intégralSheppard, Kevin. « Forecasting High Dimensional Covariance Matrices ». Dans Handbook of Volatility Models and Their Applications, 103–25. Hoboken, NJ, USA : John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781118272039.ch4.
Texte intégralKarnel, G. « Interactively Computing Robust Covariance Matrices ». Dans Compstat, 199–204. Heidelberg : Physica-Verlag HD, 1990. http://dx.doi.org/10.1007/978-3-642-50096-1_31.
Texte intégralActes de conférences sur le sujet "Covariance matrice"
Parker Burg, John, et Gary Mavko. « Structured covariance matrices ». Dans SEG Technical Program Expanded Abstracts 1987. Society of Exploration Geophysicists, 1987. http://dx.doi.org/10.1190/1.1892039.
Texte intégralTao, Shaozhe, Yifan Sun et Daniel Boley. « Inverse Covariance Estimation with Structured Groups ». Dans Twenty-Sixth International Joint Conference on Artificial Intelligence. California : International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/395.
Texte intégralWhite, Andrew, Guoming Zhu et Jongeun Choi. « A Linear Matrix Inequality Solution to the Input Covariance Constraint Control Problem ». Dans ASME 2013 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/dscc2013-3716.
Texte intégralGurve, Dharmendra, Denis Delisle-Rodriguez, Teodiano Bastos et Sridhar Krishnan. « Motor Imagery Classification with Covariance Matrices and Non-Negative Matrix Factorization ». Dans 2019 41st Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, 2019. http://dx.doi.org/10.1109/embc.2019.8856677.
Texte intégralBen-David, Avishai, et Charles E. Davidson. « Estimation of hyperspectral covariance matrices ». Dans 2011 IEEE Applied Imagery Pattern Recognition Workshop : Imaging for Decision Making (AIPR 2011). IEEE, 2011. http://dx.doi.org/10.1109/aipr.2011.6176368.
Texte intégralBen-David, Avishai, et Charles E. Davidson. « Estimation of hyperspectral covariance matrices ». Dans IGARSS 2011 - 2011 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2011. http://dx.doi.org/10.1109/igarss.2011.6050188.
Texte intégralAl-Jiboory, Ali Khudhair, Guoming Zhu et Cornel Sultan. « LMI Control Design With Input Covariance Constraint for a Tensegrity Simplex Structure ». Dans ASME 2014 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/dscc2014-6122.
Texte intégralAbu Husain, Nurulakmar, Hamed Haddad Khodaparast, John E. Mottershead et Huajiang Ouyang. « Application of the Perturbation Method With Parameter Weighting Matrix Assignments for Estimating Variability in a Set of Nominally Identical Welded Structures ». Dans ASME 2010 10th Biennial Conference on Engineering Systems Design and Analysis. ASMEDC, 2010. http://dx.doi.org/10.1115/esda2010-24272.
Texte intégralHanson, Kenneth M. « Probing the covariance matrix ». Dans Bayesian Inference and Maximum Entropy Methods In Science and Engineering. AIP, 2006. http://dx.doi.org/10.1063/1.2423282.
Texte intégralSholihat, Seli Siti, Sapto Wahyu Indratno et Utriweni Mukhaiyar. « Online Inverse Covariance Matrix ». Dans the 2019 International Conference. New York, New York, USA : ACM Press, 2019. http://dx.doi.org/10.1145/3348400.3348405.
Texte intégralRapports d'organisations sur le sujet "Covariance matrice"
McKnight, Richard D., et Karl N. Grimm. Covariance Matrix Generation at ANL. Office of Scientific and Technical Information (OSTI), décembre 2012. http://dx.doi.org/10.2172/1114909.
Texte intégralMcKnight, Richard D., et Karl N. Grimm. ANL Critical Assembly Covariance Matrix Generation. Office of Scientific and Technical Information (OSTI), janvier 2014. http://dx.doi.org/10.2172/1114907.
Texte intégralDerrien, H., N. M. Larson et L. C. Leal. Covariance matrices for use in criticality safety predictability studies. Office of Scientific and Technical Information (OSTI), septembre 1997. http://dx.doi.org/10.2172/631237.
Texte intégralBryan, M. F., G. F. Piepel et D. B. Simpson. Methods for estimation of covariance matrices and covariance components for the Hanford Waste Vitrification Plant Process. Office of Scientific and Technical Information (OSTI), mars 1996. http://dx.doi.org/10.2172/215713.
Texte intégralMcKnight, Richard D., et Karl N. Grimm. ANL Critical Assembly Covariance Matrix Generation - Addendum. Office of Scientific and Technical Information (OSTI), janvier 2014. http://dx.doi.org/10.2172/1114908.
Texte intégralWest, Kenneth, et Whitney Newey. Automatic Lag Selection in Covariance Matrix Estimation. Cambridge, MA : National Bureau of Economic Research, février 1995. http://dx.doi.org/10.3386/t0144.
Texte intégralWest, Kenneth. Another Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimator. Cambridge, MA : National Bureau of Economic Research, juillet 1995. http://dx.doi.org/10.3386/t0183.
Texte intégralHaan, Wouter J. Den, et Andrew Levin. A Practitioner's Guide to Robust Covariance Matrix Estimation. Cambridge, MA : National Bureau of Economic Research, juin 1996. http://dx.doi.org/10.3386/t0197.
Texte intégralDerrien, Herve, Luiz C. Leal et Nancy M. Larson. Neutron Resonance Parameters and Covariance Matrix of 239Pu. Office of Scientific and Technical Information (OSTI), août 2008. http://dx.doi.org/10.2172/969958.
Texte intégralSmith, D. L. Covariance matrices for nuclear cross sections derived from nuclear model calculations. Office of Scientific and Technical Information (OSTI), janvier 2005. http://dx.doi.org/10.2172/838257.
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