Relevant bibliographies by topics / Covariance

Academic literature on the topic 'Covariance'

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Published: 4 June 2021
Last updated: 1 April 2023

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Journal articles on the topic "Covariance"

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Meyer, Karin, and Mark Kirkpatrick. "Up hill, down dale: quantitative genetics of curvaceous traits." Philosophical Transactions of the Royal Society B: Biological Sciences 360, no. 1459 (July 7, 2005): 1443–55. http://dx.doi.org/10.1098/rstb.2005.1681.

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‘Repeated’ measurements for a trait and individual, taken along some continuous scale such as time, can be thought of as representing points on a curve, where both means and covariances along the trajectory can change, gradually and continually. Such traits are commonly referred to as ‘function-valued’ (FV) traits. This review shows that standard quantitative genetic concepts extend readily to FV traits, with individual statistics, such as estimated breeding values and selection response, replaced by corresponding curves, modelled by respective functions. Covariance functions are introduced as the FV equivalent to matrices of covariances. Considering the class of functions represented by a regression on the continuous covariable, FV traits can be analysed within the linear mixed model framework commonly employed in quantitative genetics, giving rise to the so-called random regression model. Estimation of covariance functions, either indirectly from estimated covariances or directly from the data using restricted maximum likelihood or Bayesian analysis, is considered. It is shown that direct estimation of the leading principal components of covariance functions is feasible and advantageous. Extensions to multi-dimensional analyses are discussed.
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Gao, Boran, Can Yang, Jin Liu, and Xiang Zhou. "Accurate genetic and environmental covariance estimation with composite likelihood in genome-wide association studies." PLOS Genetics 17, no. 1 (January 4, 2021): e1009293. http://dx.doi.org/10.1371/journal.pgen.1009293.

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Genetic and environmental covariances between pairs of complex traits are important quantitative measurements that characterize their shared genetic and environmental architectures. Accurate estimation of genetic and environmental covariances in genome-wide association studies (GWASs) can help us identify common genetic and environmental factors associated with both traits and facilitate the investigation of their causal relationship. Genetic and environmental covariances are often modeled through multivariate linear mixed models. Existing algorithms for covariance estimation include the traditional restricted maximum likelihood (REML) method and the recent method of moments (MoM). Compared to REML, MoM approaches are computationally efficient and require only GWAS summary statistics. However, MoM approaches can be statistically inefficient, often yielding inaccurate covariance estimates. In addition, existing MoM approaches have so far focused on estimating genetic covariance and have largely ignored environmental covariance estimation. Here we introduce a new computational method, GECKO, for estimating both genetic and environmental covariances, that improves the estimation accuracy of MoM while keeping computation in check. GECKO is based on composite likelihood, relies on only summary statistics for scalable computation, provides accurate genetic and environmental covariance estimates across a range of scenarios, and can accommodate SNP annotation stratified covariance estimation. We illustrate the benefits of GECKO through simulations and applications on analyzing 22 traits from five large-scale GWASs. In the real data applications, GECKO identified 50 significant genetic covariances among analyzed trait pairs, resulting in a twofold power gain compared to the previous MoM method LDSC. In addition, GECKO identified 20 significant environmental covariances. The ability of GECKO to estimate environmental covariance in addition to genetic covariance helps us reveal strong positive correlation between the genetic and environmental covariance estimates across trait pairs, suggesting that common pathways may underlie the shared genetic and environmental architectures between traits.
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Gneiting, Tilmann, Zoltán Sasvári, and Martin Schlather. "Analogies and correspondences between variograms and covariance functions." Advances in Applied Probability 33, no. 3 (September 2001): 617–30. http://dx.doi.org/10.1239/aap/1005091356.

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Variograms and covariance functions are key tools in geostatistics. However, various properties, characterizations, and decomposition theorems have been established for covariance functions only. We present analogous results for variograms and explore the connections with covariance functions. Our findings include criteria for covariance functions on intervals, and we apply them to exponential models, fractional Brownian motion, and locally polynomial covariances. In particular, we characterize isotropic locally polynomial covariance functions of degree 3.
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Gneiting, Tilmann, Zoltán Sasvári, and Martin Schlather. "Analogies and correspondences between variograms and covariance functions." Advances in Applied Probability 33, no. 03 (September 2001): 617–30. http://dx.doi.org/10.1017/s0001867800011034.

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Variograms and covariance functions are key tools in geostatistics. However, various properties, characterizations, and decomposition theorems have been established for covariance functions only. We present analogous results for variograms and explore the connections with covariance functions. Our findings include criteria for covariance functions on intervals, and we apply them to exponential models, fractional Brownian motion, and locally polynomial covariances. In particular, we characterize isotropic locally polynomial covariance functions of degree 3.
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Yuan, Ke-Hai, and Peter M. Bentler. "9. Structural Equation Modeling with Robust Covariances." Sociological Methodology 28, no. 1 (August 1998): 363–96. http://dx.doi.org/10.1111/0081-1750.00052.

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Existing methods for structural equation modeling involve fitting the ordinary sample covariance matrix by a proposed structural model. Since a sample covariance is easily influenced by a few outlying cases, the standard practice of modeling sample covariances can lead to inefficient estimates as well as inflated fit indices. By giving a proper weight to each individual case, a robust covariance will have a bounded influence function as well as a nonzero breakdown point. These robust properties of the covariance estimators will be carried over to the parameter estimators in the structural model if a technically appropriate procedure is used. We study such a procedure in which robust covariances replace ordinary sample covariances in the context of the Wishart likelihood function. This procedure is easy to implement in practice. Statistical properties of this procedure are investigated. A fit index is given based on sampling from an elliptical distribution. An estimating equation approach is used to develop a variety of robust covariances, and consistent covariances of these robust estimators, needed for standard errors and test statistics, follow from this approach. Examples illustrate the inflated statistics and distorted parameter estimates obtained by using sample covariances when compared with those obtained by using robust covariances. The merits of each method and its relevance to specific types of data are discussed.
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Shamsipour, Pejman, Denis Marcotte, Michel Chouteau, Martine Rivest, and Abderrezak Bouchedda. "3D stochastic gravity inversion using nonstationary covariances." GEOPHYSICS 78, no. 2 (March 1, 2013): G15—G24. http://dx.doi.org/10.1190/geo2012-0122.1.

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The flexibility of geostatistical inversions in geophysics is limited by the use of stationary covariances, which, implicitly and mostly for mathematical convenience, assumes statistical homogeneity of the studied field. For fields showing sharp contrasts due, for example, to faults or folds, an approach based on the use of nonstationary covariances for cokriging inversion was developed. The approach was tested on two synthetic cases and one real data set. Inversion results based on the nonstationary covariance were compared to the results from the stationary covariance for two synthetic models. The nonstationary covariance better recovered the known synthetic models. With the real data set, the nonstationary assumption resulted in a better match with the known surface geology.
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NIKOLIĆ, HRVOJE. "QUANTUM DETERMINISM FROM QUANTUM GENERAL COVARIANCE." International Journal of Modern Physics D 15, no. 12 (December 2006): 2171–75. http://dx.doi.org/10.1142/s0218271806009595.

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The requirement of general covariance of quantum field theory (QFT) naturally leads to quantization based on the manifestly covariant De Donder–Weyl formalism. To recover the standard noncovariant formalism without violating covariance, fields need to depend on time in a specific deterministic manner. This deterministic evolution of quantum fields is recognized as a covariant version of the Bohmian hidden-variable interpretation of QFT.
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Aboutaleb, Youssef M., Mazen Danaf, Yifei Xie, and Moshe E. Ben-Akiva. "Sparse covariance estimation in logit mixture models." Econometrics Journal 24, no. 3 (March 19, 2021): 377–98. http://dx.doi.org/10.1093/ectj/utab008.

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Summary This paper introduces a new data-driven methodology for estimating sparse covariance matrices of the random coefficients in logit mixture models. Researchers typically specify covariance matrices in logit mixture models under one of two extreme assumptions: either an unrestricted full covariance matrix (allowing correlations between all random coefficients), or a restricted diagonal matrix (allowing no correlations at all). Our objective is to find optimal subsets of correlated coefficients for which we estimate covariances. We propose a new estimator, called MISC (mixed integer sparse covariance), that uses a mixed-integer optimization (MIO) program to find an optimal block diagonal structure specification for the covariance matrix, corresponding to subsets of correlated coefficients, for any desired sparsity level using Markov Chain Monte Carlo (MCMC) posterior draws from the unrestricted full covariance matrix. The optimal sparsity level of the covariance matrix is determined using out-of-sample validation. We demonstrate the ability of MISC to correctly recover the true covariance structure from synthetic data. In an empirical illustration using a stated preference survey on modes of transportation, we use MISC to obtain a sparse covariance matrix indicating how preferences for attributes are related to one another.
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Wang, Xuguang, Chris Snyder, and Thomas M. Hamill. "On the Theoretical Equivalence of Differently Proposed Ensemble–3DVAR Hybrid Analysis Schemes." Monthly Weather Review 135, no. 1 (January 1, 2007): 222–27. http://dx.doi.org/10.1175/mwr3282.1.

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Abstract Hybrid ensemble–three-dimensional variational analysis schemes incorporate flow-dependent, ensemble-estimated background-error covariances into the three-dimensional variational data assimilation (3DVAR) framework. Typically the 3DVAR background-error covariance estimate is assumed to be stationary, nearly homogeneous, and isotropic. A hybrid scheme can be achieved by 1) directly replacing the background-error covariance term in the cost function by a linear combination of the original background-error covariance with the ensemble covariance or 2) through augmenting the state vector with another set of control variables preconditioned upon the square root of the ensemble covariance. These differently proposed hybrid schemes are proven to be equivalent. The latter framework may be a simpler way to incorporate ensemble information into operational 3DVAR schemes, where the preconditioning is performed with respect to the background term.
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Yaremchuk, Max, Dmitri Nechaev, and Chudong Pan. "A Hybrid Background Error Covariance Model for Assimilating Glider Data into a Coastal Ocean Model." Monthly Weather Review 139, no. 6 (June 1, 2011): 1879–90. http://dx.doi.org/10.1175/2011mwr3510.1.

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Abstract A hybrid background error covariance (BEC) model for three-dimensional variational data assimilation of glider data into the Navy Coastal Ocean Model (NCOM) is introduced. Similar to existing atmospheric hybrid BEC models, the proposed model combines low-rank ensemble covariances with the heuristic Gaussian-shaped covariances to estimate forecast error statistics. The distinctive features of the proposed BEC model are the following: (i) formulation in terms of inverse error covariances, (ii) adaptive determination of the rank m of with information criterion based on the innovation error statistics, (iii) restriction of the heuristic covariance operator to the null space of , and (iv) definition of the BEC magnitudes through separate analyses of the innovation error statistics in the state space and the null space of . The BEC model is validated by assimilation experiments with simulated and real data obtained during a glider survey of the Monterey Bay in August 2003. It is shown that the proposed hybrid scheme substantially improves the forecast skill of the heuristic covariance model.
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Dissertations / Theses on the topic "Covariance"

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Kang, Xiaoning. "Contributions to Large Covariance and Inverse Covariance Matrices Estimation." Diss., Virginia Tech, 2016. http://hdl.handle.net/10919/82150.

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Estimation of covariance matrix and its inverse is of great importance in multivariate statistics with broad applications such as dimension reduction, portfolio optimization, linear discriminant analysis and gene expression analysis. However, accurate estimation of covariance or inverse covariance matrices is challenging due to the positive definiteness constraint and large number of parameters, especially in the high-dimensional cases. In this thesis, I develop several approaches for estimating large covariance and inverse covariance matrices with different applications. In Chapter 2, I consider an estimation of time-varying covariance matrices in the analysis of multivariate financial data. An order-invariant Cholesky-log-GARCH model is developed for estimating the time-varying covariance matrices based on the modified Cholesky decomposition. This decomposition provides a statistically interpretable parametrization of the covariance matrix. The key idea of the proposed model is to consider an ensemble estimation of covariance matrix based on the multiple permutations of variables. Chapter 3 investigates the sparse estimation of inverse covariance matrix for the highdimensional data. This problem has attracted wide attention, since zero entries in the inverse covariance matrix imply the conditional independence among variables. I propose an orderinvariant sparse estimator based on the modified Cholesky decomposition. The proposed estimator is obtained by assembling a set of estimates from the multiple permutations of variables. Hard thresholding is imposed on the ensemble Cholesky factor to encourage the sparsity in the estimated inverse covariance matrix. The proposed method is able to catch the correct sparse structure of the inverse covariance matrix. Chapter 4 focuses on the sparse estimation of large covariance matrix. Traditional estimation approach is known to perform poorly in the high dimensions. I propose a positive-definite estimator for the covariance matrix using the modified Cholesky decomposition. Such a decomposition provides a exibility to obtain a set of covariance matrix estimates. The proposed method considers an ensemble estimator as the center" of these available estimates with respect to Frobenius norm. The proposed estimator is not only guaranteed to be positive definite, but also able to catch the underlying sparse structure of the true matrix.
Ph. D.
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Cissokho, Youssouph. "Extremal Covariance Matrices." Thesis, Université d'Ottawa / University of Ottawa, 2018. http://hdl.handle.net/10393/37124.

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The tail dependence coefficient (TDC) is a natural tool to describe extremal dependence. Estimation of the tail dependence coefficient can be performed via empirical process theory. In case of extremal independence, the limit degenerates and hence one cannot construct a test for extremal independence. In order to deal with this issue, we consider an analog of the covariance matrix, namely the extremogram matrix, whose entries depend only on extremal observations. We show that under the null hypothesis of extremal independence and for finite dimension d ≥ 2, the largest eigenvalue of the sample extremogram matrix converges to the maximum of d independent normal random variables. This allows us to conduct an hypothesis testing for extremal independence by means of the asymptotic distribution of the largest eigenvalue. Simulation studies are performed to further illustrate this approach.
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Dubbs, Alexander. "Beta-ensembles with covariance." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/90185.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mathematics, 2014.
67
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 73-79).
This thesis presents analytic samplers for the [beta]-Wishart and [beta]-MANOVA ensembles with diagonal covariance. These generalize the [beta]-ensembles of Dumitriu-Edelman, Lippert, Killip-Nenciu, Forrester-Rains, and Edelman-Sutton, as well as the classical [beta] = 1, 2,4 ensembles of James, Li-Xue, and Constantine. Forrester discovered a sampler for the [beta]-Wishart ensemble around the same time, although our proof has key differences. We also derive the largest eigenvalue pdf for the [beta]-MANOVA case. In infinite-dimensional random matrix theory, we find the moments of the Wachter law, and the Jacobi parameters and free cumulants of the McKay and Wachter laws. We also present an algorithm that uses complex analysis to solve "The Moment Problem." It takes the first batch of moments of an analytic, compactly-supported distribution as input, and it outputs a fine discretization of that distribution.
by Alexander Dubbs.
Ph. D.
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Armour, Bernard. "Structured covariance autoregressive parameter estimation." Thesis, McGill University, 1989. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=59559.

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In this thesis, the problem of estimating the autoregressive (AR) parameters of a wide sense stationary process is studied for the application of spectrum estimation of short data records. The approach taken is to first estimate a structured covariance matrix satisfying an optimality criterion and then map the estimate into the AR parameter estimates. Most covariance estimators are based on a least squares prediction error criterion. The new approach taken in this thesis is the use of a maximum likelihood (ML) criterion to obtain better covariance estimates. Both approximate and exact ML algorithms are developed based on an iterative Newton-Raphson technique to maximize the loglikelihood functions. Testing reveals the symmetric centro-symmetric structured covariance provides superior estimates in comparison to the Toeplitz structure and that the exact ML AR parameter estimates are among the lowest variance. Full comparison of the ML and popular AR spectrum estimation techniques is included.
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Wilkinson, Darren James. "Bayes linear covariance matrix adjustment." Thesis, Durham University, 1995. http://etheses.dur.ac.uk/5315/.

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In this thesis, a Bayes linear methodology for the adjustment of covariance matrices is presented and discussed. A geometric framework for quantifying uncertainties about covariance matrices is set up, and an inner-product for spaces of random matrices is motivated and constructed. The inner-product on this space captures aspects of belief about the relationships between covariance matrices of interest, providing a structure rich enough to adjust beliefs about unknown matrices in the light of data such as sample covariance matrices, exploiting second-order exchangeability and related specifications to obtain representations allowing analysis. Adjustment is associated with orthogonal projection, and illustrated by examples for some common problems. The difficulties of adjusting the covariance matrices underlying exchangeable random vectors is tackled and discussed. Learning about the covariance matrices associated with multivariate time series dynamic linear models is shown to be amenable to a similar approach. Diagnostics for matrix adjustments are also discussed.
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Gent, N. D. "Scale covariance and non-triviality." Thesis, Imperial College London, 1985. http://hdl.handle.net/10044/1/37703.

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Musolas, Otaño Antoni M. (Antoni Maria). "Covariance estimation on matrix manifolds." Thesis, Massachusetts Institute of Technology, 2020. https://hdl.handle.net/1721.1/127063.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, May, 2020
Cataloged from the official PDF of thesis.
Includes bibliographical references (pages 135-150).
The estimation of covariance matrices is a fundamental problem in multivariate analysis and uncertainty quantification. Covariance matrices are an essential modeling tool in climatology, econometrics, model reduction, biostatistics, signal processing, and geostatistics, among other applications. In practice, covariances often must be estimated from samples. While the sample covariance matrix is a consistent estimator, it performs poorly when the relative number of samples is small; improved estimators that impose structure must be considered. Yet standard parametric covariance families can be insufficiently flexible for many applications, and non-parametric approaches may not easily allow certain kinds of prior knowledge to be incorporated. In this thesis, we harness the structure of the manifold of symmetric positive-(semi)definite matrices to build families of covariance matrices out of geodesic curves.
These covariance families offer more flexibility for problem-specific tailoring than classical parametric families, and are preferable to simple convex combinations. Moreover, the proposed families can be interpretable: the internal parameters may serve as explicative variables for the problem of interest. Once a covariance family has been chosen, one typically needs to select a representative member by solving an optimization problem, e.g., by maximizing the likelihood associated with a data set. Consistent with the construction of the covariance family, we propose a differential geometric interpretation of this problem: minimizing the natural distance on the covariance manifold. Our approach does not require assuming a particular probability distribution for the data. Within this framework, we explore two different estimation settings.
First, we consider problems where representative "anchor" covariance matrices are available; these matrices may result from offline empirical observations or computational simulations of the relevant spatiotemporal process at related conditions. We connect multiple anchors to build multi-parametric covariance families, and then project new observations onto this family--for instance, in online estimation with limited data. We explore this problem in the full-rank and low-rank settings. In the former, we show that the proposed natural distance-minimizing projection and maximum likelihood are locally equivalent up to second order. In the latter, we devise covariance families and minimization schemes based on generalizations of multi-linear and Bézier interpolation to the appropriate manifold.
Second, for problems where anchor matrices are unavailable, we propose a geodesic reformulation of the classical shrinkage estimator: that is, we construct a geodesic family that connects the identity (or any other target) matrix to the sample covariance matrix and minimize the expected natural distance to the true covariance. The proposed estimator inherits the properties of the geodesic distance, for instance, invariance to inversion. Leveraging previous results, we propose a solution heuristic that compares favorably with recent non-linear shrinkage estimators. We demonstrate these covariance families and estimation approaches in a range of synthetic examples, and in applications including wind field modeling and groundwater hydrology.
by Antoni Musolas.
Ph. D.
Ph.D. Massachusetts Institute of Technology, Department of Aeronautics and Astronautics
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Wegelin, Jacob A. "Latent models for cross-covariance /." Thesis, Connect to this title online; UW restricted, 2001. http://hdl.handle.net/1773/8982.

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Maillard-Teyssier, Laurence Christine. "Calcul stochastique covariant à sauts & calcul stochastique à sauts covariants." Versailles-St Quentin en Yvelines, 2003. http://www.theses.fr/2003VERS0031.

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Nous proposons un calcul stochastique covariant pour des semimartingales dans le fibré tangent TM au dessus d'une variété M. Une connexion sur M permet de définir une dérivée intrrinsèque d'une courbe (Yt), C1 dans TM, la dérivée covariante. Plus précisément, c'est la dérivée de (Yt) vue dans un repère mobile, se déplaçant parallèlement le long de sa courbe (x1)projetée sur M. Avec le principe de transfert, Norris définit l'intégration covariante le long d'une semimartingale dans TM. Nous décrivons le cas où la semimartingale saute dans TM, en utilisant les travaux de Norris et les résultats de Cohen sur le calcul stochastique à sauts sur une variété. Nous comprenons, que, selon l'ordre dans lequel on compose la fonction qui donne les sauts et la connexion, on obtient un calcul stochastique covariant à sauts covariants. Tous deux dépendent du choix de la connexion et des objets (interpolateurs et connecteurs) décrivant les sauts au sens de Stratonovich ou d'Itô. Nous étudions les choix qui rendent équivalents les deux calculs. Sous certaines conditions, on retrouve les résultats de Norris lorsque (Yt) est continue. Le cas continu est décrit par un calcul covariant continu d'ordre deux, formalisme défini à l'aide de la notion de connexion d'ordre deux
We propose a stochastic covaraiant calculus for càdlàg semimartingales in the tangent bundle TM over a manifold M. A connexion on M allows us to define an intrinsic derivative of a C1 curve (Yt) in TM, the covariant derivative. More precisely, it is the derivative of (Yt) seen in a frame moving parallely along its projection curve (xt) on M. With the transfer principle, Norris defined the stochastic covariant integration along a continuous semimartingale in TM. We describe the case where the semimartingale jumps in TM, using Norris's work and Cohen's results about stochastic calculus with jumps on manifolds. We see that, depending on the order in which we compose the function giving the jumps and the connection, we obtain a stochastic covariant calculus with jumps or a stochastic calculus with covariant jumps. Both depend on the choice of the connection and of the tools (interpolation and connection rules) describing the jumps in the meaning of Stratonovich or Itô. We study the choices that make equivalent the two calculus. Under suitable conditions, we recover Norris's results when (Yt) is continuous. The continuous case is described by a covariant continuous calculus of order two, a formalism defined with the notion of connection of order two
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Heiderich, Karen Rachel. "Spin-two fields and general covariance." Thesis, University of British Columbia, 1991. http://hdl.handle.net/2429/31021.

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It has long been presumed that any consistent nonlinear theory of a spin-two field must be generally covariant. Using Wald's consistency criteria, we exhibit classes of nonlinear theories of a spin-two field that do not have general covariance. We consider four alternative formulations of the spin-two equations. As a first example, we consider a conformally invariant theory of a spin-two field coupled to a scalar field. In the next two cases, the usual symmetric rank-two tensor field, γab, is chosen as the potential. In the fourth case, a traceless symmetric rank-two tensor field is used as the potential. We find that consistent nonlinear generalization of these different formulations leads to theories of a spin-two field that are not generally covariant. In particular, we find types of theories which, when interpreted in terms of a metric, are invariant under the infinitesimal gauge transformation γab→γab + ∇ (a∇[symbol omitted]K[symbol omitted]), where Kab is an arbitrary two-form field. In addition, we find classes of theories that are conformally invariant. As a related problem, we compare the types of theories obtained from the nonlinear extension of a divergence- and curl-free vector field when it is described in terms of two of its equivalent formulations. We find that nonlinear extension of the theory is quite different in each case. Moreover, the resulting types of nonlinear theories may not necessarily be equivalent. A similar analysis is carried out for three-dimensional electromagnetism.
Science, Faculty of
Physics and Astronomy, Department of
Graduate
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Books on the topic "Covariance"

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Aubinet, Marc, Timo Vesala, and Dario Papale, eds. Eddy Covariance. Dordrecht: Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-2351-1.

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Mohsen Pourahmadi. High-Dimensional Covariance Estimation. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118573617.

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Huitema, Bradley E. Analysis of covariance and alternatives. 2nd ed. Hoboken, N.J: Wiley, 2011.

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Zagidullina, Aygul. High-Dimensional Covariance Matrix Estimation. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-80065-9.

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Franke, Richard H. Covariance functions for statistical interpolation. Monterey, California: Naval Postgraduate School, 1986.

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Huitema, Bradley E. Analysis of covariance and alternatives. 2nd ed. Hoboken, N.J: Wiley, 2011.

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Huitema, Bradley E. The Analysis of Covariance and Alternatives. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2011. http://dx.doi.org/10.1002/9781118067475.

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Lynn, Kirlin R., and Done William J, eds. Covariance analysis for seismic signal processing. Tulsa, OK: Society of Exploration Geophysicists, 1999.

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R. Rakotomanana, Lalaonirina. Covariance and Gauge Invariance in Continuum Physics. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91782-5.

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Tsukuma, Hisayuki, and Tatsuya Kubokawa. Shrinkage Estimation for Mean and Covariance Matrices. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1596-5.

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Book chapters on the topic "Covariance"

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Mainzer, Klaus. "Covariance." In Compendium of Quantum Physics, 136–38. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-70626-7_41.

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Roberts, E. A. "Covariance." In Sequential Data in Biological Experiments, 127–49. Dordrecht: Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-011-3120-9_5.

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Gooch, Jan W. "Covariance." In Encyclopedic Dictionary of Polymers, 977–78. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-6247-8_15203.

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Foken, Thomas, Marc Aubinet, and Ray Leuning. "The Eddy Covariance Method." In Eddy Covariance, 1–19. Dordrecht: Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-94-007-2351-1_1.

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Rinne, Janne, and Christof Ammann. "Disjunct Eddy Covariance Method." In Eddy Covariance, 291–307. Dordrecht: Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-94-007-2351-1_10.

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Longdoz, Bernard, and André Granier. "Eddy Covariance Measurements over Forests." In Eddy Covariance, 309–18. Dordrecht: Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-94-007-2351-1_11.

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Moureaux, Christine, Eric Ceschia, Nicola Arriga, Pierre Béziat, Werner Eugster, Werner L. Kutsch, and Elizabeth Pattey. "Eddy Covariance Measurements over Crops." In Eddy Covariance, 319–31. Dordrecht: Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-94-007-2351-1_12.

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Wohlfahrt, Georg, Katja Klumpp, and Jean-François Soussana. "Eddy Covariance Measurements over Grasslands." In Eddy Covariance, 333–44. Dordrecht: Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-94-007-2351-1_13.

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Laurila, Tuomas, Mika Aurela, and Juha-Pekka Tuovinen. "Eddy Covariance Measurements over Wetlands." In Eddy Covariance, 345–64. Dordrecht: Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-94-007-2351-1_14.

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Vesala, Timo, Werner Eugster, and Anne Ojala. "Eddy Covariance Measurements over Lakes." In Eddy Covariance, 365–76. Dordrecht: Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-94-007-2351-1_15.

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Conference papers on the topic "Covariance"

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Cheng, Chuan, Leszek J. Frasinski, Gönenç Moğol, Felix Allum, Andrew J. Howard, Philip H. Bucksbaum, Mark Brouard, Ruaridh Forbes, and Thomas Weinacht. "Ultrafast Molecular Imaging Using 4-Fold Covariance: Coincidence Insight with Covariance Speed." In International Conference on Ultrafast Phenomena. Washington, D.C.: Optica Publishing Group, 2022. http://dx.doi.org/10.1364/up.2022.tu4a.40.

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We develop mathematical tools to compute higher order covariances in charged particle detection, and demonstrate fourfold covariance measurements for molecular imaging with intense ultrafast laser pulses.
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Brooks, Caleb S., Yang Liu, Takashi Hibiki, and Mamoru Ishii. "Void Fraction Covariance in Two-Phase Flows." In 2012 20th International Conference on Nuclear Engineering and the ASME 2012 Power Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/icone20-power2012-54594.

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A void fraction covariance in the expression for area-averaged local relative velocity has been assumed to be one by current and past researchers. Similarly, in a multi-bubble group approach, void covariances for each bubble group appear in the group area-averaged relative velocity expressions. The covariance terms have been analyzed with a substantial database from literature including upward flow in pipe diameters of 1.27 cm to 15.2 cm, downward flow in pipe diameters of 2.54 cm and 5.08 cm, and upward flow in an annulus (Dh = 1.9cm) under adiabatic, boiling, and condensing conditions. Simple relations are proposed to specify the covariance in order to improve the prediction of area-averaged local relative velocity. The correlations were found to agree well with the experimental data for the flow configurations and conditions analyzed.
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Wang, Yunfeng, and Gregory S. Chirikjian. "Robustness Analysis of Kinematic Covariance Propagation in Serial Manipulators." In ASME 2006 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2006. http://dx.doi.org/10.1115/detc2006-99283.

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In this paper we show that kinematic errors in serial manipulators propagate by convolution on the Euclidean motion group. When errors are small, covariances describing error probability densities can be propagated in place of explicitly performing the convolution. We investigate the robustness of covariance propagation formulas in the context of individual joint errors that are small, but result in overall end-effector errors that are not necessary small.
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JANYŠKA, JOSEF, and MARCO MODUGNO. "UNIQUENESS RESULTS BY COVARIANCE IN COVARIANT QUANTUM MECHANICS." In Proceedings of the Second International Symposium. WORLD SCIENTIFIC, 2002. http://dx.doi.org/10.1142/9789812777850_0049.

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Serra, Giuseppe, Costantino Grana, Marco Manfredi, and Rita Cucchiara. "Covariance of Covariance Features for Image Classification." In ICMR '14: International Conference on Multimedia Retrieval. New York, NY, USA: ACM, 2014. http://dx.doi.org/10.1145/2578726.2578781.

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Dasarathy, Gautam, Parikshit Shah, Badri Narayan Bhaskar, and Robert Nowak. "Covariance sketching." In 2012 50th Annual Allerton Conference on Communication, Control, and Computing (Allerton). IEEE, 2012. http://dx.doi.org/10.1109/allerton.2012.6483331.

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Pezzella, Franco. "Two Double String Theory Actions: Non-covariance versus Covariance." In Proceedings of the Corfu Summer Institute 2014. Trieste, Italy: Sissa Medialab, 2015. http://dx.doi.org/10.22323/1.231.0158.

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Woodburn, James, and Sergei Tanygin. "Position Covariance Visualization." In AIAA/AAS Astrodynamics Specialist Conference and Exhibit. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2002. http://dx.doi.org/10.2514/6.2002-4985.

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Tanygin, Sergei, and James Woodburn. "Attitude Covariance Visualization." In AIAA/AAS Astrodynamics Specialist Conference and Exhibit. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2002. http://dx.doi.org/10.2514/6.2002-4832.

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Korkmaz, Semih, and Erchan Aptoula. "Extended morphological covariance." In 2012 20th Signal Processing and Communications Applications Conference (SIU). IEEE, 2012. http://dx.doi.org/10.1109/siu.2012.6204455.

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Reports on the topic "Covariance"

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Oblozinsky, P., P. Oblozinsky, C. M. Mattoon, M. Herman, S. F. Mughabghab, M. T. Pigni, P. Talou, et al. Progress on Nuclear Data Covariances: AFCI-1.2 Covariance Library. Office of Scientific and Technical Information (OSTI), September 2009. http://dx.doi.org/10.2172/972322.

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McKnight, Richard D., and Karl N. Grimm. Covariance Matrix Generation at ANL. Office of Scientific and Technical Information (OSTI), December 2012. http://dx.doi.org/10.2172/1114909.

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Harris, D. Covariance Modifications to Subspace Bases. Office of Scientific and Technical Information (OSTI), November 2008. http://dx.doi.org/10.2172/945871.

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McKnight, Richard D., and Karl N. Grimm. ANL Critical Assembly Covariance Matrix Generation. Office of Scientific and Technical Information (OSTI), January 2014. http://dx.doi.org/10.2172/1114907.

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Kiedrowski, Brian C. Proposed ACE Covariance Format, Version 1.0. Office of Scientific and Technical Information (OSTI), April 2013. http://dx.doi.org/10.2172/1072248.

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Tang, Haihan, Christian M. Hafner, and Oliver Linton. Estimation of a Multiplicative Covariance Structure. IFS, May 2016. http://dx.doi.org/10.1920/wp.cem.2016.2316.

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Joag-Dev, Kumar, and Frank Proschan. A Covariance Inequality for Coherent Structures. Fort Belvoir, VA: Defense Technical Information Center, June 1986. http://dx.doi.org/10.21236/ada174889.

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Bryan, M. F., G. F. Piepel, and D. B. Simpson. Methods for estimation of covariance matrices and covariance components for the Hanford Waste Vitrification Plant Process. Office of Scientific and Technical Information (OSTI), March 1996. http://dx.doi.org/10.2172/215713.

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Herman, M., M. Herman, P. Oblozinsky, C. M. Mattoon, M. Pigni, S. Hoblit, S. F. Mughabghab, et al. AFCI-2.0 Neutron Cross Section Covariance Library. Office of Scientific and Technical Information (OSTI), March 2011. http://dx.doi.org/10.2172/1013530.

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McKnight, Richard D., and Karl N. Grimm. ANL Critical Assembly Covariance Matrix Generation - Addendum. Office of Scientific and Technical Information (OSTI), January 2014. http://dx.doi.org/10.2172/1114908.

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