Relevant bibliographies by topics / Correlation Matrix

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

Academic literature on the topic 'Correlation Matrix'

Author: Grafiati
Published: 4 June 2021
Last updated: 28 January 2023

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Journal articles on the topic "Correlation Matrix"

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Sukhanova, E. M. "Matrix Correlation." Theory of Probability & Its Applications 54, no. 2 (January 2010): 347–55. http://dx.doi.org/10.1137/s0040585x97984231.

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Qian, Weixian, Xiaojun Zhou, Yingcheng Lu, and Jiang Xu. "Paths correlation matrix." Optics Letters 40, no. 18 (September 14, 2015): 4336. http://dx.doi.org/10.1364/ol.40.004336.

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Sonneveld, P., J. J. I. M. van Kan, X. Huang, and C. W. Oosterlee. "Nonnegative matrix factorization of a correlation matrix." Linear Algebra and its Applications 431, no. 3-4 (July 2009): 334–49. http://dx.doi.org/10.1016/j.laa.2009.01.004.

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Harnad, J., and M. Jacques. "The supersymmetric soliton correlation matrix." Journal of Geometry and Physics 2, no. 1 (January 1985): 1–15. http://dx.doi.org/10.1016/0393-0440(85)90015-4.

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Neudecker, H., and A. M. Wesselman. "The asymptotic variance matrix of the sample correlation matrix." Linear Algebra and its Applications 127 (1990): 589–99. http://dx.doi.org/10.1016/0024-3795(90)90363-h.

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Meng, Jong, and Chun Xia Guo. "A Optimization Model of Correlation Matrix." Advanced Materials Research 482-484 (February 2012): 270–73. http://dx.doi.org/10.4028/www.scientific.net/amr.482-484.270.

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Hamilton and Pelletier have brought forward the regime switching model for dynamic correlation matrix. It is especially for the variance time-varying financial multiple time series. In this paper, we use random matrix theory to improve the correlation matrix, which removed the noise, leaving the true information. We also present an empirical application use China's stock market data to optimize portfolio which illustrates that our model can have achieved good results.
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Foucart, Thierry. "Numerical Analysis of a Correlation Matrix." Statistics 29, no. 4 (January 1997): 347–61. http://dx.doi.org/10.1080/02331889708802595.

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Ti, Guan, Geng Yujie, Ma Qiang, Liu Yong, and Lin Lin. "Communication Network Discovery through Correlation Matrix." Journal of Physics: Conference Series 1345 (November 2019): 022070. http://dx.doi.org/10.1088/1742-6596/1345/2/022070.

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Burda, Z., A. Görlich, A. Jarosz, and J. Jurkiewicz. "Signal and noise in correlation matrix." Physica A: Statistical Mechanics and its Applications 343 (November 2004): 295–310. http://dx.doi.org/10.1016/j.physa.2004.05.048.

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Frappat, L., A. Sciarrino, and P. Sorba. "Correlation matrix for quartet codon usage." Physica A: Statistical Mechanics and its Applications 351, no. 2-4 (June 2005): 461–76. http://dx.doi.org/10.1016/j.physa.2005.01.051.

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Dissertations / Theses on the topic "Correlation Matrix"

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Martinsson, Engshagen Jan. "Nothing is normal in nance! : On Tail Correlations and Robust Higher Order Moments in Normal Portfolio Frameworks." Thesis, KTH, Matematik (Inst.), 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-102699.

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Abstract This thesis project is divided in two parts. The first part examines the possibility that correlation matrix estimates based on an outlier sample would contain information about extreme events. According to my findings, such methods do not perform better than simple shrinkage methods where robust shrinkage targets are used. The method tested is especially outperformed when it comes to the extreme events, where a shrinkage of the correlation matrix towards the identity matrix seems to give the best result. The second part is about valuation of skewness in marginal distributions and the penalizing of heavy tails. I argue that it is reasonable to use a degrees of freedom parameter instead of kurtosis and a certain regression parameter, that I develop, instead of skewness due to robustness issues. When minimizing the one period draw-down is our target, the "value" of skewness seems to have a linear relationship with expected returns. Re-valuing of expected returns, in terms of skewness, in the standard Markowitz framework will tend to lower expected shortfall (ES), increase skewness and lower the realized portfolio variance. Penalizing of heavy tails will most times in the same way lower ES, lower kurtosis and realized portfolio variance. The results indicate that the parameters representing higher order moments in some way characterize the assets and also reflect their future behavior. These properties can be used in a simple optimization framework and seem to have a positive impact even on portfolio level
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Di, Sabatino Stefano. "Reduced density-matrix functional theory : correlation and spectroscopy." Thesis, Toulouse 3, 2015. http://www.theses.fr/2015TOU30137.

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Cette thèse traite de la description de la corrélation électronique et de la spectroscopie dans le cadre de la Théorie de la Fonctionnelle de la Matrice Densité Réduite (RDMFT). Dans la RDMFT, les propriétés de l'état fondamental d'un système physique sont des fonctionnelles de la matrice densité à un corps. Plusieurs approximations à la corrélation électronique ont été proposées dans la littérature. Beaucoup d'entre elles peuvent être reliés au travail de Müller, qui en a proposé une similaire à l'approximation Hartree-Fock mais qui peut produire des nombres d'occupation fractionnaires. Cela n'est pas toujours suffisant, notamment dans les matériaux fortement corrélés. Par ailleurs, l'expression des observables du système en terme de la matrice densité n'est pas toujours connue. Tel est le cas, par exemple, pour la fonction spectrale, qui est liée aux spectres de photoémission. Dans ce cas, il y a des annulations d'erreur entre l'approximation à la corrélation électronique et l'approximation à l'observable, ce qui affaiblit la théorie. Dans cette thèse, nous recherchons des approximations plus précises en exploitant le lien entre les matrices densité et les fonctions de Green. Dans la première partie de la thèse, nous nous concentrons sur la fonction spectrale. En utilisant le modèle de Hubbard, qui peut être résolu exactement, nous analysons les approximations existantes à cette observable et nous soulignons leurs points faibles. Ensuite, à partir de sa définition en terme de la fonction de Green à un corps nous dérivons une expression pour la fonction spectrale qui dépend des nombres d'occupation naturels et d'une énergie efficace qui prend en compte toutes les excitations du système. Cette énergie efficace dépend de la matrice densité à un corps ainsi que des ordres supérieurs. Des approximations simples à cette énergie efficace donnent des spectres précis dans des systèmes modèles dans des régimes à la fois de faible et de forte corrélation. Pour illustrer notre méthode sur les matériaux réels, nous calculons le spectre de photoemission du NiO massif: notre méthode donne une image qualitativement correcte dans la phase antiferromagnétique et dans la phase paramagnétique, contrairement aux méthodes de champ moyen utilisés actuellement, qui donnent un métal dans le dernier cas. La deuxième partie de la thèse est plus explorative et traite des phénomènes dépendant du temps dans la RDMFT. En général, l'évolution temporelle des matrices densité est donnée par la hiérarchie des équations de Bogoliubov-Born-Green-Kirkwood-Yvon (BBGKY), dans lequel l'équation du mouvement de la matrice densité a n corps est donnée en termes de la matrice densité à n+1 corps. La première équation de la hiérarchie relie la matrice densité à un corps à la matrice densité à deux corps. La tâche difficile est de trouver des approximations à la matrice densité à deux corps. Les approximations existantes sont des extensions adiabatiques des approximations de l'état fondamental. Nous explorons cette question en examinant de nouvelles approximations qui nous tirons de la théorie à plusieurs corps (MBPT) basée sur les fonctions de Green ainsi que de la solution exacte du modèle de Anderson à deux niveaux dans son état fondamental. Nos premiers résultats sur le modèle de Anderson soumis à divers champs externes montrent quelques caractéristiques intéressantes, qui suggèrent d'explorer davantage ces approximations aussi sur des systèmes modèles plus grands
This thesis addresses the description of electron correlation and spectroscopy within the context of Reduced Density-Matrix Functional Theory (RDMFT). Within RDMFT the ground-state properties of a physical system are functionals of the ground-state reduced density matrix. Various approximations to electron correlation have been proposed in literature. Many of them, however, can be traced back to the work of Müller, who has proposed an approximation to the correlation which is similar to the Hartree-Fock approximation but which can produce fractional occupation numbers. This is not always sufficient. Moreover, the expression of the observables of the system in terms of the reduced density matrix is not always known. This is the case, for example, for the spectral function, which is closely related to photoemission spectra. In this case there are error cancellations between the approximation to correlation and the approximation to the observable, which weakens the theory. In this thesis we look for more accurate approximations by exploiting the link between density matrices and Green's functions. In the first part of the thesis we focus on the spectral function. Using the exactly solvable Hubbard model as illustration, we analyze the existent approximations to this observable and we point out their weak points. Then, starting from its definition in terms of the one-body Green's function, we derive an expression for the spectral function that depends on the natural occupation numbers and on an effective energy which accounts for all the charged excitations. This effective energy depends on the one-body as well as higher-order reduced density matrices. Simple approximations to this effective energy give accurate spectra in model systems in the weak as well as strong-correlation regimes. To illustrate our method on real materials we calculate the photoemission spectrum of bulk NiO: our method yields a qualitatively correct picture both in the antiferromagnetic and in the paramagnetic phases, contrary to currently used mean-field methods, which give a metal in the latter case. The second part of the thesis is more explorative and deals with time-dependent phenomena within RDMFT. In general the time evolution of the reduced density matrices is given by the Bogoliubov-Born-Green-Kirkwood-Yvon (BBGKY) hierarchy of equations, in which the equation of motion of the n-body reduced density matrix is given in terms of the (n + 1)-body reduced density matrix. The first equation of the hierarchy relates the one-body to the two-body reduced density matrix. The difficult task is to find approximations to the two-body reduced density matrix. Commonly used approximations are adiabatic extension of ground-state approximations. We explore this issue by looking at new approximations derived from Many-Body Perturbation Theory (MBPT) based on Green's functions as well as from the exact solution of the two-level Anderson impurity model in its ground state. Our first results on the two-level Anderson model subjected to various external fields show some interesting and, at the same time, puzzling features, which suggest to explore further these approximations
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Yasuda, Koji. "Correlation energy functional in the density-matrix functional theory." American Physical Society, 2001. http://hdl.handle.net/2237/8742.

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Enaganti, Srujan Kumar. "Solving correlation matrix completion problems using parallel differential evolution." Thesis, University of British Columbia, 2010. http://hdl.handle.net/2429/30302.

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Matrix Completion problems have been receiving increased attention due to their varied applicability in different domains. Correlation matrices arise often in studying multiple streams of time series data like technical analysis of stock market data. Often some of the values in the matrix are unknown and some reasonable replacements have to be found at the earliest opportunity to avert an unwanted consequence or keep up the pace in the business. After looking to background research related to solving this problem, we propose a new parallel technique that can solve general correlation matrix completion problems over a set of computers connected to a high speed network. We present some of our results where we could reduce the execution time.
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Hobson, Stephen. "Correlation matrix memories : improving performance for capacity and generalisation." Thesis, York St John University College, 2011. http://etheses.whiterose.ac.uk/2211/.

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The human brain is an extremely powerful pattern recogniser, as well as being capable of displaying amazing feats of memory. It is clear that human memory is associative; we recall information by associating items together so that one may be used to recall another. This model of memory, where items are associated as pairs rather than stored at a particular location, can be used to implement computer memories which display powerful properties such as robustness to noise, a high storage capacity and the ability to generalise. One example of such a memory is the Binary Correlation Matrix Memory (CMM), which in addition to the previously listed properties is capable of operating extremely quickly in both learning and recall, as well as being well suited for hardware implementation. These memories have been used as elements of larger pattern recognition architectures, solving problems such as object recognition, text recognition and rule chaining, with the memories being used to store rules. Clearly, the performance of the memories is a large factor in the performance of such architectures. This thesis presents a discussion of the issues involved with optimising the performance of CMMs in the context of larger architectures. Two architectures are examined in some detail, which motivates a desire to improve the storage capacity and generalisation capability of the memories. The issues surrounding the optimisation of storage capacity of CMMs are discussed, and a method for improving the capacity is presented. Additionally, while CMMs are able to generalise, this capability is often ignored. A method for producing codes suitable for storage in a CMM is presented, which provides the ability to react to previously unseen inputs. This potentially adds a powerful new capability to existing architectures.
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Fargus, Alexander. "Optimisation of correlation matrix memory prognostic and diagnostic systems." Thesis, University of York, 2015. http://etheses.whiterose.ac.uk/9032/.

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Condition monitoring systems for prognostics and diagnostics can enable large and complex systems to be operated more safely, at a lower cost and have a longer lifetime than is possible without them. AURA Alert is a condition monitoring system that uses a fast approximate k-Nearest Neighbour (kNN) search of a timeseries database containing known system states to identify anomalous system behaviour. This search algorithm, AURA kNN, uses a type of binary associative neural network called a Correlation Matrix Memory (CMM) to facilitate the search of the historical database. AURA kNN is evaluated with respect to the state of the art Locality Sensitive Hashing (LSH) approximate kNN algorithm and shown to be orders of magnitude slower to search large historical databases. As a result, it is determined that the standard AURA kNN scales poorly for large historical databases. A novel method for generating CMM input tokens called Weighted Overlap Code Construction is presented and combined with Baum Coded output tokens to reduce the query time of the CMM. These modifications are shown to improve the ability of AURA kNN to scale with large databases, but this comes at the cost of accuracy. In the best case an AURA kNN search is 3.1 times faster than LSH with an accuracy penalty of 4% on databases with 1000 features and fewer than 100,000 samples. However the modified AURA kNN is still slower than LSH with databases with fewer features or more samples. These results suggest that it may be possible for AURA kNN to be improved so that it is competitive with the state of the art LSH algorithm.
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Barker, Oliver Robin. "Recognition of distinctive features in protein structures using correlation matrix memories." Thesis, University of York, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.423740.

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Kustrin, Daniel. "Forecasting financial time series with correlation matrix memories for tactical asset allocation." Thesis, University of York, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.298529.

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Kim, Yu Shin. "Correlation Between MMP-2 and -9 Levels and Local Stresses in Arteries Using a Heterogeneous Mechanical Model." Diss., Georgia Institute of Technology, 2007. http://hdl.handle.net/1853/16134.

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The mechanical environment influences vascular smooth muscle cell (VSMC) functions related to the vascular remodeling. However, the relationships are not appropriately addressed by most mechanical models of arteries assuming homogeneity. Accounting for the effects of heterogeneity is expected to be important to our understanding of VSMC functions. We hypothesized that local stresses computed using a heterogeneous mechanical model of arteries positively correlate to the levels of matrix metalloproteinase (MMP)-2 and -9 in situ. We developed a mathematical model of an arterial wall accounting for nonlinearity, residual strain, anisotropy, and structural heterogeneity. The distributions of elastin and collagen fibers, quantified using their optical properties, showed significant structural heterogeneity. Anisotropy was represented by the direction of collagen fibers, which was measured by the helical angle of VSMC nuclei. The recruiting points of collagen fibers were computed assuming a uniform strain of collagen fibers under physiological loading conditions; an assumption motivated by the morphology. This was supported by observed uniform length and orientation of VSMC nuclei under physiological loading. The distributions of circumferential stresses computed using both heterogeneous and corresponding homogeneous models were correlated to the distributions of expression and activation of MMP-2 and -9 in porcine common carotid arteries, which were incubated in an ex vivo perfusion organ culture system under either normotensive or hypertensive conditions for 48 hours. While strains computed using incompressibility were identical in both models, the heterogeneous model, unlike the homogeneous model, predicted higher circumferential stresses in the outer layer. The tissue levels of MMP-2 and -9 were positively correlated to circumferential stresses computed using the heterogeneous model, which implies that areas of high stress are expected to be sites of localized remodeling and agrees with results from cell culture studies. The results support the role of mechanical stress in vascular remodeling and suggest the importance of structural heterogeneity in studying mechanobiological responses.
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Mandere, Edward Ondieki. "Financial Networks and Their Applications to the Stock Market." Bowling Green State University / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1234473233.

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Books on the topic "Correlation Matrix"

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Bogomolny, E. B. Random matrix theory and the Riemann zeros II: N-point correlations. Bristol [England]: Hewlett Packard, 1996.

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Weiner, Richard M. Introduction to Bose-Einstein correlations and subatomic interferometry. Chichester, England: John Wiley, 2000.

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Zabrodin, Anton. Financial applications of random matrix theory: a short review. Edited by Gernot Akemann, Jinho Baik, and Philippe Di Francesco. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198744191.013.40.

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This article reviews some applications of random matrix theory (RMT) in the context of financial markets and econometric models, with emphasis on various theoretical results (for example, the Marčenko-Pastur spectrum and its various generalizations, random singular value decomposition, free matrices, largest eigenvalue statistics) as well as some concrete applications to portfolio optimization and out-of-sample risk estimation. The discussion begins with an overview of principal component analysis (PCA) of the correlation matrix, followed by an analysis of return statistics and portfolio theory. In particular, the article considers single asset returns, multivariate distribution of returns, risk and portfolio theory, and nonequal time correlations and more general rectangular correlation matrices. It also presents several RMT results on the bulk density of states that can be obtained using the concept of matrix freeness before concluding with a description of empirical correlation matrices of stock returns.
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Borodin, Alexei. Random matrix representations of critical statistics. Edited by Gernot Akemann, Jinho Baik, and Philippe Di Francesco. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198744191.013.12.

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This article examines two random matrix ensembles that are useful for describing critical spectral statistics in systems with multifractal eigenfunction statistics: the Gaussian non-invariant ensemble and the invariant random matrix ensemble. It first provides an overview of non-invariant Gaussian random matrix theory (RMT) with multifractal eigenvectors and invariant random matrix theory (RMT) with log-square confinement before discussing self-unfolding and not self-unfolding in invariant RMT. It then considers a non-trivial unfolding and how it changes the form of the spectral correlations, along with the appearance of a ghost correlation dip in RMT and Hawking radiation. It also describes the correspondence between invariant and non-invariant ensembles and concludes by introducing a simple field theory in 1+1 dimensions which reproduces level statistics of both of the two random matrix models and the classical Wigner-Dyson spectral statistics in the framework of the unified formalism of Luttinger liquid.
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Bouchaud, Jean-Philippe. Random matrix theory and (big) data analysis. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198797319.003.0006.

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This chapter reviews methods from random matrix theory to extract information about a large signal matrix C (for example, a correlation matrix arising in big data problems), from its noisy observation matrix M. The chapter shows that the replica method can be used to obtain both the spectral density and the overlaps between noise-corrupted eigenvectors and the true ones, for both additive and multiplicative noise. This allows one to construct optimal rotationally invariant estimators of C based on the observation of M alone. This chapter also discusses the case of rectangular correlation matrices and the problem of random singular value decomposition.
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Cicuta, Giovanni, and Luca Molinari. Two-matrix models and biorthogonal polynomials. Edited by Gernot Akemann, Jinho Baik, and Philippe Di Francesco. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198744191.013.15.

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This article considers two cases of two-matrix models that are amenable to biorthogonal polynomials: Itzykson-Zuber interaction and Cauchy interaction. The features and applications of the biorthogonal polynomials relevant to either case are discussed, but first the article provides an overview of chain-matrix models. It then describes the Itzykson-Zuber Hermitian two-matrix model and the Christoffel–Darboux identities, along with the spectral curve. It also examines the so-called mixed correlation functions that are involved in the combinatorial applications of the two-matrix model before concluding with an analysis of the Cauchy two-matrix model, which in a ‘complication scale’ turns out to lie in between the one-matrix model and the Itzykson-Zuber two-matrix model.
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Akemann, Gernot. Random matrix theory and quantum chromodynamics. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198797319.003.0005.

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This chapter was originally presented to a mixed audience of physicists and mathematicians with some basic working knowledge of random matrix theory. The first part is devoted to the solution of the chiral Gaussian unitary ensemble in the presence of characteristic polynomials, using orthogonal polynomial techniques. This includes all eigenvalue density correlation functions, smallest eigenvalue distributions, and their microscopic limit at the origin. These quantities are relevant for the description of the Dirac operator spectrum in quantum chromodynamics with three colors in four Euclidean space-time dimensions. In the second part these two theories are related based on symmetries, and the random matrix approximation is explained. In the last part recent developments are covered, including the effect of finite chemical potential and finite space-time lattice spacing, and their corresponding orthogonal polynomials. This chapter also provides some open random matrix problems.
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United States. National Aeronautics and Space Administration., ed. The correlation of low-velocity impact resistance of graphite-fiber-reinforced composites with matrix properties. [Washington, D.C: National Aeronautics and Space Administration, 1986.

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The correlation of low-velocity impact resistance of graphite-fiber-reinforced composites with matrix properties. [Washington, D.C: National Aeronautics and Space Administration, 1986.

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United States. National Aeronautics and Space Administration., ed. The correlation of low-velocity impact resistance of graphite-fiber-reinforced composites with matrix properties. [Washington, D.C: National Aeronautics and Space Administration, 1986.

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Book chapters on the topic "Correlation Matrix"

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Verma, J. P. "Correlation Matrix and Partial Correlation: Explaining Relationships." In Data Analysis in Management with SPSS Software, 103–32. India: Springer India, 2012. http://dx.doi.org/10.1007/978-81-322-0786-3_4.

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Puntanen, Simo, George P. H. Styan, and Jarkko Isotalo. "Easy Correlation Tricks." In Matrix Tricks for Linear Statistical Models, 91–104. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-10473-2_4.

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Mathai, Arak M., Serge B. Provost, and Hans J. Haubold. "Canonical Correlation Analysis." In Multivariate Statistical Analysis in the Real and Complex Domains, 641–77. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-95864-0_10.

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AbstractWe will keep utilizing the same notations in this chapter. More specifically, lower-case letters x, y, … will denote real scalar variables, whether mathematical or random. Capital letters X, Y, … will be used to denote real matrix-variate mathematical or random variables, whether square or rectangular matrices are involved. A tilde will be placed above letters such as $$\tilde {x},\tilde {y},\tilde {X},\tilde {Y}$$ x ~ , y ~ , X ~ , Y ~ to denote variables in the complex domain. Constant matrices will for instance be denoted by A, B, C. A tilde will not be used on constant matrices unless the point is to be stressed that the matrix is in the complex domain. The determinant of a square matrix A will be denoted by |A| or det(A) and, in the complex case, the absolute value or modulus of the determinant of A will be denoted as |det(A)|. When matrices are square, their order will be taken as p × p, unless specified otherwise. When A is a full rank matrix in the complex domain, then AA∗ is Hermitian positive definite where an asterisk designates the complex conjugate transpose of a matrix. Additionally, dX will indicate the wedge product of all the distinct differentials of the elements of the matrix X. Letting the p × q matrix X = (xij) where the xij’s are distinct real scalar variables, $$\mathrm {d}X=\wedge _{i=1}^p\wedge _{j=1}^q\mathrm {d}x_{ij}$$ d X = ∧ i = 1 p ∧ j = 1 q d x i j . For the complex matrix $$\tilde {X}=X_1+iX_2,\ i=\sqrt {(-1)}$$ X ~ = X 1 + i X 2 , i = ( − 1 ) , where X1 and X2 are real, $$\mathrm {d}\tilde {X}=\mathrm {d}X_1\wedge \mathrm {d}X_2$$ d X ~ = d X 1 ∧ d X 2 .
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Haario, Heikki, Janne Hakkarainen, Ramona Maraia, and Sebastian Springer. "Correlation Integral Likelihood for Stochastic Differential Equations." In 2017 MATRIX Annals, 25–36. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-04161-8_3.

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Trendafilov, Nickolay, and Michele Gallo. "Cannonical correlation analysis (CCA)." In Multivariate Data Analysis on Matrix Manifolds, 269–88. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-76974-1_8.

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Devroye, Luc, and Gérard Letac. "Copulas with Prescribed Correlation Matrix." In Lecture Notes in Mathematics, 585–601. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-18585-9_25.

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Balashov, Vsevolod V., Alexei N. Grum-Grzhimailo, and Nikolai M. Kabachnik. "Density Matrix and Statistical Tensors." In Polarization and Correlation Phenomena in Atomic Collisions, 1–43. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4757-3228-3_1.

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Skillicorn, David B. "Finding Unusual Correlation Using Matrix Decompositions." In Intelligence and Security Informatics, 83–99. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-25952-7_7.

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Shevlyakov, G. L., and T. Yu Khvatova. "On Robust Estimation of a Correlation Coefficient and Correlation Matrix." In Contributions to Statistics, 153–62. Heidelberg: Physica-Verlag HD, 1998. http://dx.doi.org/10.1007/978-3-642-58988-1_17.

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Adachi, Kohei. "Canonical Correlation and Multiple Correspondence Analyses." In Matrix-Based Introduction to Multivariate Data Analysis, 211–28. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4103-2_14.

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Conference papers on the topic "Correlation Matrix"

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Ushenko, Alexander, Vladimir Ushenko, Inna Lukashevich, Galina Bodnar, Viktor Zhytaryuk, Olexander Prydiy, Galina Koval, and Oleg Vanchuliak. "3D Mueller-matrix mapping of biological optically anisotropic networks." In Correlation Optics 2017, edited by Oleg V. Angelsky. SPIE, 2018. http://dx.doi.org/10.1117/12.2304736.

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Sidor, M. I., N. Pavlyukovich, O. Pavlyukovich, Olexander V. Dubolazov, Alexander Ushenko, and Leonid Pidkamin. "Polarization-interference Jones-matrix mapping of biological crystal networks." In Correlation Optics 2017, edited by Oleg V. Angelsky. SPIE, 2018. http://dx.doi.org/10.1117/12.2305348.

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Dubolazov, Olexander V., Alexander Ushenko, Yuriy Ushenko, Leonid Pidkamin, Maxim Sidor, Marta Grytsyuk, and Pavlo Prysyazhnyuk. "Mueller matrix mapping of biological polycrystalline layers using reference wave." In Correlation Optics 2017, edited by Oleg V. Angelsky. SPIE, 2018. http://dx.doi.org/10.1117/12.2304719.

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Savenkov, Sergey N., and Konstantin E. Yushtin. "Physical realization of experimental Mueller matrix." In International Conference on Correlation Optics, edited by Oleg V. Angelsky. SPIE, 1999. http://dx.doi.org/10.1117/12.370409.

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Wang, Wei, and Mitsuo Takeda. "Optical transfer matrix: matrix correlation as frequency domain analysis of polarization imaging system." In Fifteenth International Conference on Correlation Optics, edited by Oleg V. Angelsky. SPIE, 2021. http://dx.doi.org/10.1117/12.2616423.

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Ushenko, Alexander, Vladimir Ushenko, M. Grytsyuk, G. B. Bodnar, O. Vanchulyak, and I. Meglinskiy. "Differential 3D Mueller-matrix mapping of optically anisotropic depolarizing biological layers." In Correlation Optics 2017, edited by Oleg V. Angelsky. SPIE, 2018. http://dx.doi.org/10.1117/12.2305329.

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Zabolotna, N. I., and I. V. Musiichuk. "Principles and methods of Mueller-matrix tomography of multilayer biological tissues." In Correlation Optics 2011, edited by Oleg V. Angelsky. SPIE, 2011. http://dx.doi.org/10.1117/12.920930.

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Trifonyuk, L., V. Baranovsky, Olexander V. Dubolazov, Vladimir Ushenko, Alexander Ushenko, V. G. Zhytaryuk, O. G. Prydiy, and O. Vanchulyak. "Jones-matrix tomography of biological tissues phase anisotropy in the diagnosis of uterus wall prolapse." In Correlation Optics 2017, edited by Oleg V. Angelsky. SPIE, 2018. http://dx.doi.org/10.1117/12.2305345.

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Tamma, Vincenzo, and Florian Nägele. "Correlation functions and matrix permanents." In SPIE Optical Engineering + Applications, edited by Ronald E. Meyers, Yanhua Shih, and Keith S. Deacon. SPIE, 2013. http://dx.doi.org/10.1117/12.2030215.

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Ushenko, V. O., G. D. Koval, V. T. Bachinskiy, L. Ya Kushnerick, M. Garazdiyk, M. M. Dominikov, and O. V. Dronenko. "Mueller-matrix differential diagnosis of biological crystallites phase anisotropy." In Eleventh International Conference on Correlation Optics, edited by Oleg V. Angelsky. SPIE, 2013. http://dx.doi.org/10.1117/12.2053863.

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Reports on the topic "Correlation Matrix"

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Soloviev, Vladimir N., Symon P. Yevtushenko, and Viktor V. Batareyev. Comparative analysis of the cryptocurrency and the stock markets using the Random Matrix Theory. [б. в.], February 2020. http://dx.doi.org/10.31812/123456789/3681.

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This article demonstrates the comparative possibility of constructing indicators of critical and crash phenomena in the volatile market of cryptocurrency and developed stock market. Then, combining the empirical cross-correlation matrix with the Random Matrix Theory, we mainly examine the statistical properties of cross-correlation coefficients, the evolution of the distribution of eigenvalues and corresponding eigenvectors in both markets using the daily returns of price time series. The result has indicated that the largest eigenvalue reflects a collective effect of the whole market, and is very sensitive to the crash phenomena. It has been shown that introduced the largest eigenvalue of the matrix of correlations can act like indicators-predictors of falls in both markets.
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Soloviev, V., and V. Solovieva. Quantum econophysics of cryptocurrencies crises. [б. в.], 2018. http://dx.doi.org/10.31812/0564/2464.

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From positions, attained by modern theoretical physics in understanding of the universe bases, the methodological and philosophical analysis of fundamental physical concepts and their formal and informal connections with the real economic measuring is carried out. Procedures for heterogeneous economic time determination, normalized economic coordinates and economic mass are offered, based on the analysis of time series, the concept of economic Plank's constant has been proposed. The theory has been approved on the real economic dynamic's time series, related to the cryptocurrencies market, the achieved results are open for discussion. Then, combined the empirical cross-correlation matrix with the random matrix theory, we mainly examine the statistical properties of cross-correlation coefficient, the evolution of average correlation coefficient, the distribution of eigenvalues and corresponding eigenvectors of the global cryptocurrency market using the daily returns of 15 cryptocurrencies price time series across the world from 2016 to 2018. The result indicated that the largest eigenvalue reflects a collective effect of the whole market, practically coincides with the dynamics of the mean value of the correlation coefficient and very sensitive to the crisis phenomena. It is shown that both the introduced economic mass and the largest eigenvalue of the matrix of correlations can serve as quantum indicator-predictors of crises in the market of cryptocurrencies.
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Taniguchi, M., and P. R. Krishnaiah. Asymptotic Distributions of Functions of the Eigenvalues of the Sample Covariance Matrix and Canonical Correlation Matrix in Multivariate Time Series. Fort Belvoir, VA: Defense Technical Information Center, March 1986. http://dx.doi.org/10.21236/ada170282.

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Pradeep Rohatgi. Mechanical Properties - Structure Correlation for Commercial Specification of Cast Particulate Metal Matrix Composites. Office of Scientific and Technical Information (OSTI), December 2002. http://dx.doi.org/10.2172/808536.

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Muller, Isabelle S., Innocent Joseph, and Ian L. Pegg. Preparation and Testing of LAW High-Alkali Correlation and Augmentation Matrix Glasses (Final Report, Rev. 0). Office of Scientific and Technical Information (OSTI), October 2006. http://dx.doi.org/10.2172/1523513.

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Hollas, C. L., G. Arnone, G. Brunson, and K. Coop. Determination of fission neutron transmission through waste matrix material using neutron signal correlation from active assay of {sup 239}Pu. Office of Scientific and Technical Information (OSTI), September 1996. http://dx.doi.org/10.2172/378874.

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Hodgdon, Taylor, Anthony Fuentes, Brian Quinn, Bruce Elder, and Sally Shoop. Characterizing snow surface properties using airborne hyperspectral imagery for autonomous winter mobility. Engineer Research and Development Center (U.S.), October 2021. http://dx.doi.org/10.21079/11681/42189.

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With changing conditions in northern climates it is crucial for the United States to have assured mobility in these high-latitude regions. Winter terrain conditions adversely affect vehicle mobility and, as such, they must be accurately characterized to ensure mission success. Previous studies have attempted to remotely characterize snow properties using varied sensors. However, these studies have primarily used satellite-based products that provide coarse spatial and temporal resolution, which is unsuitable for autonomous mobility. Our work employs the use of an Unmanned Aeriel Vehicle (UAV) mounted hyperspectral camera in tandem with machine learning frameworks to predict snow surface properties at finer scales. Several machine learning models were trained using hyperspectral imagery in tandem with in-situ snow measurements. The results indicate that random forest and k-nearest neighbors models had the lowest Mean Absolute Error for all surface snow properties. A pearson correlation matrix showed that density, grain size, and moisture content all had a significant positive correlation to one another. Mechanically, density and grain size had a slightly positive correlation to compressive strength, while moisture had a much weaker negative correlation. This work provides preliminary insight into the efficacy of using hyperspectral imagery for characterizing snow properties for autonomous vehicle mobility.
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Derbentsev, V., A. Ganchuk, and Володимир Миколайович Соловйов. Cross correlations and multifractal properties of Ukraine stock market. Politecnico di Torino, 2006. http://dx.doi.org/10.31812/0564/1117.

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Recently the statistical characterizations of financial markets based on physics concepts and methods attract considerable attentions. The correlation matrix formalism and concept of multifractality are used to study temporal aspects of the Ukraine Stock Market evolution. Random matrix theory (RMT) is carried out using daily returns of 431 stocks extracted from database time series of prices the First Stock Trade System index (www.kinto.com) for the ten-year period 1997-2006. We find that a majority of the eigenvalues of C fall within the RMT bounds for the eigenvalues of random correlation matrices. We test the eigenvalues of C within the RMT bound for universal properties of random matrices and find good agreement with the results for the Gaussian orthogonal ensemble of random matrices—implying a large degree of randomness in the measured cross-correlation coefficients. Further, we find that the distribution of eigenvector components for the eigenvectors corresponding to the eigenvalues outside the RMT bound display systematic deviations from the RMT prediction. We analyze the components of the deviating eigenvectors and find that the largest eigenvalue corresponds to an influence common to all stocks. Our analysis of the remaining deviating eigenvectors shows distinct groups, whose identities correspond to conventionally identified business sectors. Comparison with the Mantegna minimum spanning trees method gives a satisfactory consent. The found out the pseudoeffects related to the artificial unchanging areas of price series come into question We used two possible procedures of analyzing multifractal properties of a time series. The first one uses the continuous wavelet transform and extracts scaling exponents from the wavelet transform amplitudes over all scales. The second method is the multifractal version of the detrended fluctuation analysis method (MF-DFA). The multifractality of a time series we analysed by means of the difference of values singularity stregth (or Holder exponent) ®max and ®min as a suitable way to characterise multifractality. Singularity spectrum calculated from daily returns using a sliding 250 day time window in discrete steps of 1. . . 10 days. We discovered that changes in the multifractal spectrum display distinctive pattern around significant “drawdowns”. Finally, we discuss applications to the construction of crushes precursors at the financial markets.
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Aziz, Md Abdul, Sarah Jafrin, Md Abdul Barek, Shamima Nasrin Anonna, and Mohammad Safiqul Islam. The Association between Matrix Metalloproteinase-3 -1171 (5A/6A) Promoter Polymorphism and Cancer Susceptibility: An Updated Meta-Analysis and Trial Sequential Analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, August 2022. http://dx.doi.org/10.37766/inplasy2022.8.0049.

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Review question / Objective: The polymorphism of the 5A/6A promoter region of matrix metalloproteinases-3-1171 has been comprehensively studied to evaluate its risk associated with various cancers. We performed this updated meta-analysis to clarify the inconclusive outcomes of previous studies and to verify the link of this specific variant with the cancer risk. Eligibility criteria: For the literature covered in this meta-analysis, the authors followed some standards as inclusion criteria: (a) comparative case-control or cohort (different case groups) studies stating the correlation of MMP-3 -1171 (5A/6A) variant with cancer risk; (b) Available genotype and allele data in cases and controls; (c) Sufficient data to determine ORs (odds ratios) with 95% Cis (confidence intervals). The substandard studies were: (a) Review articles, commentaries and duplicate studies; (b) Study graph apart from case-control comparative approaches; (c) Studies having inadequate genotypic information to compute ORs with 95% CIs.
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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.

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Numerous extant studies are dedicated to enhancing the safety of active transportation modes, but very few studies are devoted to safety analysis surrounding transit stations, which serve as an important modal interface for pedestrians and bicyclists. This study bridges the gap by developing joint models based on the multivariate conditionally autoregressive (MCAR) priors with a distance-oriented neighboring weight matrix. For this purpose, transit-station-centered data in Los Angeles County were used for model development. Feature selection relying on both random forest and correlation analyses was employed, which leads to different covariate inputs to each of the two jointed models, resulting in increased model flexibility. Utilizing an Integrated Nested Laplace Approximation (INLA) algorithm and various evaluation criteria, the results demonstrate that models with a correlation effect between pedestrians and bicyclists perform much better than the models without such an effect. The joint models also aid in identifying significant covariates contributing to the safety of each of the two active transportation modes. The research results can furnish transportation professionals with additional insights to create safer access to transit and thus promote active transportation.
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