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Статті в журналах з теми "Covariance matrice"
Cappuccio, Nunzio, and Diego Lubian. "Ordering of Covariance Matrice." Econometric Theory 12, no. 4 (October 1996): 746–48. http://dx.doi.org/10.1017/s0266466600007106.
Повний текст джерелаSigaud, Olivier, and Freek Stulp. "Adaptation de la matrice de covariance pour l’apprentissage par renforcement direct." Revue d'intelligence artificielle 27, no. 2 (April 30, 2013): 243–63. http://dx.doi.org/10.3166/ria.27.243-263.
Повний текст джерелаFourdrinier, Dominique, William E. Strawderman, and 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 (May 1998): 1135–40. http://dx.doi.org/10.1016/s0764-4442(98)80076-1.
Повний текст джерелаAppourchaux, T., and L. Gizon. "The Art of Fitting P-Mode Spectra." Symposium - International Astronomical Union 185 (1998): 43–44. http://dx.doi.org/10.1017/s0074180900238230.
Повний текст джерелаKhoder, 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.
Повний текст джерелаMeyer, Karin, and Mark Kirkpatrick. "Up hill, down dale: quantitative genetics of curvaceous traits." Philosophical Transactions of the Royal Society B: Biological Sciences 360, no. 1459 (July 7, 2005): 1443–55. http://dx.doi.org/10.1098/rstb.2005.1681.
Повний текст джерелаAlekseychik, Pavel, Gabriel Katul, Ilkka Korpela, and Samuli Launiainen. "Eddies in motion: visualizing boundary-layer turbulence above an open boreal peatland using UAS thermal videos." Atmospheric Measurement Techniques 14, no. 5 (May 18, 2021): 3501–21. http://dx.doi.org/10.5194/amt-14-3501-2021.
Повний текст джерелаGonzalez-Ondina, Jose M., Lewis Sampson, and 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 (December 20, 2021): 1461. http://dx.doi.org/10.3390/jmse9121461.
Повний текст джерелаZhang, Peng, Wen Juan Qi, and Zi Li Deng. "Covariance Intersection Fusion Kalman Estimator for Multi-Sensor System with Measurements Delays." Applied Mechanics and Materials 475-476 (December 2013): 460–65. http://dx.doi.org/10.4028/www.scientific.net/amm.475-476.460.
Повний текст джерелаFang (方啸), Xiao, Tim Eifler, and 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 (June 17, 2020): 2699–714. http://dx.doi.org/10.1093/mnras/staa1726.
Повний текст джерелаДисертації з теми "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.
Повний текст джерелаA 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.
Повний текст джерелаOur 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.
Повний текст джерелаRicci, Sophie. "Assimilation variationnelle océanique : modélisation multivariée de la matrice de covariance d'erreur d'ébauche." Toulouse 3, 2004. http://www.theses.fr/2004TOU30048.
Повний текст джерелаHaddouche, Mohamed Anis. "Estimation d'une matrice d'échelle." Thesis, Normandie, 2019. http://www.theses.fr/2019NORMR058/document.
Повний текст джерелаNumerous 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.
Повний текст джерелаIn 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.
Повний текст джерелаThe 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.
Повний текст джерелаThe 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.
Повний текст джерелаScotta, 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.
Повний текст джерелаIn 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
Книги з теми "Covariance matrice"
Tsukuma, Hisayuki, and Tatsuya Kubokawa. Shrinkage Estimation for Mean and Covariance Matrices. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1596-5.
Повний текст джерелаJong, Robert M. de. Consistency of kernel estimators of heteroscedastic and autocorrelated covariance matrices. Cardiff: Cardiff Business School, 1996.
Знайти повний текст джерелаSrivastava, M. S. Classification with a preassigned error rate when two covariance matrices are equal. Toronto: University of Toronto, Dept. of Statistics, 1998.
Знайти повний текст джерелаWoodruff, David. A note on a relationship between covariance matrices and consistently estimated variance components. Iowa City, Iowa: American College Testing Program, 1995.
Знайти повний текст джерелаU.S. Nuclear Regulatory Commission. Division of Systems Analysis and Regulatory Effectiveness. and Oak Ridge National Laboratory, eds. 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.
Знайти повний текст джерелаU.S. Nuclear Regulatory Commission. Division of Systems Analysis and Regulatory Effectiveness. and Oak Ridge National Laboratory, eds. 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.
Знайти повний текст джерелаKubokawa, T. Robust improvements in estimation of mean and covariance matrices in elliptically contoured distribution. Toronto: University of Toronto, Dept. of Statistics, 1997.
Знайти повний текст джерелаPynnönen, Seppo. Testing for additional information in variables in multivariate normal classification with unequal covariance matrices. Vaasa: Universitas Wasaensis, 1988.
Знайти повний текст джерелаBose, Arup, and Monika Bhattacharjee. Large Covariance and Autocovariance Matrices. Taylor & Francis Group, 2018.
Знайти повний текст джерелаBose, Arup. Large Covariance and Autocovariance Matrices. Chapman and Hall/CRC, 2018. http://dx.doi.org/10.1201/9780203730652.
Повний текст джерелаЧастини книг з теми "Covariance matrice"
Wackernagel, Hans. "Covariance Function Matrices." In Multivariate Geostatistics, 151–53. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-662-05294-5_21.
Повний текст джерелаWackernagel, Hans. "Covariance Function Matrices." In Multivariate Geostatistics, 152–54. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-662-03550-4_22.
Повний текст джерелаWackernagel, Hans. "Covariance Function Matrices." In Multivariate Geostatistics, 137–39. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/978-3-662-03098-1_21.
Повний текст джерелаShultz, Thomas R., Scott E. Fahlman, Susan Craw, Periklis Andritsos, Panayiotis Tsaparas, Ricardo Silva, Chris Drummond, et al. "Covariance Matrix." In Encyclopedia of Machine Learning, 235–38. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_183.
Повний текст джерелаZhang, Xinhua. "Covariance Matrix." In 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.
Повний текст джерелаZhang, Xinhua. "Covariance Matrix." In 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.
Повний текст джерелаKramer, Oliver. "Covariance Matrix Estimation." In Studies in Big Data, 23–32. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-33383-0_3.
Повний текст джерелаBrown, 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.
Повний текст джерелаSheppard, Kevin. "Forecasting High Dimensional Covariance Matrices." In 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.
Повний текст джерелаKarnel, G. "Interactively Computing Robust Covariance Matrices." In Compstat, 199–204. Heidelberg: Physica-Verlag HD, 1990. http://dx.doi.org/10.1007/978-3-642-50096-1_31.
Повний текст джерелаТези доповідей конференцій з теми "Covariance matrice"
Parker Burg, John, and Gary Mavko. "Structured covariance matrices." In SEG Technical Program Expanded Abstracts 1987. Society of Exploration Geophysicists, 1987. http://dx.doi.org/10.1190/1.1892039.
Повний текст джерелаTao, Shaozhe, Yifan Sun, and Daniel Boley. "Inverse Covariance Estimation with Structured Groups." In 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.
Повний текст джерелаWhite, Andrew, Guoming Zhu, and Jongeun Choi. "A Linear Matrix Inequality Solution to the Input Covariance Constraint Control Problem." In ASME 2013 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/dscc2013-3716.
Повний текст джерелаGurve, Dharmendra, Denis Delisle-Rodriguez, Teodiano Bastos, and Sridhar Krishnan. "Motor Imagery Classification with Covariance Matrices and Non-Negative Matrix Factorization." In 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.
Повний текст джерелаBen-David, Avishai, and Charles E. Davidson. "Estimation of hyperspectral covariance matrices." In 2011 IEEE Applied Imagery Pattern Recognition Workshop: Imaging for Decision Making (AIPR 2011). IEEE, 2011. http://dx.doi.org/10.1109/aipr.2011.6176368.
Повний текст джерелаBen-David, Avishai, and Charles E. Davidson. "Estimation of hyperspectral covariance matrices." In IGARSS 2011 - 2011 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2011. http://dx.doi.org/10.1109/igarss.2011.6050188.
Повний текст джерелаAl-Jiboory, Ali Khudhair, Guoming Zhu, and Cornel Sultan. "LMI Control Design With Input Covariance Constraint for a Tensegrity Simplex Structure." In ASME 2014 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/dscc2014-6122.
Повний текст джерелаAbu Husain, Nurulakmar, Hamed Haddad Khodaparast, John E. Mottershead, and Huajiang Ouyang. "Application of the Perturbation Method With Parameter Weighting Matrix Assignments for Estimating Variability in a Set of Nominally Identical Welded Structures." In ASME 2010 10th Biennial Conference on Engineering Systems Design and Analysis. ASMEDC, 2010. http://dx.doi.org/10.1115/esda2010-24272.
Повний текст джерелаHanson, Kenneth M. "Probing the covariance matrix." In Bayesian Inference and Maximum Entropy Methods In Science and Engineering. AIP, 2006. http://dx.doi.org/10.1063/1.2423282.
Повний текст джерелаSholihat, Seli Siti, Sapto Wahyu Indratno, and Utriweni Mukhaiyar. "Online Inverse Covariance Matrix." In the 2019 International Conference. New York, New York, USA: ACM Press, 2019. http://dx.doi.org/10.1145/3348400.3348405.
Повний текст джерелаЗвіти організацій з теми "Covariance matrice"
McKnight, Richard D., and Karl N. Grimm. Covariance Matrix Generation at ANL. Office of Scientific and Technical Information (OSTI), December 2012. http://dx.doi.org/10.2172/1114909.
Повний текст джерелаMcKnight, Richard D., and Karl N. Grimm. ANL Critical Assembly Covariance Matrix Generation. Office of Scientific and Technical Information (OSTI), January 2014. http://dx.doi.org/10.2172/1114907.
Повний текст джерелаDerrien, H., N. M. Larson, and L. C. Leal. Covariance matrices for use in criticality safety predictability studies. Office of Scientific and Technical Information (OSTI), September 1997. http://dx.doi.org/10.2172/631237.
Повний текст джерелаBryan, M. F., G. F. Piepel, and 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), March 1996. http://dx.doi.org/10.2172/215713.
Повний текст джерелаMcKnight, Richard D., and Karl N. Grimm. ANL Critical Assembly Covariance Matrix Generation - Addendum. Office of Scientific and Technical Information (OSTI), January 2014. http://dx.doi.org/10.2172/1114908.
Повний текст джерелаWest, Kenneth, and Whitney Newey. Automatic Lag Selection in Covariance Matrix Estimation. Cambridge, MA: National Bureau of Economic Research, February 1995. http://dx.doi.org/10.3386/t0144.
Повний текст джерелаWest, Kenneth. Another Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimator. Cambridge, MA: National Bureau of Economic Research, July 1995. http://dx.doi.org/10.3386/t0183.
Повний текст джерелаHaan, Wouter J. Den, and Andrew Levin. A Practitioner's Guide to Robust Covariance Matrix Estimation. Cambridge, MA: National Bureau of Economic Research, June 1996. http://dx.doi.org/10.3386/t0197.
Повний текст джерелаDerrien, Herve, Luiz C. Leal, and Nancy M. Larson. Neutron Resonance Parameters and Covariance Matrix of 239Pu. Office of Scientific and Technical Information (OSTI), August 2008. http://dx.doi.org/10.2172/969958.
Повний текст джерелаSmith, 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.
Повний текст джерела