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Relevant bibliographies by topics / Distance de covariance

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Academic literature on the topic 'Distance de covariance'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "Distance de covariance"

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Székely, Gábor J., and Maria L. Rizzo. "Brownian distance covariance." Annals of Applied Statistics 3, no. 4 (December 2009): 1236–65. http://dx.doi.org/10.1214/09-aoas312.

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Székely, Gábor J., and Maria L. Rizzo. "Rejoinder: Brownian distance covariance." Annals of Applied Statistics 3, no. 4 (December 2009): 1303–8. http://dx.doi.org/10.1214/09-aoas312rej.

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Cope, Leslie. "Discussion of: Brownian distance covariance." Annals of Applied Statistics 3, no. 4 (December 2009): 1279–81. http://dx.doi.org/10.1214/00-aoas312c.

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Bickel, Peter J., and Ying Xu. "Discussion of: Brownian distance covariance." Annals of Applied Statistics 3, no. 4 (December 2009): 1266–69. http://dx.doi.org/10.1214/09-aoas312a.

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Kosorok, Michael R. "Discussion of: Brownian distance covariance." Annals of Applied Statistics 3, no. 4 (December 2009): 1270–78. http://dx.doi.org/10.1214/09-aoas312b.

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Feuerverger, Andrey. "Discussion of: Brownian distance covariance." Annals of Applied Statistics 3, no. 4 (December 2009): 1282–84. http://dx.doi.org/10.1214/09-aoas312d.

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Gretton, Arthur, Kenji Fukumizu, and Bharath K. Sriperumbudur. "Discussion of: Brownian distance covariance." Annals of Applied Statistics 3, no. 4 (December 2009): 1285–94. http://dx.doi.org/10.1214/09-aoas312e.

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Rémillard, Bruno. "Discussion of: Brownian distance covariance." Annals of Applied Statistics 3, no. 4 (December 2009): 1295–98. http://dx.doi.org/10.1214/09-aoas312f.

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Genovese, Christopher R. "Discussion of: Brownian distance covariance." Annals of Applied Statistics 3, no. 4 (December 2009): 1299–302. http://dx.doi.org/10.1214/09-aoas312g.

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Huo, Xiaoming, and Gábor J. Székely. "Fast Computing for Distance Covariance." Technometrics 58, no. 4 (October 1, 2016): 435–47. http://dx.doi.org/10.1080/00401706.2015.1054435.

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Dissertations / Theses on the topic "Distance de covariance"

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Youssef, Pierre, and Pierre Youssef. "Invertibilité restreinte, distance au cube et covariance de matrices aléatoires." Phd thesis, Université Paris-Est, 2013. http://tel.archives-ouvertes.fr/tel-00952297.

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Dans cette thèse, on aborde trois thèmes : problème de sélection de colonnes dans une matrice, distance de Banach-Mazur au cube et estimation de la covariance de matrices aléatoires. Bien que les trois thèmes paraissent éloignés, les techniques utilisées se ressemblent tout au long de la thèse. Dans un premier lieu, nous généralisons le principe d'invertibilité restreinte de Bourgain-Tzafriri. Ce résultat permet d'extraire un "grand" bloc de colonnes linéairement indépendantes dans une matrice et d'estimer la plus petite valeur singulière de la matrice extraite. Nous proposons ensuite un algorithme déterministe pour extraire d'une matrice un bloc presque isométrique c'est à dire une sous-matrice dont les valeurs singulières sont proches de 1. Ce résultat nous permet de retrouver le meilleur résultat connu sur la célèbre conjecture de Kadison-Singer. Des applications à la théorie locale des espaces de Banach ainsi qu'à l'analyse harmonique sont déduites. Nous donnons une estimation de la distance de Banach-Mazur d'un corps convexe de Rn au cube de dimension n. Nous proposons une démarche plus élémentaire, basée sur le principe d'invertibilité restreinte, pour améliorer et simplifier les résultats précédents concernant ce problème. Plusieurs travaux ont été consacrés pour approcher la matrice de covariance d'un vecteur aléatoire par la matrice de covariance empirique. Nous étendons ce problème à un cadre matriciel et on répond à la question. Notre résultat peut être interprété comme une quantification de la loi des grands nombres pour des matrices aléatoires symétriques semi-définies positives. L'estimation obtenue s'applique à une large classe de matrices aléatoires

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Youssef, Pierre. "Invertibilité restreinte, distance au cube et covariance de matrices aléatoires." Thesis, Paris Est, 2013. http://www.theses.fr/2013PEST1022/document.

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Dans cette thèse, on aborde trois thèmes : problème de sélection de colonnes dans une matrice, distance de Banach-Mazur au cube et estimation de la covariance de matrices aléatoires. Bien que les trois thèmes paraissent éloignés, les techniques utilisées se ressemblent tout au long de la thèse. Dans un premier lieu, nous généralisons le principe d'invertibilité restreinte de Bourgain-Tzafriri. Ce résultat permet d'extraire un "grand" bloc de colonnes linéairement indépendantes dans une matrice et d'estimer la plus petite valeur singulière de la matrice extraite. Nous proposons ensuite un algorithme déterministe pour extraire d'une matrice un bloc presque isométrique c’est à dire une sous-matrice dont les valeurs singulières sont proches de 1. Ce résultat nous permet de retrouver le meilleur résultat connu sur la célèbre conjecture de Kadison-Singer. Des applications à la théorie locale des espaces de Banach ainsi qu'à l'analyse harmonique sont déduites. Nous donnons une estimation de la distance de Banach-Mazur d'un corps convexe de Rn au cube de dimension n. Nous proposons une démarche plus élémentaire, basée sur le principe d'invertibilité restreinte, pour améliorer et simplifier les résultats précédents concernant ce problème. Plusieurs travaux ont été consacrés pour approcher la matrice de covariance d'un vecteur aléatoire par la matrice de covariance empirique. Nous étendons ce problème à un cadre matriciel et on répond à la question. Notre résultat peut être interprété comme une quantification de la loi des grands nombres pour des matrices aléatoires symétriques semi-définies positives. L'estimation obtenue s'applique à une large classe de matrices aléatoires
In this thesis, we address three themes : columns subset selection in a matrix, the Banach-Mazur distance to the cube and the estimation of the covariance of random matrices. Although the three themes seem distant, the techniques used are similar throughout the thesis. In the first place, we generalize the restricted invertibility principle of Bougain-Tzafriri. This result allows us to extract a "large" block of linearly independent columns inside a matrix and estimate the smallest singular value of the restricted matrix. We also propose a deterministic algorithm in order to extract an almost isometric block inside a matrix i.e a submatrix whose singular values are close to 1. This result allows us to recover the best known result on the Kadison-Singer conjecture. Applications to the local theory of Banach spaces as well as to harmonic analysis are deduced. We give an estimate of the Banach-Mazur distance between a symmetric convex body in Rn and the cube of dimension n. We propose an elementary approach, based on the restricted invertibility principle, in order to improve and simplify the previous results dealing with this problem. Several studies have been devoted to approximate the covariance matrix of a random vector by its sample covariance matrix. We extend this problem to a matrix setting and we answer the question. Our result can be interpreted as a quantified law of large numbers for positive semidefinite random matrices. The estimate we obtain, applies to a large class of random matrices

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Lescornel, Hélène. "Covariance estimation and study of models of deformations between distributions with the Wasserstein distance." Toulouse 3, 2014. http://www.theses.fr/2014TOU30045.

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La première partie de cette thèse est consacrée à l'estimation de covariance de processus stochastiques non stationnaires. Le modèle étudié amène à estimer la covariance du processus dans différents espaces vectoriels de matrices. Nous étudions dans le chapitre 3 une méthode de sélection de modèle par minimisation d'un critère pénalisé en utilisant des inégalités de concentration, et le chapitre 4 présente une méthode basée sur l'estimation sans biais du risque. Dans les deux cas des inégalités oracles sont obtenues. La seconde partie de cette thèse concerne l'étude de modèles de déformations entre distributions. On suppose observer une quantité aléatoire epsilon à travers une fonction de déformation. C'est l'importance de la déformation, représentée par un paramètre theta, que l'on cherche à retrouver. Nous présentons plusieurs méthodes d'estimation basées sur la distance de Wasserstein en alignant les lois des observations pour retrouver le paramètre de déformation. Dans le cas où les variables aléatoires sont à valeurs réelles, le chapitre 7 donne des propriétés de consistance pour un M-estimateur et sa distribution asymptotique. On y utilise des techniques de Hadamard différentiabilité pour appliquer une Delta-Méthode fonctionnelle. Le chapitre 8 concerne l'étude d'un estimateur de type Robbins-Monro et présente des propriétés de convergence pour un estimateur à noyau de la densité de la variable epsilon obtenu à l'aide des observations. Le modèle est généralisé à des variables dans des espaces métriques complets dans le chapitre 9, puis, dans l'optique de créer un test d'adéquation, le chapitre 10 donne des résultats sur la distribution asymptotique d'une statistique de test
The first part of this thesis concerns the covariance estimation of non stationary processes. We are estimating the covariance in different vectorial spaces of matrices. In Chapter 3, we give a model selection procedure by minimizing a penalized criterion and using concentration inequalities, and Chapter 4 presents an Unbiased Risk Estimation method. In both cases we give oracle inequalities. The second part deals with the study of models of deformation between distributions. We assume that we observe a random quantity epsilon through a deformation function. The importance of the deformation is represented by a parameter theta that we aim to estimate. We present several methods of estimation based on the Wasserstein distance by aligning the distributions of the observations to recover the deformation parameter. In the case of real random variables, Chapter 7 presents properties of consistency for a M-estimator and its asymptotic distribution. We use Hadamard differentiability techniques to apply a functional Delta method. Chapter 8 concerns a Robbins-Monro estimator for the deformation parameter and presents properties of convergence for a kernel estimator of the density of the variable epsilon obtained with the observations. The model is generalized to random variables in complete metric spaces in Chapter 9. Then, in the aim to build a goodness of fit test, Chapter 10 gives results on the asymptotic distribution of a test statistic

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Gunay, Melih. "Representation Of Covariance Matrices In Track Fusion Problems." Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/12609026/index.pdf.

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Covariance Matrix in target tracking algorithms has a critical role at multi- sensor track fusion systems. This matrix reveals the uncertainty of state es- timates that are obtained from diferent sensors. So, many subproblems of track fusion usually utilize this matrix to get more accurate results. That is why this matrix should be interchanged between the nodes of the multi-sensor tracking system. This thesis mainly deals with analysis of approximations of the covariance matrix that can best represent this matrix in order to efectively transmit this matrix to the demanding site. Kullback-Leibler (KL) Distance is exploited to derive some of the representations for Gaussian case. Also com- parison of these representations is another objective of this work and this is based on the fusion performance of the representations and the performance is measured for a system of a 2-radar track fusion system.

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Pierre, Cyrille. "Localisation coopérative robuste de robots mobiles par mesure d’inter-distance." Thesis, Université Clermont Auvergne‎ (2017-2020), 2020. http://www.theses.fr/2020CLFAC045.

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Il existe de plus en plus d'applications en robotique mobile faisant intervenir plusieurs robots capables de communiquer entre eux et naviguer en coopération. L'objectif de ces travaux est d'exploiter la communication et la détection des robots avoisinants afin de réaliser de la localisation coopérative. Le moyen de détection des robots utilisé ici se base sur la technologie ultra-wideband,permettant d'effectuer des mesures précises de distance entre deux capteurs. L'approche que nous avons développée privilégie la robustesse et l'intégrité des estimations. Elle permet de prendre en compte des scénarios dans lesquels le peu d'information disponible rend la tâche de localisation difficile à gérer pour les robots. Ainsi, notre solution gère deux problématiques importantes de la localisation coopérative par mesure de distance : la corrélation des données échangées entre les robots et la non-linéarité du modèle d'observation des mesures. Pour résoudre ces problématiques, nous avons fait le choix de développer une approche décentralisée dans laquelle l'aspect coopératif est pris en compte par une modélisation particulière des observations de robots. Une observation correspond ici à une mesure de distance avec une balise (représentant un robot ou un objet statique) dont la position est représentée par une distribution normale. Après plusieurs observations successives d'une même balise, la corrélation devient forte entre l'état du robot et la position de la balise observée. Notre approche s'appuie alors sur la méthode de fusion d'un filtre à intersection de covariance partitionné afin d'éviter les problèmes de sur-convergence dus à la corrélation. De plus, les estimations des états des robots sont modélisées par des mixtures de gaussiennes permettant ainsi de représenter au mieux les formes réelles des distributions obtenues après la fusion d'une mesure de distance. Notre algorithme de localisation coopérative a également la particularité de faire évoluer le nombre de particules des mixtures de gaussiennes afin de se ramener à une modélisation équivalente à un filtre gaussien lorsque les conditions le permettent. Le fonctionnement de notre approche de localisation coopérative est présenté grâce à l'étude de quelques situations basiques mettant en évidences différentes caractéristiques de l'algorithme. Le manuscrit se termine par la présentation de trois scénarios de localisation coopérative faisant intervenir plusieurs robots et objets statiques. Les deux premiers exploitent un simulateur réaliste capable de simuler la physique des robots. Le troisième est une expérimentation réelle utilisant une plateforme urbaine dont dispose le laboratoire. L'objectif de ces expérimentations est de montrer que notre approche reste intègre dans les situations les plus difficiles
There is an increasing number of applications in mobile robotics involving several robots able tocommunicate with each other to navigate cooperatively.The aim of this work is to exploit the communication and the detection of robots in order to achievecooperative localization.The perception tool used here rely on ultra-wideband technology, which allows to perfomprecise range measurements between two sensors.The approach we have developed focuses on the robustness and consistency of robot state estimation.It enables to take into account scenarios where the localization task is difficult to handle due tolimited data available.In that respect, our solution of cooperative localization by range measurements addresses twoimportant problematics: the correlation of data exchanged between robots and the non-linearity ofthe observation model.To solve these issues, we have choosed to develop a decentralized approach in which the cooperativeaspect is taken into account by a specific robot observation model.In this context, an observation corresponds to a range measurement with a beacon (that is, a robotor a static object) where the position is reprensented by a normal distribution. After several observations of the same beacon, the correlation between the robot state and thebeacon position increases.Our approach is based on the fusion method of the Split Covariance Intersection Filter in order toavoid the problem of over-convergence induced by data correlation.In addition, the robot state estimates are modeled by Gaussian mixtures allowing best representationof the distributions obtained after merging a range measurement.Our localization algorithm is also able to dynamically adjust the number of Gaussians of mixturemodels and can be reduced to a simple Gaussian filter when conditions are favorable.Our cooperative localization approach is studied using basic situations, highlighting importantcharacteristics of the algorithm.The manuscript ends with the presentation of three scenarios of cooperative localization implyingseveral robots and static objects.The first two take advantage of a realistic simulator able to simulate the physics of robots.The third is a real world experimentation using a platform for urban experimentation with vehicles.The aim of these scenarios is to show that our approach stay consistent in difficult situations

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Kashlak, Adam B. "A concentration inequality based statistical methodology for inference on covariance matrices and operators." Thesis, University of Cambridge, 2017. https://www.repository.cam.ac.uk/handle/1810/267833.

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In the modern era of high and infinite dimensional data, classical statistical methodology is often rendered inefficient and ineffective when confronted with such big data problems as arise in genomics, medical imaging, speech analysis, and many other areas of research. Many problems manifest when the practitioner is required to take into account the covariance structure of the data during his or her analysis, which takes on the form of either a high dimensional low rank matrix or a finite dimensional representation of an infinite dimensional operator acting on some underlying function space. Thus, novel methodology is required to estimate, analyze, and make inferences concerning such covariances. In this manuscript, we propose using tools from the concentration of measure literature–a theory that arose in the latter half of the 20th century from connections between geometry, probability, and functional analysis–to construct rigorous descriptive and inferential statistical methodology for covariance matrices and operators. A variety of concentration inequalities are considered, which allow for the construction of nonasymptotic dimension-free confidence sets for the unknown matrices and operators. Given such confidence sets a wide range of estimation and inferential procedures can be and are subsequently developed. For high dimensional data, we propose a method to search a concentration in- equality based confidence set using a binary search algorithm for the estimation of large sparse covariance matrices. Both sub-Gaussian and sub-exponential concentration inequalities are considered and applied to both simulated data and to a set of gene expression data from a study of small round blue-cell tumours. For infinite dimensional data, which is also referred to as functional data, we use a celebrated result, Talagrand’s concentration inequality, in the Banach space setting to construct confidence sets for covariance operators. From these confidence sets, three different inferential techniques emerge: the first is a k-sample test for equality of covariance operator; the second is a functional data classifier, which makes its decisions based on the covariance structure of the data; the third is a functional data clustering algorithm, which incorporates the concentration inequality based confidence sets into the framework of an expectation-maximization algorithm. These techniques are applied to simulated data and to speech samples from a set of spoken phoneme data. Lastly, we take a closer look at a key tool used in the construction of concentration based confidence sets: Rademacher symmetrization. The symmetrization inequality, which arises in the probability in Banach spaces literature, is shown to be connected with optimal transport theory and specifically the Wasserstein distance. This insight is used to improve the symmetrization inequality resulting in tighter concentration bounds to be used in the construction of nonasymptotic confidence sets. A variety of other applications are considered including tests for data symmetry and tightening inequalities in Banach spaces. An R package for inference on covariance operators is briefly discussed in an appendix chapter.

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Gliga, Lavinius ioan. "Diagnostic d'une Turbine Eolienne à Distance à l'aide du Réseau de Capteurs sans Fil." Thesis, Normandie, 2019. http://www.theses.fr/2019NORMR063/document.

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Les Éoliennes à Entraînement Direct (ÉED) sont équipées de Générateurs Syn- chrones à Aimants Permanents (GSAP). Leurs trois plus courantes défaillances sont la dé- magnétisation, l’excentricité (statique, dynamique et mixte) et le court-circuit inter-tour. L’analyse de la signature du courant de la machine est souvent utilisée pour rechercher des problèmes du générateur, car ces altérations introduisent des harmoniques supplémen- taires dans les courants générés. La Transformée de Fourier Rapide (TFR) est utilisée pour calculer le spectre des courants. Cependant, la TFR permet de calculer l’ensemble du spec- tre, tandis que le nombre de défauts possible et le nombre d’harmoniques introduites sont faibles. L’algorithme de Goertzel, mis en oeuvre sous forme de filtre (le filtre de Goertzel), est présenté comme une alternative plus efficace au TFR. Le spectre des courants change avec la vitesse du vent, ce qui rend la détection plus difficile. Le Filtre de Kalman Étendu (FKÉ) est proposé comme solution. Le spectre de résidus, calcule entre les courants estimés et les courants générés, est constant, quelle que soit la vitesse du vent. Cependant, l’effet des défauts est visible dans leur spectre. Lors de l’utilisation de l’FKÉ, un défi consiste à estime la matrice de covariance pour le bruit du processus. Une nouvelle méthode était développée pour ça, qui n’utilise aucune de maîtrise du filtre. Les ÉED sont placés soit dans des zones éloignées, soit dans des villes. Pour la surveillance des ÉED, des dizaines ou des centaines de kilomètres de câbles sont nécessaires. Les Réseaux de Capteurs sans Fil (RCF) sont bien adaptés pour être utilisés dans l’infrastructure de communication des ÉED. RCF ont des coûts initiaux et d’entretien plus faibles et leurs installations sont rapides. De plus, ils peuvent compléter les réseaux câblés. Différentes technologies sans fil sont comparées : les technologies à grande surface, ainsi que les technologies à courte portée qui supportent des débits de données élevés
Direct Drive Wind Turbines (DDWTs) are equipped with Permanent Magnet Syn- chronous Generators (PMSGs). Their three most common failures are demagnetization, ec- centricity (static, dynamic and mixed) and inter-turn short circuit. Machine Current Signa- ture Analysis is often used to look for generator problems, as these impairments introduce additional harmonics into the generated currents. The Fast Fourier Transform (FFT) is utilized to compute the spectrum of the currents. However, the FFT calculates the whole spectrum, while the number of possible faults and the number of introduced harmonics is low. The Goertzel algorithm, implemented as a filter (the Goertzel filter), is presented as a more efficient alternative to the FFT. The spectrum of the currents changes with the wind speed, and thus the detection is made more difficult. The Extended Kalman Filter (EKF) is proposed as a solution. The spectrum of the residuals, computed between the estimated and the generated current, is constant, regardless of the wind speed. However, the effect of the faults is visible in the spectrum. When using the EKF, one challenge is to find out the covariance matrix of the process noise. A new method was developed in this regard, which does not use any of the matrices of the filter. DDWTs are either placed in remote areas or in cities. For the monitoring of a DDWT, tens or hundreds of kilometers of cables are necessary. Wireless Sensor Networks (WSNs) are suited to be used in the communication infrastructure of DDWTs. WSNs have lower initial and maintenance costs, and they are quickly installed. Moreover, they can complement wired networks. Different wireless technologies are com- pared - both wide area ones, as well as short range technologies which support high data rates

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Young, Barrington R. St A. "Efficient Algorithms for Data Mining with Federated Databases." University of Cincinnati / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1179332091.

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Han, Zhi. "Applications of stochastic control and statistical inference in macroeconomics and high-dimensional data." Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/54401.

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This dissertation is dedicated to study the modeling of drift control in foreign exchange reserves management and design the fast algorithm of statistical inference with its application in high dimensional data analysis. The thesis has two parts. The first topic involves the modeling of foreign exchange reserve management as an drift control problem. We show that, under certain conditions, the control band policies are optimal for the discounted cost drift control problem and develop an algorithm to calculate the optimal thresholds of the optimal control band policy. The second topic involves the fast computing algorithm of partial distance covariance statistics with its application in feature screening in high dimensional data. We show that an O(n log n) algorithm for a version of the partial distance covariance exists, compared with the O(n^2) algorithm implemented directly accordingly to its definition. We further propose an iterative feature screening procedure in high dimensional data based on the partial distance covariance. This procedure enjoys two advantages over the correlation learning. First, an important predictor that is marginally uncorrelated but jointly correlated with the response can be picked by our procedure and thus entering the estimation model. Second, our procedure is robust to model mis- specification.

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Paler, Mary Elvi Aspiras. "On Modern Measures and Tests of Multivariate Independence." Bowling Green State University / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1447628176.

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Books on the topic "Distance de covariance"

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Greenspan, Donald. Completely conservative and covariant numerical methodology for N-body problems with distance-dependent potentials. Arlington, Tex: University of Texas at Arlington, Dept. of Mathematics, Research Center for Advanced Study (RCAS), 1992.

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Schmidt, Alexandra, Jennifer Hoeting, João Batista M. Pereira, and Pedro Paulo Vieira. Mapping malaria in the Amazon rain forest: A spatio-temporal mixture model. Edited by Anthony O'Hagan and Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.5.

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This article focuses on the use of a spatio-temporal mixture model for mapping malaria in the Amazon rain forest. The spatio-temporal model was developed to study malaria outbreaks over a four year period in the state of Amazonas, Brazil. The goal is to predict malaria counts for unobserved municipalities and future time periods with the aid of a free-form spatial covariance structure and a methodology that allows temporal prediction and spatial interpolation for outbreaks of malaria over time. The proposed structure is unique in that it is not a distance- or neighbourhood-based covariance model. Instead, spatial correlation is allowed among all locations to be estimated freely. To model the temporal correlation between observations, a Bayesian dynamic linear model is incorporated into one level of the spatio-temporal mixture model. The model also provides sensible ways of malaria mapping for municipalities which were not observed.

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Kennefick, Daniel. Three and a Half Principles: The Origins of Modern Relativity Theory. Edited by Jed Z. Buchwald and Robert Fox. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199696253.013.27.

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This article explores the origins of modern relativity theory. In his 1905 paper On the Electrodynamics of Moving Bodies, Albert Einstein directly addressed one of the largest issues of the time. Electrodynamics aims to describe the motion of charged particles (usually thought of as electrons), whose interaction through the electromagnetic field, as described by Maxwell’s equations, affects their respective motions. The problem was so complex because the electromagnetic field theory was not an action-at-a-distance theory. This article begins with an overview of the principle of relativity and of the constancy of the speed of light, followed by a discussion on the relativity of simultaneity, the mass–energy equivalence, and experimental tests of special relativity. It also examines the principle of equivalence, the concepts of spacetime curvature and general covariance, and Mach’s principle. Finally, it considers experimental predictions of general relativity.

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Book chapters on the topic "Distance de covariance"

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Á. Harmati, István, and Robert Fullér. "On Possibilistic Version of Distance Covariance and Correlation." In Trends in Mathematics and Computational Intelligence, 175–81. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00485-9_20.

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Nithya, S., and A. Salim. "Dimensionality Reduction by Distance Covariance and Eigen Value Decomposition." In Communications in Computer and Information Science, 112–22. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-5427-3_12.

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Minh, Hà Quang. "Affine-Invariant Riemannian Distance Between Infinite-Dimensional Covariance Operators." In Lecture Notes in Computer Science, 30–38. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25040-3_4.

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Said, Salem, Lionel Bombrun, and Yannick Berthoumieu. "Texture Classification Using Rao’s Distance on the Space of Covariance Matrices." In Lecture Notes in Computer Science, 371–78. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25040-3_40.

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Iwamura, Masakazu, Shinichiro Omachi, and Hirotomo Aso. "A Method to Estimate the True Mahalanobis Distance from Eigenvectors of Sample Covariance Matrix." In Lecture Notes in Computer Science, 498–507. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-70659-3_52.

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Kobayashi, Yasuyuki. "A Corrector for the Sample Mahalanobis Distance Free from Estimating the Population Eigenvalues of Covariance Matrix." In Neural Information Processing, 224–32. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46672-9_26.

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Quang, Minh Hà. "Riemannian Distances between Covariance Operators and Gaussian Processes." In Functional and High-Dimensional Statistics and Related Fields, 177–85. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47756-1_24.

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Muzzall, Evan, and Alfredo Coppa. "Temporal and Spatial Biological Kinship Variation at Campovalano and Alfedena in Iron Age Central Italy." In Bioarchaeology of Frontiers and Borderlands, 107–32. University Press of Florida, 2019. http://dx.doi.org/10.5744/florida/9781683400844.003.0006.

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This chapter utilizes craniometric, dental metric, and Arizona State University Dental Anthropology System (ASUDAS) data to investigate temporospatial differences in biological distance patterns at the monumental necropoles of Campovalano and Alfedena, Iron Age Central Italy. Results of craniometric one-way analyses of variance suggest that Campovalano crania exhibit great biological continuity through time, while geometric mean scaled dental metric multidimensional scaling and ASUDAS neighbor-joining clustering indicate Campovalano samples are more similar to each other than compared to Alfedena Campo Consolino, the ritual core of the broader Alfedena necropolis. Also, Mantel tests reveal that male faces and cranial bases faintly correlate with burial distances at Alfedena Campo Consolino, which is consistent with previous research. However, female tooth row metrics slightly correlate with burial distances at Campovalano, which was unexpected. Finally, logistic regression and analyses of covariance models of a small dental metric subset could suggest that Alfedena Campo Consolino is the most different of the metric samples and that more comprehensive modelling of sex-specific dental morphogenetic field variation should be considered in bioarchaeology. Results are discussed in terms of biological and physical distances, endogamy and marriage rules, heritability, and small and imbalanced samples for investigating the frontiers and borderlands of the past.

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Emam, Moataz H. "General Covariance." In Covariant Physics, 164–210. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198864899.003.0005.

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The general theory of relativity is introduced based on the principle of equivalence. Gravity is shown to arise dues to spacetime curvature. Specific examples of curved spacetimes are presented from the approximate but more intuitive to the complex: Uniform gravitational field (Galilean metric), the Newtonian weak field metric, Schwarzschild’s exterior and interior solutions, black holes, and cosmological spacetimes. A brief discussion on distances, areas and volumes in curved spaces is also given.

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van den Dool, Huug. "Empirical Orthogonal Functions." In Empirical Methods in Short-Term Climate Prediction. Oxford University Press, 2006. http://dx.doi.org/10.1093/oso/9780199202782.003.0012.

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The purpose of this chapter is to discuss Empirical Orthogonal Functions (EOF), both in method and application. When dealing with teleconnections in the previous chapter we came very close to EOF, so it will be a natural extension of that theme. However, EOF opens the way to an alternative point of view about space–time relationships, especially correlation across distant times as in analogues. EOFs have been treated in book-size texts, most recently in Jolliffe (2002), a principal older reference being Preisendorfer (1988). The subject is extremely interdisciplinary, and each field has its own nomenclature, habits and notation. Jolliffe’s book is probably the best attempt to unify various fields. The term EOF appeared first in meteorology in Lorenz (1956). Zwiers and von Storch (1999) and Wilks (1995) devote lengthy single chapters to the topic. Here we will only briefly treat EOF or PCA (Principal Component Analysis) as it is called in most fields. Specifically we discuss how to set up the covariance matrix, how to calculate the EOF, what are their properties, advantages, disadvantages etc. We will do this in both space–time set-ups already alluded to in Equations (2.14) and (2.14a). There are no concrete rules as to how one constructs the covariance matrix. Hence there are in the literature matrices based on correlation, based on covariance, etc. Here we follow the conventions laid out in Chapter 2. The post-processing and display conventions of EOFs can also be quite confusing. Examples will be shown, for both daily and seasonal mean data, for both the Northern and Southern Hemisphere. EOF may or may not look like teleconnections. Therefore, as a diagnostic tool, EOFs may not always allow the interpretation some would wish. This has led to many proposed “simplifications” of the EOFs, which hopefully are more like teleconnections. However, regardless of physical interpretation, since EOFs are maximally efficient in retaining as much of the data set’s information as possible for as few degrees of freedom as possible they are ideally suited for empirical modeling. Indeed EOFs are an extremely popular tool these days.

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Conference papers on the topic "Distance de covariance"

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Enqvist, Per, and Johan Karlsson. "Minimal Itakura-Saito distance and covariance interpolation." In 2008 47th IEEE Conference on Decision and Control. IEEE, 2008. http://dx.doi.org/10.1109/cdc.2008.4739312.

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Patil, Nishad, Sandeep Menon, Diganta Das, and Michael Pecht. "Evaluation of Robust Covariance Estimation Techniques for Anomaly Detection of Insulated Gate Bipolar Transistors (IGBT)." In ASME 2010 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. ASMEDC, 2010. http://dx.doi.org/10.1115/smasis2010-3861.

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An approach to detect anomalies in IGBTs is to monitor the collector-emitter current and voltage in application. These current and voltage parameters can then be reduced to a univariate distance measure called the Mahalanobis Distance (MD). The MD values with the use of an appropriate threshold enable anomaly detection of these devices. Mahalanobis distances (MD) are weighted Euclidean distances; the distance of each point from the center of the distribution is weighted by the inverse of the sample variance-covariance matrix. The presence of outliers in the monitored data can lead to the overestimation of the covariance matrix that in turn affects the anomaly detection results. This issue can be addressed by the use of robust covariance estimation techniques. In this study, the minimum volume ellipsoid (MVE) estimator, the minimum covariance determinant estimator (MCD) and the nearest neighbor variance estimator (NNVE) were used for anomaly detection of IGBTs. IGBTs were aged under a resistive load until failure. The monitored collector-emitter current and voltage values were used as input parameters for the MD calculation. The three robust covariance estimation techniques were used to compute the MD values and the anomaly detection times were compared to the results obtained by the classical covariance estimation technique.

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Tiomoko, Malik, Romain Couillet, Eric Moisan, and Steeve Zozor. "Improved Estimation of the Distance between Covariance Matrices." In ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2019. http://dx.doi.org/10.1109/icassp.2019.8682621.

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Pratiher, Sawon, Sabyasachi Mukhopadhyay, Ritwik Barman, Souvik Pratiher, Asima Pradhan, Nirmalya Ghosh, and Prasanta K. Panigrahi. "Covariance weighted distance metrics for optical diagnosis of cancer." In 2016 International Conference on Signal Processing and Communication (ICSC). IEEE, 2016. http://dx.doi.org/10.1109/icspcom.2016.7980603.

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Lin, Wanbiao, Lei Sun, Jianchao Song, Xinwei Chen, and Jingtai Liu. "Map Optimization with Distance-Based Covariance in Industrial Field." In 2018 IEEE 8th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER). IEEE, 2018. http://dx.doi.org/10.1109/cyber.2018.8688135.

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Sayed, Mehran, Zeeshan Bhatti, and Imdad Ali Ismaili. "Proposed Model for Facial Animation using Covariance Matrix and Mahalanobis Distance Algorithms." In 2019 2nd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET). IEEE, 2019. http://dx.doi.org/10.1109/icomet.2019.8673465.

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Peralta, Daniel, and Yvan Saeys. "Distributed, Numerically Stable Distance and Covariance Computation with MPI for Extremely Large Datasets." In 2019 IEEE International Congress on Big Data (BigData Congress). IEEE, 2019. http://dx.doi.org/10.1109/bigdatacongress.2019.00023.

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Keskin, Furkan, A. Enis Cetin, Tulin Ersahin, and Rengul Cetin-Atalay. "Microscopic image classification via ℂWT-based covariance descriptors using Kullback-Leibler distance." In 2012 IEEE International Symposium on Circuits and Systems - ISCAS 2012. IEEE, 2012. http://dx.doi.org/10.1109/iscas.2012.6271692.

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Gouskir, Mohamed, Hicham Aissaoui, Benachir Elhadadi, Mohammed Boutalline, and Belaid Bouikhalene. "Automatic brain tumor detection and segmentation for MRI using covariance and geodesic distance." In 2014 International Conference on Multimedia Computing and Systems (ICMCS). IEEE, 2014. http://dx.doi.org/10.1109/icmcs.2014.6911342.

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Lozano, Aurelie C., Huijing Jiang, and Xinwei Deng. "Robust sparse estimation of multiresponse regression and inverse covariance matrix via the L2 distance." In KDD' 13: The 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2013. http://dx.doi.org/10.1145/2487575.2487667.

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Reports on the topic "Distance de covariance"

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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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