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Journal articles on the topic 'Covariance matrice'

Author: Grafiati

Published: 11 March 2023

Last updated: 31 July 2025

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1

Cappuccio, Nunzio, and Diego Lubian. "Ordering of Covariance Matrice." Econometric Theory 12, no. 4 (1996): 746–48. http://dx.doi.org/10.1017/s0266466600007106.

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2

Sigaud, Olivier, and Freek Stulp. "Adaptation de la matrice de covariance pour l’apprentissage par renforcement direct." Revue d'intelligence artificielle 27, no. 2 (2013): 243–63. http://dx.doi.org/10.3166/ria.27.243-263.

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3

M. Abowd, John, and Kevin L. McKinney. "Mixed-Effects Methods for Search and Matching Research." Revue économique Vol. 51, no. 1 (2024): 55–72. http://dx.doi.org/10.3917/reco.751.0055.

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Nous étudions les méthodes à effets mixtes pour l’estimation d’équations contenant des effets individuels et d’entreprise. En économie, ces modèles sont généralement estimés à l’aide de méthodes à effets fixes. Les améliorations récentes de ces méthodes à effets fixes incluent des corrections du biais dans l’estimation de la matrice de covariance des effets individuels et d’entreprise, que nous considérons également.

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4

Fourdrinier, Dominique, William E. Strawderman, and Martin T. Wells. "Estimation robuste pour des lois à symétrie elliptique à matrice de covariance inconnue." Comptes Rendus de l'Académie des Sciences - Series I - Mathematics 326, no. 9 (1998): 1135–40. http://dx.doi.org/10.1016/s0764-4442(98)80076-1.

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5

Khoder, Wassim. "Recalage de la navigation inertielle hybride par le filtrage de Kalman sans parfum paramétré à quaternions." MATEC Web of Conferences 261 (2019): 06003. http://dx.doi.org/10.1051/matecconf/201926106003.

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Dans ce papier, nous avons développé un algorithme d’hybridation (recalage) de la navigation inertielle, noté Q-SUKF, qui combine le filtre de Kalman sans parfum à paramètre (SUKF) et l’utilisation des propriétés de rotation et d’unicité des quaternions (Q) pour représenter l’attitude. L’utilisation des quaternions unités dans le calcul de la matrice de covariance d’erreurs prédite empêche les problèmes de singularité et la dérive des informations d’attitude. L’augmentation de l’incertitude dans les angles d’attitude, est modélisé par un vecteur de rotation pour garantir que la normalisation d

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6

Brik, Hatem, Jihene El Ouakdi, and Zied Ftiti. "Revisiting the Contagion Effect in International Stock Markets: An Approach Based on Endogenous Crises." Recherches en Sciences de Gestion N° 159, no. 6 (2024): 41–69. http://dx.doi.org/10.3917/resg.159.0041.

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Ce papier vise à identifier la présence d'un effet de contagion en se basant sur un modèle MS VAR, avec des contraintes sur la matrice de variance-covariance et en fixant de manière endogène des intervalles caractérisés par des régimes à faible et forte volatilité. Les résultats montrent que pour les pays développés, un choc positif (négatif) sur un marché a un impact positif (négatif) à court terme sur les autres marchés boursiers. L'effet de contagion d'un pays émergent aux autres pays du continent est relativement plus important que pour les pays développés. En considérant le changement de

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7

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 (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

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8

Alekseychik, Pavel, Gabriel Katul, Ilkka Korpela, and Samuli Launiainen. "Eddies in motion: visualizing boundary-layer turbulence above an open boreal peatland using UAS thermal videos." Atmospheric Measurement Techniques 14, no. 5 (2021): 3501–21. http://dx.doi.org/10.5194/amt-14-3501-2021.

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Abstract. High-resolution thermal infrared (TIR) imaging is opening up new vistas in biosphere–atmosphere heat exchange studies. The rapidly developing unmanned aerial systems (UASs) and specially designed cameras offer opportunities for TIR survey with increasingly high resolution, reduced geometric and radiometric noise, and prolonged flight times. A state-of-the-art science platform is assembled using a Matrice 210 V2 drone equipped with a Zenmuse XT2 thermal camera and deployed over a pristine boreal peatland with the aim of testing its performance in a heterogeneous sedge-fen ecosystem. T

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9

Sole, Pierre, Vaibhav Jaiswal, Cédric Jouanne, and Vivian Salino. "Assesment of covariance processing with GAIA for nuclear data uncertainty propagation." EPJ Web of Conferences 294 (2024): 05001. http://dx.doi.org/10.1051/epjconf/202429405001.

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Nuclear data uncertainties are provided as covariance matrices in standard nuclear data libraries and propagating them trough neutronics simulations helps quantify the associated uncertainties on the final result. However, processing these matrices often poses challenges. Currently, the IRSN nuclear data processing code GAIA processes cross sections via several modules like DOP (Reconstruction and Doppler), TOP (URR), and SAB (TSL), but lacks the capability to process covariances. This paper introduces a new module named COP (COvariance Processing). The COP module aims to process covariance ma

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10

Gonzalez-Ondina, Jose M., Lewis Sampson, and Georgy I. Shapiro. "A Projection Method for the Estimation of Error Covariance Matrices for Variational Data Assimilation in Ocean Modelling." Journal of Marine Science and Engineering 9, no. 12 (2021): 1461. http://dx.doi.org/10.3390/jmse9121461.

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Data assimilation methods are an invaluable tool for operational ocean models. These methods are often based on a variational approach and require the knowledge of the spatial covariances of the background errors (differences between the numerical model and the true values) and the observation errors (differences between true and measured values). Since the true values are never known in practice, the error covariance matrices containing values of the covariance functions at different locations, are estimated approximately. Several methods have been devised to compute these matrices, one of th

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11

Zhang, Peng, Wen Juan Qi, and Zi Li Deng. "Covariance Intersection Fusion Kalman Estimator for Multi-Sensor System with Measurements Delays." Applied Mechanics and Materials 475-476 (December 2013): 460–65. http://dx.doi.org/10.4028/www.scientific.net/amm.475-476.460.

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To handle the state estimation fusion problem between local estimation errors for the system with unknown cross-covariances and to avoid a large computation complexity of cross-covariances, for a multi-sensor linear discrete time-invariant stochastic system with time-delayed measurements, by the measurement transformation method, an equivalent system without measurement delays is obtained, and then using the covariance intersection (CI) fusion method, the covariance intersection fusion steady-state Kalman estimator is presented. It is proved that its accuracy is higher than that of each local

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12

Fang (方啸), Xiao, Tim Eifler, and Elisabeth Krause. "2D-FFTLog: efficient computation of real-space covariance matrices for galaxy clustering and weak lensing." Monthly Notices of the Royal Astronomical Society 497, no. 3 (2020): 2699–714. http://dx.doi.org/10.1093/mnras/staa1726.

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ABSTRACT Accurate covariance matrices for two-point functions are critical for inferring cosmological parameters in likelihood analyses of large-scale structure surveys. Among various approaches to obtaining the covariance, analytic computation is much faster and less noisy than estimation from data or simulations. However, the transform of covariances from Fourier space to real space involves integrals with two Bessel integrals, which are numerically slow and easily affected by numerical uncertainties. Inaccurate covariances may lead to significant errors in the inference of the cosmological

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13

Aboutaleb, Youssef M., Mazen Danaf, Yifei Xie, and Moshe E. Ben-Akiva. "Sparse covariance estimation in logit mixture models." Econometrics Journal 24, no. 3 (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

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14

Silverstein, Jack W., and Z. D. Bai. "Covariance Matrices." Annals of Probability 27, no. 3 (1999): 1536–55. http://dx.doi.org/10.1214/aop/1022677458.

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15

Klypin, Anatoly, Francisco Prada, and Joyce Byun. "Suppressing cosmic variance with paired-and-fixed cosmological simulations: average properties and covariances of dark matter clustering statistics." Monthly Notices of the Royal Astronomical Society 496, no. 3 (2020): 3862–69. http://dx.doi.org/10.1093/mnras/staa734.

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ABSTRACT Making cosmological inferences from the observed galaxy clustering requires accurate predictions for the mean clustering statistics and their covariances. Those are affected by cosmic variance – the statistical noise due to the finite number of harmonics. The cosmic variance can be suppressed by fixing the amplitudes of the harmonics instead of drawing them from a Gaussian distribution predicted by the inflation models. Initial realizations also can be generated in pairs with 180○ flipped phases to further reduce the variance. Here, we compare the consequences of using paired-and-fixe

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16

Philcox, Oliver H. E., Daniel J. Eisenstein, Ross O’Connell, and Alexander Wiegand. "rascalc: a jackknife approach to estimating single- and multitracer galaxy covariance matrices." Monthly Notices of the Royal Astronomical Society 491, no. 3 (2019): 3290–317. http://dx.doi.org/10.1093/mnras/stz3218.

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ABSTRACT To make use of clustering statistics from large cosmological surveys, accurate and precise covariance matrices are needed. We present a new code to estimate large-scale galaxy two-point correlation function (2PCF) covariances in arbitrary survey geometries that, due to new sampling techniques, runs ∼104 times faster than previous codes, computing finely binned covariance matrices with negligible noise in less than 100 CPU-hours. As in previous works, non-Gaussianity is approximated via a small rescaling of shot noise in the theoretical model, calibrated by comparing jackknife survey c

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17

Bongiorno, C., D. Challet, and G. Loeper. "Filtering time-dependent covariance matrices using time-independent eigenvalues." Journal of Statistical Mechanics: Theory and Experiment 2023, no. 2 (2023): 023402. http://dx.doi.org/10.1088/1742-5468/acb7ed.

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Abstract We propose a data-driven, model-free, way to reduce the noise of covariance matrices of time-varying systems. If the true covariance matrix is time-invariant, non-linear shrinkage of the eigenvalues is known to yield the optimal estimator for large matrices. Such a method outputs eigenvalues that are highly dependent on the inputs, as common sense suggests. When the covariance matrix is time-dependent, we show that it is generally better to use the set of eigenvalues that encode the average influence of the future on present eigenvalues resulting in a set of time-independent average e

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18

Smith, Kimberly, Courtenay Strong, and Firas Rassoul-Agha. "Multisite Generalization of the SHArP Weather Generator." Journal of Applied Meteorology and Climatology 57, no. 9 (2018): 2113–27. http://dx.doi.org/10.1175/jamc-d-17-0236.1.

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AbstractGeneralization of point-scale stochastic weather generators to simultaneously produce output at multiple sites provides more powerful support for hydrology and climate change impact studies. Generalization preserves the statistical properties of each individual site while maintaining proper spatial correlation over the domain. Here, generalization of the daily precipitation and temperature components of the stochastic harmonic autoregressive parametric (SHArP) weather generator is presented. The generalization process for temperature involves conversion of vector time series to matrix

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19

Koch, K. R., H. Kuhlmann, and W. D. Schuh. "Approximating covariance matrices estimated in multivariate models by estimated auto- and cross-covariances." Journal of Geodesy 84, no. 6 (2010): 383–97. http://dx.doi.org/10.1007/s00190-010-0375-5.

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20

Blot, Linda, Martin Crocce, Emiliano Sefusatti, et al. "Comparing approximate methods for mock catalogues and covariance matrices II: power spectrum multipoles." Monthly Notices of the Royal Astronomical Society 485, no. 2 (2019): 2806–24. http://dx.doi.org/10.1093/mnras/stz507.

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ABSTRACT We study the accuracy of several approximate methods for gravitational dynamics in terms of halo power spectrum multipoles and their estimated covariance matrix. We propagate the differences in covariances into parameter constraints related to growth rate of structure, Alcock–Paczynski distortions, and biasing. We consider seven methods in three broad categories: algorithms that solve for halo density evolution deterministically using Lagrangian trajectories (ICE–COLA, pinocchio, and peakpatch), methods that rely on halo assignment schemes on to dark matter overdensities calibrated wi

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21

Fumagalli, Alessandra, Matteo Biagetti, Alex Saro, et al. "Fitting covariance matrix models to simulations." Journal of Cosmology and Astroparticle Physics 2022, no. 12 (2022): 022. http://dx.doi.org/10.1088/1475-7516/2022/12/022.

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Abstract Data analysis in cosmology requires reliable covariance matrices. Covariance matrices derived from numerical simulations often require a very large number of realizations to be accurate. When a theoretical model for the covariance matrix exists, the parameters of the model can often be fit with many fewer simulations. We write a likelihood-based method for performing such a fit. We demonstrate how a model covariance matrix can be tested by examining the appropriate χ 2 distributions from simulations. We show that if model covariance has amplitude freedom, the expectation value of seco

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22

Stępniak, Czesław. "Inverting covariance matrices." Discussiones Mathematicae Probability and Statistics 26, no. 2 (2006): 163. http://dx.doi.org/10.7151/dmps.1080.

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23

Dorvlo, Atsu S. S. "Generating covariance matrices." International Journal of Mathematical Education in Science and Technology 31, sup2 (2000): 287–89. http://dx.doi.org/10.1080/00207390050032261.

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24

Loh, Wei-Liem. "Estimating Covariance Matrices." Annals of Statistics 19, no. 1 (1991): 283–96. http://dx.doi.org/10.1214/aos/1176347982.

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25

Tian, Yongge. "Matrix rank and inertia formulas in the analysis of general linear models." Open Mathematics 15, no. 1 (2017): 126–50. http://dx.doi.org/10.1515/math-2017-0013.

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Abstract Matrix mathematics provides a powerful tool set for addressing statistical problems, in particular, the theory of matrix ranks and inertias has been developed as effective methodology of simplifying various complicated matrix expressions, and establishing equalities and inequalities occurred in statistical analysis. This paper describes how to establish exact formulas for calculating ranks and inertias of covariances of predictors and estimators of parameter spaces in general linear models (GLMs), and how to use the formulas in statistical analysis of GLMs. We first derive analytical

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26

Carta, Lynn, and David Carta. "Nematode specific gravity profiles and applications to flotation extraction and taxonomy." Nematology 2, no. 2 (2000): 201–10. http://dx.doi.org/10.1163/156854100508935.

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AbstractA technique is described that refines the standard sugar flotation procedure used to isolate nematodes from their surroundings. By centrifuging nematodes in a number of increasing specific gravity solutions and plotting the fraction floating, the cumulative probability distribution of the population’s specific gravity is generated. By assuming normality, the population mean, μ, and standard deviation, σ, are found by a nonlinear least squares procedure. These density parameters along with their error covariance matrix may be used as a taxonomic physical character. A chi-squared test is

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27

Bishop, Craig H., Bo Huang, and Xuguang Wang. "A Nonvariational Consistent Hybrid Ensemble Filter." Monthly Weather Review 143, no. 12 (2015): 5073–90. http://dx.doi.org/10.1175/mwr-d-14-00391.1.

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Abstract A consistent hybrid ensemble filter (CHEF) for using hybrid forecast error covariance matrices that linearly combine aspects of both climatological and flow-dependent matrices within a nonvariational ensemble data assimilation scheme is described. The CHEF accommodates the ensemble data assimilation enhancements of (i) model space ensemble covariance localization for satellite data assimilation and (ii) Hodyss’s method for improving accuracy using ensemble skewness. Like the local ensemble transform Kalman filter (LETKF), the CHEF is computationally scalable because it updates local p

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28

Appourchaux, T., and L. Gizon. "The Art of Fitting P-Mode Spectra." Symposium - International Astronomical Union 185 (1998): 43–44. http://dx.doi.org/10.1017/s0074180900238230.

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For deriving p-mode parameters from m, v diagrammes, one has to treat correctly the statistics of the observation. The correct statistical treatment of these diagrammes was first achieved by Schou (1992) (PhD thesis, Aarhus University). Fitting p-mode spectra requires 4 major steps: 1.Compute the mode leakage matrices2.Compute mode covariance matrices from the previous matrices3.Compute the noise covariance matrices4.Compute and maximize the likelihood of the observation

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29

Zhang, Peng, Wen Juan Qi, and Zi Li Deng. "Parallel Covariance Intersection Fusion Optimal Kalman Filter." Applied Mechanics and Materials 475-476 (December 2013): 436–41. http://dx.doi.org/10.4028/www.scientific.net/amm.475-476.436.

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For multisensor network systems with unknown cross-covariances, a novel multi-level parallel covariance intersection (PCI) fusion Kalman filter is presented in this paper, which is realized by the multi-level parallel two-sensor covariance intersection (CI) fusers, so it only requires to solve the optimization problems of several one-dimensional nonlinear cost functions in parallel with loss computation burden. It can significantly reduce the computation time and increase data processing rate when the number of sensors is very large. It is proved that the PCI fuser is consistent, and its accur

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30

Yuan, Sihan, and Daniel J. Eisenstein. "Decorrelating the errors of the galaxy correlation function with compact transformation matrices." Monthly Notices of the Royal Astronomical Society 486, no. 1 (2019): 708–24. http://dx.doi.org/10.1093/mnras/stz899.

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Abstract Covariance matrix estimation is a persistent challenge for cosmology, often requiring a large number of synthetic mock catalogues. The off-diagonal components of the covariance matrix also make it difficult to show representative error bars on the 2-point correlation function (2PCF) since errors computed from the diagonal values of the covariance matrix greatly underestimate the uncertainties. We develop a routine for decorrelating the projected and anisotropic 2PCF with simple and scale-compact transformations on the 2PCF. These transformation matrices are modelled after the Cholesky

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31

Deng, Di, Peng Yi, and Junlin Xiong. "An Adaptive Robust Event-Triggered Variational Bayesian Filtering Method with Heavy-Tailed Noise." Sensors 25, no. 10 (2025): 3130. https://doi.org/10.3390/s25103130.

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Event-triggered state estimation has attracted significant attention due to the advantage of efficiently utilizing communication resources in wireless sensor networks. In this paper, an adaptive robust event-triggered variational Bayesian filtering method is designed for heavy-tailed noise with inaccurate nominal covariance matrices. The one-step state prediction probability density function and the measurement likelihood function are modeled as Student’s t-distributions. By choosing inverse Wishart priors, the system state, the prediction error covariance, and the measurement noise covariance

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32

Knavoot, Jiamwattanapong, Ingadapa Nisanad, and Phrueksawatnon Piyada. "A Comparative Study of Two-Sample Tests for High-Dimensional Covariance Matrices." International Journal of Current Science Research and Review 05, no. 04 (2022): 1073–80. https://doi.org/10.5281/zenodo.6469107.

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Abstract : The equality of covariance matrices is an essential assumption in means and discriminant analyses for high-dimensional data. The performance of tests for covariance matrices may vary substantially depending on the covariance structure, so using inappropriate methods to verify the assumption will result in worse performance. The purpose of this study is to assess and compare the performance of three tests for two-sample high-dimensional covariance matrices: Schott’s (2007), Srivastava and Yanagihara’s (2010), and Li and Chen’s (2012) under various covariance structu

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33

Engle, Robert F., Olivier Ledoit, and Michael Wolf. "Large Dynamic Covariance Matrices." Journal of Business & Economic Statistics 37, no. 2 (2017): 363–75. http://dx.doi.org/10.1080/07350015.2017.1345683.

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34

Trenkler, Götz. "Ordering of Covariance Matrices." Econometric Theory 11, no. 4 (1995): 796. http://dx.doi.org/10.1017/s0266466600009750.

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35

Loh, Wei-Liem. "Estimating covariance matrices II." Journal of Multivariate Analysis 36, no. 2 (1991): 163–74. http://dx.doi.org/10.1016/0047-259x(91)90055-7.

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36

Eriksen, P. Svante. "Proportionality of Covariance Matrices." Annals of Statistics 15, no. 2 (1987): 732–48. http://dx.doi.org/10.1214/aos/1176350372.

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37

Carroll, T. L., and J. M. Byers. "Dimension from covariance matrices." Chaos: An Interdisciplinary Journal of Nonlinear Science 27, no. 2 (2017): 023101. http://dx.doi.org/10.1063/1.4975063.

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38

Pillai, Natesh S., and Jun Yin. "Universality of covariance matrices." Annals of Applied Probability 24, no. 3 (2014): 935–1001. http://dx.doi.org/10.1214/13-aap939.

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39

Ludwig, Monika. "Covariance matrices and valuations." Advances in Applied Mathematics 51, no. 3 (2013): 359–66. http://dx.doi.org/10.1016/j.aam.2012.12.003.

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Gunawan, B., and JW James. "The use of 'bending' in multiple trait selection of Border Leicester - Merino synthetic populations." Australian Journal of Agricultural Research 37, no. 5 (1986): 539. http://dx.doi.org/10.1071/ar9860539.

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The consistency of phenotypic and genetic parameters estimated for various body weight and wool characters in Border Leicester-Merino synthetic populations was investigated by calculating the eigenvalues of matrices of phenotypic covariances (P), genetic covariances (G), and the product of the inverse of the phenotypic with the genetic covariance matrix (P-1G). If these estimates were found to be inconsistent (non-positive definite), the bending technique was applied before genetic selection indices were calculated. In general, the P were positive definite, but the G or P-1G were always non-po

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Alshawi, Aymen, Stefano De Pinto, Pietro Stano, et al. "An Adaptive Unscented Kalman Filter for the Estimation of the Vehicle Velocity Components, Slip Angles, and Slip Ratios in Extreme Driving Manoeuvres." Sensors 24, no. 2 (2024): 436. http://dx.doi.org/10.3390/s24020436.

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This paper presents a novel unscented Kalman filter (UKF) implementation with adaptive covariance matrices (ACMs), to accurately estimate the longitudinal and lateral components of vehicle velocity, and thus the sideslip angle, tire slip angles, and tire slip ratios, also in extreme driving conditions, including tyre–road friction variations. The adaptation strategies are implemented on both the process noise and measurement noise covariances. The resulting UKF ACM is compared against a well-tuned baseline UKF with fixed covariances. Experimental test results in high tyre–road friction conditi

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42

Blais, J. A. Rod. "Optimal Modeling and Filtering of Stochastic Time Series for Geoscience Applications." Mathematical Problems in Engineering 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/895061.

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Sequences of observations or measurements are often modeled as realizations of stochastic processes with some stationary properties in the first and second moments. However in practice, the noise biases and variances are likely to be different for different epochs in time or regions in space, and hence such stationarity assumptions are often questionable. In the case of strict stationarity with equally spaced data, the Wiener-Hopf equations can readily be solved with fast Fourier transforms (FFTs) with optimal computational efficiency. In more general contexts, covariance matrices can also be

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Qi, Wen Juan, Peng Zhang, Zi Li Deng, and Yuan Gao. "Covariance Intersection Fusion Smoothers for Multichannel ARMA Signal with Colored Measurement Noises." Applied Mechanics and Materials 373-375 (August 2013): 716–22. http://dx.doi.org/10.4028/www.scientific.net/amm.373-375.716.

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For multichannel autoregressive moving average (ARMA) signal with colored measurement noises, based on classical Kalman filtering theory, a covariance intersection (CI) fusion smoother without cross-covariances is presented by the augmented state space model. It has the advantage that the computation of cross-covariances is avoid, so it can significantly reduce the computational burden, and it can solve the fusion problem for multi-sensor systems with unknown cross-covariances. Under the unbiased linear minimum variance (ULMV) criterion, three optimal weighted fusion smoothers with matrix weig

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44

Tieplova, D. "Distribution of Eigenvalues of Sample Covariance Matrices with Tensor Product Samples." Zurnal matematiceskoj fiziki, analiza, geometrii 13, no. 1 (2017): 82–98. http://dx.doi.org/10.15407/mag13.01.082.

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Trucíos, Carlos, Mauricio Zevallos, Luiz K. Hotta, and André A. P. Santos. "Covariance Prediction in Large Portfolio Allocation." Econometrics 7, no. 2 (2019): 19. http://dx.doi.org/10.3390/econometrics7020019.

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Many financial decisions, such as portfolio allocation, risk management, option pricing and hedge strategies, are based on forecasts of the conditional variances, covariances and correlations of financial returns. The paper shows an empirical comparison of several methods to predict one-step-ahead conditional covariance matrices. These matrices are used as inputs to obtain out-of-sample minimum variance portfolios based on stocks belonging to the S&P500 index from 2000 to 2017 and sub-periods. The analysis is done through several metrics, including standard deviation, turnover, net average

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46

Philcox, Oliver H. E., and Daniel J. Eisenstein. "Estimating covariance matrices for two- and three-point correlation function moments in Arbitrary Survey Geometries." Monthly Notices of the Royal Astronomical Society 490, no. 4 (2019): 5931–51. http://dx.doi.org/10.1093/mnras/stz2896.

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ABSTRACT We present configuration-space estimators for the auto- and cross-covariance of two- and three-point correlation functions (2PCF and 3PCF) in general survey geometries. These are derived in the Gaussian limit (setting higher order correlation functions to zero), but for arbitrary non-linear 2PCFs (which may be estimated from the survey itself), with a shot-noise rescaling parameter included to capture non-Gaussianity. We generalize previous approaches to include Legendre moments via a geometry-correction function calibrated from measured pair and triple counts. Making use of importanc

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Penny, Stephen G. "The Hybrid Local Ensemble Transform Kalman Filter." Monthly Weather Review 142, no. 6 (2014): 2139–49. http://dx.doi.org/10.1175/mwr-d-13-00131.1.

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Abstract Hybrid data assimilation methods combine elements of ensemble Kalman filters (EnKF) and variational methods. While most approaches have focused on augmenting an operational variational system with dynamic error covariance information from an ensemble, this study takes the opposite perspective of augmenting an operational EnKF with information from a simple 3D variational data assimilation (3D-Var) method. A class of hybrid methods is introduced that combines the gain matrices of the ensemble and variational methods, rather than linearly combining the respective background error covari

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Nye, Tom M. W., Brad J. C. Baxter, and Walter R. Gilks. "A Covariance Matrix Inversion Problem arising from the Construction of Phylogenetic Trees." LMS Journal of Computation and Mathematics 10 (2007): 119–31. http://dx.doi.org/10.1112/s1461157000001327.

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AbstractWe describe an efficient algorithm for the inversion of covariance matrices that arise in the context of phylogenetic tree construction. Phylogenetic trees describe the evolutionary relationships between species, and their construction is computationally demanding. Many approaches involve the symmetric matrix of evolutionary distances between species. Regarding these distances as random variables, the corresponding set of variances and covariances form a rank-4 tensor, and the inner-product defined by its inverse can be used to assign statistical scores to candidate trees. We describe

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Chang, Wei-Yu, Jothiram Vivekanandan, and Tai-Chi Chen Wang. "Estimation of X-Band Polarimetric Radar Attenuation and Measurement Uncertainty Using a Variational Method." Journal of Applied Meteorology and Climatology 53, no. 4 (2014): 1099–119. http://dx.doi.org/10.1175/jamc-d-13-0191.1.

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AbstractA variational algorithm for estimating measurement error covariance and the attenuation of X-band polarimetric radar measurements is described. It concurrently uses both the differential reflectivity ZDR and propagation phase ΦDP. The majority of the current attenuation estimation techniques use only ΦDP. A few of the ΦDP-based methods use ZDR as a constraint for verifying estimated attenuation. In this paper, a detailed observing system simulation experiment was used for evaluating the performance of the variational algorithm. The results were compared with a single-coefficient ΦDP-ba

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de Santi, Natalí S. M., and L. Raul Abramo. "Improving cosmological covariance matrices with machine learning." Journal of Cosmology and Astroparticle Physics 2022, no. 09 (2022): 013. http://dx.doi.org/10.1088/1475-7516/2022/09/013.

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Abstract Cosmological covariance matrices are fundamental for parameter inference, since they are responsible for propagating uncertainties from the data down to the model parameters. However, when data vectors are large, in order to estimate accurate and precise covariance matrices we need huge numbers of observations, or rather costly simulations - neither of which may be viable. In this work we propose a machine learning approach to alleviate this problem in the context of the covariance matrices used in the study of large-scale structure. With only a small amount of data (matrices built wi

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