WorldWideScience

Sample records for covariance matrix adaptation

  1. Convex Banding of the Covariance Matrix.

    Science.gov (United States)

    Bien, Jacob; Bunea, Florentina; Xiao, Luo

    2016-01-01

    We introduce a new sparse estimator of the covariance matrix for high-dimensional models in which the variables have a known ordering. Our estimator, which is the solution to a convex optimization problem, is equivalently expressed as an estimator which tapers the sample covariance matrix by a Toeplitz, sparsely-banded, data-adaptive matrix. As a result of this adaptivity, the convex banding estimator enjoys theoretical optimality properties not attained by previous banding or tapered estimators. In particular, our convex banding estimator is minimax rate adaptive in Frobenius and operator norms, up to log factors, over commonly-studied classes of covariance matrices, and over more general classes. Furthermore, it correctly recovers the bandwidth when the true covariance is exactly banded. Our convex formulation admits a simple and efficient algorithm. Empirical studies demonstrate its practical effectiveness and illustrate that our exactly-banded estimator works well even when the true covariance matrix is only close to a banded matrix, confirming our theoretical results. Our method compares favorably with all existing methods, in terms of accuracy and speed. We illustrate the practical merits of the convex banding estimator by showing that it can be used to improve the performance of discriminant analysis for classifying sound recordings.

  2. APPLICATION OF RESTART COVARIANCE MATRIX ADAPTATION EVOLUTION STRATEGY (RCMA-ES TO GENERATION EXPANSION PLANNING PROBLEM

    Directory of Open Access Journals (Sweden)

    K. Karthikeyan

    2012-10-01

    Full Text Available This paper describes the application of an evolutionary algorithm, Restart Covariance Matrix Adaptation Evolution Strategy (RCMA-ES to the Generation Expansion Planning (GEP problem. RCMA-ES is a class of continuous Evolutionary Algorithm (EA derived from the concept of self-adaptation in evolution strategies, which adapts the covariance matrix of a multivariate normal search distribution. The original GEP problem is modified by incorporating Virtual Mapping Procedure (VMP. The GEP problem of a synthetic test systems for 6-year, 14-year and 24-year planning horizons having five types of candidate units is considered. Two different constraint-handling methods are incorporated and impact of each method has been compared. In addition, comparison and validation has also made with dynamic programming method.

  3. How much do genetic covariances alter the rate of adaptation?

    Science.gov (United States)

    Agrawal, Aneil F; Stinchcombe, John R

    2009-03-22

    Genetically correlated traits do not evolve independently, and the covariances between traits affect the rate at which a population adapts to a specified selection regime. To measure the impact of genetic covariances on the rate of adaptation, we compare the rate fitness increases given the observed G matrix to the expected rate if all the covariances in the G matrix are set to zero. Using data from the literature, we estimate the effect of genetic covariances in real populations. We find no net tendency for covariances to constrain the rate of adaptation, though the quality and heterogeneity of the data limit the certainty of this result. There are some examples in which covariances strongly constrain the rate of adaptation but these are balanced by counter examples in which covariances facilitate the rate of adaptation; in many cases, covariances have little or no effect. We also discuss how our metric can be used to identify traits or suites of traits whose genetic covariances to other traits have a particularly large impact on the rate of adaptation.

  4. Spatio-Temporal Audio Enhancement Based on IAA Noise Covariance Matrix Estimates

    DEFF Research Database (Denmark)

    Nørholm, Sidsel Marie; Jensen, Jesper Rindom; Christensen, Mads Græsbøll

    2014-01-01

    A method for estimating the noise covariance matrix in a mul- tichannel setup is proposed. The method is based on the iter- ative adaptive approach (IAA), which only needs short seg- ments of data to estimate the covariance matrix. Therefore, the method can be used for fast varying signals....... The method is based on an assumption of the desired signal being harmonic, which is used for estimating the noise covariance matrix from the covariance matrix of the observed signal. The noise co- variance estimate is used in the linearly constrained minimum variance (LCMV) filter and compared...

  5. HIGH DIMENSIONAL COVARIANCE MATRIX ESTIMATION IN APPROXIMATE FACTOR MODELS.

    Science.gov (United States)

    Fan, Jianqing; Liao, Yuan; Mincheva, Martina

    2011-01-01

    The variance covariance matrix plays a central role in the inferential theories of high dimensional factor models in finance and economics. Popular regularization methods of directly exploiting sparsity are not directly applicable to many financial problems. Classical methods of estimating the covariance matrices are based on the strict factor models, assuming independent idiosyncratic components. This assumption, however, is restrictive in practical applications. By assuming sparse error covariance matrix, we allow the presence of the cross-sectional correlation even after taking out common factors, and it enables us to combine the merits of both methods. We estimate the sparse covariance using the adaptive thresholding technique as in Cai and Liu (2011), taking into account the fact that direct observations of the idiosyncratic components are unavailable. The impact of high dimensionality on the covariance matrix estimation based on the factor structure is then studied.

  6. Robust adaptive multichannel SAR processing based on covariance matrix reconstruction

    Science.gov (United States)

    Tan, Zhen-ya; He, Feng

    2018-04-01

    With the combination of digital beamforming (DBF) processing, multichannel synthetic aperture radar(SAR) systems in azimuth promise well in high-resolution and wide-swath imaging, whereas conventional processing methods don't take the nonuniformity of scattering coefficient into consideration. This paper brings up a robust adaptive Multichannel SAR processing method which utilizes the Capon spatial spectrum estimator to obtain the spatial spectrum distribution over all ambiguous directions first, and then the interference-plus-noise covariance Matrix is reconstructed based on definition to acquire the Multichannel SAR processing filter. The performance of processing under nonuniform scattering coefficient is promoted by this novel method and it is robust again array errors. The experiments with real measured data demonstrate the effectiveness and robustness of the proposed method.

  7. Covariance matrix estimation for stationary time series

    OpenAIRE

    Xiao, Han; Wu, Wei Biao

    2011-01-01

    We obtain a sharp convergence rate for banded covariance matrix estimates of stationary processes. A precise order of magnitude is derived for spectral radius of sample covariance matrices. We also consider a thresholded covariance matrix estimator that can better characterize sparsity if the true covariance matrix is sparse. As our main tool, we implement Toeplitz [Math. Ann. 70 (1911) 351–376] idea and relate eigenvalues of covariance matrices to the spectral densities or Fourier transforms...

  8. Robust Adaptive Beamforming with Sensor Position Errors Using Weighted Subspace Fitting-Based Covariance Matrix Reconstruction.

    Science.gov (United States)

    Chen, Peng; Yang, Yixin; Wang, Yong; Ma, Yuanliang

    2018-05-08

    When sensor position errors exist, the performance of recently proposed interference-plus-noise covariance matrix (INCM)-based adaptive beamformers may be severely degraded. In this paper, we propose a weighted subspace fitting-based INCM reconstruction algorithm to overcome sensor displacement for linear arrays. By estimating the rough signal directions, we construct a novel possible mismatched steering vector (SV) set. We analyze the proximity of the signal subspace from the sample covariance matrix (SCM) and the space spanned by the possible mismatched SV set. After solving an iterative optimization problem, we reconstruct the INCM using the estimated sensor position errors. Then we estimate the SV of the desired signal by solving an optimization problem with the reconstructed INCM. The main advantage of the proposed algorithm is its robustness against SV mismatches dominated by unknown sensor position errors. Numerical examples show that even if the position errors are up to half of the assumed sensor spacing, the output signal-to-interference-plus-noise ratio is only reduced by 4 dB. Beam patterns plotted using experiment data show that the interference suppression capability of the proposed beamformer outperforms other tested beamformers.

  9. Construction of covariance matrix for experimental data

    International Nuclear Information System (INIS)

    Liu Tingjin; Zhang Jianhua

    1992-01-01

    For evaluators and experimenters, the information is complete only in the case when the covariance matrix is given. The covariance matrix of the indirectly measured data has been constructed and discussed. As an example, the covariance matrix of 23 Na(n, 2n) cross section is constructed. A reasonable result is obtained

  10. An Adaptive Estimation of Forecast Error Covariance Parameters for Kalman Filtering Data Assimilation

    Institute of Scientific and Technical Information of China (English)

    Xiaogu ZHENG

    2009-01-01

    An adaptive estimation of forecast error covariance matrices is proposed for Kalman filtering data assimilation. A forecast error covariance matrix is initially estimated using an ensemble of perturbation forecasts. This initially estimated matrix is then adjusted with scale parameters that are adaptively estimated by minimizing -2log-likelihood of observed-minus-forecast residuals. The proposed approach could be applied to Kalman filtering data assimilation with imperfect models when the model error statistics are not known. A simple nonlinear model (Burgers' equation model) is used to demonstrate the efficacy of the proposed approach.

  11. ACORNS, Covariance and Correlation Matrix Diagonalization

    International Nuclear Information System (INIS)

    Szondi, E.J.

    1990-01-01

    1 - Description of program or function: The program allows the user to verify the different types of covariance/correlation matrices used in the activation neutron spectrometry. 2 - Method of solution: The program performs the diagonalization of the input covariance/relative covariance/correlation matrices. The Eigen values are then analyzed to determine the rank of the matrices. If the Eigen vectors of the pertinent correlation matrix have also been calculated, the program can perform a complete factor analysis (generation of the factor matrix and its rotation in Kaiser's 'varimax' sense to select the origin of the correlations). 3 - Restrictions on the complexity of the problem: Matrix size is limited to 60 on PDP and to 100 on IBM PC/AT

  12. Heteroscedasticity resistant robust covariance matrix estimator

    Czech Academy of Sciences Publication Activity Database

    Víšek, Jan Ámos

    2010-01-01

    Roč. 17, č. 27 (2010), s. 33-49 ISSN 1212-074X Grant - others:GA UK(CZ) GA402/09/0557 Institutional research plan: CEZ:AV0Z10750506 Keywords : Regression * Covariance matrix * Heteroscedasticity * Resistant Subject RIV: BB - Applied Statistics, Operational Research http://library.utia.cas.cz/separaty/2011/SI/visek-heteroscedasticity resistant robust covariance matrix estimator.pdf

  13. The K-Step Spatial Sign Covariance Matrix

    NARCIS (Netherlands)

    Croux, C.; Dehon, C.; Yadine, A.

    2010-01-01

    The Sign Covariance Matrix is an orthogonal equivariant estimator of mul- tivariate scale. It is often used as an easy-to-compute and highly robust estimator. In this paper we propose a k-step version of the Sign Covariance Matrix, which improves its e±ciency while keeping the maximal breakdown

  14. The Performance Analysis Based on SAR Sample Covariance Matrix

    Directory of Open Access Journals (Sweden)

    Esra Erten

    2012-03-01

    Full Text Available Multi-channel systems appear in several fields of application in science. In the Synthetic Aperture Radar (SAR context, multi-channel systems may refer to different domains, as multi-polarization, multi-interferometric or multi-temporal data, or even a combination of them. Due to the inherent speckle phenomenon present in SAR images, the statistical description of the data is almost mandatory for its utilization. The complex images acquired over natural media present in general zero-mean circular Gaussian characteristics. In this case, second order statistics as the multi-channel covariance matrix fully describe the data. For practical situations however, the covariance matrix has to be estimated using a limited number of samples, and this sample covariance matrix follow the complex Wishart distribution. In this context, the eigendecomposition of the multi-channel covariance matrix has been shown in different areas of high relevance regarding the physical properties of the imaged scene. Specifically, the maximum eigenvalue of the covariance matrix has been frequently used in different applications as target or change detection, estimation of the dominant scattering mechanism in polarimetric data, moving target indication, etc. In this paper, the statistical behavior of the maximum eigenvalue derived from the eigendecomposition of the sample multi-channel covariance matrix in terms of multi-channel SAR images is simplified for SAR community. Validation is performed against simulated data and examples of estimation and detection problems using the analytical expressions are as well given.

  15. Bayesian hierarchical model for large-scale covariance matrix estimation.

    Science.gov (United States)

    Zhu, Dongxiao; Hero, Alfred O

    2007-12-01

    Many bioinformatics problems implicitly depend on estimating large-scale covariance matrix. The traditional approaches tend to give rise to high variance and low accuracy due to "overfitting." We cast the large-scale covariance matrix estimation problem into the Bayesian hierarchical model framework, and introduce dependency between covariance parameters. We demonstrate the advantages of our approaches over the traditional approaches using simulations and OMICS data analysis.

  16. An Empirical State Error Covariance Matrix for Batch State Estimation

    Science.gov (United States)

    Frisbee, Joseph H., Jr.

    2011-01-01

    State estimation techniques serve effectively to provide mean state estimates. However, the state error covariance matrices provided as part of these techniques suffer from some degree of lack of confidence in their ability to adequately describe the uncertainty in the estimated states. A specific problem with the traditional form of state error covariance matrices is that they represent only a mapping of the assumed observation error characteristics into the state space. Any errors that arise from other sources (environment modeling, precision, etc.) are not directly represented in a traditional, theoretical state error covariance matrix. Consider that an actual observation contains only measurement error and that an estimated observation contains all other errors, known and unknown. It then follows that a measurement residual (the difference between expected and observed measurements) contains all errors for that measurement. Therefore, a direct and appropriate inclusion of the actual measurement residuals in the state error covariance matrix will result in an empirical state error covariance matrix. This empirical state error covariance matrix will fully account for the error in the state estimate. By way of a literal reinterpretation of the equations involved in the weighted least squares estimation algorithm, it is possible to arrive at an appropriate, and formally correct, empirical state error covariance matrix. The first specific step of the method is to use the average form of the weighted measurement residual variance performance index rather than its usual total weighted residual form. Next it is helpful to interpret the solution to the normal equations as the average of a collection of sample vectors drawn from a hypothetical parent population. From here, using a standard statistical analysis approach, it directly follows as to how to determine the standard empirical state error covariance matrix. This matrix will contain the total uncertainty in the

  17. ANL Critical Assembly Covariance Matrix Generation - Addendum

    Energy Technology Data Exchange (ETDEWEB)

    McKnight, Richard D. [Argonne National Lab. (ANL), Argonne, IL (United States); Grimm, Karl N. [Argonne National Lab. (ANL), Argonne, IL (United States)

    2014-01-13

    In March 2012, a report was issued on covariance matrices for Argonne National Laboratory (ANL) critical experiments. That report detailed the theory behind the calculation of covariance matrices and the methodology used to determine the matrices for a set of 33 ANL experimental set-ups. Since that time, three new experiments have been evaluated and approved. This report essentially updates the previous report by adding in these new experiments to the preceding covariance matrix structure.

  18. ARMA Cholesky Factor Models for the Covariance Matrix of Linear Models.

    Science.gov (United States)

    Lee, Keunbaik; Baek, Changryong; Daniels, Michael J

    2017-11-01

    In longitudinal studies, serial dependence of repeated outcomes must be taken into account to make correct inferences on covariate effects. As such, care must be taken in modeling the covariance matrix. However, estimation of the covariance matrix is challenging because there are many parameters in the matrix and the estimated covariance matrix should be positive definite. To overcomes these limitations, two Cholesky decomposition approaches have been proposed: modified Cholesky decomposition for autoregressive (AR) structure and moving average Cholesky decomposition for moving average (MA) structure, respectively. However, the correlations of repeated outcomes are often not captured parsimoniously using either approach separately. In this paper, we propose a class of flexible, nonstationary, heteroscedastic models that exploits the structure allowed by combining the AR and MA modeling of the covariance matrix that we denote as ARMACD. We analyze a recent lung cancer study to illustrate the power of our proposed methods.

  19. An Empirical State Error Covariance Matrix Orbit Determination Example

    Science.gov (United States)

    Frisbee, Joseph H., Jr.

    2015-01-01

    State estimation techniques serve effectively to provide mean state estimates. However, the state error covariance matrices provided as part of these techniques suffer from some degree of lack of confidence in their ability to adequately describe the uncertainty in the estimated states. A specific problem with the traditional form of state error covariance matrices is that they represent only a mapping of the assumed observation error characteristics into the state space. Any errors that arise from other sources (environment modeling, precision, etc.) are not directly represented in a traditional, theoretical state error covariance matrix. First, consider that an actual observation contains only measurement error and that an estimated observation contains all other errors, known and unknown. Then it follows that a measurement residual (the difference between expected and observed measurements) contains all errors for that measurement. Therefore, a direct and appropriate inclusion of the actual measurement residuals in the state error covariance matrix of the estimate will result in an empirical state error covariance matrix. This empirical state error covariance matrix will fully include all of the errors in the state estimate. The empirical error covariance matrix is determined from a literal reinterpretation of the equations involved in the weighted least squares estimation algorithm. It is a formally correct, empirical state error covariance matrix obtained through use of the average form of the weighted measurement residual variance performance index rather than the usual total weighted residual form. Based on its formulation, this matrix will contain the total uncertainty in the state estimate, regardless as to the source of the uncertainty and whether the source is anticipated or not. It is expected that the empirical error covariance matrix will give a better, statistical representation of the state error in poorly modeled systems or when sensor performance

  20. A Comparison of Methods for Estimating the Determinant of High-Dimensional Covariance Matrix

    KAUST Repository

    Hu, Zongliang; Dong, Kai; Dai, Wenlin; Tong, Tiejun

    2017-01-01

    The determinant of the covariance matrix for high-dimensional data plays an important role in statistical inference and decision. It has many real applications including statistical tests and information theory. Due to the statistical and computational challenges with high dimensionality, little work has been proposed in the literature for estimating the determinant of high-dimensional covariance matrix. In this paper, we estimate the determinant of the covariance matrix using some recent proposals for estimating high-dimensional covariance matrix. Specifically, we consider a total of eight covariance matrix estimation methods for comparison. Through extensive simulation studies, we explore and summarize some interesting comparison results among all compared methods. We also provide practical guidelines based on the sample size, the dimension, and the correlation of the data set for estimating the determinant of high-dimensional covariance matrix. Finally, from a perspective of the loss function, the comparison study in this paper may also serve as a proxy to assess the performance of the covariance matrix estimation.

  1. A Comparison of Methods for Estimating the Determinant of High-Dimensional Covariance Matrix

    KAUST Repository

    Hu, Zongliang

    2017-09-27

    The determinant of the covariance matrix for high-dimensional data plays an important role in statistical inference and decision. It has many real applications including statistical tests and information theory. Due to the statistical and computational challenges with high dimensionality, little work has been proposed in the literature for estimating the determinant of high-dimensional covariance matrix. In this paper, we estimate the determinant of the covariance matrix using some recent proposals for estimating high-dimensional covariance matrix. Specifically, we consider a total of eight covariance matrix estimation methods for comparison. Through extensive simulation studies, we explore and summarize some interesting comparison results among all compared methods. We also provide practical guidelines based on the sample size, the dimension, and the correlation of the data set for estimating the determinant of high-dimensional covariance matrix. Finally, from a perspective of the loss function, the comparison study in this paper may also serve as a proxy to assess the performance of the covariance matrix estimation.

  2. A Comparison of Methods for Estimating the Determinant of High-Dimensional Covariance Matrix.

    Science.gov (United States)

    Hu, Zongliang; Dong, Kai; Dai, Wenlin; Tong, Tiejun

    2017-09-21

    The determinant of the covariance matrix for high-dimensional data plays an important role in statistical inference and decision. It has many real applications including statistical tests and information theory. Due to the statistical and computational challenges with high dimensionality, little work has been proposed in the literature for estimating the determinant of high-dimensional covariance matrix. In this paper, we estimate the determinant of the covariance matrix using some recent proposals for estimating high-dimensional covariance matrix. Specifically, we consider a total of eight covariance matrix estimation methods for comparison. Through extensive simulation studies, we explore and summarize some interesting comparison results among all compared methods. We also provide practical guidelines based on the sample size, the dimension, and the correlation of the data set for estimating the determinant of high-dimensional covariance matrix. Finally, from a perspective of the loss function, the comparison study in this paper may also serve as a proxy to assess the performance of the covariance matrix estimation.

  3. Covariance, correlation matrix, and the multiscale community structure of networks.

    Science.gov (United States)

    Shen, Hua-Wei; Cheng, Xue-Qi; Fang, Bin-Xing

    2010-07-01

    Empirical studies show that real world networks often exhibit multiple scales of topological descriptions. However, it is still an open problem how to identify the intrinsic multiple scales of networks. In this paper, we consider detecting the multiscale community structure of network from the perspective of dimension reduction. According to this perspective, a covariance matrix of network is defined to uncover the multiscale community structure through the translation and rotation transformations. It is proved that the covariance matrix is the unbiased version of the well-known modularity matrix. We then point out that the translation and rotation transformations fail to deal with the heterogeneous network, which is very common in nature and society. To address this problem, a correlation matrix is proposed through introducing the rescaling transformation into the covariance matrix. Extensive tests on real world and artificial networks demonstrate that the correlation matrix significantly outperforms the covariance matrix, identically the modularity matrix, as regards identifying the multiscale community structure of network. This work provides a novel perspective to the identification of community structure and thus various dimension reduction methods might be used for the identification of community structure. Through introducing the correlation matrix, we further conclude that the rescaling transformation is crucial to identify the multiscale community structure of network, as well as the translation and rotation transformations.

  4. Hierarchical matrix approximation of large covariance matrices

    KAUST Repository

    Litvinenko, Alexander

    2015-01-05

    We approximate large non-structured covariance matrices in the H-matrix format with a log-linear computational cost and storage O(nlogn). We compute inverse, Cholesky decomposition and determinant in H-format. As an example we consider the class of Matern covariance functions, which are very popular in spatial statistics, geostatistics, machine learning and image analysis. Applications are: kriging and op- timal design.

  5. Hierarchical matrix approximation of large covariance matrices

    KAUST Repository

    Litvinenko, Alexander; Genton, Marc G.; Sun, Ying; Tempone, Raul

    2015-01-01

    We approximate large non-structured covariance matrices in the H-matrix format with a log-linear computational cost and storage O(nlogn). We compute inverse, Cholesky decomposition and determinant in H-format. As an example we consider the class of Matern covariance functions, which are very popular in spatial statistics, geostatistics, machine learning and image analysis. Applications are: kriging and op- timal design.

  6. Rotational covariance and light-front current matrix elements

    International Nuclear Information System (INIS)

    Keister, B.D.

    1994-01-01

    Light-front current matrix elements for elastic scattering from hadrons with spin 1 or greater must satisfy a nontrivial constraint associated with the requirement of rotational covariance for the current operator. Using a model ρ meson as a prototype for hadronic quark models, this constraint and its implications are studied at both low and high momentum transfers. In the kinematic region appropriate for asymptotic QCD, helicity rules, together with the rotational covariance condition, yield an additional relation between the light-front current matrix elements

  7. Hierarchical matrix approximation of large covariance matrices

    KAUST Repository

    Litvinenko, Alexander

    2015-01-07

    We approximate large non-structured covariance matrices in the H-matrix format with a log-linear computational cost and storage O(n log n). We compute inverse, Cholesky decomposition and determinant in H-format. As an example we consider the class of Matern covariance functions, which are very popular in spatial statistics, geostatistics, machine learning and image analysis. Applications are: kriging and optimal design

  8. Hierarchical matrix approximation of large covariance matrices

    KAUST Repository

    Litvinenko, Alexander; Genton, Marc G.; Sun, Ying; Tempone, Raul

    2015-01-01

    We approximate large non-structured covariance matrices in the H-matrix format with a log-linear computational cost and storage O(n log n). We compute inverse, Cholesky decomposition and determinant in H-format. As an example we consider the class of Matern covariance functions, which are very popular in spatial statistics, geostatistics, machine learning and image analysis. Applications are: kriging and optimal design

  9. ℋ-matrix techniques for approximating large covariance matrices and estimating its parameters

    KAUST Repository

    Litvinenko, Alexander; Genton, Marc G.; Sun, Ying; Keyes, David E.

    2016-01-01

    In this work the task is to use the available measurements to estimate unknown hyper-parameters (variance, smoothness parameter and covariance length) of the covariance function. We do it by maximizing the joint log-likelihood function. This is a non-convex and non-linear problem. To overcome cubic complexity in linear algebra, we approximate the discretised covariance function in the hierarchical (ℋ-) matrix format. The ℋ-matrix format has a log-linear computational cost and storage O(knlogn), where rank k is a small integer. On each iteration step of the optimization procedure the covariance matrix itself, its determinant and its Cholesky decomposition are recomputed within ℋ-matrix format. (© 2016 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)

  10. ℋ-matrix techniques for approximating large covariance matrices and estimating its parameters

    KAUST Repository

    Litvinenko, Alexander

    2016-10-25

    In this work the task is to use the available measurements to estimate unknown hyper-parameters (variance, smoothness parameter and covariance length) of the covariance function. We do it by maximizing the joint log-likelihood function. This is a non-convex and non-linear problem. To overcome cubic complexity in linear algebra, we approximate the discretised covariance function in the hierarchical (ℋ-) matrix format. The ℋ-matrix format has a log-linear computational cost and storage O(knlogn), where rank k is a small integer. On each iteration step of the optimization procedure the covariance matrix itself, its determinant and its Cholesky decomposition are recomputed within ℋ-matrix format. (© 2016 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)

  11. Estimation of covariance matrix on the experimental data for nuclear data evaluation

    International Nuclear Information System (INIS)

    Murata, T.

    1985-01-01

    In order to evaluate fission and capture cross sections of some U and Pu isotopes for JENDL-3, we have a plan for evaluating them simultaneously with a least-squares method. For the simultaneous evaluation, the covariance matrix is required for each experimental data set. In the present work, we have studied the procedures for deriving the covariance matrix from the error data given in the experimental papers. The covariance matrices were obtained using the partial errors and estimated correlation coefficients between the same type partial errors for different neutron energy. Some examples of the covariance matrix estimation are explained and the preliminary results of the simultaneous evaluation are presented. (author)

  12. Estimation of Covariance Matrix on Bi-Response Longitudinal Data Analysis with Penalized Spline Regression

    Science.gov (United States)

    Islamiyati, A.; Fatmawati; Chamidah, N.

    2018-03-01

    The correlation assumption of the longitudinal data with bi-response occurs on the measurement between the subjects of observation and the response. It causes the auto-correlation of error, and this can be overcome by using a covariance matrix. In this article, we estimate the covariance matrix based on the penalized spline regression model. Penalized spline involves knot points and smoothing parameters simultaneously in controlling the smoothness of the curve. Based on our simulation study, the estimated regression model of the weighted penalized spline with covariance matrix gives a smaller error value compared to the error of the model without covariance matrix.

  13. The covariance matrix of derived quantities and their combination

    International Nuclear Information System (INIS)

    Zhao, Z.; Perey, F.G.

    1992-06-01

    The covariance matrix of quantities derived from measured data via nonlinear relations are only approximate since they are functions of the measured data taken as estimates for the true values of the measured quantities. The evaluation of such derived quantities entails new estimates for the true values of the measured quantities and consequently implies a modification of the covariance matrix of the derived quantities that was used in the evaluation process. Failure to recognize such an implication can lead to inconsistencies between the results of different evaluation strategies. In this report we show that an iterative procedure can eliminate such inconsistencies

  14. MIMO Radar Transmit Beampattern Design Without Synthesising the Covariance Matrix

    KAUST Repository

    Ahmed, Sajid

    2013-10-28

    Compared to phased-array, multiple-input multiple-output (MIMO) radars provide more degrees-offreedom (DOF) that can be exploited for improved spatial resolution, better parametric identifiability, lower side-lobe levels at the transmitter/receiver, and design variety of transmit beampatterns. The design of the transmit beampattern generally requires the waveforms to have arbitrary auto- and crosscorrelation properties. The generation of such waveforms is a two step complicated process. In the first step a waveform covariance matrix is synthesised, which is a constrained optimisation problem. In the second step, to realise this covariance matrix actual waveforms are designed, which is also a constrained optimisation problem. Our proposed scheme converts this two step constrained optimisation problem into a one step unconstrained optimisation problem. In the proposed scheme, in contrast to synthesising the covariance matrix for the desired beampattern, nT independent finite-alphabet constantenvelope waveforms are generated and pre-processed, with weight matrix W, before transmitting from the antennas. In this work, two weight matrices are proposed that can be easily optimised for the desired symmetric and non-symmetric beampatterns and guarantee equal average power transmission from each antenna. Simulation results validate our claims.

  15. An Adaptive Approach to Mitigate Background Covariance Limitations in the Ensemble Kalman Filter

    KAUST Repository

    Song, Hajoon

    2010-07-01

    A new approach is proposed to address the background covariance limitations arising from undersampled ensembles and unaccounted model errors in the ensemble Kalman filter (EnKF). The method enhances the representativeness of the EnKF ensemble by augmenting it with new members chosen adaptively to add missing information that prevents the EnKF from fully fitting the data to the ensemble. The vectors to be added are obtained by back projecting the residuals of the observation misfits from the EnKF analysis step onto the state space. The back projection is done using an optimal interpolation (OI) scheme based on an estimated covariance of the subspace missing from the ensemble. In the experiments reported here, the OI uses a preselected stationary background covariance matrix, as in the hybrid EnKF–three-dimensional variational data assimilation (3DVAR) approach, but the resulting correction is included as a new ensemble member instead of being added to all existing ensemble members. The adaptive approach is tested with the Lorenz-96 model. The hybrid EnKF–3DVAR is used as a benchmark to evaluate the performance of the adaptive approach. Assimilation experiments suggest that the new adaptive scheme significantly improves the EnKF behavior when it suffers from small size ensembles and neglected model errors. It was further found to be competitive with the hybrid EnKF–3DVAR approach, depending on ensemble size and data coverage.

  16. Estimation of genetic connectedness diagnostics based on prediction errors without the prediction error variance-covariance matrix.

    Science.gov (United States)

    Holmes, John B; Dodds, Ken G; Lee, Michael A

    2017-03-02

    An important issue in genetic evaluation is the comparability of random effects (breeding values), particularly between pairs of animals in different contemporary groups. This is usually referred to as genetic connectedness. While various measures of connectedness have been proposed in the literature, there is general agreement that the most appropriate measure is some function of the prediction error variance-covariance matrix. However, obtaining the prediction error variance-covariance matrix is computationally demanding for large-scale genetic evaluations. Many alternative statistics have been proposed that avoid the computational cost of obtaining the prediction error variance-covariance matrix, such as counts of genetic links between contemporary groups, gene flow matrices, and functions of the variance-covariance matrix of estimated contemporary group fixed effects. In this paper, we show that a correction to the variance-covariance matrix of estimated contemporary group fixed effects will produce the exact prediction error variance-covariance matrix averaged by contemporary group for univariate models in the presence of single or multiple fixed effects and one random effect. We demonstrate the correction for a series of models and show that approximations to the prediction error matrix based solely on the variance-covariance matrix of estimated contemporary group fixed effects are inappropriate in certain circumstances. Our method allows for the calculation of a connectedness measure based on the prediction error variance-covariance matrix by calculating only the variance-covariance matrix of estimated fixed effects. Since the number of fixed effects in genetic evaluation is usually orders of magnitudes smaller than the number of random effect levels, the computational requirements for our method should be reduced.

  17. Multi-Target Angle Tracking Algorithm for Bistatic Multiple-Input Multiple-Output (MIMO Radar Based on the Elements of the Covariance Matrix

    Directory of Open Access Journals (Sweden)

    Zhengyan Zhang

    2018-03-01

    Full Text Available In this paper, we consider the problem of tracking the direction of arrivals (DOA and the direction of departure (DOD of multiple targets for bistatic multiple-input multiple-output (MIMO radar. A high-precision tracking algorithm for target angle is proposed. First, the linear relationship between the covariance matrix difference and the angle difference of the adjacent moment was obtained through three approximate relations. Then, the proposed algorithm obtained the relationship between the elements in the covariance matrix difference. On this basis, the performance of the algorithm was improved by averaging the covariance matrix element. Finally, the least square method was used to estimate the DOD and DOA. The algorithm realized the automatic correlation of the angle and provided better performance when compared with the adaptive asymmetric joint diagonalization (AAJD algorithm. The simulation results demonstrated the effectiveness of the proposed algorithm. The algorithm provides the technical support for the practical application of MIMO radar.

  18. Multi-Target Angle Tracking Algorithm for Bistatic Multiple-Input Multiple-Output (MIMO) Radar Based on the Elements of the Covariance Matrix.

    Science.gov (United States)

    Zhang, Zhengyan; Zhang, Jianyun; Zhou, Qingsong; Li, Xiaobo

    2018-03-07

    In this paper, we consider the problem of tracking the direction of arrivals (DOA) and the direction of departure (DOD) of multiple targets for bistatic multiple-input multiple-output (MIMO) radar. A high-precision tracking algorithm for target angle is proposed. First, the linear relationship between the covariance matrix difference and the angle difference of the adjacent moment was obtained through three approximate relations. Then, the proposed algorithm obtained the relationship between the elements in the covariance matrix difference. On this basis, the performance of the algorithm was improved by averaging the covariance matrix element. Finally, the least square method was used to estimate the DOD and DOA. The algorithm realized the automatic correlation of the angle and provided better performance when compared with the adaptive asymmetric joint diagonalization (AAJD) algorithm. The simulation results demonstrated the effectiveness of the proposed algorithm. The algorithm provides the technical support for the practical application of MIMO radar.

  19. On the regularity of the covariance matrix of a discretized scalar field on the sphere

    Energy Technology Data Exchange (ETDEWEB)

    Bilbao-Ahedo, J.D. [Departamento de Física Moderna, Universidad de Cantabria, Av. los Castros s/n, 39005 Santander (Spain); Barreiro, R.B.; Herranz, D.; Vielva, P.; Martínez-González, E., E-mail: bilbao@ifca.unican.es, E-mail: barreiro@ifca.unican.es, E-mail: herranz@ifca.unican.es, E-mail: vielva@ifca.unican.es, E-mail: martinez@ifca.unican.es [Instituto de Física de Cantabria (CSIC-UC), Av. los Castros s/n, 39005 Santander (Spain)

    2017-02-01

    We present a comprehensive study of the regularity of the covariance matrix of a discretized field on the sphere. In a particular situation, the rank of the matrix depends on the number of pixels, the number of spherical harmonics, the symmetries of the pixelization scheme and the presence of a mask. Taking into account the above mentioned components, we provide analytical expressions that constrain the rank of the matrix. They are obtained by expanding the determinant of the covariance matrix as a sum of determinants of matrices made up of spherical harmonics. We investigate these constraints for five different pixelizations that have been used in the context of Cosmic Microwave Background (CMB) data analysis: Cube, Icosahedron, Igloo, GLESP and HEALPix, finding that, at least in the considered cases, the HEALPix pixelization tends to provide a covariance matrix with a rank closer to the maximum expected theoretical value than the other pixelizations. The effect of the propagation of numerical errors in the regularity of the covariance matrix is also studied for different computational precisions, as well as the effect of adding a certain level of noise in order to regularize the matrix. In addition, we investigate the application of the previous results to a particular example that requires the inversion of the covariance matrix: the estimation of the CMB temperature power spectrum through the Quadratic Maximum Likelihood algorithm. Finally, some general considerations in order to achieve a regular covariance matrix are also presented.

  20. Hierarchical matrix approximation of large covariance matrices

    KAUST Repository

    Litvinenko, Alexander; Genton, Marc G.; Sun, Ying

    2015-01-01

    We approximate large non-structured Matérn covariance matrices of size n×n in the H-matrix format with a log-linear computational cost and storage O(kn log n), where rank k ≪ n is a small integer. Applications are: spatial statistics, machine learning and image analysis, kriging and optimal design.

  1. Hierarchical matrix approximation of large covariance matrices

    KAUST Repository

    Litvinenko, Alexander

    2015-11-30

    We approximate large non-structured Matérn covariance matrices of size n×n in the H-matrix format with a log-linear computational cost and storage O(kn log n), where rank k ≪ n is a small integer. Applications are: spatial statistics, machine learning and image analysis, kriging and optimal design.

  2. R-matrix and q-covariant oscillators for Uq(sl(n|m))

    International Nuclear Information System (INIS)

    Leblanc, Y.; Wallet, J.C.

    1993-02-01

    An R-matrix formalism is used to construct covariant quantum oscillator algebras for U q (sl(n|m)). It is shown that the complete structure of the twisted oscillator algebras can be obtained from the properties of the intertwining matrix obeying a Hecke type relation, combined with covariance of the oscillators at the deformed level and associativity. The resulting twisted algebras, involving q-bosons and q-fermions, are invariant under natural q-transformations of the oscillators induced by the coproduct. (author) 11 refs

  3. Evolution of the additive genetic variance–covariance matrix under continuous directional selection on a complex behavioural phenotype

    Science.gov (United States)

    Careau, Vincent; Wolak, Matthew E.; Carter, Patrick A.; Garland, Theodore

    2015-01-01

    Given the pace at which human-induced environmental changes occur, a pressing challenge is to determine the speed with which selection can drive evolutionary change. A key determinant of adaptive response to multivariate phenotypic selection is the additive genetic variance–covariance matrix (G). Yet knowledge of G in a population experiencing new or altered selection is not sufficient to predict selection response because G itself evolves in ways that are poorly understood. We experimentally evaluated changes in G when closely related behavioural traits experience continuous directional selection. We applied the genetic covariance tensor approach to a large dataset (n = 17 328 individuals) from a replicated, 31-generation artificial selection experiment that bred mice for voluntary wheel running on days 5 and 6 of a 6-day test. Selection on this subset of G induced proportional changes across the matrix for all 6 days of running behaviour within the first four generations. The changes in G induced by selection resulted in a fourfold slower-than-predicted rate of response to selection. Thus, selection exacerbated constraints within G and limited future adaptive response, a phenomenon that could have profound consequences for populations facing rapid environmental change. PMID:26582016

  4. Evolution of the additive genetic variance-covariance matrix under continuous directional selection on a complex behavioural phenotype.

    Science.gov (United States)

    Careau, Vincent; Wolak, Matthew E; Carter, Patrick A; Garland, Theodore

    2015-11-22

    Given the pace at which human-induced environmental changes occur, a pressing challenge is to determine the speed with which selection can drive evolutionary change. A key determinant of adaptive response to multivariate phenotypic selection is the additive genetic variance-covariance matrix ( G: ). Yet knowledge of G: in a population experiencing new or altered selection is not sufficient to predict selection response because G: itself evolves in ways that are poorly understood. We experimentally evaluated changes in G: when closely related behavioural traits experience continuous directional selection. We applied the genetic covariance tensor approach to a large dataset (n = 17 328 individuals) from a replicated, 31-generation artificial selection experiment that bred mice for voluntary wheel running on days 5 and 6 of a 6-day test. Selection on this subset of G: induced proportional changes across the matrix for all 6 days of running behaviour within the first four generations. The changes in G: induced by selection resulted in a fourfold slower-than-predicted rate of response to selection. Thus, selection exacerbated constraints within G: and limited future adaptive response, a phenomenon that could have profound consequences for populations facing rapid environmental change. © 2015 The Author(s).

  5. Exploiting Data Sparsity In Covariance Matrix Computations on Heterogeneous Systems

    KAUST Repository

    Charara, Ali M.

    2018-05-24

    Covariance matrices are ubiquitous in computational sciences, typically describing the correlation of elements of large multivariate spatial data sets. For example, covari- ance matrices are employed in climate/weather modeling for the maximum likelihood estimation to improve prediction, as well as in computational ground-based astronomy to enhance the observed image quality by filtering out noise produced by the adap- tive optics instruments and atmospheric turbulence. The structure of these covariance matrices is dense, symmetric, positive-definite, and often data-sparse, therefore, hier- archically of low-rank. This thesis investigates the performance limit of dense matrix computations (e.g., Cholesky factorization) on covariance matrix problems as the number of unknowns grows, and in the context of the aforementioned applications. We employ recursive formulations of some of the basic linear algebra subroutines (BLAS) to accelerate the covariance matrix computation further, while reducing data traffic across the memory subsystems layers. However, dealing with large data sets (i.e., covariance matrices of billions in size) can rapidly become prohibitive in memory footprint and algorithmic complexity. Most importantly, this thesis investigates the tile low-rank data format (TLR), a new compressed data structure and layout, which is valuable in exploiting data sparsity by approximating the operator. The TLR com- pressed data structure allows approximating the original problem up to user-defined numerical accuracy. This comes at the expense of dealing with tasks with much lower arithmetic intensities than traditional dense computations. In fact, this thesis con- solidates the two trends of dense and data-sparse linear algebra for HPC. Not only does the thesis leverage recursive formulations for dense Cholesky-based matrix al- gorithms, but it also implements a novel TLR-Cholesky factorization using batched linear algebra operations to increase hardware occupancy and

  6. Co-movements among financial stocks and covariance matrix analysis

    OpenAIRE

    Sharifi, Saba

    2003-01-01

    The major theories of finance leading into the main body of this research are discussed and our experiments on studying the risk and co-movements among stocks are presented. This study leads to the application of Random Matrix Theory (RMT) The idea of this theory refers to the importance of the empirically measured correlation (or covariance) matrix, C, in finance and particularly in the theory of optimal portfolios However, this matrix has recently come into question, as a large part of ...

  7. Multiple feature fusion via covariance matrix for visual tracking

    Science.gov (United States)

    Jin, Zefenfen; Hou, Zhiqiang; Yu, Wangsheng; Wang, Xin; Sun, Hui

    2018-04-01

    Aiming at the problem of complicated dynamic scenes in visual target tracking, a multi-feature fusion tracking algorithm based on covariance matrix is proposed to improve the robustness of the tracking algorithm. In the frame-work of quantum genetic algorithm, this paper uses the region covariance descriptor to fuse the color, edge and texture features. It also uses a fast covariance intersection algorithm to update the model. The low dimension of region covariance descriptor, the fast convergence speed and strong global optimization ability of quantum genetic algorithm, and the fast computation of fast covariance intersection algorithm are used to improve the computational efficiency of fusion, matching, and updating process, so that the algorithm achieves a fast and effective multi-feature fusion tracking. The experiments prove that the proposed algorithm can not only achieve fast and robust tracking but also effectively handle interference of occlusion, rotation, deformation, motion blur and so on.

  8. SIMULATIONS OF WIDE-FIELD WEAK-LENSING SURVEYS. II. COVARIANCE MATRIX OF REAL-SPACE CORRELATION FUNCTIONS

    International Nuclear Information System (INIS)

    Sato, Masanori; Matsubara, Takahiko; Takada, Masahiro; Hamana, Takashi

    2011-01-01

    Using 1000 ray-tracing simulations for a Λ-dominated cold dark model in Sato et al., we study the covariance matrix of cosmic shear correlation functions, which is the standard statistics used in previous measurements. The shear correlation function of a particular separation angle is affected by Fourier modes over a wide range of multipoles, even beyond a survey area, which complicates the analysis of the covariance matrix. To overcome such obstacles we first construct Gaussian shear simulations from the 1000 realizations and then use the Gaussian simulations to disentangle the Gaussian covariance contribution to the covariance matrix we measured from the original simulations. We found that an analytical formula of Gaussian covariance overestimates the covariance amplitudes due to an effect of the finite survey area. Furthermore, the clean separation of the Gaussian covariance allows us to examine the non-Gaussian covariance contributions as a function of separation angles and source redshifts. For upcoming surveys with typical source redshifts of z s = 0.6 and 1.0, the non-Gaussian contribution to the diagonal covariance components at 1 arcmin scales is greater than the Gaussian contribution by a factor of 20 and 10, respectively. Predictions based on the halo model qualitatively well reproduce the simulation results, however show a sizable disagreement in the covariance amplitudes. By combining these simulation results we develop a fitting formula to the covariance matrix for a survey with arbitrary area coverage, taking into account effects of the finiteness of survey area on the Gaussian covariance.

  9. Some Algorithms for the Conditional Mean Vector and Covariance Matrix

    Directory of Open Access Journals (Sweden)

    John F. Monahan

    2006-08-01

    Full Text Available We consider here the problem of computing the mean vector and covariance matrix for a conditional normal distribution, considering especially a sequence of problems where the conditioning variables are changing. The sweep operator provides one simple general approach that is easy to implement and update. A second, more goal-oriented general method avoids explicit computation of the vector and matrix, while enabling easy evaluation of the conditional density for likelihood computation or easy generation from the conditional distribution. The covariance structure that arises from the special case of an ARMA(p, q time series can be exploited for substantial improvements in computational efficiency.

  10. ANGELO-LAMBDA, Covariance matrix interpolation and mathematical verification

    International Nuclear Information System (INIS)

    Kodeli, Ivo

    2007-01-01

    1 - Description of program or function: The codes ANGELO-2.3 and LAMBDA-2.3 are used for the interpolation of the cross section covariance data from the original to a user defined energy group structure, and for the mathematical tests of the matrices, respectively. The LAMBDA-2.3 code calculates the eigenvalues of the matrices (both for the original or the converted) and lists them accordingly into positive and negative matrices. This verification is strongly recommended before using any covariance matrices. These versions of the two codes are the extended versions of the previous codes available in the Packages NEA-1264 - ZZ-VITAMIN-J/COVA. They were specifically developed for the purposes of the OECD LWR UAM benchmark, in particular for the processing of the ZZ-SCALE5.1/COVA-44G cross section covariance matrix library retrieved from the SCALE-5.1 package. Either the original SCALE-5.1 libraries or the libraries separated into several files by Nuclides can be (in principle) processed by ANGELO/LAMBDA codes, but the use of the one-nuclide data is strongly recommended. Due to large deviations of the correlation matrix terms from unity observed in some SCALE5.1 covariance matrices, the previous more severe acceptance condition in the ANGELO2.3 code was released. In case the correlation coefficients exceed 1.0, only a warning message is issued, and coefficients are replaced by 1.0. 2 - Methods: ANGELO-2.3 interpolates the covariance matrices to a union grid using flat weighting. LAMBDA-2.3 code includes the mathematical routines to calculate the eigenvalues of the covariance matrices. 3 - Restrictions on the complexity of the problem: The algorithm used in ANGELO is relatively simple, therefore the interpolations involving energy group structure which are very different from the original (e.g. large difference in the number of energy groups) may not be accurate. In particular in the case of the MT=1018 data (fission spectra covariances) the algorithm may not be

  11. On the use of the covariance matrix to fit correlated data

    Science.gov (United States)

    D'Agostini, G.

    1994-07-01

    Best fits to data which are affected by systematic uncertainties on the normalization factor have the tendency to produce curves lower than expected if the covariance matrix of the data points is used in the definition of the χ2. This paper shows that the effect is a direct consequence of the hypothesis used to estimate the empirical covariance matrix, namely the linearization on which the usual error propagation relies. The bias can become unacceptable if the normalization error is large, or a large number of data points are fitted.

  12. Shrinkage covariance matrix approach based on robust trimmed mean in gene sets detection

    Science.gov (United States)

    Karjanto, Suryaefiza; Ramli, Norazan Mohamed; Ghani, Nor Azura Md; Aripin, Rasimah; Yusop, Noorezatty Mohd

    2015-02-01

    Microarray involves of placing an orderly arrangement of thousands of gene sequences in a grid on a suitable surface. The technology has made a novelty discovery since its development and obtained an increasing attention among researchers. The widespread of microarray technology is largely due to its ability to perform simultaneous analysis of thousands of genes in a massively parallel manner in one experiment. Hence, it provides valuable knowledge on gene interaction and function. The microarray data set typically consists of tens of thousands of genes (variables) from just dozens of samples due to various constraints. Therefore, the sample covariance matrix in Hotelling's T2 statistic is not positive definite and become singular, thus it cannot be inverted. In this research, the Hotelling's T2 statistic is combined with a shrinkage approach as an alternative estimation to estimate the covariance matrix to detect significant gene sets. The use of shrinkage covariance matrix overcomes the singularity problem by converting an unbiased to an improved biased estimator of covariance matrix. Robust trimmed mean is integrated into the shrinkage matrix to reduce the influence of outliers and consequently increases its efficiency. The performance of the proposed method is measured using several simulation designs. The results are expected to outperform existing techniques in many tested conditions.

  13. Testing Constancy of the Error Covariance Matrix in Vector Models against Parametric Alternatives using a Spectral Decomposition

    DEFF Research Database (Denmark)

    Yang, Yukay

    I consider multivariate (vector) time series models in which the error covariance matrix may be time-varying. I derive a test of constancy of the error covariance matrix against the alternative that the covariance matrix changes over time. I design a new family of Lagrange-multiplier tests against...... to consider multivariate volatility modelling....

  14. Some remarks on estimating a covariance structure model from a sample correlation matrix

    OpenAIRE

    Maydeu Olivares, Alberto; Hernández Estrada, Adolfo

    2000-01-01

    A popular model in structural equation modeling involves a multivariate normal density with a structured covariance matrix that has been categorized according to a set of thresholds. In this setup one may estimate the covariance structure parameters from the sample tetrachoricl polychoric correlations but only if the covariance structure is scale invariant. Doing so when the covariance structure is not scale invariant results in estimating a more restricted covariance structure than the one i...

  15. Large Covariance Estimation by Thresholding Principal Orthogonal Complements.

    Science.gov (United States)

    Fan, Jianqing; Liao, Yuan; Mincheva, Martina

    2013-09-01

    This paper deals with the estimation of a high-dimensional covariance with a conditional sparsity structure and fast-diverging eigenvalues. By assuming sparse error covariance matrix in an approximate factor model, we allow for the presence of some cross-sectional correlation even after taking out common but unobservable factors. We introduce the Principal Orthogonal complEment Thresholding (POET) method to explore such an approximate factor structure with sparsity. The POET estimator includes the sample covariance matrix, the factor-based covariance matrix (Fan, Fan, and Lv, 2008), the thresholding estimator (Bickel and Levina, 2008) and the adaptive thresholding estimator (Cai and Liu, 2011) as specific examples. We provide mathematical insights when the factor analysis is approximately the same as the principal component analysis for high-dimensional data. The rates of convergence of the sparse residual covariance matrix and the conditional sparse covariance matrix are studied under various norms. It is shown that the impact of estimating the unknown factors vanishes as the dimensionality increases. The uniform rates of convergence for the unobserved factors and their factor loadings are derived. The asymptotic results are also verified by extensive simulation studies. Finally, a real data application on portfolio allocation is presented.

  16. Asymptotic theory for the sample covariance matrix of a heavy-tailed multivariate time series

    DEFF Research Database (Denmark)

    Davis, Richard A.; Mikosch, Thomas Valentin; Pfaffel, Olivier

    2016-01-01

    In this paper we give an asymptotic theory for the eigenvalues of the sample covariance matrix of a multivariate time series. The time series constitutes a linear process across time and between components. The input noise of the linear process has regularly varying tails with index α∈(0,4) in...... particular, the time series has infinite fourth moment. We derive the limiting behavior for the largest eigenvalues of the sample covariance matrix and show point process convergence of the normalized eigenvalues. The limiting process has an explicit form involving points of a Poisson process and eigenvalues...... of a non-negative definite matrix. Based on this convergence we derive limit theory for a host of other continuous functionals of the eigenvalues, including the joint convergence of the largest eigenvalues, the joint convergence of the largest eigenvalue and the trace of the sample covariance matrix...

  17. Large Covariance Estimation by Thresholding Principal Orthogonal Complements

    Science.gov (United States)

    Fan, Jianqing; Liao, Yuan; Mincheva, Martina

    2012-01-01

    This paper deals with the estimation of a high-dimensional covariance with a conditional sparsity structure and fast-diverging eigenvalues. By assuming sparse error covariance matrix in an approximate factor model, we allow for the presence of some cross-sectional correlation even after taking out common but unobservable factors. We introduce the Principal Orthogonal complEment Thresholding (POET) method to explore such an approximate factor structure with sparsity. The POET estimator includes the sample covariance matrix, the factor-based covariance matrix (Fan, Fan, and Lv, 2008), the thresholding estimator (Bickel and Levina, 2008) and the adaptive thresholding estimator (Cai and Liu, 2011) as specific examples. We provide mathematical insights when the factor analysis is approximately the same as the principal component analysis for high-dimensional data. The rates of convergence of the sparse residual covariance matrix and the conditional sparse covariance matrix are studied under various norms. It is shown that the impact of estimating the unknown factors vanishes as the dimensionality increases. The uniform rates of convergence for the unobserved factors and their factor loadings are derived. The asymptotic results are also verified by extensive simulation studies. Finally, a real data application on portfolio allocation is presented. PMID:24348088

  18. Bayesian tests on components of the compound symmetry covariance matrix

    NARCIS (Netherlands)

    Mulder, J.; Fox, J.P.

    2013-01-01

    Complex dependency structures are often conditionally modeled, where random effects parameters are used to specify the natural heterogeneity in the population. When interest is focused on the dependency structure, inferences can be made from a complex covariance matrix using a marginal modeling

  19. A Concise Method for Storing and Communicating the Data Covariance Matrix

    Energy Technology Data Exchange (ETDEWEB)

    Larson, Nancy M [ORNL

    2008-10-01

    The covariance matrix associated with experimental cross section or transmission data consists of several components. Statistical uncertainties on the measured quantity (counts) provide a diagonal contribution. Off-diagonal components arise from uncertainties on the parameters (such as normalization or background) that figure into the data reduction process; these are denoted systematic or common uncertainties, since they affect all data points. The full off-diagonal data covariance matrix (DCM) can be extremely large, since the size is the square of the number of data points. Fortunately, it is not necessary to explicitly calculate, store, or invert the DCM. Likewise, it is not necessary to explicitly calculate, store, or use the inverse of the DCM. Instead, it is more efficient to accomplish the same results using only the various component matrices that appear in the definition of the DCM. Those component matrices are either diagonal or small (the number of data points times the number of data-reduction parameters); hence, this implicit data covariance method requires far less array storage and far fewer computations while producing more accurate results.

  20. Covariance Matrix of a Double-Differential Doppler-Broadened Elastic Scattering Cross Section

    Science.gov (United States)

    Arbanas, G.; Becker, B.; Dagan, R.; Dunn, M. E.; Larson, N. M.; Leal, L. C.; Williams, M. L.

    2012-05-01

    Legendre moments of a double-differential Doppler-broadened elastic neutron scattering cross section on 238U are computed near the 6.67 eV resonance at temperature T = 103 K up to angular order 14. A covariance matrix of these Legendre moments is computed as a functional of the covariance matrix of the elastic scattering cross section. A variance of double-differential Doppler-broadened elastic scattering cross section is computed from the covariance of Legendre moments. Notice: This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes.

  1. Fault estimation of satellite reaction wheels using covariance based adaptive unscented Kalman filter

    Science.gov (United States)

    Rahimi, Afshin; Kumar, Krishna Dev; Alighanbari, Hekmat

    2017-05-01

    Reaction wheels, as one of the most commonly used actuators in satellite attitude control systems, are prone to malfunction which could lead to catastrophic failures. Such malfunctions can be detected and addressed in time if proper analytical redundancy algorithms such as parameter estimation and control reconfiguration are employed. Major challenges in parameter estimation include speed and accuracy of the employed algorithm. This paper presents a new approach for improving parameter estimation with adaptive unscented Kalman filter. The enhancement in tracking speed of unscented Kalman filter is achieved by systematically adapting the covariance matrix to the faulty estimates using innovation and residual sequences combined with an adaptive fault annunciation scheme. The proposed approach provides the filter with the advantage of tracking sudden changes in the system non-measurable parameters accurately. Results showed successful detection of reaction wheel malfunctions without requiring a priori knowledge about system performance in the presence of abrupt, transient, intermittent, and incipient faults. Furthermore, the proposed approach resulted in superior filter performance with less mean squared errors for residuals compared to generic and adaptive unscented Kalman filters, and thus, it can be a promising method for the development of fail-safe satellites.

  2. A Note on the Eigensystem of the Covariance Matrix of Dichotomous Guttman Items.

    Science.gov (United States)

    Davis-Stober, Clintin P; Doignon, Jean-Paul; Suck, Reinhard

    2015-01-01

    We consider the covariance matrix for dichotomous Guttman items under a set of uniformity conditions, and obtain closed-form expressions for the eigenvalues and eigenvectors of the matrix. In particular, we describe the eigenvalues and eigenvectors of the matrix in terms of trigonometric functions of the number of items. Our results parallel those of Zwick (1987) for the correlation matrix under the same uniformity conditions. We provide an explanation for certain properties of principal components under Guttman scalability which have been first reported by Guttman (1950).

  3. DANTE, Activation Analysis Neutron Spectra Unfolding by Covariance Matrix Method

    International Nuclear Information System (INIS)

    Petilli, M.

    1981-01-01

    1 - Description of problem or function: The program evaluates activation measurements of reactor neutron spectra and unfolds the results for dosimetry purposes. Different evaluation options are foreseen: absolute or relative fluxes and different iteration algorithms. 2 - Method of solution: A least-square fit method is used. A correlation between available data and their uncertainties has been introduced by means of flux and activity variance-covariance matrices. Cross sections are assumed to be constant, i.e. with variance-covariance matrix equal to zero. The Lagrange multipliers method has been used for calculating the solution. 3 - Restrictions on the complexity of the problem: 9 activation experiments can be analyzed. 75 energy groups are accepted

  4. HLIBCov: Parallel Hierarchical Matrix Approximation of Large Covariance Matrices and Likelihoods with Applications in Parameter Identification

    KAUST Repository

    Litvinenko, Alexander

    2017-09-26

    The main goal of this article is to introduce the parallel hierarchical matrix library HLIBpro to the statistical community. We describe the HLIBCov package, which is an extension of the HLIBpro library for approximating large covariance matrices and maximizing likelihood functions. We show that an approximate Cholesky factorization of a dense matrix of size $2M\\\\times 2M$ can be computed on a modern multi-core desktop in few minutes. Further, HLIBCov is used for estimating the unknown parameters such as the covariance length, variance and smoothness parameter of a Mat\\\\\\'ern covariance function by maximizing the joint Gaussian log-likelihood function. The computational bottleneck here is expensive linear algebra arithmetics due to large and dense covariance matrices. Therefore covariance matrices are approximated in the hierarchical ($\\\\H$-) matrix format with computational cost $\\\\mathcal{O}(k^2n \\\\log^2 n/p)$ and storage $\\\\mathcal{O}(kn \\\\log n)$, where the rank $k$ is a small integer (typically $k<25$), $p$ the number of cores and $n$ the number of locations on a fairly general mesh. We demonstrate a synthetic example, where the true values of known parameters are known. For reproducibility we provide the C++ code, the documentation, and the synthetic data.

  5. HLIBCov: Parallel Hierarchical Matrix Approximation of Large Covariance Matrices and Likelihoods with Applications in Parameter Identification

    KAUST Repository

    Litvinenko, Alexander

    2017-09-24

    The main goal of this article is to introduce the parallel hierarchical matrix library HLIBpro to the statistical community. We describe the HLIBCov package, which is an extension of the HLIBpro library for approximating large covariance matrices and maximizing likelihood functions. We show that an approximate Cholesky factorization of a dense matrix of size $2M\\\\times 2M$ can be computed on a modern multi-core desktop in few minutes. Further, HLIBCov is used for estimating the unknown parameters such as the covariance length, variance and smoothness parameter of a Mat\\\\\\'ern covariance function by maximizing the joint Gaussian log-likelihood function. The computational bottleneck here is expensive linear algebra arithmetics due to large and dense covariance matrices. Therefore covariance matrices are approximated in the hierarchical ($\\\\mathcal{H}$-) matrix format with computational cost $\\\\mathcal{O}(k^2n \\\\log^2 n/p)$ and storage $\\\\mathcal{O}(kn \\\\log n)$, where the rank $k$ is a small integer (typically $k<25$), $p$ the number of cores and $n$ the number of locations on a fairly general mesh. We demonstrate a synthetic example, where the true values of known parameters are known. For reproducibility we provide the C++ code, the documentation, and the synthetic data.

  6. DFT-Based Closed-form Covariance Matrix and Direct Waveforms Design for MIMO Radar to Achieve Desired Beampatterns

    KAUST Repository

    Bouchoucha, Taha

    2017-01-23

    In multiple-input multiple-out (MIMO) radar, for desired transmit beampatterns, appropriate correlated waveforms are designed. To design such waveforms, conventional MIMO radar methods use two steps. In the first step, the waveforms covariance matrix, R, is synthesized to achieve the desired beampattern. While in the second step, to realize the synthesized covariance matrix, actual waveforms are designed. Most of the existing methods use iterative algorithms to solve these constrained optimization problems. The computational complexity of these algorithms is very high, which makes them difficult to use in practice. In this paper, to achieve the desired beampattern, a low complexity discrete-Fourier-transform based closed-form covariance matrix design technique is introduced for a MIMO radar. The designed covariance matrix is then exploited to derive a novel closed-form algorithm to directly design the finite-alphabet constant-envelope waveforms for the desired beampattern. The proposed technique can be used to design waveforms for large antenna array to change the beampattern in real time. It is also shown that the number of transmitted symbols from each antenna depends on the beampattern and is less than the total number of transmit antenna elements.

  7. Least-squares adjustment of a 'known' neutron spectrum: The importance of the covariance matrix of the input spectrum

    International Nuclear Information System (INIS)

    Mannhart, W.

    1986-01-01

    Based on the responses of 25 different neutron activation detectors, the neutron spectrum of Cf-252 hs been adjusted with least-squares methods. For a fixed input neutron spectrum, the covariance matrix of this spectrum has been systematically varied to investigate the influence of this matrix on the final result. The investigation showed that the adjusted neutron spectrum is rather sensitive to the structure of the covariance matrix for the input spectrum. (author)

  8. A note on the eigensystem of the covariance matrix of dichotomous Guttman items

    Directory of Open Access Journals (Sweden)

    Clintin P Davis-Stober

    2015-12-01

    Full Text Available We consider the sample covariance matrix for dichotomous Guttman items under a set of uniformity conditions, and obtain closed-form expressions for the eigenvalues and eigenvectors of the matrix. In particular, we describe the eigenvalues and eigenvectors of the matrix in terms of trigonometric functions of the number of items. Our results parallel those of Zwick (1987 for the correlation matrix under the same uniformity conditions. We provide an explanation for certain properties of principal components under Guttman scalability which have been first reported by Guttman (1950.

  9. Cloud-Based DDoS HTTP Attack Detection Using Covariance Matrix Approach

    Directory of Open Access Journals (Sweden)

    Abdulaziz Aborujilah

    2017-01-01

    Full Text Available In this era of technology, cloud computing technology has become essential part of the IT services used the daily life. In this regard, website hosting services are gradually moving to the cloud. This adds new valued feature to the cloud-based websites and at the same time introduces new threats for such services. DDoS attack is one such serious threat. Covariance matrix approach is used in this article to detect such attacks. The results were encouraging, according to confusion matrix and ROC descriptors.

  10. Direct closed-form covariance matrix and finite alphabet constant-envelope waveforms for planar array beampatterns

    KAUST Repository

    Ahmed, Sajid

    2016-11-24

    Various examples of methods and systems are provided for direct closed-form finite alphabet constant-envelope waveforms for planar array beampatterns. In one example, a method includes defining a waveform covariance matrix based at least in part upon a two-dimensional fast Fourier transform (2D-FFT) analysis of a frequency domain matrix Hf associated with a planar array of antennas. Symbols can be encoded based upon the waveform covariance matrix and the encoded symbols can be transmitted via the planar array of antennas. In another embodiment, a system comprises an N x M planar array of antennas and transmission circuitry configured to transmit symbols via a two-dimensional waveform beampattern defined based at least in part upon a 2D-FFT analysis of a frequency domain matrix Hf associated with the planar array of antennas.

  11. Ultracentrifuge separative power modeling with multivariate regression using covariance matrix

    International Nuclear Information System (INIS)

    Migliavacca, Elder

    2004-01-01

    In this work, the least-squares methodology with covariance matrix is applied to determine a data curve fitting to obtain a performance function for the separative power δU of a ultracentrifuge as a function of variables that are experimentally controlled. The experimental data refer to 460 experiments on the ultracentrifugation process for uranium isotope separation. The experimental uncertainties related with these independent variables are considered in the calculation of the experimental separative power values, determining an experimental data input covariance matrix. The process variables, which significantly influence the δU values are chosen in order to give information on the ultracentrifuge behaviour when submitted to several levels of feed flow rate F, cut θ and product line pressure P p . After the model goodness-of-fit validation, a residual analysis is carried out to verify the assumed basis concerning its randomness and independence and mainly the existence of residual heteroscedasticity with any explained regression model variable. The surface curves are made relating the separative power with the control variables F, θ and P p to compare the fitted model with the experimental data and finally to calculate their optimized values. (author)

  12. VITAMIN-J/COVA/EFF-3 cross-section covariance matrix library and its use to analyse benchmark experiments in sinbad database

    International Nuclear Information System (INIS)

    Kodeli, Ivan-Alexander

    2005-01-01

    The new cross-section covariance matrix library ZZ-VITAMIN-J/COVA/EFF3 intended to simplify and encourage sensitivity and uncertainty analysis was prepared and is available from the NEA Data Bank. The library is organised in a ready-to-use form including both the covariance matrix data as well as processing tools:-Cross-section covariance matrices from the EFF-3 evaluation for five materials: 9 Be, 28 Si, 56 Fe, 58 Ni and 60 Ni. Other data will be included when available. -FORTRAN program ANGELO-2 to extrapolate/interpolate the covariance matrices to a users' defined energy group structure. -FORTRAN program LAMBDA to verify the mathematical properties of the covariance matrices, like symmetry, positive definiteness, etc. The preparation, testing and use of the covariance matrix library are presented. The uncertainties based on the cross-section covariance data were compared with those based on other evaluations, like ENDF/B-VI. The collapsing procedure used in the ANGELO-2 code was compared and validated with the one used in the NJOY system

  13. A Robust Adaptive Unscented Kalman Filter for Nonlinear Estimation with Uncertain Noise Covariance.

    Science.gov (United States)

    Zheng, Binqi; Fu, Pengcheng; Li, Baoqing; Yuan, Xiaobing

    2018-03-07

    The Unscented Kalman filter (UKF) may suffer from performance degradation and even divergence while mismatch between the noise distribution assumed as a priori by users and the actual ones in a real nonlinear system. To resolve this problem, this paper proposes a robust adaptive UKF (RAUKF) to improve the accuracy and robustness of state estimation with uncertain noise covariance. More specifically, at each timestep, a standard UKF will be implemented first to obtain the state estimations using the new acquired measurement data. Then an online fault-detection mechanism is adopted to judge if it is necessary to update current noise covariance. If necessary, innovation-based method and residual-based method are used to calculate the estimations of current noise covariance of process and measurement, respectively. By utilizing a weighting factor, the filter will combine the last noise covariance matrices with the estimations as the new noise covariance matrices. Finally, the state estimations will be corrected according to the new noise covariance matrices and previous state estimations. Compared with the standard UKF and other adaptive UKF algorithms, RAUKF converges faster to the actual noise covariance and thus achieves a better performance in terms of robustness, accuracy, and computation for nonlinear estimation with uncertain noise covariance, which is demonstrated by the simulation results.

  14. Genome-Wide Scan for Adaptive Divergence and Association with Population-Specific Covariates.

    Science.gov (United States)

    Gautier, Mathieu

    2015-12-01

    In population genomics studies, accounting for the neutral covariance structure across population allele frequencies is critical to improve the robustness of genome-wide scan approaches. Elaborating on the BayEnv model, this study investigates several modeling extensions (i) to improve the estimation accuracy of the population covariance matrix and all the related measures, (ii) to identify significantly overly differentiated SNPs based on a calibration procedure of the XtX statistics, and (iii) to consider alternative covariate models for analyses of association with population-specific covariables. In particular, the auxiliary variable model allows one to deal with multiple testing issues and, providing the relative marker positions are available, to capture some linkage disequilibrium information. A comprehensive simulation study was carried out to evaluate the performances of these different models. Also, when compared in terms of power, robustness, and computational efficiency to five other state-of-the-art genome-scan methods (BayEnv2, BayScEnv, BayScan, flk, and lfmm), the proposed approaches proved highly effective. For illustration purposes, genotyping data on 18 French cattle breeds were analyzed, leading to the identification of 13 strong signatures of selection. Among these, four (surrounding the KITLG, KIT, EDN3, and ALB genes) contained SNPs strongly associated with the piebald coloration pattern while a fifth (surrounding PLAG1) could be associated to morphological differences across the populations. Finally, analysis of Pool-Seq data from 12 populations of Littorina saxatilis living in two different ecotypes illustrates how the proposed framework might help in addressing relevant ecological issues in nonmodel species. Overall, the proposed methods define a robust Bayesian framework to characterize adaptive genetic differentiation across populations. The BayPass program implementing the different models is available at http://www1.montpellier

  15. A robust bi-orthogonal/dynamically-orthogonal method using the covariance pseudo-inverse with application to stochastic flow problems

    Science.gov (United States)

    Babaee, Hessam; Choi, Minseok; Sapsis, Themistoklis P.; Karniadakis, George Em

    2017-09-01

    We develop a new robust methodology for the stochastic Navier-Stokes equations based on the dynamically-orthogonal (DO) and bi-orthogonal (BO) methods [1-3]. Both approaches are variants of a generalized Karhunen-Loève (KL) expansion in which both the stochastic coefficients and the spatial basis evolve according to system dynamics, hence, capturing the low-dimensional structure of the solution. The DO and BO formulations are mathematically equivalent [3], but they exhibit computationally complimentary properties. Specifically, the BO formulation may fail due to crossing of the eigenvalues of the covariance matrix, while both BO and DO become unstable when there is a high condition number of the covariance matrix or zero eigenvalues. To this end, we combine the two methods into a robust hybrid framework and in addition we employ a pseudo-inverse technique to invert the covariance matrix. The robustness of the proposed method stems from addressing the following issues in the DO/BO formulation: (i) eigenvalue crossing: we resolve the issue of eigenvalue crossing in the BO formulation by switching to the DO near eigenvalue crossing using the equivalence theorem and switching back to BO when the distance between eigenvalues is larger than a threshold value; (ii) ill-conditioned covariance matrix: we utilize a pseudo-inverse strategy to invert the covariance matrix; (iii) adaptivity: we utilize an adaptive strategy to add/remove modes to resolve the covariance matrix up to a threshold value. In particular, we introduce a soft-threshold criterion to allow the system to adapt to the newly added/removed mode and therefore avoid repetitive and unnecessary mode addition/removal. When the total variance approaches zero, we show that the DO/BO formulation becomes equivalent to the evolution equation of the Optimally Time-Dependent modes [4]. We demonstrate the capability of the proposed methodology with several numerical examples, namely (i) stochastic Burgers equation: we

  16. Two-antenna GNSS Aided-INS Alignment Using Adaptive Control of Filter Noise Covariance

    Directory of Open Access Journals (Sweden)

    HAO Yushi

    2018-04-01

    Full Text Available This paper developed a theory of INS fine alignment in order to restrain the divergence of yaw angle,two antennas GNSS aided-INS integrated alignment algorithm was utilized.An attitude error measurement equation was conducted based on the relationship between baseline vectors calculated by two sensors and attitude error.The algorithm was executed by EKF using adaptive control of filter noise covariance.The experimental results showed that stability of the integrated system was improved under the system noise covariance adaptive control mechanism;The measurement noise covariance adaptive control mechanism can reduce the influence of measurement noise and improve the alignment absolute accuracy;Further improvement was achieved under the condition of minim bias of baseline length.The accuracy of roll and pitch was 0.02°,the accuracy of yaw was 0.04°.

  17. Condition Number Regularized Covariance Estimation.

    Science.gov (United States)

    Won, Joong-Ho; Lim, Johan; Kim, Seung-Jean; Rajaratnam, Bala

    2013-06-01

    Estimation of high-dimensional covariance matrices is known to be a difficult problem, has many applications, and is of current interest to the larger statistics community. In many applications including so-called the "large p small n " setting, the estimate of the covariance matrix is required to be not only invertible, but also well-conditioned. Although many regularization schemes attempt to do this, none of them address the ill-conditioning problem directly. In this paper, we propose a maximum likelihood approach, with the direct goal of obtaining a well-conditioned estimator. No sparsity assumption on either the covariance matrix or its inverse are are imposed, thus making our procedure more widely applicable. We demonstrate that the proposed regularization scheme is computationally efficient, yields a type of Steinian shrinkage estimator, and has a natural Bayesian interpretation. We investigate the theoretical properties of the regularized covariance estimator comprehensively, including its regularization path, and proceed to develop an approach that adaptively determines the level of regularization that is required. Finally, we demonstrate the performance of the regularized estimator in decision-theoretic comparisons and in the financial portfolio optimization setting. The proposed approach has desirable properties, and can serve as a competitive procedure, especially when the sample size is small and when a well-conditioned estimator is required.

  18. Four-Component Scattering Power Decomposition Algorithm with Rotation of Covariance Matrix Using ALOS-PALSAR Polarimetric Data

    Directory of Open Access Journals (Sweden)

    Yasuhiro Nakamura

    2012-07-01

    Full Text Available The present study introduces the four-component scattering power decomposition (4-CSPD algorithm with rotation of covariance matrix, and presents an experimental proof of the equivalence between the 4-CSPD algorithms based on rotation of covariance matrix and coherency matrix. From a theoretical point of view, the 4-CSPD algorithms with rotation of the two matrices are identical. Although it seems obvious, no experimental evidence has yet been presented. In this paper, using polarimetric synthetic aperture radar (POLSAR data acquired by Phased Array L-band SAR (PALSAR on board of Advanced Land Observing Satellite (ALOS, an experimental proof is presented to show that both algorithms indeed produce identical results.

  19. Approaches for the generation of a covariance matrix for the Cf-252 fission-neutron spectrum

    International Nuclear Information System (INIS)

    Mannhart, W.

    1983-01-01

    After a brief retrospective glance is cast at the situation, the evaluation of the Cf-252 neutron spectrum with a complete covariance matrix based on the results of integral experiments is proposed. The different steps already taken in such an evaluation and work in progress are reviewed. It is shown that special attention should be given to the normalization of the neutron spectrum which must be reflected in the covariance matrix. The result of the least-squares adjustment procedure applied can easily be combined with the results of direct spectrum measurements and should be regarded as the first step in a new evaluation of the Cf-252 fission-neutron spectrum. (author)

  20. Robust Markowitz mean-variance portfolio selection under ambiguous covariance matrix *

    OpenAIRE

    Ismail, Amine; Pham, Huyên

    2016-01-01

    This paper studies a robust continuous-time Markowitz portfolio selection pro\\-blem where the model uncertainty carries on the covariance matrix of multiple risky assets. This problem is formulated into a min-max mean-variance problem over a set of non-dominated probability measures that is solved by a McKean-Vlasov dynamic programming approach, which allows us to characterize the solution in terms of a Bellman-Isaacs equation in the Wasserstein space of probability measures. We provide expli...

  1. Persymmetric Adaptive Detectors of Subspace Signals in Homogeneous and Partially Homogeneous Clutter

    Directory of Open Access Journals (Sweden)

    Ding Hao

    2015-08-01

    Full Text Available In the field of adaptive radar detection, an effective strategy to improve the detection performance is to exploit the structural information of the covariance matrix, especially in the case of insufficient reference cells. Thus, in this study, the problem of detecting multidimensional subspace signals is discussed by considering the persymmetric structure of the clutter covariance matrix, which implies that the covariance matrix is persymmetric about its cross diagonal. Persymmetric adaptive detectors are derived on the basis of the one-step principle as well as the two-step Generalized Likelihood Ratio Test (GLRT in homogeneous and partially homogeneous clutter. The proposed detectors consider the structural information of the covariance matrix at the design stage. Simulation results suggest performance improvement compared with existing detectors when reference cells are insufficient. Moreover, the detection performance is assessed with respect to the effects of the covariance matrix, signal subspace dimension, and mismatched performance of signal subspace as well as signal fluctuations.

  2. Condition Number Regularized Covariance Estimation*

    Science.gov (United States)

    Won, Joong-Ho; Lim, Johan; Kim, Seung-Jean; Rajaratnam, Bala

    2012-01-01

    Estimation of high-dimensional covariance matrices is known to be a difficult problem, has many applications, and is of current interest to the larger statistics community. In many applications including so-called the “large p small n” setting, the estimate of the covariance matrix is required to be not only invertible, but also well-conditioned. Although many regularization schemes attempt to do this, none of them address the ill-conditioning problem directly. In this paper, we propose a maximum likelihood approach, with the direct goal of obtaining a well-conditioned estimator. No sparsity assumption on either the covariance matrix or its inverse are are imposed, thus making our procedure more widely applicable. We demonstrate that the proposed regularization scheme is computationally efficient, yields a type of Steinian shrinkage estimator, and has a natural Bayesian interpretation. We investigate the theoretical properties of the regularized covariance estimator comprehensively, including its regularization path, and proceed to develop an approach that adaptively determines the level of regularization that is required. Finally, we demonstrate the performance of the regularized estimator in decision-theoretic comparisons and in the financial portfolio optimization setting. The proposed approach has desirable properties, and can serve as a competitive procedure, especially when the sample size is small and when a well-conditioned estimator is required. PMID:23730197

  3. Construction and use of gene expression covariation matrix

    Directory of Open Access Journals (Sweden)

    Bellis Michel

    2009-07-01

    strings of symbols. Conclusion This new method, applied to four different large data sets, has allowed us to construct distinct covariation matrices with similar properties. We have also developed a technique to translate these covariation networks into graphical 3D representations and found that the local assignation of the probe sets was conserved across the four chip set models used which encompass three different species (humans, mice, and rats. The application of adapted clustering methods succeeded in delineating six conserved functional regions that we characterized using Gene Ontology information.

  4. Covariant field equations, gauge fields and conservation laws from Yang-Mills matrix models

    International Nuclear Information System (INIS)

    Steinacker, Harold

    2009-01-01

    The effective geometry and the gravitational coupling of nonabelian gauge and scalar fields on generic NC branes in Yang-Mills matrix models is determined. Covariant field equations are derived from the basic matrix equations of motions, known as Yang-Mills algebra. Remarkably, the equations of motion for the Poisson structure and for the nonabelian gauge fields follow from a matrix Noether theorem, and are therefore protected from quantum corrections. This provides a transparent derivation and generalization of the effective action governing the SU(n) gauge fields obtained in [1], including the would-be topological term. In particular, the IKKT matrix model is capable of describing 4-dimensional NC space-times with a general effective metric. Metric deformations of flat Moyal-Weyl space are briefly discussed.

  5. Object Tracking Using Adaptive Covariance Descriptor and Clustering-Based Model Updating for Visual Surveillance

    Directory of Open Access Journals (Sweden)

    Lei Qin

    2014-05-01

    Full Text Available We propose a novel approach for tracking an arbitrary object in video sequences for visual surveillance. The first contribution of this work is an automatic feature extraction method that is able to extract compact discriminative features from a feature pool before computing the region covariance descriptor. As the feature extraction method is adaptive to a specific object of interest, we refer to the region covariance descriptor computed using the extracted features as the adaptive covariance descriptor. The second contribution is to propose a weakly supervised method for updating the object appearance model during tracking. The method performs a mean-shift clustering procedure among the tracking result samples accumulated during a period of time and selects a group of reliable samples for updating the object appearance model. As such, the object appearance model is kept up-to-date and is prevented from contamination even in case of tracking mistakes. We conducted comparing experiments on real-world video sequences, which confirmed the effectiveness of the proposed approaches. The tracking system that integrates the adaptive covariance descriptor and the clustering-based model updating method accomplished stable object tracking on challenging video sequences.

  6. A comparison of likelihood ratio tests and Rao's score test for three separable covariance matrix structures.

    Science.gov (United States)

    Filipiak, Katarzyna; Klein, Daniel; Roy, Anuradha

    2017-01-01

    The problem of testing the separability of a covariance matrix against an unstructured variance-covariance matrix is studied in the context of multivariate repeated measures data using Rao's score test (RST). The RST statistic is developed with the first component of the separable structure as a first-order autoregressive (AR(1)) correlation matrix or an unstructured (UN) covariance matrix under the assumption of multivariate normality. It is shown that the distribution of the RST statistic under the null hypothesis of any separability does not depend on the true values of the mean or the unstructured components of the separable structure. A significant advantage of the RST is that it can be performed for small samples, even smaller than the dimension of the data, where the likelihood ratio test (LRT) cannot be used, and it outperforms the standard LRT in a number of contexts. Monte Carlo simulations are then used to study the comparative behavior of the null distribution of the RST statistic, as well as that of the LRT statistic, in terms of sample size considerations, and for the estimation of the empirical percentiles. Our findings are compared with existing results where the first component of the separable structure is a compound symmetry (CS) correlation matrix. It is also shown by simulations that the empirical null distribution of the RST statistic converges faster than the empirical null distribution of the LRT statistic to the limiting χ 2 distribution. The tests are implemented on a real dataset from medical studies. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  7. Automated vessel segmentation using cross-correlation and pooled covariance matrix analysis.

    Science.gov (United States)

    Du, Jiang; Karimi, Afshin; Wu, Yijing; Korosec, Frank R; Grist, Thomas M; Mistretta, Charles A

    2011-04-01

    Time-resolved contrast-enhanced magnetic resonance angiography (CE-MRA) provides contrast dynamics in the vasculature and allows vessel segmentation based on temporal correlation analysis. Here we present an automated vessel segmentation algorithm including automated generation of regions of interest (ROIs), cross-correlation and pooled sample covariance matrix analysis. The dynamic images are divided into multiple equal-sized regions. In each region, ROIs for artery, vein and background are generated using an iterative thresholding algorithm based on the contrast arrival time map and contrast enhancement map. Region-specific multi-feature cross-correlation analysis and pooled covariance matrix analysis are performed to calculate the Mahalanobis distances (MDs), which are used to automatically separate arteries from veins. This segmentation algorithm is applied to a dual-phase dynamic imaging acquisition scheme where low-resolution time-resolved images are acquired during the dynamic phase followed by high-frequency data acquisition at the steady-state phase. The segmented low-resolution arterial and venous images are then combined with the high-frequency data in k-space and inverse Fourier transformed to form the final segmented arterial and venous images. Results from volunteer and patient studies demonstrate the advantages of this automated vessel segmentation and dual phase data acquisition technique. Copyright © 2011 Elsevier Inc. All rights reserved.

  8. The Requirement of a Positive Definite Covariance Matrix of Security Returns for Mean-Variance Portfolio Analysis: A Pedagogic Illustration

    Directory of Open Access Journals (Sweden)

    Clarence C. Y. Kwan

    2010-07-01

    Full Text Available This study considers, from a pedagogic perspective, a crucial requirement for the covariance matrix of security returns in mean-variance portfolio analysis. Although the requirement that the covariance matrix be positive definite is fundamental in modern finance, it has not received any attention in standard investment textbooks. Being unaware of the requirement could cause confusion for students over some strange portfolio results that are based on seemingly reasonable input parameters. This study considers the requirement both informally and analytically. Electronic spreadsheet tools for constrained optimization and basic matrix operations are utilized to illustrate the various concepts involved.

  9. Quality Quantification of Evaluated Cross Section Covariances

    International Nuclear Information System (INIS)

    Varet, S.; Dossantos-Uzarralde, P.; Vayatis, N.

    2015-01-01

    Presently, several methods are used to estimate the covariance matrix of evaluated nuclear cross sections. Because the resulting covariance matrices can be different according to the method used and according to the assumptions of the method, we propose a general and objective approach to quantify the quality of the covariance estimation for evaluated cross sections. The first step consists in defining an objective criterion. The second step is computation of the criterion. In this paper the Kullback-Leibler distance is proposed for the quality quantification of a covariance matrix estimation and its inverse. It is based on the distance to the true covariance matrix. A method based on the bootstrap is presented for the estimation of this criterion, which can be applied with most methods for covariance matrix estimation and without the knowledge of the true covariance matrix. The full approach is illustrated on the 85 Rb nucleus evaluations and the results are then used for a discussion on scoring and Monte Carlo approaches for covariance matrix estimation of the cross section evaluations

  10. A special form of SPD covariance matrix for interpretation and visualization of data manipulated with Riemannian geometry

    Science.gov (United States)

    Congedo, Marco; Barachant, Alexandre

    2015-01-01

    Currently the Riemannian geometry of symmetric positive definite (SPD) matrices is gaining momentum as a powerful tool in a wide range of engineering applications such as image, radar and biomedical data signal processing. If the data is not natively represented in the form of SPD matrices, typically we may summarize them in such form by estimating covariance matrices of the data. However once we manipulate such covariance matrices on the Riemannian manifold we lose the representation in the original data space. For instance, we can evaluate the geometric mean of a set of covariance matrices, but not the geometric mean of the data generating the covariance matrices, the space of interest in which the geometric mean can be interpreted. As a consequence, Riemannian information geometry is often perceived by non-experts as a "black-box" tool and this perception prevents a wider adoption in the scientific community. Hereby we show that we can overcome this limitation by constructing a special form of SPD matrix embedding both the covariance structure of the data and the data itself. Incidentally, whenever the original data can be represented in the form of a generic data matrix (not even square), this special SPD matrix enables an exhaustive and unique description of the data up to second-order statistics. This is achieved embedding the covariance structure of both the rows and columns of the data matrix, allowing naturally a wide range of possible applications and bringing us over and above just an interpretability issue. We demonstrate the method by manipulating satellite images (pansharpening) and event-related potentials (ERPs) of an electroencephalography brain-computer interface (BCI) study. The first example illustrates the effect of moving along geodesics in the original data space and the second provides a novel estimation of ERP average (geometric mean), showing that, in contrast to the usual arithmetic mean, this estimation is robust to outliers. In

  11. Beamforming using subspace estimation from a diagonally averaged sample covariance.

    Science.gov (United States)

    Quijano, Jorge E; Zurk, Lisa M

    2017-08-01

    The potential benefit of a large-aperture sonar array for high resolution target localization is often challenged by the lack of sufficient data required for adaptive beamforming. This paper introduces a Toeplitz-constrained estimator of the clairvoyant signal covariance matrix corresponding to multiple far-field targets embedded in background isotropic noise. The estimator is obtained by averaging along subdiagonals of the sample covariance matrix, followed by covariance extrapolation using the method of maximum entropy. The sample covariance is computed from limited data snapshots, a situation commonly encountered with large-aperture arrays in environments characterized by short periods of local stationarity. Eigenvectors computed from the Toeplitz-constrained covariance are used to construct signal-subspace projector matrices, which are shown to reduce background noise and improve detection of closely spaced targets when applied to subspace beamforming. Monte Carlo simulations corresponding to increasing array aperture suggest convergence of the proposed projector to the clairvoyant signal projector, thereby outperforming the classic projector obtained from the sample eigenvectors. Beamforming performance of the proposed method is analyzed using simulated data, as well as experimental data from the Shallow Water Array Performance experiment.

  12. On the mean squared error of the ridge estimator of the covariance and precision matrix

    NARCIS (Netherlands)

    van Wieringen, Wessel N.

    2017-01-01

    For a suitably chosen ridge penalty parameter, the ridge regression estimator uniformly dominates the maximum likelihood regression estimator in terms of the mean squared error. Analogous results for the ridge maximum likelihood estimators of covariance and precision matrix are presented.

  13. Optimal Design of Large Dimensional Adaptive Subspace Detectors

    KAUST Repository

    Ben Atitallah, Ismail; Kammoun, Abla; Alouini, Mohamed-Slim; Alnaffouri, Tareq Y.

    2016-01-01

    This paper addresses the design of Adaptive Subspace Matched Filter (ASMF) detectors in the presence of a mismatch in the steering vector. These detectors are coined as adaptive in reference to the step of utilizing an estimate of the clutter covariance matrix using training data of signalfree observations. To estimate the clutter covariance matrix, we employ regularized covariance estimators that, by construction, force the eigenvalues of the covariance estimates to be greater than a positive scalar . While this feature is likely to increase the bias of the covariance estimate, it presents the advantage of improving its conditioning, thus making the regularization suitable for handling high dimensional regimes. In this paper, we consider the setting of the regularization parameter and the threshold for ASMF detectors in both Gaussian and Compound Gaussian clutters. In order to allow for a proper selection of these parameters, it is essential to analyze the false alarm and detection probabilities. For tractability, such a task is carried out under the asymptotic regime in which the number of observations and their dimensions grow simultaneously large, thereby allowing us to leverage existing results from random matrix theory. Simulation results are provided in order to illustrate the relevance of the proposed design strategy and to compare the performances of the proposed ASMF detectors versus Adaptive normalized Matched Filter (ANMF) detectors under mismatch scenarios.

  14. Optimal Design of Large Dimensional Adaptive Subspace Detectors

    KAUST Repository

    Ben Atitallah, Ismail

    2016-05-27

    This paper addresses the design of Adaptive Subspace Matched Filter (ASMF) detectors in the presence of a mismatch in the steering vector. These detectors are coined as adaptive in reference to the step of utilizing an estimate of the clutter covariance matrix using training data of signalfree observations. To estimate the clutter covariance matrix, we employ regularized covariance estimators that, by construction, force the eigenvalues of the covariance estimates to be greater than a positive scalar . While this feature is likely to increase the bias of the covariance estimate, it presents the advantage of improving its conditioning, thus making the regularization suitable for handling high dimensional regimes. In this paper, we consider the setting of the regularization parameter and the threshold for ASMF detectors in both Gaussian and Compound Gaussian clutters. In order to allow for a proper selection of these parameters, it is essential to analyze the false alarm and detection probabilities. For tractability, such a task is carried out under the asymptotic regime in which the number of observations and their dimensions grow simultaneously large, thereby allowing us to leverage existing results from random matrix theory. Simulation results are provided in order to illustrate the relevance of the proposed design strategy and to compare the performances of the proposed ASMF detectors versus Adaptive normalized Matched Filter (ANMF) detectors under mismatch scenarios.

  15. Synthesis of linear regression coefficients by recovering the within-study covariance matrix from summary statistics.

    Science.gov (United States)

    Yoneoka, Daisuke; Henmi, Masayuki

    2017-06-01

    Recently, the number of regression models has dramatically increased in several academic fields. However, within the context of meta-analysis, synthesis methods for such models have not been developed in a commensurate trend. One of the difficulties hindering the development is the disparity in sets of covariates among literature models. If the sets of covariates differ across models, interpretation of coefficients will differ, thereby making it difficult to synthesize them. Moreover, previous synthesis methods for regression models, such as multivariate meta-analysis, often have problems because covariance matrix of coefficients (i.e. within-study correlations) or individual patient data are not necessarily available. This study, therefore, proposes a brief explanation regarding a method to synthesize linear regression models under different covariate sets by using a generalized least squares method involving bias correction terms. Especially, we also propose an approach to recover (at most) threecorrelations of covariates, which is required for the calculation of the bias term without individual patient data. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

  16. Contributions to Large Covariance and Inverse Covariance Matrices Estimation

    OpenAIRE

    Kang, Xiaoning

    2016-01-01

    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 estimat...

  17. Least square methods and covariance matrix applied to the relative efficiency calibration of a Ge(Li) detector

    International Nuclear Information System (INIS)

    Geraldo, L.P.; Smith, D.L.

    1989-01-01

    The methodology of covariance matrix and square methods have been applied in the relative efficiency calibration for a Ge(Li) detector apllied in the relative efficiency calibration for a Ge(Li) detector. Procedures employed to generate, manipulate and test covariance matrices which serve to properly represent uncertainties of experimental data are discussed. Calibration data fitting using least square methods has been performed for a particular experimental data set. (author) [pt

  18. Low-Rank Matrix Factorization With Adaptive Graph Regularizer.

    Science.gov (United States)

    Lu, Gui-Fu; Wang, Yong; Zou, Jian

    2016-05-01

    In this paper, we present a novel low-rank matrix factorization algorithm with adaptive graph regularizer (LMFAGR). We extend the recently proposed low-rank matrix with manifold regularization (MMF) method with an adaptive regularizer. Different from MMF, which constructs an affinity graph in advance, LMFAGR can simultaneously seek graph weight matrix and low-dimensional representations of data. That is, graph construction and low-rank matrix factorization are incorporated into a unified framework, which results in an automatically updated graph rather than a predefined one. The experimental results on some data sets demonstrate that the proposed algorithm outperforms the state-of-the-art low-rank matrix factorization methods.

  19. Covariance matrices of experimental data

    International Nuclear Information System (INIS)

    Perey, F.G.

    1978-01-01

    A complete statement of the uncertainties in data is given by its covariance matrix. It is shown how the covariance matrix of data can be generated using the information available to obtain their standard deviations. Determination of resonance energies by the time-of-flight method is used as an example. The procedure for combining data when the covariance matrix is non-diagonal is given. The method is illustrated by means of examples taken from the recent literature to obtain an estimate of the energy of the first resonance in carbon and for five resonances of 238 U

  20. Sparse Covariance Matrix Estimation by DCA-Based Algorithms.

    Science.gov (United States)

    Phan, Duy Nhat; Le Thi, Hoai An; Dinh, Tao Pham

    2017-11-01

    This letter proposes a novel approach using the [Formula: see text]-norm regularization for the sparse covariance matrix estimation (SCME) problem. The objective function of SCME problem is composed of a nonconvex part and the [Formula: see text] term, which is discontinuous and difficult to tackle. Appropriate DC (difference of convex functions) approximations of [Formula: see text]-norm are used that result in approximation SCME problems that are still nonconvex. DC programming and DCA (DC algorithm), powerful tools in nonconvex programming framework, are investigated. Two DC formulations are proposed and corresponding DCA schemes developed. Two applications of the SCME problem that are considered are classification via sparse quadratic discriminant analysis and portfolio optimization. A careful empirical experiment is performed through simulated and real data sets to study the performance of the proposed algorithms. Numerical results showed their efficiency and their superiority compared with seven state-of-the-art methods.

  1. How to deal with the high condition number of the noise covariance matrix of gravity field functionals synthesised from a satellite-only global gravity field model?

    Science.gov (United States)

    Klees, R.; Slobbe, D. C.; Farahani, H. H.

    2018-03-01

    The posed question arises for instance in regional gravity field modelling using weighted least-squares techniques if the gravity field functionals are synthesised from the spherical harmonic coefficients of a satellite-only global gravity model (GGM), and are used as one of the noisy datasets. The associated noise covariance matrix, appeared to be extremely ill-conditioned with a singular value spectrum that decayed gradually to zero without any noticeable gap. We analysed three methods to deal with the ill-conditioned noise covariance matrix: Tihonov regularisation of the noise covariance matrix in combination with the standard formula for the weighted least-squares estimator, a formula of the weighted least-squares estimator, which does not involve the inverse noise covariance matrix, and an estimator based on Rao's unified theory of least-squares. Our analysis was based on a numerical experiment involving a set of height anomalies synthesised from the GGM GOCO05s, which is provided with a full noise covariance matrix. We showed that the three estimators perform similar, provided that the two regularisation parameters each method knows were chosen properly. As standard regularisation parameter choice rules do not apply here, we suggested a new parameter choice rule, and demonstrated its performance. Using this rule, we found that the differences between the three least-squares estimates were within noise. For the standard formulation of the weighted least-squares estimator with regularised noise covariance matrix, this required an exceptionally strong regularisation, much larger than one expected from the condition number of the noise covariance matrix. The preferred method is the inversion-free formulation of the weighted least-squares estimator, because of its simplicity with respect to the choice of the two regularisation parameters.

  2. Control Systems with Normalized and Covariance Adaptation by Optimal Control Modification

    Science.gov (United States)

    Nguyen, Nhan T. (Inventor); Burken, John J. (Inventor); Hanson, Curtis E. (Inventor)

    2016-01-01

    Disclosed is a novel adaptive control method and system called optimal control modification with normalization and covariance adjustment. The invention addresses specifically to current challenges with adaptive control in these areas: 1) persistent excitation, 2) complex nonlinear input-output mapping, 3) large inputs and persistent learning, and 4) the lack of stability analysis tools for certification. The invention has been subject to many simulations and flight testing. The results substantiate the effectiveness of the invention and demonstrate the technical feasibility for use in modern aircraft flight control systems.

  3. HLIBCov: Parallel Hierarchical Matrix Approximation of Large Covariance Matrices and Likelihoods with Applications in Parameter Identification

    KAUST Repository

    Litvinenko, Alexander

    2017-01-01

    matrices. Therefore covariance matrices are approximated in the hierarchical ($\\H$-) matrix format with computational cost $\\mathcal{O}(k^2n \\log^2 n/p)$ and storage $\\mathcal{O}(kn \\log n)$, where the rank $k$ is a small integer (typically $k<25$), $p

  4. Generation of the covariance matrix for a set of nuclear data produced by collapsing a larger parent set through the weighted averaging of equivalent data points

    International Nuclear Information System (INIS)

    Smith, D.L.

    1987-01-01

    A method is described for generating the covariance matrix of a set of experimental nuclear data which has been collapsed in size by the averaging of equivalent data points belonging to a larger parent data set. It is assumed that the data values and covariance matrix for the parent set are provided. The collapsed set is obtained by a proper weighted-averaging procedure based on the method of least squares. It is then shown by means of the law of error propagation that the elements of the covariance matrix for the collapsed set are linear combinations of elements from the parent set covariance matrix. The coefficients appearing in these combinations are binary products of the same coefficients which appear as weighting factors in the data collapsing procedure. As an example, the procedure is applied to a collection of recently-measured integral neutron-fission cross-section ratios. (orig.)

  5. Multi types DG expansion dynamic planning in distribution system under stochastic conditions using Covariance Matrix Adaptation Evolutionary Strategy and Monte-Carlo simulation

    International Nuclear Information System (INIS)

    Sadeghi, Mahmood; Kalantar, Mohsen

    2014-01-01

    Highlights: • Defining a DG dynamic planning problem. • Applying a new evolutionary algorithm called “CMAES” in planning process. • Considering electricity price and fuel price variation stochastic conditions. • Scenario generation and reduction with MCS and backward reduction programs. • Considering approximately all of the costs of the distribution system. - Abstract: This paper presents a dynamic DG planning problem considering uncertainties related to the intermittent nature of the DG technologies such as wind turbines and solar units in addition to the stochastic economic conditions. The stochastic economic situation includes the uncertainties related to the fuel and electricity price of each year. The Monte Carlo simulation is used to generate the possible scenarios of uncertain situations and the produced scenarios are reduced through backward reduction program. The aim of this paper is to maximize the revenue of the distribution system through the benefit cost analysis alongside the encouraging and punishment functions. In order to close to reality, the different growth rates for the planning period are selected. In this paper the Covariance Matrix Adaptation Evolutionary Strategy is introduced and is used to find the best planning scheme of the DG units. The different DG types are considered in the planning problem. The main assumption of this paper is that the DISCO is the owner of the distribution system and the DG units. The proposed method is tested on a 9 bus test distribution system and the results are compared with the known genetic algorithm and PSO methods to show the applicability of the CMAES method in this problem

  6. On-Line Identification of Simulation Examples for Forgetting Methods to Track Time Varying Parameters Using the Alternative Covariance Matrix in Matlab

    Science.gov (United States)

    Vachálek, Ján

    2011-12-01

    The paper compares the abilities of forgetting methods to track time varying parameters of two different simulated models with different types of excitation. The observed parameters in the simulations are the integral sum of the Euclidean norm, deviation of the parameter estimates from their true values and a selected band prediction error count. As supplementary information, we observe the eigenvalues of the covariance matrix. In the paper we used a modified method of Regularized Exponential Forgetting with Alternative Covariance Matrix (REFACM) along with Directional Forgetting (DF) and three standard regularized methods.

  7. A New Heteroskedastic Consistent Covariance Matrix Estimator using Deviance Measure

    Directory of Open Access Journals (Sweden)

    Nuzhat Aftab

    2016-06-01

    Full Text Available In this article we propose a new heteroskedastic consistent covariance matrix estimator, HC6, based on deviance measure. We have studied and compared the finite sample behavior of the new test and compared it with other this kind of estimators, HC1, HC3 and HC4m, which are used in case of leverage observations. Simulation study is conducted to study the effect of various levels of heteroskedasticity on the size and power of quasi-t test with HC estimators. Results show that the test statistic based on our new suggested estimator has better asymptotic approximation and less size distortion as compared to other estimators for small sample sizes when high level ofheteroskedasticity is present in data.

  8. An Adaptive Low-Cost INS/GNSS Tightly-Coupled Integration Architecture Based on Redundant Measurement Noise Covariance Estimation.

    Science.gov (United States)

    Li, Zheng; Zhang, Hai; Zhou, Qifan; Che, Huan

    2017-09-05

    The main objective of the introduced study is to design an adaptive Inertial Navigation System/Global Navigation Satellite System (INS/GNSS) tightly-coupled integration system that can provide more reliable navigation solutions by making full use of an adaptive Kalman filter (AKF) and satellite selection algorithm. To achieve this goal, we develop a novel redundant measurement noise covariance estimation (RMNCE) theorem, which adaptively estimates measurement noise properties by analyzing the difference sequences of system measurements. The proposed RMNCE approach is then applied to design both a modified weighted satellite selection algorithm and a type of adaptive unscented Kalman filter (UKF) to improve the performance of the tightly-coupled integration system. In addition, an adaptive measurement noise covariance expanding algorithm is developed to mitigate outliers when facing heavy multipath and other harsh situations. Both semi-physical simulation and field experiments were conducted to evaluate the performance of the proposed architecture and were compared with state-of-the-art algorithms. The results validate that the RMNCE provides a significant improvement in the measurement noise covariance estimation and the proposed architecture can improve the accuracy and reliability of the INS/GNSS tightly-coupled systems. The proposed architecture can effectively limit positioning errors under conditions of poor GNSS measurement quality and outperforms all the compared schemes.

  9. Partial covariance based functional connectivity computation using Ledoit-Wolf covariance regularization.

    Science.gov (United States)

    Brier, Matthew R; Mitra, Anish; McCarthy, John E; Ances, Beau M; Snyder, Abraham Z

    2015-11-01

    Functional connectivity refers to shared signals among brain regions and is typically assessed in a task free state. Functional connectivity commonly is quantified between signal pairs using Pearson correlation. However, resting-state fMRI is a multivariate process exhibiting a complicated covariance structure. Partial covariance assesses the unique variance shared between two brain regions excluding any widely shared variance, hence is appropriate for the analysis of multivariate fMRI datasets. However, calculation of partial covariance requires inversion of the covariance matrix, which, in most functional connectivity studies, is not invertible owing to rank deficiency. Here we apply Ledoit-Wolf shrinkage (L2 regularization) to invert the high dimensional BOLD covariance matrix. We investigate the network organization and brain-state dependence of partial covariance-based functional connectivity. Although RSNs are conventionally defined in terms of shared variance, removal of widely shared variance, surprisingly, improved the separation of RSNs in a spring embedded graphical model. This result suggests that pair-wise unique shared variance plays a heretofore unrecognized role in RSN covariance organization. In addition, application of partial correlation to fMRI data acquired in the eyes open vs. eyes closed states revealed focal changes in uniquely shared variance between the thalamus and visual cortices. This result suggests that partial correlation of resting state BOLD time series reflect functional processes in addition to structural connectivity. Copyright © 2015 Elsevier Inc. All rights reserved.

  10. Real-time probabilistic covariance tracking with efficient model update.

    Science.gov (United States)

    Wu, Yi; Cheng, Jian; Wang, Jinqiao; Lu, Hanqing; Wang, Jun; Ling, Haibin; Blasch, Erik; Bai, Li

    2012-05-01

    The recently proposed covariance region descriptor has been proven robust and versatile for a modest computational cost. The covariance matrix enables efficient fusion of different types of features, where the spatial and statistical properties, as well as their correlation, are characterized. The similarity between two covariance descriptors is measured on Riemannian manifolds. Based on the same metric but with a probabilistic framework, we propose a novel tracking approach on Riemannian manifolds with a novel incremental covariance tensor learning (ICTL). To address the appearance variations, ICTL incrementally learns a low-dimensional covariance tensor representation and efficiently adapts online to appearance changes of the target with only O(1) computational complexity, resulting in a real-time performance. The covariance-based representation and the ICTL are then combined with the particle filter framework to allow better handling of background clutter, as well as the temporary occlusions. We test the proposed probabilistic ICTL tracker on numerous benchmark sequences involving different types of challenges including occlusions and variations in illumination, scale, and pose. The proposed approach demonstrates excellent real-time performance, both qualitatively and quantitatively, in comparison with several previously proposed trackers.

  11. Position Error Covariance Matrix Validation and Correction

    Science.gov (United States)

    Frisbee, Joe, Jr.

    2016-01-01

    In order to calculate operationally accurate collision probabilities, the position error covariance matrices predicted at times of closest approach must be sufficiently accurate representations of the position uncertainties. This presentation will discuss why the Gaussian distribution is a reasonable expectation for the position uncertainty and how this assumed distribution type is used in the validation and correction of position error covariance matrices.

  12. Adaptive Federal Kalman Filtering for SINS/GPS Integrated System

    Institute of Scientific and Technical Information of China (English)

    杨勇; 缪玲娟

    2003-01-01

    A new adaptive federal Kalman filter for a strapdown integrated navigation system/global positioning system (SINS/GPS) is given. The developed federal Kalman filter is based on the trace operation of parameters estimation's error covariance matrix and the spectral radius of update measurement noise variance-covariance matrix for the proper choice of the filter weight and hence the filter gain factors. Theoretical analysis and results from simulation in which the SINS/GPS was compared to conventional Kalman filter are presented. Results show that the algorithm of this adaptive federal Kalman filter is simpler than that of the conventional one. Furthermore, it outperforms the conventional Kalman filter when the system is undertaken measurement malfunctions because of its possession of adaptive ability. This filter can be used in the vehicle integrated navigation system.

  13. The covariance matrix of the Potts model: A random cluster analysis

    International Nuclear Information System (INIS)

    Borgs, C.; Chayes, J.T.

    1996-01-01

    We consider the covariance matrix, G mn = q 2 x ,m); δ(σ y ,n)>, of the d-dimensional q-states Potts model, rewriting it in the random cluster representation of Fortuin and Kasteleyn. In many of the q ordered phases, we identify the eigenvalues of this matrix both in terms of representations of the unbroken symmetry group of the model and in terms of random cluster connectivities and covariances, thereby attributing algebraic significance to these stochastic geometric quantities. We also show that the correlation length and the correlation length corresponding to the decay rate of one on the eigenvalues in the same as the inverse decay rate of the diameter of finite clusters. For dimension of d=2, we show that this correlation length and the correlation length of two-point function with free boundary conditions at the corresponding dual temperature are equal up to a factor of two. For systems with first-order transitions, this relation helps to resolve certain inconsistencies between recent exact and numerical work on correlation lengths at the self-dual point β o . For systems with second order transitions, this relation implies the equality of the correlation length exponents from above below threshold, as well as an amplitude ratio of two. In the course of proving the above results, we establish several properties of independent interest, including left continuity of the inverse correlation length with free boundary conditions and upper semicontinuity of the decay rate for finite clusters in all dimensions, and left continuity of the two-dimensional free boundary condition percolation probability at β o . We also introduce DLR equations for the random cluster model and use them to establish ergodicity of the free measure. In order to prove these results, we introduce a new class of events which we call decoupling events and two inequalities for these events

  14. Massive data compression for parameter-dependent covariance matrices

    Science.gov (United States)

    Heavens, Alan F.; Sellentin, Elena; de Mijolla, Damien; Vianello, Alvise

    2017-12-01

    We show how the massive data compression algorithm MOPED can be used to reduce, by orders of magnitude, the number of simulated data sets which are required to estimate the covariance matrix required for the analysis of Gaussian-distributed data. This is relevant when the covariance matrix cannot be calculated directly. The compression is especially valuable when the covariance matrix varies with the model parameters. In this case, it may be prohibitively expensive to run enough simulations to estimate the full covariance matrix throughout the parameter space. This compression may be particularly valuable for the next generation of weak lensing surveys, such as proposed for Euclid and Large Synoptic Survey Telescope, for which the number of summary data (such as band power or shear correlation estimates) is very large, ∼104, due to the large number of tomographic redshift bins which the data will be divided into. In the pessimistic case where the covariance matrix is estimated separately for all points in an Monte Carlo Markov Chain analysis, this may require an unfeasible 109 simulations. We show here that MOPED can reduce this number by a factor of 1000, or a factor of ∼106 if some regularity in the covariance matrix is assumed, reducing the number of simulations required to a manageable 103, making an otherwise intractable analysis feasible.

  15. The Bayesian Covariance Lasso.

    Science.gov (United States)

    Khondker, Zakaria S; Zhu, Hongtu; Chu, Haitao; Lin, Weili; Ibrahim, Joseph G

    2013-04-01

    Estimation of sparse covariance matrices and their inverse subject to positive definiteness constraints has drawn a lot of attention in recent years. The abundance of high-dimensional data, where the sample size ( n ) is less than the dimension ( d ), requires shrinkage estimation methods since the maximum likelihood estimator is not positive definite in this case. Furthermore, when n is larger than d but not sufficiently larger, shrinkage estimation is more stable than maximum likelihood as it reduces the condition number of the precision matrix. Frequentist methods have utilized penalized likelihood methods, whereas Bayesian approaches rely on matrix decompositions or Wishart priors for shrinkage. In this paper we propose a new method, called the Bayesian Covariance Lasso (BCLASSO), for the shrinkage estimation of a precision (covariance) matrix. We consider a class of priors for the precision matrix that leads to the popular frequentist penalties as special cases, develop a Bayes estimator for the precision matrix, and propose an efficient sampling scheme that does not precalculate boundaries for positive definiteness. The proposed method is permutation invariant and performs shrinkage and estimation simultaneously for non-full rank data. Simulations show that the proposed BCLASSO performs similarly as frequentist methods for non-full rank data.

  16. Flexible Bayesian Dynamic Modeling of Covariance and Correlation Matrices

    KAUST Repository

    Lan, Shiwei

    2017-11-08

    Modeling covariance (and correlation) matrices is a challenging problem due to the large dimensionality and positive-definiteness constraint. In this paper, we propose a novel Bayesian framework based on decomposing the covariance matrix into variance and correlation matrices. The highlight is that the correlations are represented as products of vectors on unit spheres. We propose a variety of distributions on spheres (e.g. the squared-Dirichlet distribution) to induce flexible prior distributions for covariance matrices that go beyond the commonly used inverse-Wishart prior. To handle the intractability of the resulting posterior, we introduce the adaptive $\\\\Delta$-Spherical Hamiltonian Monte Carlo. We also extend our structured framework to dynamic cases and introduce unit-vector Gaussian process priors for modeling the evolution of correlation among multiple time series. Using an example of Normal-Inverse-Wishart problem, a simulated periodic process, and an analysis of local field potential data (collected from the hippocampus of rats performing a complex sequence memory task), we demonstrated the validity and effectiveness of our proposed framework for (dynamic) modeling covariance and correlation matrices.

  17. Adaptive Kalman Filter of Transfer Alignment with Un-modeled Wing Flexure of Aircraft

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    The alignment accuracy of the strap-down inertial navigation system (SINS) of airborne weapon is greatly degraded by the dynamic wing flexure of the aircraft. An adaptive Kalman filter uses innovation sequences based on the maximum likelihood estimated criterion to adapt the system noise covariance matrix and the measurement noise covariance matrix on line, which is used to estimate the misalignment if the model of wing flexure of the aircraft is unknown. From a number of simulations, it is shown that the accuracy of the adaptive Kalman filter is better than the conventional Kalman filter, and the erroneous misalignment models of the wing flexure of aircraft will cause bad estimation results of Kalman filter using attitude match method.

  18. Progress on Nuclear Data Covariances: AFCI-1.2 Covariance Library

    International Nuclear Information System (INIS)

    Oblozinsky, P.; Oblozinsky, P.; Mattoon, C.M.; Herman, M.; Mughabghab, S.F.; Pigni, M.T.; Talou, P.; Hale, G.M.; Kahler, A.C.; Kawano, T.; Little, R.C.; Young, P.G

    2009-01-01

    Improved neutron cross section covariances were produced for 110 materials including 12 light nuclei (coolants and moderators), 78 structural materials and fission products, and 20 actinides. Improved covariances were organized into AFCI-1.2 covariance library in 33-energy groups, from 10 -5 eV to 19.6 MeV. BNL contributed improved covariance data for the following materials: 23 Na and 55 Mn where more detailed evaluation was done; improvements in major structural materials 52 Cr, 56 Fe and 58 Ni; improved estimates for remaining structural materials and fission products; improved covariances for 14 minor actinides, and estimates of mubar covariances for 23 Na and 56 Fe. LANL contributed improved covariance data for 235 U and 239 Pu including prompt neutron fission spectra and completely new evaluation for 240 Pu. New R-matrix evaluation for 16 O including mubar covariances is under completion. BNL assembled the library and performed basic testing using improved procedures including inspection of uncertainty and correlation plots for each material. The AFCI-1.2 library was released to ANL and INL in August 2009.

  19. A bias correction for covariance estimators to improve inference with generalized estimating equations that use an unstructured correlation matrix.

    Science.gov (United States)

    Westgate, Philip M

    2013-07-20

    Generalized estimating equations (GEEs) are routinely used for the marginal analysis of correlated data. The efficiency of GEE depends on how closely the working covariance structure resembles the true structure, and therefore accurate modeling of the working correlation of the data is important. A popular approach is the use of an unstructured working correlation matrix, as it is not as restrictive as simpler structures such as exchangeable and AR-1 and thus can theoretically improve efficiency. However, because of the potential for having to estimate a large number of correlation parameters, variances of regression parameter estimates can be larger than theoretically expected when utilizing the unstructured working correlation matrix. Therefore, standard error estimates can be negatively biased. To account for this additional finite-sample variability, we derive a bias correction that can be applied to typical estimators of the covariance matrix of parameter estimates. Via simulation and in application to a longitudinal study, we show that our proposed correction improves standard error estimation and statistical inference. Copyright © 2012 John Wiley & Sons, Ltd.

  20. A special covariance structure for random coefficient models with both between and within covariates

    International Nuclear Information System (INIS)

    Riedel, K.S.

    1990-07-01

    We review random coefficient (RC) models in linear regression and propose a bias correction to the maximum likelihood (ML) estimator. Asymmptotic expansion of the ML equations are given when the between individual variance is much larger or smaller than the variance from within individual fluctuations. The standard model assumes all but one covariate varies within each individual, (we denote the within covariates by vector χ 1 ). We consider random coefficient models where some of the covariates do not vary in any single individual (we denote the between covariates by vector χ 0 ). The regression coefficients, vector β k , can only be estimated in the subspace X k of X. Thus the number of individuals necessary to estimate vector β and the covariance matrix Δ of vector β increases significantly in the presence of more than one between covariate. When the number of individuals is sufficient to estimate vector β but not the entire matrix Δ , additional assumptions must be imposed on the structure of Δ. A simple reduced model is that the between component of vector β is fixed and only the within component varies randomly. This model fails because it is not invariant under linear coordinate transformations and it can significantly overestimate the variance of new observations. We propose a covariance structure for Δ without these difficulties by first projecting the within covariates onto the space perpendicular to be between covariates. (orig.)

  1. Asset allocation with different covariance/correlation estimators

    OpenAIRE

    Μανταφούνη, Σοφία

    2007-01-01

    The subject of the study is to test whether the use of different covariance – correlation estimators than the historical covariance matrix that is widely used, would help in portfolio optimization through the mean-variance analysis. In other words, if an investor would like to use the mean-variance analysis in order to invest in assets like stocks or indices, would it be of some help to use more sophisticated estimators for the covariance matrix of the returns of his portfolio? The procedure ...

  2. Adaptive Detectors for Two Types of Subspace Targets in an Inverse Gamma Textured Background

    Directory of Open Access Journals (Sweden)

    Ding Hao

    2017-06-01

    Full Text Available Considering an inverse Gamma prior distribution model for texture, the adaptive detection problems for both first order Gaussian and second order Gaussian subspace targets are researched in a compound Gaussian sea clutter. Test statistics are derived on the basis of the two-step generalized likelihood ratio test. From these tests, new adaptive detectors are proposed by substituting the covariance matrix with estimation results from the Sample Covariance Matrix (SCM, normalized SCM, and fixed point estimator. The proposed detectors consider the prior distribution model for sea clutter during the design stage, and they model parameters that match the working environment during the detection stage. Analysis and validation results indicate that the detection performance of the proposed detectors out performs existing AMF (Adaptive Matched Filter, AMF and ANMF (Adaptive Normalized Matched Filter, ANMF detectors.

  3. Modeling Covariance Breakdowns in Multivariate GARCH

    OpenAIRE

    Jin, Xin; Maheu, John M

    2014-01-01

    This paper proposes a flexible way of modeling dynamic heterogeneous covariance breakdowns in multivariate GARCH (MGARCH) models. During periods of normal market activity, volatility dynamics are governed by an MGARCH specification. A covariance breakdown is any significant temporary deviation of the conditional covariance matrix from its implied MGARCH dynamics. This is captured through a flexible stochastic component that allows for changes in the conditional variances, covariances and impl...

  4. A heteroskedastic error covariance matrix estimator using a first-order conditional autoregressive Markov simulation for deriving asympotical efficient estimates from ecological sampled Anopheles arabiensis aquatic habitat covariates

    Directory of Open Access Journals (Sweden)

    Githure John I

    2009-09-01

    Full Text Available Abstract Background Autoregressive regression coefficients for Anopheles arabiensis aquatic habitat models are usually assessed using global error techniques and are reported as error covariance matrices. A global statistic, however, will summarize error estimates from multiple habitat locations. This makes it difficult to identify where there are clusters of An. arabiensis aquatic habitats of acceptable prediction. It is therefore useful to conduct some form of spatial error analysis to detect clusters of An. arabiensis aquatic habitats based on uncertainty residuals from individual sampled habitats. In this research, a method of error estimation for spatial simulation models was demonstrated using autocorrelation indices and eigenfunction spatial filters to distinguish among the effects of parameter uncertainty on a stochastic simulation of ecological sampled Anopheles aquatic habitat covariates. A test for diagnostic checking error residuals in an An. arabiensis aquatic habitat model may enable intervention efforts targeting productive habitats clusters, based on larval/pupal productivity, by using the asymptotic distribution of parameter estimates from a residual autocovariance matrix. The models considered in this research extends a normal regression analysis previously considered in the literature. Methods Field and remote-sampled data were collected during July 2006 to December 2007 in Karima rice-village complex in Mwea, Kenya. SAS 9.1.4® was used to explore univariate statistics, correlations, distributions, and to generate global autocorrelation statistics from the ecological sampled datasets. A local autocorrelation index was also generated using spatial covariance parameters (i.e., Moran's Indices in a SAS/GIS® database. The Moran's statistic was decomposed into orthogonal and uncorrelated synthetic map pattern components using a Poisson model with a gamma-distributed mean (i.e. negative binomial regression. The eigenfunction

  5. Research on Modified Root-MUSIC Algorithm of DOA Estimation Based on Covariance Matrix Reconstruction

    Directory of Open Access Journals (Sweden)

    Changgan SHU

    2014-09-01

    Full Text Available In the standard root multiple signal classification algorithm, the performance of direction of arrival estimation will reduce and even lose effect in circumstances that a low signal noise ratio and a small signals interval. By reconstructing and weighting the covariance matrix of received signal, the modified algorithm can provide more accurate estimation results. The computer simulation and performance analysis are given next, which show that under the condition of lower signal noise ratio and stronger correlation between signals, the proposed modified algorithm could provide preferable azimuth estimating performance than the standard method.

  6. Phenotypic Covariation and Morphological Diversification in the Ruminant Skull.

    Science.gov (United States)

    Haber, Annat

    2016-05-01

    Differences among clades in their diversification patterns result from a combination of extrinsic and intrinsic factors. In this study, I examined the role of intrinsic factors in the morphological diversification of ruminants, in general, and in the differences between bovids and cervids, in particular. Using skull morphology, which embodies many of the adaptations that distinguish bovids and cervids, I examined 132 of the 200 extant ruminant species. As a proxy for intrinsic constraints, I quantified different aspects of the phenotypic covariation structure within species and compared them with the among-species divergence patterns, using phylogenetic comparative methods. My results show that for most species, divergence is well aligned with their phenotypic covariance matrix and that those that are better aligned have diverged further away from their ancestor. Bovids have dispersed into a wider range of directions in morphospace than cervids, and their overall disparity is higher. This difference is best explained by the lower eccentricity of bovids' within-species covariance matrices. These results are consistent with the role of intrinsic constraints in determining amount, range, and direction of dispersion and demonstrate that intrinsic constraints can influence macroevolutionary patterns even as the covariance structure evolves.

  7. MATXTST, Basic Operations for Covariance Matrices

    International Nuclear Information System (INIS)

    Geraldo, Luiz P.; Smith, Donald

    1989-01-01

    1 - Description of program or function: MATXTST and MATXTST1 perform the following operations for a covariance matrix: - test for singularity; - test for positive definiteness; - compute the inverse if the matrix is non-singular; - compute the determinant; - determine the number of positive, negative, and zero eigenvalues; - examine all possible 3 X 3 cross correlations within a sub-matrix corresponding to a leading principal minor which is non-positive definite. While the two programs utilize the same input, the calculational procedures employed are somewhat different and their functions are complementary. The available input options include: i) the full covariance matrix, ii) the basic variables plus the relative covariance matrix, or iii) uncertainties in the basic variables plus the correlation matrix. 2 - Method of solution: MATXTST employs LINPACK subroutines SPOFA and SPODI to test for positive definiteness and to perform further optional calculations. Subroutine SPOFA factors a symmetric matrix M using the Cholesky algorithm to determine the elements of a matrix R which satisfies the relation M=R'R, where R' is the transposed matrix of R. Each leading principal minor of M is tested until the first one is found which is not positive definite. MATXTST1 uses LINPACK subroutines SSICO, SSIFA, and SSIDI to estimate whether the matrix is near to singularity or not (SSICO), and to perform the matrix diagonalization process (SSIFA). The algorithm used in SSIFA is generalization of the Method of Lagrange Reduction. SSIDI is used to compute the determinant and inertia of the matrix. 3 - Restrictions on the complexity of the problem: Matrices of sizes up to 50 X 50 elements can be treated by present versions of the programs

  8. Impact of the 235U Covariance Data in Benchmark Calculations

    International Nuclear Information System (INIS)

    Leal, Luiz C.; Mueller, D.; Arbanas, G.; Wiarda, D.; Derrien, H.

    2008-01-01

    The error estimation for calculated quantities relies on nuclear data uncertainty information available in the basic nuclear data libraries such as the U.S. Evaluated Nuclear Data File (ENDF/B). The uncertainty files (covariance matrices) in the ENDF/B library are generally obtained from analysis of experimental data. In the resonance region, the computer code SAMMY is used for analyses of experimental data and generation of resonance parameters. In addition to resonance parameters evaluation, SAMMY also generates resonance parameter covariance matrices (RPCM). SAMMY uses the generalized least-squares formalism (Bayes method) together with the resonance formalism (R-matrix theory) for analysis of experimental data. Two approaches are available for creation of resonance-parameter covariance data. (1) During the data-evaluation process, SAMMY generates both a set of resonance parameters that fit the experimental data and the associated resonance-parameter covariance matrix. (2) For existing resonance-parameter evaluations for which no resonance-parameter covariance data are available, SAMMY can retroactively create a resonance-parameter covariance matrix. The retroactive method was used to generate covariance data for 235U. The resulting 235U covariance matrix was then used as input to the PUFF-IV code, which processed the covariance data into multigroup form, and to the TSUNAMI code, which calculated the uncertainty in the multiplication factor due to uncertainty in the experimental cross sections. The objective of this work is to demonstrate the use of the 235U covariance data in calculations of critical benchmark systems

  9. Impact of the 235U covariance data in benchmark calculations

    International Nuclear Information System (INIS)

    Leal, Luiz; Mueller, Don; Arbanas, Goran; Wiarda, Dorothea; Derrien, Herve

    2008-01-01

    The error estimation for calculated quantities relies on nuclear data uncertainty information available in the basic nuclear data libraries such as the U.S. Evaluated Nuclear Data File (ENDF/B). The uncertainty files (covariance matrices) in the ENDF/B library are generally obtained from analysis of experimental data. In the resonance region, the computer code SAMMY is used for analyses of experimental data and generation of resonance parameters. In addition to resonance parameters evaluation, SAMMY also generates resonance parameter covariance matrices (RPCM). SAMMY uses the generalized least-squares formalism (Bayes' method) together with the resonance formalism (R-matrix theory) for analysis of experimental data. Two approaches are available for creation of resonance-parameter covariance data. (1) During the data-evaluation process, SAMMY generates both a set of resonance parameters that fit the experimental data and the associated resonance-parameter covariance matrix. (2) For existing resonance-parameter evaluations for which no resonance-parameter covariance data are available, SAMMY can retroactively create a resonance-parameter covariance matrix. The retroactive method was used to generate covariance data for 235 U. The resulting 235 U covariance matrix was then used as input to the PUFF-IV code, which processed the covariance data into multigroup form, and to the TSUNAMI code, which calculated the uncertainty in the multiplication factor due to uncertainty in the experimental cross sections. The objective of this work is to demonstrate the use of the 235 U covariance data in calculations of critical benchmark systems. (authors)

  10. The method of covariant symbols in curved space-time

    International Nuclear Information System (INIS)

    Salcedo, L.L.

    2007-01-01

    Diagonal matrix elements of pseudodifferential operators are needed in order to compute effective Lagrangians and currents. For this purpose the method of symbols is often used, which however lacks manifest covariance. In this work the method of covariant symbols, introduced by Pletnev and Banin, is extended to curved space-time with arbitrary gauge and coordinate connections. For the Riemannian connection we compute the covariant symbols corresponding to external fields, the covariant derivative and the Laplacian, to fourth order in a covariant derivative expansion. This allows one to obtain the covariant symbol of general operators to the same order. The procedure is illustrated by computing the diagonal matrix element of a nontrivial operator to second order. Applications of the method are discussed. (orig.)

  11. Physical properties of the Schur complement of local covariance matrices

    International Nuclear Information System (INIS)

    Haruna, L F; Oliveira, M C de

    2007-01-01

    General properties of global covariance matrices representing bipartite Gaussian states can be decomposed into properties of local covariance matrices and their Schur complements. We demonstrate that given a bipartite Gaussian state ρ 12 described by a 4 x 4 covariance matrix V, the Schur complement of a local covariance submatrix V 1 of it can be interpreted as a new covariance matrix representing a Gaussian operator of party 1 conditioned to local parity measurements on party 2. The connection with a partial parity measurement over a bipartite quantum state and the determination of the reduced Wigner function is given and an operational process of parity measurement is developed. Generalization of this procedure to an n-partite Gaussian state is given, and it is demonstrated that the n - 1 system state conditioned to a partial parity projection is given by a covariance matrix such that its 2 x 2 block elements are Schur complements of special local matrices

  12. ENDF-6 File 30: Data covariances obtained from parameter covariances and sensitivities

    International Nuclear Information System (INIS)

    Muir, D.W.

    1989-01-01

    File 30 is provided as a means of describing the covariances of tabulated cross sections, multiplicities, and energy-angle distributions that result from propagating the covariances of a set of underlying parameters (for example, the input parameters of a nuclear-model code), using an evaluator-supplied set of parameter covariances and sensitivities. Whenever nuclear data are evaluated primarily through the application of nuclear models, the covariances of the resulting data can be described very adequately, and compactly, by specifying the covariance matrix for the underlying nuclear parameters, along with a set of sensitivity coefficients giving the rate of change of each nuclear datum of interest with respect to each of the model parameters. Although motivated primarily by these applications of nuclear theory, use of File 30 is not restricted to any one particular evaluation methodology. It can be used to describe data covariances of any origin, so long as they can be formally separated into a set of parameters with specified covariances and a set of data sensitivities

  13. Pu239 Cross-Section Variations Based on Experimental Uncertainties and Covariances

    Energy Technology Data Exchange (ETDEWEB)

    Sigeti, David Edward [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Williams, Brian J. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Parsons, D. Kent [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2016-10-18

    Algorithms and software have been developed for producing variations in plutonium-239 neutron cross sections based on experimental uncertainties and covariances. The varied cross-section sets may be produced as random samples from the multi-variate normal distribution defined by an experimental mean vector and covariance matrix, or they may be produced as Latin-Hypercube/Orthogonal-Array samples (based on the same means and covariances) for use in parametrized studies. The variations obey two classes of constraints that are obligatory for cross-section sets and which put related constraints on the mean vector and covariance matrix that detemine the sampling. Because the experimental means and covariances do not obey some of these constraints to sufficient precision, imposing the constraints requires modifying the experimental mean vector and covariance matrix. Modification is done with an algorithm based on linear algebra that minimizes changes to the means and covariances while insuring that the operations that impose the different constraints do not conflict with each other.

  14. Reconstruction of sparse connectivity in neural networks from spike train covariances

    International Nuclear Information System (INIS)

    Pernice, Volker; Rotter, Stefan

    2013-01-01

    The inference of causation from correlation is in general highly problematic. Correspondingly, it is difficult to infer the existence of physical synaptic connections between neurons from correlations in their activity. Covariances in neural spike trains and their relation to network structure have been the subject of intense research, both experimentally and theoretically. The influence of recurrent connections on covariances can be characterized directly in linear models, where connectivity in the network is described by a matrix of linear coupling kernels. However, as indirect connections also give rise to covariances, the inverse problem of inferring network structure from covariances can generally not be solved unambiguously. Here we study to what degree this ambiguity can be resolved if the sparseness of neural networks is taken into account. To reconstruct a sparse network, we determine the minimal set of linear couplings consistent with the measured covariances by minimizing the L 1 norm of the coupling matrix under appropriate constraints. Contrary to intuition, after stochastic optimization of the coupling matrix, the resulting estimate of the underlying network is directed, despite the fact that a symmetric matrix of count covariances is used for inference. The performance of the new method is best if connections are neither exceedingly sparse, nor too dense, and it is easily applicable for networks of a few hundred nodes. Full coupling kernels can be obtained from the matrix of full covariance functions. We apply our method to networks of leaky integrate-and-fire neurons in an asynchronous–irregular state, where spike train covariances are well described by a linear model. (paper)

  15. Information matrix estimation procedures for cognitive diagnostic models.

    Science.gov (United States)

    Liu, Yanlou; Xin, Tao; Andersson, Björn; Tian, Wei

    2018-03-06

    Two new methods to estimate the asymptotic covariance matrix for marginal maximum likelihood estimation of cognitive diagnosis models (CDMs), the inverse of the observed information matrix and the sandwich-type estimator, are introduced. Unlike several previous covariance matrix estimators, the new methods take into account both the item and structural parameters. The relationships between the observed information matrix, the empirical cross-product information matrix, the sandwich-type covariance matrix and the two approaches proposed by de la Torre (2009, J. Educ. Behav. Stat., 34, 115) are discussed. Simulation results show that, for a correctly specified CDM and Q-matrix or with a slightly misspecified probability model, the observed information matrix and the sandwich-type covariance matrix exhibit good performance with respect to providing consistent standard errors of item parameter estimates. However, with substantial model misspecification only the sandwich-type covariance matrix exhibits robust performance. © 2018 The British Psychological Society.

  16. EEG-Based Emotion Recognition Using Deep Learning Network with Principal Component Based Covariate Shift Adaptation

    Directory of Open Access Journals (Sweden)

    Suwicha Jirayucharoensak

    2014-01-01

    Full Text Available Automatic emotion recognition is one of the most challenging tasks. To detect emotion from nonstationary EEG signals, a sophisticated learning algorithm that can represent high-level abstraction is required. This study proposes the utilization of a deep learning network (DLN to discover unknown feature correlation between input signals that is crucial for the learning task. The DLN is implemented with a stacked autoencoder (SAE using hierarchical feature learning approach. Input features of the network are power spectral densities of 32-channel EEG signals from 32 subjects. To alleviate overfitting problem, principal component analysis (PCA is applied to extract the most important components of initial input features. Furthermore, covariate shift adaptation of the principal components is implemented to minimize the nonstationary effect of EEG signals. Experimental results show that the DLN is capable of classifying three different levels of valence and arousal with accuracy of 49.52% and 46.03%, respectively. Principal component based covariate shift adaptation enhances the respective classification accuracy by 5.55% and 6.53%. Moreover, DLN provides better performance compared to SVM and naive Bayes classifiers.

  17. A cautionary note on generalized linear models for covariance of unbalanced longitudinal data

    KAUST Repository

    Huang, Jianhua Z.

    2012-03-01

    Missing data in longitudinal studies can create enormous challenges in data analysis when coupled with the positive-definiteness constraint on a covariance matrix. For complete balanced data, the Cholesky decomposition of a covariance matrix makes it possible to remove the positive-definiteness constraint and use a generalized linear model setup to jointly model the mean and covariance using covariates (Pourahmadi, 2000). However, this approach may not be directly applicable when the longitudinal data are unbalanced, as coherent regression models for the dependence across all times and subjects may not exist. Within the existing generalized linear model framework, we show how to overcome this and other challenges by embedding the covariance matrix of the observed data for each subject in a larger covariance matrix and employing the familiar EM algorithm to compute the maximum likelihood estimates of the parameters and their standard errors. We illustrate and assess the methodology using real data sets and simulations. © 2011 Elsevier B.V.

  18. AMA- and RWE- Based Adaptive Kalman Filter for Denoising Fiber Optic Gyroscope Drift Signal.

    Science.gov (United States)

    Yang, Gongliu; Liu, Yuanyuan; Li, Ming; Song, Shunguang

    2015-10-23

    An improved double-factor adaptive Kalman filter called AMA-RWE-DFAKF is proposed to denoise fiber optic gyroscope (FOG) drift signal in both static and dynamic conditions. The first factor is Kalman gain updated by random weighting estimation (RWE) of the covariance matrix of innovation sequence at any time to ensure the lowest noise level of output, but the inertia of KF response increases in dynamic condition. To decrease the inertia, the second factor is the covariance matrix of predicted state vector adjusted by RWE only when discontinuities are detected by adaptive moving average (AMA).The AMA-RWE-DFAKF is applied for denoising FOG static and dynamic signals, its performance is compared with conventional KF (CKF), RWE-based adaptive KF with gain correction (RWE-AKFG), AMA- and RWE- based dual mode adaptive KF (AMA-RWE-DMAKF). Results of Allan variance on static signal and root mean square error (RMSE) on dynamic signal show that this proposed algorithm outperforms all the considered methods in denoising FOG signal.

  19. Fuzzy Adaptive Cubature Kalman Filter for Integrated Navigation Systems.

    Science.gov (United States)

    Tseng, Chien-Hao; Lin, Sheng-Fuu; Jwo, Dah-Jing

    2016-07-26

    This paper presents a sensor fusion method based on the combination of cubature Kalman filter (CKF) and fuzzy logic adaptive system (FLAS) for the integrated navigation systems, such as the GPS/INS (Global Positioning System/inertial navigation system) integration. The third-degree spherical-radial cubature rule applied in the CKF has been employed to avoid the numerically instability in the system model. In processing navigation integration, the performance of nonlinear filter based estimation of the position and velocity states may severely degrade caused by modeling errors due to dynamics uncertainties of the vehicle. In order to resolve the shortcoming for selecting the process noise covariance through personal experience or numerical simulation, a scheme called the fuzzy adaptive cubature Kalman filter (FACKF) is presented by introducing the FLAS to adjust the weighting factor of the process noise covariance matrix. The FLAS is incorporated into the CKF framework as a mechanism for timely implementing the tuning of process noise covariance matrix based on the information of degree of divergence (DOD) parameter. The proposed FACKF algorithm shows promising accuracy improvement as compared to the extended Kalman filter (EKF), unscented Kalman filter (UKF), and CKF approaches.

  20. Fuzzy Adaptive Cubature Kalman Filter for Integrated Navigation Systems

    Directory of Open Access Journals (Sweden)

    Chien-Hao Tseng

    2016-07-01

    Full Text Available This paper presents a sensor fusion method based on the combination of cubature Kalman filter (CKF and fuzzy logic adaptive system (FLAS for the integrated navigation systems, such as the GPS/INS (Global Positioning System/inertial navigation system integration. The third-degree spherical-radial cubature rule applied in the CKF has been employed to avoid the numerically instability in the system model. In processing navigation integration, the performance of nonlinear filter based estimation of the position and velocity states may severely degrade caused by modeling errors due to dynamics uncertainties of the vehicle. In order to resolve the shortcoming for selecting the process noise covariance through personal experience or numerical simulation, a scheme called the fuzzy adaptive cubature Kalman filter (FACKF is presented by introducing the FLAS to adjust the weighting factor of the process noise covariance matrix. The FLAS is incorporated into the CKF framework as a mechanism for timely implementing the tuning of process noise covariance matrix based on the information of degree of divergence (DOD parameter. The proposed FACKF algorithm shows promising accuracy improvement as compared to the extended Kalman filter (EKF, unscented Kalman filter (UKF, and CKF approaches.

  1. Optimal Design of the Adaptive Normalized Matched Filter Detector using Regularized Tyler Estimators

    KAUST Repository

    Kammoun, Abla; Couillet, Romain; Pascal, Frederic; Alouini, Mohamed-Slim

    2017-01-01

    This article addresses improvements on the design of the adaptive normalized matched filter (ANMF) for radar detection. It is well-acknowledged that the estimation of the noise-clutter covariance matrix is a fundamental step in adaptive radar detection. In this paper, we consider regularized estimation methods which force by construction the eigenvalues of the covariance estimates to be greater than a positive regularization parameter ρ. This makes them more suitable for high dimensional problems with a limited number of secondary data samples than traditional sample covariance estimates. The motivation behind this work is to understand the effect and properly set the value of ρthat improves estimate conditioning while maintaining a low estimation bias. More specifically, we consider the design of the ANMF detector for two kinds of regularized estimators, namely the regularized sample covariance matrix (RSCM), the regularized Tyler estimator (RTE). The rationale behind this choice is that the RTE is efficient in mitigating the degradation caused by the presence of impulsive noises while inducing little loss when the noise is Gaussian. Based on asymptotic results brought by recent tools from random matrix theory, we propose a design for the regularization parameter that maximizes the asymptotic detection probability under constant asymptotic false alarm rates. Provided Simulations support the efficiency of the proposed method, illustrating its gain over conventional settings of the regularization parameter.

  2. Optimal Design of the Adaptive Normalized Matched Filter Detector using Regularized Tyler Estimators

    KAUST Repository

    Kammoun, Abla

    2017-10-25

    This article addresses improvements on the design of the adaptive normalized matched filter (ANMF) for radar detection. It is well-acknowledged that the estimation of the noise-clutter covariance matrix is a fundamental step in adaptive radar detection. In this paper, we consider regularized estimation methods which force by construction the eigenvalues of the covariance estimates to be greater than a positive regularization parameter ρ. This makes them more suitable for high dimensional problems with a limited number of secondary data samples than traditional sample covariance estimates. The motivation behind this work is to understand the effect and properly set the value of ρthat improves estimate conditioning while maintaining a low estimation bias. More specifically, we consider the design of the ANMF detector for two kinds of regularized estimators, namely the regularized sample covariance matrix (RSCM), the regularized Tyler estimator (RTE). The rationale behind this choice is that the RTE is efficient in mitigating the degradation caused by the presence of impulsive noises while inducing little loss when the noise is Gaussian. Based on asymptotic results brought by recent tools from random matrix theory, we propose a design for the regularization parameter that maximizes the asymptotic detection probability under constant asymptotic false alarm rates. Provided Simulations support the efficiency of the proposed method, illustrating its gain over conventional settings of the regularization parameter.

  3. TRANSPOSABLE REGULARIZED COVARIANCE MODELS WITH AN APPLICATION TO MISSING DATA IMPUTATION.

    Science.gov (United States)

    Allen, Genevera I; Tibshirani, Robert

    2010-06-01

    Missing data estimation is an important challenge with high-dimensional data arranged in the form of a matrix. Typically this data matrix is transposable , meaning that either the rows, columns or both can be treated as features. To model transposable data, we present a modification of the matrix-variate normal, the mean-restricted matrix-variate normal , in which the rows and columns each have a separate mean vector and covariance matrix. By placing additive penalties on the inverse covariance matrices of the rows and columns, these so called transposable regularized covariance models allow for maximum likelihood estimation of the mean and non-singular covariance matrices. Using these models, we formulate EM-type algorithms for missing data imputation in both the multivariate and transposable frameworks. We present theoretical results exploiting the structure of our transposable models that allow these models and imputation methods to be applied to high-dimensional data. Simulations and results on microarray data and the Netflix data show that these imputation techniques often outperform existing methods and offer a greater degree of flexibility.

  4. On estimating cosmology-dependent covariance matrices

    International Nuclear Information System (INIS)

    Morrison, Christopher B.; Schneider, Michael D.

    2013-01-01

    We describe a statistical model to estimate the covariance matrix of matter tracer two-point correlation functions with cosmological simulations. Assuming a fixed number of cosmological simulation runs, we describe how to build a 'statistical emulator' of the two-point function covariance over a specified range of input cosmological parameters. Because the simulation runs with different cosmological models help to constrain the form of the covariance, we predict that the cosmology-dependent covariance may be estimated with a comparable number of simulations as would be needed to estimate the covariance for fixed cosmology. Our framework is a necessary first step in planning a simulations campaign for analyzing the next generation of cosmological surveys

  5. Bayesian estimation of covariance matrices: Application to market risk management at EDF

    International Nuclear Information System (INIS)

    Jandrzejewski-Bouriga, M.

    2012-01-01

    In this thesis, we develop new methods of regularized covariance matrix estimation, under the Bayesian setting. The regularization methodology employed is first related to shrinkage. We investigate a new Bayesian modeling of covariance matrix, based on hierarchical inverse-Wishart distribution, and then derive different estimators under standard loss functions. Comparisons between shrunk and empirical estimators are performed in terms of frequentist performance under different losses. It allows us to highlight the critical importance of the definition of cost function and show the persistent effect of the shrinkage-type prior on inference. In a second time, we consider the problem of covariance matrix estimation in Gaussian graphical models. If the issue is well treated for the decomposable case, it is not the case if you also consider non-decomposable graphs. We then describe a Bayesian and operational methodology to carry out the estimation of covariance matrix of Gaussian graphical models, decomposable or not. This procedure is based on a new and objective method of graphical-model selection, combined with a constrained and regularized estimation of the covariance matrix of the model chosen. The procedures studied effectively manage missing data. These estimation techniques were applied to calculate the covariance matrices involved in the market risk management for portfolios of EDF (Electricity of France), in particular for problems of calculating Value-at-Risk or in Asset Liability Management. (author)

  6. MIMO-radar Waveform Covariance Matrices for High SINR and Low Side-lobe Levels

    KAUST Repository

    Ahmed, Sajid

    2012-12-29

    MIMO-radar has better parametric identifiability but compared to phased-array radar it shows loss in signal-to-noise ratio due to non-coherent processing. To exploit the benefits of both MIMO-radar and phased-array two transmit covariance matrices are found. Both of the covariance matrices yield gain in signal-to-interference-plus-noise ratio (SINR) compared to MIMO-radar and have lower side-lobe levels (SLL)\\'s compared to phased-array and MIMO-radar. Moreover, in contrast to recently introduced phased-MIMO scheme, where each antenna transmit different power, our proposed schemes allows same power transmission from each antenna. The SLL\\'s of the proposed first covariance matrix are higher than the phased-MIMO scheme while the SLL\\'s of the second proposed covariance matrix are lower than the phased-MIMO scheme. The first covariance matrix is generated using an auto-regressive process, which allow us to change the SINR and side lobe levels by changing the auto-regressive parameter, while to generate the second covariance matrix the values of sine function between 0 and $\\\\pi$ with the step size of $\\\\pi/n_T$ are used to form a positive-semidefinite Toeplitiz matrix, where $n_T$ is the number of transmit antennas. Simulation results validate our analytical results.

  7. Covariance fitting of highly-correlated data in lattice QCD

    Science.gov (United States)

    Yoon, Boram; Jang, Yong-Chull; Jung, Chulwoo; Lee, Weonjong

    2013-07-01

    We address a frequently-asked question on the covariance fitting of highly-correlated data such as our B K data based on the SU(2) staggered chiral perturbation theory. Basically, the essence of the problem is that we do not have a fitting function accurate enough to fit extremely precise data. When eigenvalues of the covariance matrix are small, even a tiny error in the fitting function yields a large chi-square value and spoils the fitting procedure. We have applied a number of prescriptions available in the market, such as the cut-off method, modified covariance matrix method, and Bayesian method. We also propose a brand new method, the eigenmode shift (ES) method, which allows a full covariance fitting without modifying the covariance matrix at all. We provide a pedagogical example of data analysis in which the cut-off method manifestly fails in fitting, but the rest work well. In our case of the B K fitting, the diagonal approximation, the cut-off method, the ES method, and the Bayesian method work reasonably well in an engineering sense. However, interpreting the meaning of χ 2 is easier in the case of the ES method and the Bayesian method in a theoretical sense aesthetically. Hence, the ES method can be a useful alternative optional tool to check the systematic error caused by the covariance fitting procedure.

  8. A cautionary note on generalized linear models for covariance of unbalanced longitudinal data

    KAUST Repository

    Huang, Jianhua Z.; Chen, Min; Maadooliat, Mehdi; Pourahmadi, Mohsen

    2012-01-01

    Missing data in longitudinal studies can create enormous challenges in data analysis when coupled with the positive-definiteness constraint on a covariance matrix. For complete balanced data, the Cholesky decomposition of a covariance matrix makes

  9. Countermeasures of adaptation to climate change: establishment and application for implementation matrix

    Institute of Scientific and Technical Information of China (English)

    Jiang Dongmei; Liu Bin

    2009-01-01

    In the conceptual framework of adaptation policy assessment to climate change,adaptation measures can be categorized as two groupsL:facilitation and implementation.Facilitation measures refers to activities that enhance adaptive capacity,while implementation refers to activities that actually avoid adverse climate impacts on a system by reducing its exposure or sensitivity to climatic hazards,or by moderating relevant non-climatic factors.This paper aims to establish a matrix of implementation measures of adaptation to climate change,through four different ways how adaptation can influence the relevant elements of climate change:reducing the exposure,reducing the sensitivity,alleviating the adverse impacts and reducing the negative non-climatic factors,and then further discuss the particular implementation measures of adaptation to climate change,through application studies on the selected sub-systems,intend to organize the disordered implementation measures in existent,and put forward some new mea sures under the guidance of this matrix,which could enrich and promote the system and content of implementation measures of adaptation.

  10. Flexible Bayesian Dynamic Modeling of Covariance and Correlation Matrices

    KAUST Repository

    Lan, Shiwei; Holbrook, Andrew; Fortin, Norbert J.; Ombao, Hernando; Shahbaba, Babak

    2017-01-01

    Modeling covariance (and correlation) matrices is a challenging problem due to the large dimensionality and positive-definiteness constraint. In this paper, we propose a novel Bayesian framework based on decomposing the covariance matrix

  11. Undesirable effects of covariance matrix techniques for error analysis

    International Nuclear Information System (INIS)

    Seibert, D.

    1994-01-01

    Regression with χ 2 constructed from covariance matrices should not be used for some combinations of covariance matrices and fitting functions. Using the technique for unsuitable combinations can amplify systematic errors. This amplification is uncontrolled, and can produce arbitrarily inaccurate results that might not be ruled out by a χ 2 test. In addition, this technique can give incorrect (artificially small) errors for fit parameters. I give a test for this instability and a more robust (but computationally more intensive) method for fitting correlated data

  12. Bayesian source term determination with unknown covariance of measurements

    Science.gov (United States)

    Belal, Alkomiet; Tichý, Ondřej; Šmídl, Václav

    2017-04-01

    Determination of a source term of release of a hazardous material into the atmosphere is a very important task for emergency response. We are concerned with the problem of estimation of the source term in the conventional linear inverse problem, y = Mx, where the relationship between the vector of observations y is described using the source-receptor-sensitivity (SRS) matrix M and the unknown source term x. Since the system is typically ill-conditioned, the problem is recast as an optimization problem minR,B(y - Mx)TR-1(y - Mx) + xTB-1x. The first term minimizes the error of the measurements with covariance matrix R, and the second term is a regularization of the source term. There are different types of regularization arising for different choices of matrices R and B, for example, Tikhonov regularization assumes covariance matrix B as the identity matrix multiplied by scalar parameter. In this contribution, we adopt a Bayesian approach to make inference on the unknown source term x as well as unknown R and B. We assume prior on x to be a Gaussian with zero mean and unknown diagonal covariance matrix B. The covariance matrix of the likelihood R is also unknown. We consider two potential choices of the structure of the matrix R. First is the diagonal matrix and the second is a locally correlated structure using information on topology of the measuring network. Since the inference of the model is intractable, iterative variational Bayes algorithm is used for simultaneous estimation of all model parameters. The practical usefulness of our contribution is demonstrated on an application of the resulting algorithm to real data from the European Tracer Experiment (ETEX). This research is supported by EEA/Norwegian Financial Mechanism under project MSMT-28477/2014 Source-Term Determination of Radionuclide Releases by Inverse Atmospheric Dispersion Modelling (STRADI).

  13. Covariance expressions for eigenvalue and eigenvector problems

    Science.gov (United States)

    Liounis, Andrew J.

    There are a number of important scientific and engineering problems whose solutions take the form of an eigenvalue--eigenvector problem. Some notable examples include solutions to linear systems of ordinary differential equations, controllability of linear systems, finite element analysis, chemical kinetics, fitting ellipses to noisy data, and optimal estimation of attitude from unit vectors. In many of these problems, having knowledge of the eigenvalue and eigenvector Jacobians is either necessary or is nearly as important as having the solution itself. For instance, Jacobians are necessary to find the uncertainty in a computed eigenvalue or eigenvector estimate. This uncertainty, which is usually represented as a covariance matrix, has been well studied for problems similar to the eigenvalue and eigenvector problem, such as singular value decomposition. There has been substantially less research on the covariance of an optimal estimate originating from an eigenvalue-eigenvector problem. In this thesis we develop two general expressions for the Jacobians of eigenvalues and eigenvectors with respect to the elements of their parent matrix. The expressions developed make use of only the parent matrix and the eigenvalue and eigenvector pair under consideration. In addition, they are applicable to any general matrix (including complex valued matrices, eigenvalues, and eigenvectors) as long as the eigenvalues are simple. Alongside this, we develop expressions that determine the uncertainty in a vector estimate obtained from an eigenvalue-eigenvector problem given the uncertainty of the terms of the matrix. The Jacobian expressions developed are numerically validated with forward finite, differencing and the covariance expressions are validated using Monte Carlo analysis. Finally, the results from this work are used to determine covariance expressions for a variety of estimation problem examples and are also applied to the design of a dynamical system.

  14. Recent Advances with the AMPX Covariance Processing Capabilities in PUFF-IV

    International Nuclear Information System (INIS)

    Wiarda, Dorothea; Arbanas, Goran; Leal, Luiz C.; Dunn, Michael E.

    2008-01-01

    The program PUFF-IV is used to process resonance parameter covariance information given in ENDF/B File 32 and point-wise covariance matrices given in ENDF/B File 33 into group-averaged covariances matrices on a user-supplied group structure. For large resonance covariance matrices, found for example in 235U, the execution time of PUFF-IV can be quite long. Recently the code was modified to take advandage of Basic Linear Algebra Subprograms (BLAS) routines for the most time-consuming matrix multiplications. This led to a substantial decrease in execution time. This faster processing capability allowed us to investigate the conversion of File 32 data into File 33 data using a larger number of user-defined groups. While conversion substantially reduces the ENDF/B file size requirements for evaluations with a large number of resonances, a trade-off is made between the number of groups used to represent the resonance parameter covariance as a point-wise covariance matrix and the file size. We are also investigating a hybrid version of the conversion, in which the low-energy part of the File 32 resonance parameter covariances matrix is retained and the correlations with higher energies as well as the high energy part are given in File 33.

  15. Positive semidefinite integrated covariance estimation, factorizations and asynchronicity

    DEFF Research Database (Denmark)

    Boudt, Kris; Laurent, Sébastien; Lunde, Asger

    2017-01-01

    An estimator of the ex-post covariation of log-prices under asynchronicity and microstructure noise is proposed. It uses the Cholesky factorization of the covariance matrix in order to exploit the heterogeneity in trading intensities to estimate the different parameters sequentially with as many...

  16. A scale invariant covariance structure on jet space

    DEFF Research Database (Denmark)

    Pedersen, Kim Steenstrup; Loog, Marco; Markussen, Bo

    2005-01-01

    This paper considers scale invariance of statistical image models. We study statistical scale invariance of the covariance structure of jet space under scale space blurring and derive the necessary structure and conditions of the jet covariance matrix in order for it to be scale invariant. As par...

  17. Land cover change detection using the internal covariance matrix of the extended kalman filter over multiple spectral bands

    CSIR Research Space (South Africa)

    Salmon

    2013-06-01

    Full Text Available stream_source_info Salmon_10577_2013.pdf.txt stream_content_type text/plain stream_size 1183 Content-Encoding ISO-8859-1 stream_name Salmon_10577_2013.pdf.txt Content-Type text/plain; charset=ISO-8859-1 IEEE Journal... of Selected Topics in Applied Earth Observations and Remote Sensing, vol, 6(3): 1079- 1085 Land cover change detection using the internal covariance matrix of the extended kalman filter over multiple spectral bands Salmon BP Kleynhans W Van den Bergh...

  18. On spectral distribution of high dimensional covariation matrices

    DEFF Research Database (Denmark)

    Heinrich, Claudio; Podolskij, Mark

    In this paper we present the asymptotic theory for spectral distributions of high dimensional covariation matrices of Brownian diffusions. More specifically, we consider N-dimensional Itô integrals with time varying matrix-valued integrands. We observe n equidistant high frequency data points...... of the underlying Brownian diffusion and we assume that N/n -> c in (0,oo). We show that under a certain mixed spectral moment condition the spectral distribution of the empirical covariation matrix converges in distribution almost surely. Our proof relies on method of moments and applications of graph theory....

  19. Covariance Partition Priors: A Bayesian Approach to Simultaneous Covariance Estimation for Longitudinal Data.

    Science.gov (United States)

    Gaskins, J T; Daniels, M J

    2016-01-02

    The estimation of the covariance matrix is a key concern in the analysis of longitudinal data. When data consists of multiple groups, it is often assumed the covariance matrices are either equal across groups or are completely distinct. We seek methodology to allow borrowing of strength across potentially similar groups to improve estimation. To that end, we introduce a covariance partition prior which proposes a partition of the groups at each measurement time. Groups in the same set of the partition share dependence parameters for the distribution of the current measurement given the preceding ones, and the sequence of partitions is modeled as a Markov chain to encourage similar structure at nearby measurement times. This approach additionally encourages a lower-dimensional structure of the covariance matrices by shrinking the parameters of the Cholesky decomposition toward zero. We demonstrate the performance of our model through two simulation studies and the analysis of data from a depression study. This article includes Supplementary Material available online.

  20. Dimension from covariance matrices.

    Science.gov (United States)

    Carroll, T L; Byers, J M

    2017-02-01

    We describe a method to estimate embedding dimension from a time series. This method includes an estimate of the probability that the dimension estimate is valid. Such validity estimates are not common in algorithms for calculating the properties of dynamical systems. The algorithm described here compares the eigenvalues of covariance matrices created from an embedded signal to the eigenvalues for a covariance matrix of a Gaussian random process with the same dimension and number of points. A statistical test gives the probability that the eigenvalues for the embedded signal did not come from the Gaussian random process.

  1. A three domain covariance framework for EEG/MEG data.

    Science.gov (United States)

    Roś, Beata P; Bijma, Fetsje; de Gunst, Mathisca C M; de Munck, Jan C

    2015-10-01

    In this paper we introduce a covariance framework for the analysis of single subject EEG and MEG data that takes into account observed temporal stationarity on small time scales and trial-to-trial variations. We formulate a model for the covariance matrix, which is a Kronecker product of three components that correspond to space, time and epochs/trials, and consider maximum likelihood estimation of the unknown parameter values. An iterative algorithm that finds approximations of the maximum likelihood estimates is proposed. Our covariance model is applicable in a variety of cases where spontaneous EEG or MEG acts as source of noise and realistic noise covariance estimates are needed, such as in evoked activity studies, or where the properties of spontaneous EEG or MEG are themselves the topic of interest, like in combined EEG-fMRI experiments in which the correlation between EEG and fMRI signals is investigated. We use a simulation study to assess the performance of the estimator and investigate the influence of different assumptions about the covariance factors on the estimated covariance matrix and on its components. We apply our method to real EEG and MEG data sets. Copyright © 2015 Elsevier Inc. All rights reserved.

  2. Dark matter statistics for large galaxy catalogs: power spectra and covariance matrices

    Science.gov (United States)

    Klypin, Anatoly; Prada, Francisco

    2018-06-01

    Large-scale surveys of galaxies require accurate theoretical predictions of the dark matter clustering for thousands of mock galaxy catalogs. We demonstrate that this goal can be achieve with the new Parallel Particle-Mesh (PM) N-body code GLAM at a very low computational cost. We run ˜22, 000 simulations with ˜2 billion particles that provide ˜1% accuracy of the dark matter power spectra P(k) for wave-numbers up to k ˜ 1hMpc-1. Using this large data-set we study the power spectrum covariance matrix. In contrast to many previous analytical and numerical results, we find that the covariance matrix normalised to the power spectrum C(k, k΄)/P(k)P(k΄) has a complex structure of non-diagonal components: an upturn at small k, followed by a minimum at k ≈ 0.1 - 0.2 hMpc-1, and a maximum at k ≈ 0.5 - 0.6 hMpc-1. The normalised covariance matrix strongly evolves with redshift: C(k, k΄)∝δα(t)P(k)P(k΄), where δ is the linear growth factor and α ≈ 1 - 1.25, which indicates that the covariance matrix depends on cosmological parameters. We also show that waves longer than 1h-1Gpc have very little impact on the power spectrum and covariance matrix. This significantly reduces the computational costs and complexity of theoretical predictions: relatively small volume ˜(1h-1Gpc)3 simulations capture the necessary properties of dark matter clustering statistics. As our results also indicate, achieving ˜1% errors in the covariance matrix for k < 0.50 hMpc-1 requires a resolution better than ɛ ˜ 0.5h-1Mpc.

  3. Fabrication of metal-matrix composites and adaptive composites using ultrasonic consolidation process

    International Nuclear Information System (INIS)

    Kong, C.Y.; Soar, R.C.

    2005-01-01

    Ultrasonic consolidation (UC) has been used to embed thermally sensitive and damage intolerant fibres within aluminium matrix structures using high frequency, low amplitude, mechanical vibrations. The UC process can induce plastic flow in the metal foils being bonded, to allow the embedding of fibres at typically 25% of the melting temperature of the base metal and at a fraction of the clamping force when compared to fusion processes. To date, the UC process has successfully embedded Sigma silicon carbide (SiC) fibres, shape memory alloy wires and optical fibres, which are presented in this paper. The eventual aim of this research is targeted at the fabrication of adaptive composite structures having the ability to measure external stimuli and respond by adapting their structure accordingly, through the action of embedded active and passive functional fibres within a freeform fabricated metal-matrix structure. This paper presents the fundamental studies of this research to identify embedding methods and working range for the fabrication of adaptive composite structures. The methods considered have produced embedded fibre specimens in which large amounts of plastic flow have been observed, within the matrix, as it is deformed around the fibres, resulting in fully consolidated specimens without damage to the fibres. The microscopic observation techniques and macroscopic functionality tests confirms that the UC process could be applied to the fabrication of metal-matrix composites and adaptive composites, where fusion techniques are not feasible and where a 'cold' process is necessary

  4. An adaptive Multiplicative Extened Kalman Filter for Attitude Estimation of Marine Satellite Tracking Antenna

    DEFF Research Database (Denmark)

    Wang, Yunlong; Soltani, Mohsen; Hussain, Dil muhammed Akbar

    2016-01-01

    , an adaptive Multiplicative Extended Kalman Filter (MEKF) for attitude estimation of Marine Satellite Tracking Antenna (MSTA) is presented with the measurement noise covariance matrix adjusted according to the norm of accelerometer measurements, which can significantly reduce the slamming influence from waves...

  5. Predicting the required number of training samples. [for remotely sensed image data based on covariance matrix estimate quality criterion of normal distribution

    Science.gov (United States)

    Kalayeh, H. M.; Landgrebe, D. A.

    1983-01-01

    A criterion which measures the quality of the estimate of the covariance matrix of a multivariate normal distribution is developed. Based on this criterion, the necessary number of training samples is predicted. Experimental results which are used as a guide for determining the number of training samples are included. Previously announced in STAR as N82-28109

  6. Explicit Covariance Matrix for Particle Measurement Precision

    CERN Document Server

    Karimäki, Veikko

    1997-01-01

    We derive explicit and precise formulae for 3 by 3 error matrix of the particle transverse momentum, direction and impact parameter. The error matrix elements are expressed as functions of up to fourth order statistical moments of the measured coordinates. The formulae are valid for any curvature and track length in case of negligible multiple scattering.

  7. Universal correlations and power-law tails in financial covariance matrices

    Science.gov (United States)

    Akemann, G.; Fischmann, J.; Vivo, P.

    2010-07-01

    We investigate whether quantities such as the global spectral density or individual eigenvalues of financial covariance matrices can be best modelled by standard random matrix theory or rather by its generalisations displaying power-law tails. In order to generate individual eigenvalue distributions a chopping procedure is devised, which produces a statistical ensemble of asset-price covariances from a single instance of financial data sets. Local results for the smallest eigenvalue and individual spacings are very stable upon reshuffling the time windows and assets. They are in good agreement with the universal Tracy-Widom distribution and Wigner surmise, respectively. This suggests a strong degree of robustness especially in the low-lying sector of the spectra, most relevant for portfolio selections. Conversely, the global spectral density of a single covariance matrix as well as the average over all unfolded nearest-neighbour spacing distributions deviate from standard Gaussian random matrix predictions. The data are in fair agreement with a recently introduced generalised random matrix model, with correlations showing a power-law decay.

  8. Sparse reduced-rank regression with covariance estimation

    KAUST Repository

    Chen, Lisha

    2014-12-08

    Improving the predicting performance of the multiple response regression compared with separate linear regressions is a challenging question. On the one hand, it is desirable to seek model parsimony when facing a large number of parameters. On the other hand, for certain applications it is necessary to take into account the general covariance structure for the errors of the regression model. We assume a reduced-rank regression model and work with the likelihood function with general error covariance to achieve both objectives. In addition we propose to select relevant variables for reduced-rank regression by using a sparsity-inducing penalty, and to estimate the error covariance matrix simultaneously by using a similar penalty on the precision matrix. We develop a numerical algorithm to solve the penalized regression problem. In a simulation study and real data analysis, the new method is compared with two recent methods for multivariate regression and exhibits competitive performance in prediction and variable selection.

  9. Sparse reduced-rank regression with covariance estimation

    KAUST Repository

    Chen, Lisha; Huang, Jianhua Z.

    2014-01-01

    Improving the predicting performance of the multiple response regression compared with separate linear regressions is a challenging question. On the one hand, it is desirable to seek model parsimony when facing a large number of parameters. On the other hand, for certain applications it is necessary to take into account the general covariance structure for the errors of the regression model. We assume a reduced-rank regression model and work with the likelihood function with general error covariance to achieve both objectives. In addition we propose to select relevant variables for reduced-rank regression by using a sparsity-inducing penalty, and to estimate the error covariance matrix simultaneously by using a similar penalty on the precision matrix. We develop a numerical algorithm to solve the penalized regression problem. In a simulation study and real data analysis, the new method is compared with two recent methods for multivariate regression and exhibits competitive performance in prediction and variable selection.

  10. The covariance matrix of neutron spectra used in the REAL 84 exercise

    International Nuclear Information System (INIS)

    Matzke, M.

    1986-08-01

    Covariance matrices of continuous functions are discussed. It is pointed out that the number of non-vanishing eigenvalues corresponds to the number of random variables (parameters) involved in the construction of the continuous functions. The covariance matrices used in the REAL 84 international intercomparison of unfolding methods of neutron spectra are investigated. It is shown that a small rank of these covariance matrices leads to a restriction of the possible solution spectra. (orig.) [de

  11. New perspective in covariance evaluation for nuclear data

    International Nuclear Information System (INIS)

    Kanda, Y.

    1992-01-01

    Methods of nuclear data evaluation have been highly developed during the past decade, especially after introducing the concept of covariance. This makes it utmost important how to evaluate covariance matrices for nuclear data. It can be said that covariance evaluation is just the nuclear data evaluation, because the covariance matrix has quantitatively decisive function in current evaluation methods. The covariance primarily represents experimental uncertainties. However, correlation of individual uncertainties between different data must be taken into account and it can not be conducted without detailed physical considerations on experimental conditions. This procedure depends on the evaluator and the estimated covariance does also. The mathematical properties of the covariance have been intensively discussed. Their physical properties should be studied to apply it to the nuclear data evaluation, and then, in this report, are reviewed to give the base for further development of the covariance application. (orig.)

  12. Multivariate covariance generalized linear models

    DEFF Research Database (Denmark)

    Bonat, W. H.; Jørgensen, Bent

    2016-01-01

    are fitted by using an efficient Newton scoring algorithm based on quasi-likelihood and Pearson estimating functions, using only second-moment assumptions. This provides a unified approach to a wide variety of types of response variables and covariance structures, including multivariate extensions......We propose a general framework for non-normal multivariate data analysis called multivariate covariance generalized linear models, designed to handle multivariate response variables, along with a wide range of temporal and spatial correlation structures defined in terms of a covariance link...... function combined with a matrix linear predictor involving known matrices. The method is motivated by three data examples that are not easily handled by existing methods. The first example concerns multivariate count data, the second involves response variables of mixed types, combined with repeated...

  13. Video based object representation and classification using multiple covariance matrices.

    Science.gov (United States)

    Zhang, Yurong; Liu, Quan

    2017-01-01

    Video based object recognition and classification has been widely studied in computer vision and image processing area. One main issue of this task is to develop an effective representation for video. This problem can generally be formulated as image set representation. In this paper, we present a new method called Multiple Covariance Discriminative Learning (MCDL) for image set representation and classification problem. The core idea of MCDL is to represent an image set using multiple covariance matrices with each covariance matrix representing one cluster of images. Firstly, we use the Nonnegative Matrix Factorization (NMF) method to do image clustering within each image set, and then adopt Covariance Discriminative Learning on each cluster (subset) of images. At last, we adopt KLDA and nearest neighborhood classification method for image set classification. Promising experimental results on several datasets show the effectiveness of our MCDL method.

  14. A three domain covariance framework for EEG/MEG data

    NARCIS (Netherlands)

    Ros, B.P.; Bijma, F.; de Gunst, M.C.M.; de Munck, J.C.

    2015-01-01

    In this paper we introduce a covariance framework for the analysis of single subject EEG and MEG data that takes into account observed temporal stationarity on small time scales and trial-to-trial variations. We formulate a model for the covariance matrix, which is a Kronecker product of three

  15. Treating Sample Covariances for Use in Strongly Coupled Atmosphere-Ocean Data Assimilation

    Science.gov (United States)

    Smith, Polly J.; Lawless, Amos S.; Nichols, Nancy K.

    2018-01-01

    Strongly coupled data assimilation requires cross-domain forecast error covariances; information from ensembles can be used, but limited sampling means that ensemble derived error covariances are routinely rank deficient and/or ill-conditioned and marred by noise. Thus, they require modification before they can be incorporated into a standard assimilation framework. Here we compare methods for improving the rank and conditioning of multivariate sample error covariance matrices for coupled atmosphere-ocean data assimilation. The first method, reconditioning, alters the matrix eigenvalues directly; this preserves the correlation structures but does not remove sampling noise. We show that it is better to recondition the correlation matrix rather than the covariance matrix as this prevents small but dynamically important modes from being lost. The second method, model state-space localization via the Schur product, effectively removes sample noise but can dampen small cross-correlation signals. A combination that exploits the merits of each is found to offer an effective alternative.

  16. Detection of Differential Item Functioning on the Kirton Adaption-Innovation Inventory Using Multiple-Group Mean and Covariance Structure Analyses.

    Science.gov (United States)

    Chan, David

    2000-01-01

    Demonstrates how the mean and covariance structure analysis model of D. Sorbom (1974) can be used to detect uniform and nonuniform differential item functioning (DIF) on polytomous ordered response items assumed to approximate a continuous scale. Uses results from 773 civil service employees administered the Kirton Adaption-Innovation Inventory…

  17. A Small Guide to Generating Covariances of Experimental Data

    International Nuclear Information System (INIS)

    Mannhart, Wolf

    2011-05-01

    A complete description of the uncertainties of an experiment can only be realized by a detailed list of all the uncertainty components, their value and a specification of existing correlations between the data. Based on such information the covariance matrix can be generated, which is necessary for any further proceeding with the experimental data. It is not necessary, and not recommended, that an experimenter evaluates this covariance matrix. The reason for this is that a incorrectly evaluated final covariance matrix can never be corrected if the details are not given. (Such obviously wrong covariance matrices have recently occasionally been found in the literature). Hence quotation of a covariance matrix is an additional step which should not occur without quoting a detailed list of the various uncertainty components and their correlations as well. It must be hoped that editors of journals will understand these necessary requirements. The generalized least squares procedure shown permits an easy way of interchanging data D 0 with parameter estimates P. This means new data can easily be combined with an earlier evaluation. However, it must be mentioned that this is only valid as long as the new data have no correlation with any of the older data of the prior evaluation. Otherwise the old data which show correlation with new data have to be extracted from the evaluation and then, together with the new data and taking account of the correlation, have again to be added to the reduced evaluation. In most cases this step cannot be performed and the evaluation has to be completely redone. A partial way out is given if the evaluation is performed step by step and the results of each step are stored. Then the evaluation need only be repeated from the step which contains correlated data for the first time while all earlier steps remain unchanged. Finally it should be noted that the addition of a small set of new data to a prior evaluation consisting of a large number of

  18. Fast covariance estimation for innovations computed from a spatial Gibbs point process

    DEFF Research Database (Denmark)

    Coeurjolly, Jean-Francois; Rubak, Ege

    In this paper, we derive an exact formula for the covariance of two innovations computed from a spatial Gibbs point process and suggest a fast method for estimating this covariance. We show how this methodology can be used to estimate the asymptotic covariance matrix of the maximum pseudo...

  19. On covariance structure in noisy, big data

    Science.gov (United States)

    Paffenroth, Randy C.; Nong, Ryan; Du Toit, Philip C.

    2013-09-01

    Herein we describe theory and algorithms for detecting covariance structures in large, noisy data sets. Our work uses ideas from matrix completion and robust principal component analysis to detect the presence of low-rank covariance matrices, even when the data is noisy, distorted by large corruptions, and only partially observed. In fact, the ability to handle partial observations combined with ideas from randomized algorithms for matrix decomposition enables us to produce asymptotically fast algorithms. Herein we will provide numerical demonstrations of the methods and their convergence properties. While such methods have applicability to many problems, including mathematical finance, crime analysis, and other large-scale sensor fusion problems, our inspiration arises from applying these methods in the context of cyber network intrusion detection.

  20. Characteristic Polynomials of Sample Covariance Matrices: The Non-Square Case

    OpenAIRE

    Kösters, Holger

    2009-01-01

    We consider the sample covariance matrices of large data matrices which have i.i.d. complex matrix entries and which are non-square in the sense that the difference between the number of rows and the number of columns tends to infinity. We show that the second-order correlation function of the characteristic polynomial of the sample covariance matrix is asymptotically given by the sine kernel in the bulk of the spectrum and by the Airy kernel at the edge of the spectrum. Similar results are g...

  1. Pairwise registration of TLS point clouds using covariance descriptors and a non-cooperative game

    Science.gov (United States)

    Zai, Dawei; Li, Jonathan; Guo, Yulan; Cheng, Ming; Huang, Pengdi; Cao, Xiaofei; Wang, Cheng

    2017-12-01

    It is challenging to automatically register TLS point clouds with noise, outliers and varying overlap. In this paper, we propose a new method for pairwise registration of TLS point clouds. We first generate covariance matrix descriptors with an adaptive neighborhood size from point clouds to find candidate correspondences, we then construct a non-cooperative game to isolate mutual compatible correspondences, which are considered as true positives. The method was tested on three models acquired by two different TLS systems. Experimental results demonstrate that our proposed adaptive covariance (ACOV) descriptor is invariant to rigid transformation and robust to noise and varying resolutions. The average registration errors achieved on three models are 0.46 cm, 0.32 cm and 1.73 cm, respectively. The computational times cost on these models are about 288 s, 184 s and 903 s, respectively. Besides, our registration framework using ACOV descriptors and a game theoretic method is superior to the state-of-the-art methods in terms of both registration error and computational time. The experiment on a large outdoor scene further demonstrates the feasibility and effectiveness of our proposed pairwise registration framework.

  2. Covariant n2-plet mass formulas

    International Nuclear Information System (INIS)

    Davidson, A.

    1979-01-01

    Using a generalized internal symmetry group analogous to the Lorentz group, we have constructed a covariant n 2 -plet mass operator. This operator is built as a scalar matrix in the (n;n*) representation, and its SU(n) breaking parameters are identified as intrinsic boost ones. Its basic properties are: covariance, Hermiticity, positivity, charge conjugation, quark contents, and a self-consistent n 2 -1, 1 mixing. The GMO and the Okubo formulas are obtained by considering two different limits of the same generalized mass formula

  3. Modelling the Covariance Structure in Marginal Multivariate Count Models

    DEFF Research Database (Denmark)

    Bonat, W. H.; Olivero, J.; Grande-Vega, M.

    2017-01-01

    The main goal of this article is to present a flexible statistical modelling framework to deal with multivariate count data along with longitudinal and repeated measures structures. The covariance structure for each response variable is defined in terms of a covariance link function combined...... be used to indicate whether there was statistical evidence of a decline in blue duikers and other species hunted during the study period. Determining whether observed drops in the number of animals hunted are indeed true is crucial to assess whether species depletion effects are taking place in exploited...... with a matrix linear predictor involving known matrices. In order to specify the joint covariance matrix for the multivariate response vector, the generalized Kronecker product is employed. We take into account the count nature of the data by means of the power dispersion function associated with the Poisson...

  4. COVARIANCE ASSISTED SCREENING AND ESTIMATION.

    Science.gov (United States)

    Ke, By Tracy; Jin, Jiashun; Fan, Jianqing

    2014-11-01

    Consider a linear model Y = X β + z , where X = X n,p and z ~ N (0, I n ). The vector β is unknown and it is of interest to separate its nonzero coordinates from the zero ones (i.e., variable selection). Motivated by examples in long-memory time series (Fan and Yao, 2003) and the change-point problem (Bhattacharya, 1994), we are primarily interested in the case where the Gram matrix G = X ' X is non-sparse but sparsifiable by a finite order linear filter. We focus on the regime where signals are both rare and weak so that successful variable selection is very challenging but is still possible. We approach this problem by a new procedure called the Covariance Assisted Screening and Estimation (CASE). CASE first uses a linear filtering to reduce the original setting to a new regression model where the corresponding Gram (covariance) matrix is sparse. The new covariance matrix induces a sparse graph, which guides us to conduct multivariate screening without visiting all the submodels. By interacting with the signal sparsity, the graph enables us to decompose the original problem into many separated small-size subproblems (if only we know where they are!). Linear filtering also induces a so-called problem of information leakage , which can be overcome by the newly introduced patching technique. Together, these give rise to CASE, which is a two-stage Screen and Clean (Fan and Song, 2010; Wasserman and Roeder, 2009) procedure, where we first identify candidates of these submodels by patching and screening , and then re-examine each candidate to remove false positives. For any procedure β̂ for variable selection, we measure the performance by the minimax Hamming distance between the sign vectors of β̂ and β. We show that in a broad class of situations where the Gram matrix is non-sparse but sparsifiable, CASE achieves the optimal rate of convergence. The results are successfully applied to long-memory time series and the change-point model.

  5. Evaluating dynamic covariance matrix forecasting and portfolio optimization

    OpenAIRE

    Sendstad, Lars Hegnes; Holten, Dag Martin

    2012-01-01

    In this thesis we have evaluated the covariance forecasting ability of the simple moving average, the exponential moving average and the dynamic conditional correlation models. Overall we found that a dynamic portfolio can gain significant improvements by implementing a multivariate GARCH forecast. We further divided the global investment universe into sectors and regions in order to investigate the relative portfolio performance of several asset allocation strategies with both variance and c...

  6. Existence and uniqueness of the maximum likelihood estimator for models with a Kronecker product covariance structure

    NARCIS (Netherlands)

    Ros, B.P.; Bijma, F.; de Munck, J.C.; de Gunst, M.C.M.

    2016-01-01

    This paper deals with multivariate Gaussian models for which the covariance matrix is a Kronecker product of two matrices. We consider maximum likelihood estimation of the model parameters, in particular of the covariance matrix. There is no explicit expression for the maximum likelihood estimator

  7. Neutron cross section and covariance data evaluation of experimental data for 27Al

    International Nuclear Information System (INIS)

    Li Chunjuan; Liu Jianfeng; Liu Tingjin

    2006-01-01

    The evaluation of neutron cross section and covariance data for 27 Al in the energy range from 210 keV to 20 MeV was carried out on the basis of the experimental data mainly taken from EXFOR library. After the experimental data and their errors were analyzed, selected and corrected, SPCC code was used to fit the data and merge the covariance matrix. The evaluated neutron cross section data and covariance matrix for 27 Al given can be collected for the evaluated library and also can be used as the basis of theoretical calculation concerned. (authors)

  8. Hamiltonian formalism, quantization and S matrix for supergravity. [S matrix, canonical constraints

    Energy Technology Data Exchange (ETDEWEB)

    Fradkin, E S; Vasiliev, M A [AN SSSR, Moscow. Fizicheskij Inst.

    1977-12-05

    The canonical formalism for supergravity is constructed. The algebra of canonical constraints is found. The correct expression for the S matrix is obtained. Usual 'covariant methods' lead to an incorrect S matrix in supergravity since a new four-particle interaction of ghostfields survives in the Lagrangian expression of the S matrix.

  9. Convergence of the standard RLS method and UDUT factorisation of covariance matrix for solving the algebraic Riccati equation of the DLQR via heuristic approximate dynamic programming

    Science.gov (United States)

    Moraes Rêgo, Patrícia Helena; Viana da Fonseca Neto, João; Ferreira, Ernesto M.

    2015-08-01

    The main focus of this article is to present a proposal to solve, via UDUT factorisation, the convergence and numerical stability problems that are related to the covariance matrix ill-conditioning of the recursive least squares (RLS) approach for online approximations of the algebraic Riccati equation (ARE) solution associated with the discrete linear quadratic regulator (DLQR) problem formulated in the actor-critic reinforcement learning and approximate dynamic programming context. The parameterisations of the Bellman equation, utility function and dynamic system as well as the algebra of Kronecker product assemble a framework for the solution of the DLQR problem. The condition number and the positivity parameter of the covariance matrix are associated with statistical metrics for evaluating the approximation performance of the ARE solution via RLS-based estimators. The performance of RLS approximators is also evaluated in terms of consistence and polarisation when associated with reinforcement learning methods. The used methodology contemplates realisations of online designs for DLQR controllers that is evaluated in a multivariable dynamic system model.

  10. Covariance and sensitivity data generation at ORNL

    International Nuclear Information System (INIS)

    Leal, L. C.; Derrien, H.; Larson, N. M.; Alpan, A.

    2005-01-01

    Covariance data are required to assess uncertainties in design parameters in several nuclear applications. The error estimation of calculated quantities relies on the nuclear data uncertainty information available in the basic nuclear data libraries, such as the US Evaluated Nuclear Data Library, ENDF/B. The uncertainty files in the ENDF/B library are obtained from the analysis of experimental data and are stored as variance and covariance data. In this paper we address the generation of covariance data in the resonance region done with the computer code SAMMY. SAMMY is used in the evaluation of the experimental data in the resolved and unresolved resonance energy regions. The data fitting of cross sections is based on the generalised least-squares formalism (Bayesian theory) together with the resonance formalism described by R-matrix theory. Two approaches are used in SAMMY for the generation of resonance parameter covariance data. In the evaluation process SAMMY generates a set of resonance parameters that fit the data, and, it provides the resonance parameter covariances. For resonance parameter evaluations where there are no resonance parameter covariance data available, the alternative is to use an approach called the 'retroactive' resonance parameter covariance generation. In this paper, we describe the application of the retroactive covariance generation approach for the gadolinium isotopes. (authors)

  11. Parametric number covariance in quantum chaotic spectra.

    Science.gov (United States)

    Vinayak; Kumar, Sandeep; Pandey, Akhilesh

    2016-03-01

    We study spectral parametric correlations in quantum chaotic systems and introduce the number covariance as a measure of such correlations. We derive analytic results for the classical random matrix ensembles using the binary correlation method and obtain compact expressions for the covariance. We illustrate the universality of this measure by presenting the spectral analysis of the quantum kicked rotors for the time-reversal invariant and time-reversal noninvariant cases. A local version of the parametric number variance introduced earlier is also investigated.

  12. Adaptive OFDM Waveform Design for Spatio-Temporal-Sparsity Exploited STAP Radar

    Energy Technology Data Exchange (ETDEWEB)

    Sen, Satyabrata [ORNL

    2017-11-01

    In this chapter, we describe a sparsity-based space-time adaptive processing (STAP) algorithm to detect a slowly moving target using an orthogonal frequency division multiplexing (OFDM) radar. The motivation of employing an OFDM signal is that it improves the target-detectability from the interfering signals by increasing the frequency diversity of the system. However, due to the addition of one extra dimension in terms of frequency, the adaptive degrees-of-freedom in an OFDM-STAP also increases. Therefore, to avoid the construction a fully adaptive OFDM-STAP, we develop a sparsity-based STAP algorithm. We observe that the interference spectrum is inherently sparse in the spatio-temporal domain, as the clutter responses occupy only a diagonal ridge on the spatio-temporal plane and the jammer signals interfere only from a few spatial directions. Hence, we exploit that sparsity to develop an efficient STAP technique that utilizes considerably lesser number of secondary data compared to the other existing STAP techniques, and produces nearly optimum STAP performance. In addition to designing the STAP filter, we optimally design the transmit OFDM signals by maximizing the output signal-to-interference-plus-noise ratio (SINR) in order to improve the STAP performance. The computation of output SINR depends on the estimated value of the interference covariance matrix, which we obtain by applying the sparse recovery algorithm. Therefore, we analytically assess the effects of the synthesized OFDM coefficients on the sparse recovery of the interference covariance matrix by computing the coherence measure of the sparse measurement matrix. Our numerical examples demonstrate the achieved STAP-performance due to sparsity-based technique and adaptive waveform design.

  13. Multi-level restricted maximum likelihood covariance estimation and kriging for large non-gridded spatial datasets

    KAUST Repository

    Castrillon, Julio

    2015-11-10

    We develop a multi-level restricted Gaussian maximum likelihood method for estimating the covariance function parameters and computing the best unbiased predictor. Our approach produces a new set of multi-level contrasts where the deterministic parameters of the model are filtered out thus enabling the estimation of the covariance parameters to be decoupled from the deterministic component. Moreover, the multi-level covariance matrix of the contrasts exhibit fast decay that is dependent on the smoothness of the covariance function. Due to the fast decay of the multi-level covariance matrix coefficients only a small set is computed with a level dependent criterion. We demonstrate our approach on problems of up to 512,000 observations with a Matérn covariance function and highly irregular placements of the observations. In addition, these problems are numerically unstable and hard to solve with traditional methods.

  14. Resonance Region Covariance Analysis Method and New Covariance Data for Th-232, U-233, U-235, U-238, and Pu-239

    International Nuclear Information System (INIS)

    Leal, Luiz C.; Arbanas, Goran; Derrien, Herve; Wiarda, Dorothea

    2008-01-01

    Resonance-parameter covariance matrix (RPCM) evaluations in the resolved resonance region were done for 232Th, 233U, 235U, 238U, and 239Pu using the computer code SAMMY. The retroactive approach of the code SAMMY was used to generate the RPCMs for 233U, 235U. RPCMs for 232Th, 238U and 239Pu were generated together with the resonance parameter evaluations. The RPCMs were then converted in the ENDF format using the FILE32 representation. Alternatively, for computer storage reasons, the FILE32 was converted in the FILE33 cross section covariance matrix (CSCM). Both representations were processed using the computer code PUFF-IV. This paper describes the procedures used to generate the RPCM with SAMMY.

  15. More on Estimation of Banded and Banded Toeplitz Covariance Matrices

    OpenAIRE

    Berntsson, Fredrik; Ohlson, Martin

    2017-01-01

    In this paper we consider two different linear covariance structures, e.g., banded and bended Toeplitz, and how to estimate them using different methods, e.g., by minimizing different norms. One way to estimate the parameters in a linear covariance structure is to use tapering, which has been shown to be the solution to a universal least squares problem. We know that tapering not always guarantee the positive definite constraints on the estimated covariance matrix and may not be a suitable me...

  16. Neutron cross section and covariance data evaluation of experimental data for {sup 27}Al

    Energy Technology Data Exchange (ETDEWEB)

    Chunjuan, Li; Jianfeng, Liu [Physics Department , Zhengzhou Univ., Zhengzhou (China); Tingjin, Liu [China Nuclear Data Center, China Inst. of Atomic Energy, Beijing (China)

    2006-07-15

    The evaluation of neutron cross section and covariance data for {sup 27}Al in the energy range from 210 keV to 20 MeV was carried out on the basis of the experimental data mainly taken from EXFOR library. After the experimental data and their errors were analyzed, selected and corrected, SPCC code was used to fit the data and merge the covariance matrix. The evaluated neutron cross section data and covariance matrix for {sup 27}Al given can be collected for the evaluated library and also can be used as the basis of theoretical calculation concerned. (authors)

  17. PUFF-III: A Code for Processing ENDF Uncertainty Data Into Multigroup Covariance Matrices

    International Nuclear Information System (INIS)

    Dunn, M.E.

    2000-01-01

    PUFF-III is an extension of the previous PUFF-II code that was developed in the 1970s and early 1980s. The PUFF codes process the Evaluated Nuclear Data File (ENDF) covariance data and generate multigroup covariance matrices on a user-specified energy grid structure. Unlike its predecessor, PUFF-III can process the new ENDF/B-VI data formats. In particular, PUFF-III has the capability to process the spontaneous fission covariances for fission neutron multiplicity. With regard to the covariance data in File 33 of the ENDF system, PUFF-III has the capability to process short-range variance formats, as well as the lumped reaction covariance data formats that were introduced in ENDF/B-V. In addition to the new ENDF formats, a new directory feature is now available that allows the user to obtain a detailed directory of the uncertainty information in the data files without visually inspecting the ENDF data. Following the correlation matrix calculation, PUFF-III also evaluates the eigenvalues of each correlation matrix and tests each matrix for positive definiteness. Additional new features are discussed in the manual. PUFF-III has been developed for implementation in the AMPX code system, and several modifications were incorporated to improve memory allocation tasks and input/output operations. Consequently, the resulting code has a structure that is similar to other modules in the AMPX code system. With the release of PUFF-III, a new and improved covariance processing code is available to process ENDF covariance formats through Version VI

  18. Group covariance and metrical theory

    International Nuclear Information System (INIS)

    Halpern, L.

    1983-01-01

    The a priori introduction of a Lie group of transformations into a physical theory has often proved to be useful; it usually serves to describe special simplified conditions before a general theory can be worked out. Newton's assumptions of absolute space and time are examples where the Euclidian group and translation group have been introduced. These groups were extended to the Galilei group and modified in the special theory of relativity to the Poincare group to describe physics under the given conditions covariantly in the simplest way. The criticism of the a priori character leads to the formulation of the general theory of relativity. The general metric theory does not really give preference to a particular invariance group - even the principle of equivalence can be adapted to a whole family of groups. The physical laws covariantly inserted into the metric space are however adapted to the Poincare group. 8 references

  19. Gaussian covariance graph models accounting for correlated marker effects in genome-wide prediction.

    Science.gov (United States)

    Martínez, C A; Khare, K; Rahman, S; Elzo, M A

    2017-10-01

    Several statistical models used in genome-wide prediction assume uncorrelated marker allele substitution effects, but it is known that these effects may be correlated. In statistics, graphical models have been identified as a useful tool for covariance estimation in high-dimensional problems and it is an area that has recently experienced a great expansion. In Gaussian covariance graph models (GCovGM), the joint distribution of a set of random variables is assumed to be Gaussian and the pattern of zeros of the covariance matrix is encoded in terms of an undirected graph G. In this study, methods adapting the theory of GCovGM to genome-wide prediction were developed (Bayes GCov, Bayes GCov-KR and Bayes GCov-H). In simulated data sets, improvements in correlation between phenotypes and predicted breeding values and accuracies of predicted breeding values were found. Our models account for correlation of marker effects and permit to accommodate general structures as opposed to models proposed in previous studies, which consider spatial correlation only. In addition, they allow incorporation of biological information in the prediction process through its use when constructing graph G, and their extension to the multi-allelic loci case is straightforward. © 2017 Blackwell Verlag GmbH.

  20. The application of sparse estimation of covariance matrix to quadratic discriminant analysis

    OpenAIRE

    Sun, Jiehuan; Zhao, Hongyu

    2015-01-01

    Background Although Linear Discriminant Analysis (LDA) is commonly used for classification, it may not be directly applied in genomics studies due to the large p, small n problem in these studies. Different versions of sparse LDA have been proposed to address this significant challenge. One implicit assumption of various LDA-based methods is that the covariance matrices are the same across different classes. However, rewiring of genetic networks (therefore different covariance matrices) acros...

  1. Updated Covariance Processing Capabilities in the AMPX Code System

    International Nuclear Information System (INIS)

    Wiarda, Dorothea; Dunn, Michael E.

    2007-01-01

    A concerted effort is in progress within the nuclear data community to provide new cross-section covariance data evaluations to support sensitivity/uncertainty analyses of fissionable systems. The objective of this work is to update processing capabilities of the AMPX library to process the latest Evaluated Nuclear Data File (ENDF)/B formats to generate covariance data libraries for radiation transport software such as SCALE. The module PUFF-IV was updated to allow processing of new ENDF covariance formats in the resolved resonance region. In the resolved resonance region, covariance matrices are given in terms of resonance parameters, which need to be processed into covariance matrices with respect to the group-averaged cross-section data. The parameter covariance matrix can be quite large if the evaluation has many resonances. The PUFF-IV code has recently been used to process an evaluation of 235U, which was prepared in collaboration between Oak Ridge National Laboratory and Los Alamos National Laboratory.

  2. A multivariate multilevel Gaussian model with a mixed effects structure in the mean and covariance part.

    Science.gov (United States)

    Li, Baoyue; Bruyneel, Luk; Lesaffre, Emmanuel

    2014-05-20

    A traditional Gaussian hierarchical model assumes a nested multilevel structure for the mean and a constant variance at each level. We propose a Bayesian multivariate multilevel factor model that assumes a multilevel structure for both the mean and the covariance matrix. That is, in addition to a multilevel structure for the mean we also assume that the covariance matrix depends on covariates and random effects. This allows to explore whether the covariance structure depends on the values of the higher levels and as such models heterogeneity in the variances and correlation structure of the multivariate outcome across the higher level values. The approach is applied to the three-dimensional vector of burnout measurements collected on nurses in a large European study to answer the research question whether the covariance matrix of the outcomes depends on recorded system-level features in the organization of nursing care, but also on not-recorded factors that vary with countries, hospitals, and nursing units. Simulations illustrate the performance of our modeling approach. Copyright © 2013 John Wiley & Sons, Ltd.

  3. Fast Adaptive Blind MMSE Equalizer for Multichannel FIR Systems

    Directory of Open Access Journals (Sweden)

    Abed-Meraim Karim

    2006-01-01

    Full Text Available We propose a new blind minimum mean square error (MMSE equalization algorithm of noisy multichannel finite impulse response (FIR systems, that relies only on second-order statistics. The proposed algorithm offers two important advantages: a low computational complexity and a relative robustness against channel order overestimation errors. Exploiting the fact that the columns of the equalizer matrix filter belong both to the signal subspace and to the kernel of truncated data covariance matrix, the proposed algorithm achieves blindly a direct estimation of the zero-delay MMSE equalizer parameters. We develop a two-step procedure to further improve the performance gain and control the equalization delay. An efficient fast adaptive implementation of our equalizer, based on the projection approximation and the shift invariance property of temporal data covariance matrix, is proposed for reducing the computational complexity from to , where is the number of emitted signals, the data vector length, and the dimension of the signal subspace. We then derive a statistical performance analysis to compare the equalization performance with that of the optimal MMSE equalizer. Finally, simulation results are provided to illustrate the effectiveness of the proposed blind equalization algorithm.

  4. Optimization of MIMO Systems Capacity Using Large Random Matrix Methods

    Directory of Open Access Journals (Sweden)

    Philippe Loubaton

    2012-11-01

    Full Text Available This paper provides a comprehensive introduction of large random matrix methods for input covariance matrix optimization of mutual information of MIMO systems. It is first recalled informally how large system approximations of mutual information can be derived. Then, the optimization of the approximations is discussed, and important methodological points that are not necessarily covered by the existing literature are addressed, including the strict concavity of the approximation, the structure of the argument of its maximum, the accuracy of the large system approach with regard to the number of antennas, or the justification of iterative water-filling optimization algorithms. While the existing papers have developed methods adapted to a specific model, this contribution tries to provide a unified view of the large system approximation approach.

  5. PENERAPAN METODE LEAST MEDIAN SQUARE-MINIMUM COVARIANCE DETERMINANT (LMS-MCD DALAM REGRESI KOMPONEN UTAMA

    Directory of Open Access Journals (Sweden)

    I PUTU EKA IRAWAN

    2013-11-01

    Full Text Available Principal Component Regression is a method to overcome multicollinearity techniques by combining principal component analysis with regression analysis. The calculation of classical principal component analysis is based on the regular covariance matrix. The covariance matrix is optimal if the data originated from a multivariate normal distribution, but is very sensitive to the presence of outliers. Alternatives are used to overcome this problem the method of Least Median Square-Minimum Covariance Determinant (LMS-MCD. The purpose of this research is to conduct a comparison between Principal Component Regression (RKU and Method of Least Median Square - Minimum Covariance Determinant (LMS-MCD in dealing with outliers. In this study, Method of Least Median Square - Minimum Covariance Determinant (LMS-MCD has a bias and mean square error (MSE is smaller than the parameter RKU. Based on the difference of parameter estimators, still have a test that has a difference of parameter estimators method LMS-MCD greater than RKU method.

  6. The Covariance Adjustment Approaches for Combining Incomparable Cox Regressions Caused by Unbalanced Covariates Adjustment: A Multivariate Meta-Analysis Study

    Directory of Open Access Journals (Sweden)

    Tania Dehesh

    2015-01-01

    Full Text Available Background. Univariate meta-analysis (UM procedure, as a technique that provides a single overall result, has become increasingly popular. Neglecting the existence of other concomitant covariates in the models leads to loss of treatment efficiency. Our aim was proposing four new approximation approaches for the covariance matrix of the coefficients, which is not readily available for the multivariate generalized least square (MGLS method as a multivariate meta-analysis approach. Methods. We evaluated the efficiency of four new approaches including zero correlation (ZC, common correlation (CC, estimated correlation (EC, and multivariate multilevel correlation (MMC on the estimation bias, mean square error (MSE, and 95% probability coverage of the confidence interval (CI in the synthesis of Cox proportional hazard models coefficients in a simulation study. Result. Comparing the results of the simulation study on the MSE, bias, and CI of the estimated coefficients indicated that MMC approach was the most accurate procedure compared to EC, CC, and ZC procedures. The precision ranking of the four approaches according to all above settings was MMC ≥ EC ≥ CC ≥ ZC. Conclusion. This study highlights advantages of MGLS meta-analysis on UM approach. The results suggested the use of MMC procedure to overcome the lack of information for having a complete covariance matrix of the coefficients.

  7. The Covariance Adjustment Approaches for Combining Incomparable Cox Regressions Caused by Unbalanced Covariates Adjustment: A Multivariate Meta-Analysis Study.

    Science.gov (United States)

    Dehesh, Tania; Zare, Najaf; Ayatollahi, Seyyed Mohammad Taghi

    2015-01-01

    Univariate meta-analysis (UM) procedure, as a technique that provides a single overall result, has become increasingly popular. Neglecting the existence of other concomitant covariates in the models leads to loss of treatment efficiency. Our aim was proposing four new approximation approaches for the covariance matrix of the coefficients, which is not readily available for the multivariate generalized least square (MGLS) method as a multivariate meta-analysis approach. We evaluated the efficiency of four new approaches including zero correlation (ZC), common correlation (CC), estimated correlation (EC), and multivariate multilevel correlation (MMC) on the estimation bias, mean square error (MSE), and 95% probability coverage of the confidence interval (CI) in the synthesis of Cox proportional hazard models coefficients in a simulation study. Comparing the results of the simulation study on the MSE, bias, and CI of the estimated coefficients indicated that MMC approach was the most accurate procedure compared to EC, CC, and ZC procedures. The precision ranking of the four approaches according to all above settings was MMC ≥ EC ≥ CC ≥ ZC. This study highlights advantages of MGLS meta-analysis on UM approach. The results suggested the use of MMC procedure to overcome the lack of information for having a complete covariance matrix of the coefficients.

  8. Covariate analysis of bivariate survival data

    Energy Technology Data Exchange (ETDEWEB)

    Bennett, L.E.

    1992-01-01

    The methods developed are used to analyze the effects of covariates on bivariate survival data when censoring and ties are present. The proposed method provides models for bivariate survival data that include differential covariate effects and censored observations. The proposed models are based on an extension of the univariate Buckley-James estimators which replace censored data points by their expected values, conditional on the censoring time and the covariates. For the bivariate situation, it is necessary to determine the expectation of the failure times for one component conditional on the failure or censoring time of the other component. Two different methods have been developed to estimate these expectations. In the semiparametric approach these expectations are determined from a modification of Burke's estimate of the bivariate empirical survival function. In the parametric approach censored data points are also replaced by their conditional expected values where the expected values are determined from a specified parametric distribution. The model estimation will be based on the revised data set, comprised of uncensored components and expected values for the censored components. The variance-covariance matrix for the estimated covariate parameters has also been derived for both the semiparametric and parametric methods. Data from the Demographic and Health Survey was analyzed by these methods. The two outcome variables are post-partum amenorrhea and breastfeeding; education and parity were used as the covariates. Both the covariate parameter estimates and the variance-covariance estimates for the semiparametric and parametric models will be compared. In addition, a multivariate test statistic was used in the semiparametric model to examine contrasts. The significance of the statistic was determined from a bootstrap distribution of the test statistic.

  9. Are Low-order Covariance Estimates Useful in Error Analyses?

    Science.gov (United States)

    Baker, D. F.; Schimel, D.

    2005-12-01

    Atmospheric trace gas inversions, using modeled atmospheric transport to infer surface sources and sinks from measured concentrations, are most commonly done using least-squares techniques that return not only an estimate of the state (the surface fluxes) but also the covariance matrix describing the uncertainty in that estimate. Besides allowing one to place error bars around the estimate, the covariance matrix may be used in simulation studies to learn what uncertainties would be expected from various hypothetical observing strategies. This error analysis capability is routinely used in designing instrumentation, measurement campaigns, and satellite observing strategies. For example, Rayner, et al (2002) examined the ability of satellite-based column-integrated CO2 measurements to constrain monthly-average CO2 fluxes for about 100 emission regions using this approach. Exact solutions for both state vector and covariance matrix become computationally infeasible, however, when the surface fluxes are solved at finer resolution (e.g., daily in time, under 500 km in space). It is precisely at these finer scales, however, that one would hope to be able to estimate fluxes using high-density satellite measurements. Non-exact estimation methods such as variational data assimilation or the ensemble Kalman filter could be used, but they achieve their computational savings by obtaining an only approximate state estimate and a low-order approximation of the true covariance. One would like to be able to use this covariance matrix to do the same sort of error analyses as are done with the full-rank covariance, but is it correct to do so? Here we compare uncertainties and `information content' derived from full-rank covariance matrices obtained from a direct, batch least squares inversion to those from the incomplete-rank covariance matrices given by a variational data assimilation approach solved with a variable metric minimization technique (the Broyden-Fletcher- Goldfarb

  10. Eigenvalue-eigenvector decomposition (EED) analysis of dissimilarity and covariance matrix obtained from total synchronous fluorescence spectral (TSFS) data sets of herbal preparations: Optimizing the classification approach

    Science.gov (United States)

    Tarai, Madhumita; Kumar, Keshav; Divya, O.; Bairi, Partha; Mishra, Kishor Kumar; Mishra, Ashok Kumar

    2017-09-01

    The present work compares the dissimilarity and covariance based unsupervised chemometric classification approaches by taking the total synchronous fluorescence spectroscopy data sets acquired for the cumin and non-cumin based herbal preparations. The conventional decomposition method involves eigenvalue-eigenvector analysis of the covariance of the data set and finds the factors that can explain the overall major sources of variation present in the data set. The conventional approach does this irrespective of the fact that the samples belong to intrinsically different groups and hence leads to poor class separation. The present work shows that classification of such samples can be optimized by performing the eigenvalue-eigenvector decomposition on the pair-wise dissimilarity matrix.

  11. Covariance Estimation and Autocorrelation of NORAD Two-Line Element Sets

    National Research Council Canada - National Science Library

    Osweiler, Victor P

    2006-01-01

    This thesis investigates NORAD two-line element sets (TLE) containing satellite mean orbital elements for the purpose of estimating a covariance matrix and formulating an autocorrelation relationship...

  12. Optimal covariate designs theory and applications

    CERN Document Server

    Das, Premadhis; Mandal, Nripes Kumar; Sinha, Bikas Kumar

    2015-01-01

    This book primarily addresses the optimality aspects of covariate designs. A covariate model is a combination of ANOVA and regression models. Optimal estimation of the parameters of the model using a suitable choice of designs is of great importance; as such choices allow experimenters to extract maximum information for the unknown model parameters. The main emphasis of this monograph is to start with an assumed covariate model in combination with some standard ANOVA set-ups such as CRD, RBD, BIBD, GDD, BTIBD, BPEBD, cross-over, multi-factor, split-plot and strip-plot designs, treatment control designs, etc. and discuss the nature and availability of optimal covariate designs. In some situations, optimal estimations of both ANOVA and the regression parameters are provided. Global optimality and D-optimality criteria are mainly used in selecting the design. The standard optimality results of both discrete and continuous set-ups have been adapted, and several novel combinatorial techniques have been applied for...

  13. Extended covariance data formats for the ENDF/B-VI differential data evaluation

    International Nuclear Information System (INIS)

    Peelle, R.W.; Muir, D.W.

    1988-01-01

    The ENDF/B-V included cross section covariance data, but covariances could not be encoded for all the important data types. New ENDF-6 covariance formats are outlined including those for cross-file (MF) covariances, resonance parameters over the whole range, and secondary energy and angle distributions. One ''late entry'' format encodes covariance data for cross sections that are output from model or fitting codes in terms of the model parameter covariance matrix and the tabulated derivatives of cross sections with respect to the model parameters. Another new format yields multigroup cross section variances that increase as the group width decreases. When evaluators use the new formats, the files can be processed and used for improved uncertainty propagation and data combination. 22 refs

  14. Ellipsoids and matrix-valued valuations

    OpenAIRE

    Ludwig, Monika

    2003-01-01

    We obtain a classification of Borel measurable, GL(n) covariant, symmetric-matrix-valued valuations on the space of n-dimensional convex polytopes. The only ones turn out to be the moment matrix corresponding to the classical Legendre ellipsoid and the matrix corresponding to the ellipsoid recently discovered by E. Lutwak, D. Yang, and G. Zhang.

  15. Matrix algebra for higher order moments

    NARCIS (Netherlands)

    Meijer, Erik

    2005-01-01

    A large part of statistics is devoted to the estimation of models from the sample covariance matrix. The development of the statistical theory and estimators has been greatly facilitated by the introduction of special matrices, such as the commutation matrix and the duplication matrix, and the

  16. Bayes Factor Covariance Testing in Item Response Models.

    Science.gov (United States)

    Fox, Jean-Paul; Mulder, Joris; Sinharay, Sandip

    2017-12-01

    Two marginal one-parameter item response theory models are introduced, by integrating out the latent variable or random item parameter. It is shown that both marginal response models are multivariate (probit) models with a compound symmetry covariance structure. Several common hypotheses concerning the underlying covariance structure are evaluated using (fractional) Bayes factor tests. The support for a unidimensional factor (i.e., assumption of local independence) and differential item functioning are evaluated by testing the covariance components. The posterior distribution of common covariance components is obtained in closed form by transforming latent responses with an orthogonal (Helmert) matrix. This posterior distribution is defined as a shifted-inverse-gamma, thereby introducing a default prior and a balanced prior distribution. Based on that, an MCMC algorithm is described to estimate all model parameters and to compute (fractional) Bayes factor tests. Simulation studies are used to show that the (fractional) Bayes factor tests have good properties for testing the underlying covariance structure of binary response data. The method is illustrated with two real data studies.

  17. COVAR: Computer Program for Multifactor Relative Risks and Tests of Hypotheses Using a Variance-Covariance Matrix from Linear and Log-Linear Regression

    Directory of Open Access Journals (Sweden)

    Leif E. Peterson

    1997-11-01

    Full Text Available A computer program for multifactor relative risks, confidence limits, and tests of hypotheses using regression coefficients and a variance-covariance matrix obtained from a previous additive or multiplicative regression analysis is described in detail. Data used by the program can be stored and input from an external disk-file or entered via the keyboard. The output contains a list of the input data, point estimates of single or joint effects, confidence intervals and tests of hypotheses based on a minimum modified chi-square statistic. Availability of the program is also discussed.

  18. Covariance specification and estimation to improve top-down Green House Gas emission estimates

    Science.gov (United States)

    Ghosh, S.; Lopez-Coto, I.; Prasad, K.; Whetstone, J. R.

    2015-12-01

    The National Institute of Standards and Technology (NIST) operates the North-East Corridor (NEC) project and the Indianapolis Flux Experiment (INFLUX) in order to develop measurement methods to quantify sources of Greenhouse Gas (GHG) emissions as well as their uncertainties in urban domains using a top down inversion method. Top down inversion updates prior knowledge using observations in a Bayesian way. One primary consideration in a Bayesian inversion framework is the covariance structure of (1) the emission prior residuals and (2) the observation residuals (i.e. the difference between observations and model predicted observations). These covariance matrices are respectively referred to as the prior covariance matrix and the model-data mismatch covariance matrix. It is known that the choice of these covariances can have large effect on estimates. The main objective of this work is to determine the impact of different covariance models on inversion estimates and their associated uncertainties in urban domains. We use a pseudo-data Bayesian inversion framework using footprints (i.e. sensitivities of tower measurements of GHGs to surface emissions) and emission priors (based on Hestia project to quantify fossil-fuel emissions) to estimate posterior emissions using different covariance schemes. The posterior emission estimates and uncertainties are compared to the hypothetical truth. We find that, if we correctly specify spatial variability and spatio-temporal variability in prior and model-data mismatch covariances respectively, then we can compute more accurate posterior estimates. We discuss few covariance models to introduce space-time interacting mismatches along with estimation of the involved parameters. We then compare several candidate prior spatial covariance models from the Matern covariance class and estimate their parameters with specified mismatches. We find that best-fitted prior covariances are not always best in recovering the truth. To achieve

  19. Cross-covariance functions for multivariate geostatistics

    KAUST Repository

    Genton, Marc G.

    2015-05-01

    Continuously indexed datasets with multiple variables have become ubiquitous in the geophysical, ecological, environmental and climate sciences, and pose substantial analysis challenges to scientists and statisticians. For many years, scientists developed models that aimed at capturing the spatial behavior for an individual process; only within the last few decades has it become commonplace to model multiple processes jointly. The key difficulty is in specifying the cross-covariance function, that is, the function responsible for the relationship between distinct variables. Indeed, these cross-covariance functions must be chosen to be consistent with marginal covariance functions in such a way that the second-order structure always yields a nonnegative definite covariance matrix. We review the main approaches to building cross-covariance models, including the linear model of coregionalization, convolution methods, the multivariate Matérn and nonstationary and space-time extensions of these among others. We additionally cover specialized constructions, including those designed for asymmetry, compact support and spherical domains, with a review of physics-constrained models. We illustrate select models on a bivariate regional climate model output example for temperature and pressure, along with a bivariate minimum and maximum temperature observational dataset; we compare models by likelihood value as well as via cross-validation co-kriging studies. The article closes with a discussion of unsolved problems. © Institute of Mathematical Statistics, 2015.

  20. Cross-covariance functions for multivariate geostatistics

    KAUST Repository

    Genton, Marc G.; Kleiber, William

    2015-01-01

    Continuously indexed datasets with multiple variables have become ubiquitous in the geophysical, ecological, environmental and climate sciences, and pose substantial analysis challenges to scientists and statisticians. For many years, scientists developed models that aimed at capturing the spatial behavior for an individual process; only within the last few decades has it become commonplace to model multiple processes jointly. The key difficulty is in specifying the cross-covariance function, that is, the function responsible for the relationship between distinct variables. Indeed, these cross-covariance functions must be chosen to be consistent with marginal covariance functions in such a way that the second-order structure always yields a nonnegative definite covariance matrix. We review the main approaches to building cross-covariance models, including the linear model of coregionalization, convolution methods, the multivariate Matérn and nonstationary and space-time extensions of these among others. We additionally cover specialized constructions, including those designed for asymmetry, compact support and spherical domains, with a review of physics-constrained models. We illustrate select models on a bivariate regional climate model output example for temperature and pressure, along with a bivariate minimum and maximum temperature observational dataset; we compare models by likelihood value as well as via cross-validation co-kriging studies. The article closes with a discussion of unsolved problems. © Institute of Mathematical Statistics, 2015.

  1. STEADY ESTIMATION ALGORITHMS OF THE DYNAMIC SYSTEMS CONDITION ON THE BASIS OF CONCEPTS OF THE ADAPTIVE FILTRATION AND CONTROL

    Directory of Open Access Journals (Sweden)

    H.Z. Igamberdiyev

    2014-07-01

    Full Text Available Dynamic systems condition estimation regularization algorithms in the conditions of signals and hindrances statistical characteristics aprioristic uncertainty are offered. Regular iterative algorithms of strengthening matrix factor elements of the Kalman filter, allowing to adapt the filter to changing hindrance-alarm conditions are developed. Steady adaptive estimation algorithms of a condition vector in the aprioristic uncertainty conditions of covariance matrixes of object noise and the measurements hindrances providing a certain roughness of filtration process in relation to changing statistical characteristics of signals information parameters are offered. Offered practical realization results of the dynamic systems condition estimation algorithms are given at the adaptive management systems synthesis problems solution by technological processes of granulation drying of an ammophos pulp and receiving ammonia.

  2. Positive Semidefinite Integrated Covariance Estimation, Factorizations and Asynchronicity

    DEFF Research Database (Denmark)

    Boudt, Kris; Laurent, Sébastien; Lunde, Asger

    An estimator of the ex-post covariation of log-prices under asynchronicity and microstructure noise is proposed. It uses the Cholesky factorization on the correlation matrix in order to exploit the heterogeneity in trading intensity to estimate the different parameters sequentially with as many...

  3. Eigenvalue-eigenvector decomposition (EED) analysis of dissimilarity and covariance matrix obtained from total synchronous fluorescence spectral (TSFS) data sets of herbal preparations: Optimizing the classification approach.

    Science.gov (United States)

    Tarai, Madhumita; Kumar, Keshav; Divya, O; Bairi, Partha; Mishra, Kishor Kumar; Mishra, Ashok Kumar

    2017-09-05

    The present work compares the dissimilarity and covariance based unsupervised chemometric classification approaches by taking the total synchronous fluorescence spectroscopy data sets acquired for the cumin and non-cumin based herbal preparations. The conventional decomposition method involves eigenvalue-eigenvector analysis of the covariance of the data set and finds the factors that can explain the overall major sources of variation present in the data set. The conventional approach does this irrespective of the fact that the samples belong to intrinsically different groups and hence leads to poor class separation. The present work shows that classification of such samples can be optimized by performing the eigenvalue-eigenvector decomposition on the pair-wise dissimilarity matrix. Copyright © 2017 Elsevier B.V. All rights reserved.

  4. The Joint Adaptive Kalman Filter (JAKF) for Vehicle Motion State Estimation.

    Science.gov (United States)

    Gao, Siwei; Liu, Yanheng; Wang, Jian; Deng, Weiwen; Oh, Heekuck

    2016-07-16

    This paper proposes a multi-sensory Joint Adaptive Kalman Filter (JAKF) through extending innovation-based adaptive estimation (IAE) to estimate the motion state of the moving vehicles ahead. JAKF views Lidar and Radar data as the source of the local filters, which aims to adaptively adjust the measurement noise variance-covariance (V-C) matrix 'R' and the system noise V-C matrix 'Q'. Then, the global filter uses R to calculate the information allocation factor 'β' for data fusion. Finally, the global filter completes optimal data fusion and feeds back to the local filters to improve the measurement accuracy of the local filters. Extensive simulation and experimental results show that the JAKF has better adaptive ability and fault tolerance. JAKF enables one to bridge the gap of the accuracy difference of various sensors to improve the integral filtering effectivity. If any sensor breaks down, the filtered results of JAKF still can maintain a stable convergence rate. Moreover, the JAKF outperforms the conventional Kalman filter (CKF) and the innovation-based adaptive Kalman filter (IAKF) with respect to the accuracy of displacement, velocity, and acceleration, respectively.

  5. Numerical Differentiation Methods for Computing Error Covariance Matrices in Item Response Theory Modeling: An Evaluation and a New Proposal

    Science.gov (United States)

    Tian, Wei; Cai, Li; Thissen, David; Xin, Tao

    2013-01-01

    In item response theory (IRT) modeling, the item parameter error covariance matrix plays a critical role in statistical inference procedures. When item parameters are estimated using the EM algorithm, the parameter error covariance matrix is not an automatic by-product of item calibration. Cai proposed the use of Supplemented EM algorithm for…

  6. Threat Object Detection using Covariance Matrix Modeling in X-ray Images

    International Nuclear Information System (INIS)

    Jeon, Byoun Gil; Kim, Jong Yul; Moon, Myung Kook

    2016-01-01

    The X-ray imaging system for the aviation security is one of the applications. In airports, all passengers and properties should be inspected and accepted by security machines before boarding on aircrafts to avoid all treat factors. That treat factors might be directly connected on terrorist threats awfully hazardous to not only passengers but also people in highly populated area such as major cities or buildings. Because the performance of the system is increasing along with the growth of IT technology, information that has various type and good quality can be provided for security check. However, human factors are mainly affected on the inspections. It means that human inspectors should be proficient corresponding to the growth of technology for efficient and effective inspection but there is clear limit of proficiency. Human being is not a computer. Because of the limitation, the aviation security techniques have the tendencies to provide not only numerous and nice information but also effective assistance for security inspectors. Many image processing applications already have been developed to provide efficient assistance for the security systems. Naturally, the security check procedure should not be altered by automatic software because it's not guaranteed that the automatic system will never make any mistake. This paper addressed an application of threat object detection using the covariance matrix modeling. The algorithm is implemented in MATLAB environment and evaluated the performance by comparing with other detection algorithms. Considering the shape of an object on an image is changed by the attitude of that to the imaging machine, the implemented detector has the robustness for rotation and scale of an object

  7. Threat Object Detection using Covariance Matrix Modeling in X-ray Images

    Energy Technology Data Exchange (ETDEWEB)

    Jeon, Byoun Gil; Kim, Jong Yul; Moon, Myung Kook [KAERI, Daejeon (Korea, Republic of)

    2016-05-15

    The X-ray imaging system for the aviation security is one of the applications. In airports, all passengers and properties should be inspected and accepted by security machines before boarding on aircrafts to avoid all treat factors. That treat factors might be directly connected on terrorist threats awfully hazardous to not only passengers but also people in highly populated area such as major cities or buildings. Because the performance of the system is increasing along with the growth of IT technology, information that has various type and good quality can be provided for security check. However, human factors are mainly affected on the inspections. It means that human inspectors should be proficient corresponding to the growth of technology for efficient and effective inspection but there is clear limit of proficiency. Human being is not a computer. Because of the limitation, the aviation security techniques have the tendencies to provide not only numerous and nice information but also effective assistance for security inspectors. Many image processing applications already have been developed to provide efficient assistance for the security systems. Naturally, the security check procedure should not be altered by automatic software because it's not guaranteed that the automatic system will never make any mistake. This paper addressed an application of threat object detection using the covariance matrix modeling. The algorithm is implemented in MATLAB environment and evaluated the performance by comparing with other detection algorithms. Considering the shape of an object on an image is changed by the attitude of that to the imaging machine, the implemented detector has the robustness for rotation and scale of an object.

  8. The application of sparse estimation of covariance matrix to quadratic discriminant analysis.

    Science.gov (United States)

    Sun, Jiehuan; Zhao, Hongyu

    2015-02-18

    Although Linear Discriminant Analysis (LDA) is commonly used for classification, it may not be directly applied in genomics studies due to the large p, small n problem in these studies. Different versions of sparse LDA have been proposed to address this significant challenge. One implicit assumption of various LDA-based methods is that the covariance matrices are the same across different classes. However, rewiring of genetic networks (therefore different covariance matrices) across different diseases has been observed in many genomics studies, which suggests that LDA and its variations may be suboptimal for disease classifications. However, it is not clear whether considering differing genetic networks across diseases can improve classification in genomics studies. We propose a sparse version of Quadratic Discriminant Analysis (SQDA) to explicitly consider the differences of the genetic networks across diseases. Both simulation and real data analysis are performed to compare the performance of SQDA with six commonly used classification methods. SQDA provides more accurate classification results than other methods for both simulated and real data. Our method should prove useful for classification in genomics studies and other research settings, where covariances differ among classes.

  9. The count variance-covariance matrix in a critical reactor

    International Nuclear Information System (INIS)

    Carloni, F.; Giovannini, R.

    1984-01-01

    The present paper deals with a critical reactor containing a set of neutron detectors operating one at time in different time intervals. The analysis makes use of the Kolmogorov backward formalism for the branching processes, in the framework of the one-velocity, point reactor model, explicitly taking into account the six groups of delayed neutrons. The expression of the mean value, the covariance of the counting distribution are reported. The list of the Fortran 4. subroutine CRITIC which computes these moments is also reported

  10. Construction of covariance matrix for absolute fission yield data measurement

    International Nuclear Information System (INIS)

    Liu Tingjin; Sun Zhengjun

    1999-01-01

    The purpose is to provide a tool for experimenters and evaluators to conveniently construct the covariance based on the information of the experiment. The method used is so called as parameter analysis one. The basic method and formula are given in the first section, a practical program is introduced in the second section, and finally, some examples are given in the third section

  11. Yield response of winter wheat cultivars to environments modeled by different variance-covariance structures in linear mixed models

    Energy Technology Data Exchange (ETDEWEB)

    Studnicki, M.; Mądry, W.; Noras, K.; Wójcik-Gront, E.; Gacek, E.

    2016-11-01

    The main objectives of multi-environmental trials (METs) are to assess cultivar adaptation patterns under different environmental conditions and to investigate genotype by environment (G×E) interactions. Linear mixed models (LMMs) with more complex variance-covariance structures have become recognized and widely used for analyzing METs data. Best practice in METs analysis is to carry out a comparison of competing models with different variance-covariance structures. Improperly chosen variance-covariance structures may lead to biased estimation of means resulting in incorrect conclusions. In this work we focused on adaptive response of cultivars on the environments modeled by the LMMs with different variance-covariance structures. We identified possible limitations of inference when using an inadequate variance-covariance structure. In the presented study we used the dataset on grain yield for 63 winter wheat cultivars, evaluated across 18 locations, during three growing seasons (2008/2009-2010/2011) from the Polish Post-registration Variety Testing System. For the evaluation of variance-covariance structures and the description of cultivars adaptation to environments, we calculated adjusted means for the combination of cultivar and location in models with different variance-covariance structures. We concluded that in order to fully describe cultivars adaptive patterns modelers should use the unrestricted variance-covariance structure. The restricted compound symmetry structure may interfere with proper interpretation of cultivars adaptive patterns. We found, that the factor-analytic structure is also a good tool to describe cultivars reaction on environments, and it can be successfully used in METs data after determining the optimal component number for each dataset. (Author)

  12. An adaptive angle-doppler compensation method for airborne bistatic radar based on PAST

    Science.gov (United States)

    Hang, Xu; Jun, Zhao

    2018-05-01

    Adaptive angle-Doppler compensation method extract the requisite information based on the data itself adaptively, thus avoiding the problem of performance degradation caused by inertia system error. However, this method requires estimation and egiendecomposition of sample covariance matrix, which has a high computational complexity and limits its real-time application. In this paper, an adaptive angle Doppler compensation method based on projection approximation subspace tracking (PAST) is studied. The method uses cyclic iterative processing to quickly estimate the positions of the spectral center of the maximum eigenvector of each range cell, and the computational burden of matrix estimation and eigen-decompositon is avoided, and then the spectral centers of all range cells is overlapped by two dimensional compensation. Simulation results show the proposed method can effectively reduce the no homogeneity of airborne bistatic radar, and its performance is similar to that of egien-decomposition algorithms, but the computation load is obviously reduced and easy to be realized.

  13. Galaxy-galaxy lensing estimators and their covariance properties

    Science.gov (United States)

    Singh, Sukhdeep; Mandelbaum, Rachel; Seljak, Uroš; Slosar, Anže; Vazquez Gonzalez, Jose

    2017-11-01

    We study the covariance properties of real space correlation function estimators - primarily galaxy-shear correlations, or galaxy-galaxy lensing - using SDSS data for both shear catalogues and lenses (specifically the BOSS LOWZ sample). Using mock catalogues of lenses and sources, we disentangle the various contributions to the covariance matrix and compare them with a simple analytical model. We show that not subtracting the lensing measurement around random points from the measurement around the lens sample is equivalent to performing the measurement using the lens density field instead of the lens overdensity field. While the measurement using the lens density field is unbiased (in the absence of systematics), its error is significantly larger due to an additional term in the covariance. Therefore, this subtraction should be performed regardless of its beneficial effects on systematics. Comparing the error estimates from data and mocks for estimators that involve the overdensity, we find that the errors are dominated by the shape noise and lens clustering, which empirically estimated covariances (jackknife and standard deviation across mocks) that are consistent with theoretical estimates, and that both the connected parts of the four-point function and the supersample covariance can be neglected for the current levels of noise. While the trade-off between different terms in the covariance depends on the survey configuration (area, source number density), the diagnostics that we use in this work should be useful for future works to test their empirically determined covariances.

  14. Galaxy–galaxy lensing estimators and their covariance properties

    International Nuclear Information System (INIS)

    Singh, Sukhdeep; Mandelbaum, Rachel; Seljak, Uros; Slosar, Anze; Gonzalez, Jose Vazquez

    2017-01-01

    Here, we study the covariance properties of real space correlation function estimators – primarily galaxy–shear correlations, or galaxy–galaxy lensing – using SDSS data for both shear catalogues and lenses (specifically the BOSS LOWZ sample). Using mock catalogues of lenses and sources, we disentangle the various contributions to the covariance matrix and compare them with a simple analytical model. We show that not subtracting the lensing measurement around random points from the measurement around the lens sample is equivalent to performing the measurement using the lens density field instead of the lens overdensity field. While the measurement using the lens density field is unbiased (in the absence of systematics), its error is significantly larger due to an additional term in the covariance. Therefore, this subtraction should be performed regardless of its beneficial effects on systematics. Comparing the error estimates from data and mocks for estimators that involve the overdensity, we find that the errors are dominated by the shape noise and lens clustering, which empirically estimated covariances (jackknife and standard deviation across mocks) that are consistent with theoretical estimates, and that both the connected parts of the four-point function and the supersample covariance can be neglected for the current levels of noise. While the trade-off between different terms in the covariance depends on the survey configuration (area, source number density), the diagnostics that we use in this work should be useful for future works to test their empirically determined covariances.

  15. Error estimation for ADS nuclear properties by using nuclear data covariances

    International Nuclear Information System (INIS)

    Tsujimoto, Kazufumi

    2005-01-01

    Error for nuclear properties of accelerator-driven subcritical system by the uncertainties of nuclear data was performed. An uncertainty analysis was done using the sensitivity coefficients based on the generalized perturbation theory and the variance matrix data. For major actinides and structural material, the covariance data in JENDL-3.3 library were used. For MA, newly evaluated covariance data was used since there had been no reliable data in all libraries. (author)

  16. Modeling the Conditional Covariance between Stock and Bond Returns

    NARCIS (Netherlands)

    P. de Goeij (Peter); W.A. Marquering (Wessel)

    2002-01-01

    textabstractTo analyze the intertemporal interaction between the stock and bond market returns, we allow the conditional covariance matrix to vary over time according to a multivariate GARCH model similar to Bollerslev, Engle and Wooldridge (1988). We extend the model such that it allows for

  17. Research Article Comparing covariance matrices: random skewers method compared to the common principal components model

    Directory of Open Access Journals (Sweden)

    James M. Cheverud

    2007-03-01

    Full Text Available Comparisons of covariance patterns are becoming more common as interest in the evolution of relationships between traits and in the evolutionary phenotypic diversification of clades have grown. We present parallel analyses of covariance matrix similarity for cranial traits in 14 New World Monkey genera using the Random Skewers (RS, T-statistics, and Common Principal Components (CPC approaches. We find that the CPC approach is very powerful in that with adequate sample sizes, it can be used to detect significant differences in matrix structure, even between matrices that are virtually identical in their evolutionary properties, as indicated by the RS results. We suggest that in many instances the assumption that population covariance matrices are identical be rejected out of hand. The more interesting and relevant question is, How similar are two covariance matrices with respect to their predicted evolutionary responses? This issue is addressed by the random skewers method described here.

  18. Recursive Principal Components Analysis Using Eigenvector Matrix Perturbation

    Directory of Open Access Journals (Sweden)

    Deniz Erdogmus

    2004-10-01

    Full Text Available Principal components analysis is an important and well-studied subject in statistics and signal processing. The literature has an abundance of algorithms for solving this problem, where most of these algorithms could be grouped into one of the following three approaches: adaptation based on Hebbian updates and deflation, optimization of a second-order statistical criterion (like reconstruction error or output variance, and fixed point update rules with deflation. In this paper, we take a completely different approach that avoids deflation and the optimization of a cost function using gradients. The proposed method updates the eigenvector and eigenvalue matrices simultaneously with every new sample such that the estimates approximately track their true values as would be calculated from the current sample estimate of the data covariance matrix. The performance of this algorithm is compared with that of traditional methods like Sanger's rule and APEX, as well as a structurally similar matrix perturbation-based method.

  19. The impact of covariance misspecification in group-based trajectory models for longitudinal data with non-stationary covariance structure.

    Science.gov (United States)

    Davies, Christopher E; Glonek, Gary Fv; Giles, Lynne C

    2017-08-01

    One purpose of a longitudinal study is to gain a better understanding of how an outcome of interest changes among a given population over time. In what follows, a trajectory will be taken to mean the series of measurements of the outcome variable for an individual. Group-based trajectory modelling methods seek to identify subgroups of trajectories within a population, such that trajectories that are grouped together are more similar to each other than to trajectories in distinct groups. Group-based trajectory models generally assume a certain structure in the covariances between measurements, for example conditional independence, homogeneous variance between groups or stationary variance over time. Violations of these assumptions could be expected to result in poor model performance. We used simulation to investigate the effect of covariance misspecification on misclassification of trajectories in commonly used models under a range of scenarios. To do this we defined a measure of performance relative to the ideal Bayesian correct classification rate. We found that the more complex models generally performed better over a range of scenarios. In particular, incorrectly specified covariance matrices could significantly bias the results but using models with a correct but more complicated than necessary covariance matrix incurred little cost.

  20. Adaptive generalized matrix projective lag synchronization between two different complex networks with non-identical nodes and different dimensions

    International Nuclear Information System (INIS)

    Dai Hao; Jia Li-Xin; Zhang Yan-Bin

    2012-01-01

    The adaptive generalized matrix projective lag synchronization between two different complex networks with non-identical nodes and different dimensions is investigated in this paper. Based on Lyapunov stability theory and Barbalat's lemma, generalized matrix projective lag synchronization criteria are derived by using the adaptive control method. Furthermore, each network can be undirected or directed, connected or disconnected, and nodes in either network may have identical or different dynamics. The proposed strategy is applicable to almost all kinds of complex networks. In addition, numerical simulation results are presented to illustrate the effectiveness of this method, showing that the synchronization speed is sensitively influenced by the adaptive law strength, the network size, and the network topological structure. (general)

  1. Adaptation of chemical methods of analysis to the matrix of pyrite-acidified mining lakes

    International Nuclear Information System (INIS)

    Herzsprung, P.; Friese, K.

    2000-01-01

    Owing to the unusual matrix of pyrite-acidified mining lakes, the analysis of chemical parameters may be difficult. A number of methodological improvements have been developed so far, and a comprehensive validation of methods is envisaged. The adaptation of the available methods to small-volume samples of sediment pore waters and the adaptation of sensitivity to the expected concentration ranges is an important element of the methods applied in analyses of biogeochemical processes in mining lakes [de

  2. On the Methodology to Calculate the Covariance of Estimated Resonance Parameters

    International Nuclear Information System (INIS)

    Becker, B.; Kopecky, S.; Schillebeeckx, P.

    2015-01-01

    Principles to determine resonance parameters and their covariance from experimental data are discussed. Different methods to propagate the covariance of experimental parameters are compared. A full Bayesian statistical analysis reveals that the level to which the initial uncertainty of the experimental parameters propagates, strongly depends on the experimental conditions. For high precision data the initial uncertainties of experimental parameters, like a normalization factor, has almost no impact on the covariance of the parameters in case of thick sample measurements and conventional uncertainty propagation or full Bayesian analysis. The covariances derived from a full Bayesian analysis and least-squares fit are derived under the condition that the model describing the experimental observables is perfect. When the quality of the model can not be verified a more conservative method based on a renormalization of the covariance matrix is recommended to propagate fully the uncertainty of experimental systematic effects. Finally, neutron resonance transmission analysis is proposed as an accurate method to validate evaluated data libraries in the resolved resonance region

  3. An Adjoint-Based Adaptive Ensemble Kalman Filter

    KAUST Repository

    Song, Hajoon

    2013-10-01

    A new hybrid ensemble Kalman filter/four-dimensional variational data assimilation (EnKF/4D-VAR) approach is introduced to mitigate background covariance limitations in the EnKF. The work is based on the adaptive EnKF (AEnKF) method, which bears a strong resemblance to the hybrid EnKF/three-dimensional variational data assimilation (3D-VAR) method. In the AEnKF, the representativeness of the EnKF ensemble is regularly enhanced with new members generated after back projection of the EnKF analysis residuals to state space using a 3D-VAR [or optimal interpolation (OI)] scheme with a preselected background covariance matrix. The idea here is to reformulate the transformation of the residuals as a 4D-VAR problem, constraining the new member with model dynamics and the previous observations. This should provide more information for the estimation of the new member and reduce dependence of the AEnKF on the assumed stationary background covariance matrix. This is done by integrating the analysis residuals backward in time with the adjoint model. Numerical experiments are performed with the Lorenz-96 model under different scenarios to test the new approach and to evaluate its performance with respect to the EnKF and the hybrid EnKF/3D-VAR. The new method leads to the least root-mean-square estimation errors as long as the linear assumption guaranteeing the stability of the adjoint model holds. It is also found to be less sensitive to choices of the assimilation system inputs and parameters.

  4. An Adjoint-Based Adaptive Ensemble Kalman Filter

    KAUST Repository

    Song, Hajoon; Hoteit, Ibrahim; Cornuelle, Bruce D.; Luo, Xiaodong; Subramanian, Aneesh C.

    2013-01-01

    A new hybrid ensemble Kalman filter/four-dimensional variational data assimilation (EnKF/4D-VAR) approach is introduced to mitigate background covariance limitations in the EnKF. The work is based on the adaptive EnKF (AEnKF) method, which bears a strong resemblance to the hybrid EnKF/three-dimensional variational data assimilation (3D-VAR) method. In the AEnKF, the representativeness of the EnKF ensemble is regularly enhanced with new members generated after back projection of the EnKF analysis residuals to state space using a 3D-VAR [or optimal interpolation (OI)] scheme with a preselected background covariance matrix. The idea here is to reformulate the transformation of the residuals as a 4D-VAR problem, constraining the new member with model dynamics and the previous observations. This should provide more information for the estimation of the new member and reduce dependence of the AEnKF on the assumed stationary background covariance matrix. This is done by integrating the analysis residuals backward in time with the adjoint model. Numerical experiments are performed with the Lorenz-96 model under different scenarios to test the new approach and to evaluate its performance with respect to the EnKF and the hybrid EnKF/3D-VAR. The new method leads to the least root-mean-square estimation errors as long as the linear assumption guaranteeing the stability of the adjoint model holds. It is also found to be less sensitive to choices of the assimilation system inputs and parameters.

  5. Generation of covariance data among values from a single set of experiments

    International Nuclear Information System (INIS)

    Smith, D.L.

    1992-01-01

    Modern nuclear data evaluation methods demand detailed uncertainty information for all input results to be considered. It can be shown from basic statistical principles that provision of a covariance matrix for a set of data provides the necessary information for its proper consideration in the context of other included experimental data and/or a priori representations of the physical parameters in question. This paper examines how an experimenter should go about preparing the covariance matrix for any single experimental data set he intends to report. The process involves detailed examination of the experimental procedures, identification of all error sources (both random and systematic); and consideration of any internal discrepancies. Some specific examples are given to illustrate the methods and principles involved

  6. Covariance structure in the skull of Catarrhini: a case of pattern stasis and magnitude evolution.

    Science.gov (United States)

    de Oliveira, Felipe Bandoni; Porto, Arthur; Marroig, Gabriel

    2009-04-01

    The study of the genetic variance/covariance matrix (G-matrix) is a recent and fruitful approach in evolutionary biology, providing a window of investigating for the evolution of complex characters. Although G-matrix studies were originally conducted for microevolutionary timescales, they could be extrapolated to macroevolution as long as the G-matrix remains relatively constant, or proportional, along the period of interest. A promising approach to investigating the constancy of G-matrices is to compare their phenotypic counterparts (P-matrices) in a large group of related species; if significant similarity is found among several taxa, it is very likely that the underlying G-matrices are also equivalent. Here we study the similarity of covariance and correlation structure in a broad sample of Old World monkeys and apes (Catarrhini). We made phylogenetically structured comparisons of correlation and covariance matrices derived from 39 skull traits, ranging from between species to the superfamily level. We also compared the overall magnitude of integration between skull traits (r2) for all Catarrhini genera. Our results show that P-matrices were not strictly constant among catarrhines, but the amount of divergence observed among taxa was generally low. There was significant and positive correlation between the amount of divergence in correlation and covariance patterns among the 30 genera and their phylogenetic distances derived from a recently proposed phylogenetic hypothesis. Our data demonstrate that the P-matrices remained relatively similar along the evolutionary history of catarrhines, and comparisons with the G-matrix available for a New World monkey genus (Saguinus) suggests that the same holds for all anthropoids. The magnitude of integration, in contrast, varied considerably among genera, indicating that evolution of the magnitude, rather than the pattern of inter-trait correlations, might have played an important role in the diversification of the

  7. Visualization and assessment of spatio-temporal covariance properties

    KAUST Repository

    Huang, Huang

    2017-11-23

    Spatio-temporal covariances are important for describing the spatio-temporal variability of underlying random fields in geostatistical data. For second-order stationary random fields, there exist subclasses of covariance functions that assume a simpler spatio-temporal dependence structure with separability and full symmetry. However, it is challenging to visualize and assess separability and full symmetry from spatio-temporal observations. In this work, we propose a functional data analysis approach that constructs test functions using the cross-covariances from time series observed at each pair of spatial locations. These test functions of temporal lags summarize the properties of separability or symmetry for the given spatial pairs. We use functional boxplots to visualize the functional median and the variability of the test functions, where the extent of departure from zero at all temporal lags indicates the degree of non-separability or asymmetry. We also develop a rank-based nonparametric testing procedure for assessing the significance of the non-separability or asymmetry. Essentially, the proposed methods only require the analysis of temporal covariance functions. Thus, a major advantage over existing approaches is that there is no need to estimate any covariance matrix for selected spatio-temporal lags. The performances of the proposed methods are examined by simulations with various commonly used spatio-temporal covariance models. To illustrate our methods in practical applications, we apply it to real datasets, including weather station data and climate model outputs.

  8. Adaptive robust Kalman filtering for precise point positioning

    International Nuclear Information System (INIS)

    Guo, Fei; Zhang, Xiaohong

    2014-01-01

    The optimality of precise point postioning (PPP) solution using a Kalman filter is closely connected to the quality of the a priori information about the process noise and the updated mesurement noise, which are sometimes difficult to obtain. Also, the estimation enviroment in the case of dynamic or kinematic applications is not always fixed but is subject to change. To overcome these problems, an adaptive robust Kalman filtering algorithm, the main feature of which introduces an equivalent covariance matrix to resist the unexpected outliers and an adaptive factor to balance the contribution of observational information and predicted information from the system dynamic model, is applied for PPP processing. The basic models of PPP including the observation model, dynamic model and stochastic model are provided first. Then an adaptive robust Kalmam filter is developed for PPP. Compared with the conventional robust estimator, only the observation with largest standardized residual will be operated by the IGG III function in each iteration to avoid reducing the contribution of the normal observations or even filter divergence. Finally, tests carried out in both static and kinematic modes have confirmed that the adaptive robust Kalman filter outperforms the classic Kalman filter by turning either the equivalent variance matrix or the adaptive factor or both of them. This becomes evident when analyzing the positioning errors in flight tests at the turns due to the target maneuvering and unknown process/measurement noises. (paper)

  9. Thinking outside the box: effects of modes larger than the survey on matter power spectrum covariance

    International Nuclear Information System (INIS)

    Putter, Roland de; Wagner, Christian; Verde, Licia; Mena, Olga; Percival, Will J.

    2012-01-01

    Accurate power spectrum (or correlation function) covariance matrices are a crucial requirement for cosmological parameter estimation from large scale structure surveys. In order to minimize reliance on computationally expensive mock catalogs, it is important to have a solid analytic understanding of the different components that make up a covariance matrix. Considering the matter power spectrum covariance matrix, it has recently been found that there is a potentially dominant effect on mildly non-linear scales due to power in modes of size equal to and larger than the survey volume. This beat coupling effect has been derived analytically in perturbation theory and while it has been tested with simulations, some questions remain unanswered. Moreover, there is an additional effect of these large modes, which has so far not been included in analytic studies, namely the effect on the estimated average density which enters the power spectrum estimate. In this article, we work out analytic, perturbation theory based expressions including both the beat coupling and this local average effect and we show that while, when isolated, beat coupling indeed causes large excess covariance in agreement with the literature, in a realistic scenario this is compensated almost entirely by the local average effect, leaving only ∼ 10% of the excess. We test our analytic expressions by comparison to a suite of large N-body simulations, using both full simulation boxes and subboxes thereof to study cases without beat coupling, with beat coupling and with both beat coupling and the local average effect. For the variances, we find excellent agreement with the analytic expressions for k −1 at z = 0.5, while the correlation coefficients agree to beyond k = 0.4 hMpc −1 . As expected, the range of agreement increases towards higher redshift and decreases slightly towards z = 0. We finish by including the large-mode effects in a full covariance matrix description for arbitrary survey

  10. Quantum channels irreducibly covariant with respect to the finite group generated by the Weyl operators

    Science.gov (United States)

    Siudzińska, Katarzyna; Chruściński, Dariusz

    2018-03-01

    In matrix algebras, we introduce a class of linear maps that are irreducibly covariant with respect to the finite group generated by the Weyl operators. In particular, we analyze the irreducibly covariant quantum channels, that is, the completely positive and trace-preserving linear maps. Interestingly, imposing additional symmetries leads to the so-called generalized Pauli channels, which were recently considered in the context of the non-Markovian quantum evolution. Finally, we provide examples of irreducibly covariant positive but not necessarily completely positive maps.

  11. Distributed Remote Vector Gaussian Source Coding with Covariance Distortion Constraints

    DEFF Research Database (Denmark)

    Zahedi, Adel; Østergaard, Jan; Jensen, Søren Holdt

    2014-01-01

    In this paper, we consider a distributed remote source coding problem, where a sequence of observations of source vectors is available at the encoder. The problem is to specify the optimal rate for encoding the observations subject to a covariance matrix distortion constraint and in the presence...

  12. Improving the ensemble-optimization method through covariance-matrix adaptation

    NARCIS (Netherlands)

    Fonseca, R.M.; Leeuwenburgh, O.; Hof, P.M.J. van den; Jansen, J.D.

    2015-01-01

    Ensemble optimization (referred to throughout the remainder of the paper as EnOpt) is a rapidly emerging method for reservoirmodel-based production optimization. EnOpt uses an ensemble of controls to approximate the gradient of the objective function with respect to the controls. Current

  13. A New Approach for Nuclear Data Covariance and Sensitivity Generation

    International Nuclear Information System (INIS)

    Leal, L.C.; Larson, N.M.; Derrien, H.; Kawano, T.; Chadwick, M.B.

    2005-01-01

    Covariance data are required to correctly assess uncertainties in design parameters in nuclear applications. The error estimation of calculated quantities relies on the nuclear data uncertainty information available in the basic nuclear data libraries, such as the U.S. Evaluated Nuclear Data File, ENDF/B. The uncertainty files in the ENDF/B library are obtained from the analysis of experimental data and are stored as variance and covariance data. The computer code SAMMY is used in the analysis of the experimental data in the resolved and unresolved resonance energy regions. The data fitting of cross sections is based on generalized least-squares formalism (Bayes' theory) together with the resonance formalism described by R-matrix theory. Two approaches are used in SAMMY for the generation of resonance-parameter covariance data. In the evaluation process SAMMY generates a set of resonance parameters that fit the data, and, in addition, it also provides the resonance-parameter covariances. For existing resonance-parameter evaluations where no resonance-parameter covariance data are available, the alternative is to use an approach called the 'retroactive' resonance-parameter covariance generation. In the high-energy region the methodology for generating covariance data consists of least-squares fitting and model parameter adjustment. The least-squares fitting method calculates covariances directly from experimental data. The parameter adjustment method employs a nuclear model calculation such as the optical model and the Hauser-Feshbach model, and estimates a covariance for the nuclear model parameters. In this paper we describe the application of the retroactive method and the parameter adjustment method to generate covariance data for the gadolinium isotopes

  14. Robust adaptive subspace detection in impulsive noise

    KAUST Repository

    Ben Atitallah, Ismail

    2016-09-13

    This paper addresses the design of the Adaptive Subspace Matched Filter (ASMF) detector in the presence of compound Gaussian clutters and a mismatch in the steering vector. In particular, we consider the case wherein the ASMF uses the regularized Tyler estimator (RTE) to estimate the clutter covariance matrix. Under this setting, a major question that needs to be addressed concerns the setting of the threshold and the regularization parameter. To answer this question, we consider the regime in which the number of observations used to estimate the RTE and their dimensions grow large together. Recent results from random matrix theory are then used in order to approximate the false alarm and detection probabilities by deterministic quantities. The latter are optimized in order to maximize an upper bound on the asymptotic detection probability while keeping the asymptotic false alarm probability at a fixed rate. © 2016 IEEE.

  15. Robust adaptive subspace detection in impulsive noise

    KAUST Repository

    Ben Atitallah, Ismail; Kammoun, Abla; Alouini, Mohamed-Slim; Al-Naffouri, Tareq Y.

    2016-01-01

    This paper addresses the design of the Adaptive Subspace Matched Filter (ASMF) detector in the presence of compound Gaussian clutters and a mismatch in the steering vector. In particular, we consider the case wherein the ASMF uses the regularized Tyler estimator (RTE) to estimate the clutter covariance matrix. Under this setting, a major question that needs to be addressed concerns the setting of the threshold and the regularization parameter. To answer this question, we consider the regime in which the number of observations used to estimate the RTE and their dimensions grow large together. Recent results from random matrix theory are then used in order to approximate the false alarm and detection probabilities by deterministic quantities. The latter are optimized in order to maximize an upper bound on the asymptotic detection probability while keeping the asymptotic false alarm probability at a fixed rate. © 2016 IEEE.

  16. Technical Note: Variance-covariance matrix and averaging kernels for the Levenberg-Marquardt solution of the retrieval of atmospheric vertical profiles

    Directory of Open Access Journals (Sweden)

    S. Ceccherini

    2010-03-01

    Full Text Available The variance-covariance matrix (VCM and the averaging kernel matrix (AKM are widely used tools to characterize atmospheric vertical profiles retrieved from remote sensing measurements. Accurate estimation of these quantities is essential for both the evaluation of the quality of the retrieved profiles and for the correct use of the profiles themselves in subsequent applications such as data comparison, data assimilation and data fusion. We propose a new method to estimate the VCM and AKM of vertical profiles retrieved using the Levenberg-Marquardt iterative technique. We apply the new method to the inversion of simulated limb emission measurements. Then we compare the obtained VCM and AKM with those resulting from other methods already published in the literature and with accurate estimates derived using statistical and numerical estimators. The proposed method accounts for all the iterations done in the inversion and provides the most accurate VCM and AKM. Furthermore, it correctly estimates the VCM and the AKM also if the retrieval iterations are stopped when a physically meaningful convergence criterion is fulfilled, i.e. before achievement of the numerical convergence at machine precision. The method can be easily implemented in any Levenberg-Marquardt iterative retrieval scheme, either constrained or unconstrained, without significant computational overhead.

  17. Perils of parsimony: properties of reduced-rank estimates of genetic covariance matrices.

    Science.gov (United States)

    Meyer, Karin; Kirkpatrick, Mark

    2008-10-01

    Eigenvalues and eigenvectors of covariance matrices are important statistics for multivariate problems in many applications, including quantitative genetics. Estimates of these quantities are subject to different types of bias. This article reviews and extends the existing theory on these biases, considering a balanced one-way classification and restricted maximum-likelihood estimation. Biases are due to the spread of sample roots and arise from ignoring selected principal components when imposing constraints on the parameter space, to ensure positive semidefinite estimates or to estimate covariance matrices of chosen, reduced rank. In addition, it is shown that reduced-rank estimators that consider only the leading eigenvalues and -vectors of the "between-group" covariance matrix may be biased due to selecting the wrong subset of principal components. In a genetic context, with groups representing families, this bias is inverse proportional to the degree of genetic relationship among family members, but is independent of sample size. Theoretical results are supplemented by a simulation study, demonstrating close agreement between predicted and observed bias for large samples. It is emphasized that the rank of the genetic covariance matrix should be chosen sufficiently large to accommodate all important genetic principal components, even though, paradoxically, this may require including a number of components with negligible eigenvalues. A strategy for rank selection in practical analyses is outlined.

  18. Non-stationary pre-envelope covariances of non-classically damped systems

    Science.gov (United States)

    Muscolino, G.

    1991-08-01

    A new formulation is given to evaluate the stationary and non-stationary response of linear non-classically damped systems subjected to multi-correlated non-separable Gaussian input processes. This formulation is based on a new and more suitable definition of the impulse response function matrix for such systems. It is shown that, when using this definition, the stochastic response of non-classically damped systems involves the evaluation of quantities similar to those of classically damped ones. Furthermore, considerations about non-stationary cross-covariances, spectral moments and pre-envelope cross-covariances are presented for a monocorrelated input process.

  19. Homonuclear long-range correlation spectra from HMBC experiments by covariance processing.

    Science.gov (United States)

    Schoefberger, Wolfgang; Smrecki, Vilko; Vikić-Topić, Drazen; Müller, Norbert

    2007-07-01

    We present a new application of covariance nuclear magnetic resonance processing based on 1H--13C-HMBC experiments which provides an effective way for establishing indirect 1H--1H and 13C--13C nuclear spin connectivity at natural isotope abundance. The method, which identifies correlated spin networks in terms of covariance between one-dimensional traces from a single decoupled HMBC experiment, derives 13C--13C as well as 1H--1H spin connectivity maps from the two-dimensional frequency domain heteronuclear long-range correlation data matrix. The potential and limitations of this novel covariance NMR application are demonstrated on two compounds: eugenyl-beta-D-glucopyranoside and an emodin-derivative. Copyright (c) 2007 John Wiley & Sons, Ltd.

  20. Extreme eigenvalues of sample covariance and correlation matrices

    DEFF Research Database (Denmark)

    Heiny, Johannes

    This thesis is concerned with asymptotic properties of the eigenvalues of high-dimensional sample covariance and correlation matrices under an infinite fourth moment of the entries. In the first part, we study the joint distributional convergence of the largest eigenvalues of the sample covariance...... matrix of a p-dimensional heavy-tailed time series when p converges to infinity together with the sample size n. We generalize the growth rates of p existing in the literature. Assuming a regular variation condition with tail index ... eigenvalues are essentially determined by the extreme order statistics from an array of iid random variables. The asymptotic behavior of the extreme eigenvalues is then derived routinely from classical extreme value theory. The resulting approximations are strikingly simple considering the high dimension...

  1. Robust Adaptive LCMV Beamformer Based On An Iterative Suboptimal Solution

    Directory of Open Access Journals (Sweden)

    Xiansheng Guo

    2015-06-01

    Full Text Available The main drawback of closed-form solution of linearly constrained minimum variance (CF-LCMV beamformer is the dilemma of acquiring long observation time for stable covariance matrix estimates and short observation time to track dynamic behavior of targets, leading to poor performance including low signal-noise-ratio (SNR, low jammer-to-noise ratios (JNRs and small number of snapshots. Additionally, CF-LCMV suffers from heavy computational burden which mainly comes from two matrix inverse operations for computing the optimal weight vector. In this paper, we derive a low-complexity Robust Adaptive LCMV beamformer based on an Iterative Suboptimal solution (RAIS-LCMV using conjugate gradient (CG optimization method. The merit of our proposed method is threefold. Firstly, RAIS-LCMV beamformer can reduce the complexity of CF-LCMV remarkably. Secondly, RAIS-LCMV beamformer can adjust output adaptively based on measurement and its convergence speed is comparable. Finally, RAIS-LCMV algorithm has robust performance against low SNR, JNRs, and small number of snapshots. Simulation results demonstrate the superiority of our proposed algorithms.

  2. Adaptive learning with covariate shift-detection for motor imagery-based brain–computer interface

    OpenAIRE

    Raza, H; Cecotti, H; Li, Y; Prasad, G

    2015-01-01

    A common assumption in traditional supervised learning is the similar probability distribution of data between the training phase and the testing/operating phase. When transitioning from the training to testing phase, a shift in the probability distribution of input data is known as a covariate shift. Covariate shifts commonly arise in a wide range of real-world systems such as electroencephalogram-based brain–computer interfaces (BCIs). In such systems, there is a necessity for continuous mo...

  3. Large-region acoustic source mapping using a movable array and sparse covariance fitting.

    Science.gov (United States)

    Zhao, Shengkui; Tuna, Cagdas; Nguyen, Thi Ngoc Tho; Jones, Douglas L

    2017-01-01

    Large-region acoustic source mapping is important for city-scale noise monitoring. Approaches using a single-position measurement scheme to scan large regions using small arrays cannot provide clean acoustic source maps, while deploying large arrays spanning the entire region of interest is prohibitively expensive. A multiple-position measurement scheme is applied to scan large regions at multiple spatial positions using a movable array of small size. Based on the multiple-position measurement scheme, a sparse-constrained multiple-position vectorized covariance matrix fitting approach is presented. In the proposed approach, the overall sample covariance matrix of the incoherent virtual array is first estimated using the multiple-position array data and then vectorized using the Khatri-Rao (KR) product. A linear model is then constructed for fitting the vectorized covariance matrix and a sparse-constrained reconstruction algorithm is proposed for recovering source powers from the model. The user parameter settings are discussed. The proposed approach is tested on a 30 m × 40 m region and a 60 m × 40 m region using simulated and measured data. Much cleaner acoustic source maps and lower sound pressure level errors are obtained compared to the beamforming approaches and the previous sparse approach [Zhao, Tuna, Nguyen, and Jones, Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP) (2016)].

  4. Exact Covariance Thresholding into Connected Components for Large-Scale Graphical Lasso.

    Science.gov (United States)

    Mazumder, Rahul; Hastie, Trevor

    2012-03-01

    We consider the sparse inverse covariance regularization problem or graphical lasso with regularization parameter λ. Suppose the sample covariance graph formed by thresholding the entries of the sample covariance matrix at λ is decomposed into connected components. We show that the vertex-partition induced by the connected components of the thresholded sample covariance graph (at λ) is exactly equal to that induced by the connected components of the estimated concentration graph, obtained by solving the graphical lasso problem for the same λ. This characterizes a very interesting property of a path of graphical lasso solutions. Furthermore, this simple rule, when used as a wrapper around existing algorithms for the graphical lasso, leads to enormous performance gains. For a range of values of λ, our proposal splits a large graphical lasso problem into smaller tractable problems, making it possible to solve an otherwise infeasible large-scale problem. We illustrate the graceful scalability of our proposal via synthetic and real-life microarray examples.

  5. Structure of Pioncare covariant tensor operators in quantum mechanical models

    International Nuclear Information System (INIS)

    Polyzou, W.N.; Klink, W.H.

    1988-01-01

    The structure of operators that transform covariantly in Poincare invariant quantum mechanical models is analyzed. These operators are shown to have an interaction dependence that comes from the geometry of the Poincare group. The operators can be expressed in terms of matrix elements in a complete set of eigenstates of the mass and spin operators associated with the dynamical representation of the Poincare group. The matrix elements are factored into geometrical coefficients (Clebsch--Gordan coefficients for the Poincare group) and invariant matrix elements. The geometrical coefficients are fixed by the transformation properties of the operator and the eigenvalue spectrum of the mass and spin. The invariant matrix elements, which distinguish between different operators with the same transformation properties, are given in terms of a set of invariant form factors. copyright 1988 Academic Press, Inc

  6. lme4qtl: linear mixed models with flexible covariance structure for genetic studies of related individuals.

    Science.gov (United States)

    Ziyatdinov, Andrey; Vázquez-Santiago, Miquel; Brunel, Helena; Martinez-Perez, Angel; Aschard, Hugues; Soria, Jose Manuel

    2018-02-27

    Quantitative trait locus (QTL) mapping in genetic data often involves analysis of correlated observations, which need to be accounted for to avoid false association signals. This is commonly performed by modeling such correlations as random effects in linear mixed models (LMMs). The R package lme4 is a well-established tool that implements major LMM features using sparse matrix methods; however, it is not fully adapted for QTL mapping association and linkage studies. In particular, two LMM features are lacking in the base version of lme4: the definition of random effects by custom covariance matrices; and parameter constraints, which are essential in advanced QTL models. Apart from applications in linkage studies of related individuals, such functionalities are of high interest for association studies in situations where multiple covariance matrices need to be modeled, a scenario not covered by many genome-wide association study (GWAS) software. To address the aforementioned limitations, we developed a new R package lme4qtl as an extension of lme4. First, lme4qtl contributes new models for genetic studies within a single tool integrated with lme4 and its companion packages. Second, lme4qtl offers a flexible framework for scenarios with multiple levels of relatedness and becomes efficient when covariance matrices are sparse. We showed the value of our package using real family-based data in the Genetic Analysis of Idiopathic Thrombophilia 2 (GAIT2) project. Our software lme4qtl enables QTL mapping models with a versatile structure of random effects and efficient computation for sparse covariances. lme4qtl is available at https://github.com/variani/lme4qtl .

  7. Few group collapsing of covariance matrix data based on a conservation principle

    International Nuclear Information System (INIS)

    Hiruta, H.; Palmiotti, G.; Salvatores, M.; Arcilla, R. Jr.; Oblozinsky, P.; McKnight, R.D.

    2008-01-01

    A new algorithm for a rigorous collapsing of covariance data is proposed, derived, implemented, and tested. The method is based on a conservation principle that allows preserving at a broad energy group structure the uncertainty calculated in a fine group energy structure for a specific integral parameter, using as weights the associated sensitivity coefficients

  8. Performance of penalized maximum likelihood in estimation of genetic covariances matrices

    Directory of Open Access Journals (Sweden)

    Meyer Karin

    2011-11-01

    Full Text Available Abstract Background Estimation of genetic covariance matrices for multivariate problems comprising more than a few traits is inherently problematic, since sampling variation increases dramatically with the number of traits. This paper investigates the efficacy of regularized estimation of covariance components in a maximum likelihood framework, imposing a penalty on the likelihood designed to reduce sampling variation. In particular, penalties that "borrow strength" from the phenotypic covariance matrix are considered. Methods An extensive simulation study was carried out to investigate the reduction in average 'loss', i.e. the deviation in estimated matrices from the population values, and the accompanying bias for a range of parameter values and sample sizes. A number of penalties are examined, penalizing either the canonical eigenvalues or the genetic covariance or correlation matrices. In addition, several strategies to determine the amount of penalization to be applied, i.e. to estimate the appropriate tuning factor, are explored. Results It is shown that substantial reductions in loss for estimates of genetic covariance can be achieved for small to moderate sample sizes. While no penalty performed best overall, penalizing the variance among the estimated canonical eigenvalues on the logarithmic scale or shrinking the genetic towards the phenotypic correlation matrix appeared most advantageous. Estimating the tuning factor using cross-validation resulted in a loss reduction 10 to 15% less than that obtained if population values were known. Applying a mild penalty, chosen so that the deviation in likelihood from the maximum was non-significant, performed as well if not better than cross-validation and can be recommended as a pragmatic strategy. Conclusions Penalized maximum likelihood estimation provides the means to 'make the most' of limited and precious data and facilitates more stable estimation for multi-dimensional analyses. It should

  9. Efficient estimation of three-dimensional covariance and its application in the analysis of heterogeneous samples in cryo-electron microscopy.

    Science.gov (United States)

    Liao, Hstau Y; Hashem, Yaser; Frank, Joachim

    2015-06-02

    Single-particle cryogenic electron microscopy (cryo-EM) is a powerful tool for the study of macromolecular structures at high resolution. Classification allows multiple structural states to be extracted and reconstructed from the same sample. One classification approach is via the covariance matrix, which captures the correlation between every pair of voxels. Earlier approaches employ computing-intensive resampling and estimate only the eigenvectors of the matrix, which are then used in a separate fast classification step. We propose an iterative scheme to explicitly estimate the covariance matrix in its entirety. In our approach, the flexibility in choosing the solution domain allows us to examine a part of the molecule in greater detail. Three-dimensional covariance maps obtained in this way from experimental data (cryo-EM images of the eukaryotic pre-initiation complex) prove to be in excellent agreement with conclusions derived by using traditional approaches, revealing in addition the interdependencies of ligand bindings and structural changes. Copyright © 2015 Elsevier Ltd. All rights reserved.

  10. Approximations of noise covariance in multi-slice helical CT scans: impact on lung nodule size estimation.

    Science.gov (United States)

    Zeng, Rongping; Petrick, Nicholas; Gavrielides, Marios A; Myers, Kyle J

    2011-10-07

    Multi-slice computed tomography (MSCT) scanners have become popular volumetric imaging tools. Deterministic and random properties of the resulting CT scans have been studied in the literature. Due to the large number of voxels in the three-dimensional (3D) volumetric dataset, full characterization of the noise covariance in MSCT scans is difficult to tackle. However, as usage of such datasets for quantitative disease diagnosis grows, so does the importance of understanding the noise properties because of their effect on the accuracy of the clinical outcome. The goal of this work is to study noise covariance in the helical MSCT volumetric dataset. We explore possible approximations to the noise covariance matrix with reduced degrees of freedom, including voxel-based variance, one-dimensional (1D) correlation, two-dimensional (2D) in-plane correlation and the noise power spectrum (NPS). We further examine the effect of various noise covariance models on the accuracy of a prewhitening matched filter nodule size estimation strategy. Our simulation results suggest that the 1D longitudinal, 2D in-plane and NPS prewhitening approaches can improve the performance of nodule size estimation algorithms. When taking into account computational costs in determining noise characterizations, the NPS model may be the most efficient approximation to the MSCT noise covariance matrix.

  11. Covariate selection for the semiparametric additive risk model

    DEFF Research Database (Denmark)

    Martinussen, Torben; Scheike, Thomas

    2009-01-01

    This paper considers covariate selection for the additive hazards model. This model is particularly simple to study theoretically and its practical implementation has several major advantages to the similar methodology for the proportional hazards model. One complication compared...... and study their large sample properties for the situation where the number of covariates p is smaller than the number of observations. We also show that the adaptive Lasso has the oracle property. In many practical situations, it is more relevant to tackle the situation with large p compared with the number...... of observations. We do this by studying the properties of the so-called Dantzig selector in the setting of the additive risk model. Specifically, we establish a bound on how close the solution is to a true sparse signal in the case where the number of covariates is large. In a simulation study, we also compare...

  12. Maximum a posteriori covariance estimation using a power inverse wishart prior

    DEFF Research Database (Denmark)

    Nielsen, Søren Feodor; Sporring, Jon

    2012-01-01

    The estimation of the covariance matrix is an initial step in many multivariate statistical methods such as principal components analysis and factor analysis, but in many practical applications the dimensionality of the sample space is large compared to the number of samples, and the usual maximum...

  13. Multilevel covariance regression with correlated random effects in the mean and variance structure.

    Science.gov (United States)

    Quintero, Adrian; Lesaffre, Emmanuel

    2017-09-01

    Multivariate regression methods generally assume a constant covariance matrix for the observations. In case a heteroscedastic model is needed, the parametric and nonparametric covariance regression approaches can be restrictive in the literature. We propose a multilevel regression model for the mean and covariance structure, including random intercepts in both components and allowing for correlation between them. The implied conditional covariance function can be different across clusters as a result of the random effect in the variance structure. In addition, allowing for correlation between the random intercepts in the mean and covariance makes the model convenient for skewedly distributed responses. Furthermore, it permits us to analyse directly the relation between the mean response level and the variability in each cluster. Parameter estimation is carried out via Gibbs sampling. We compare the performance of our model to other covariance modelling approaches in a simulation study. Finally, the proposed model is applied to the RN4CAST dataset to identify the variables that impact burnout of nurses in Belgium. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  14. Lorentz covariant canonical symplectic algorithms for dynamics of charged particles

    Science.gov (United States)

    Wang, Yulei; Liu, Jian; Qin, Hong

    2016-12-01

    In this paper, the Lorentz covariance of algorithms is introduced. Under Lorentz transformation, both the form and performance of a Lorentz covariant algorithm are invariant. To acquire the advantages of symplectic algorithms and Lorentz covariance, a general procedure for constructing Lorentz covariant canonical symplectic algorithms (LCCSAs) is provided, based on which an explicit LCCSA for dynamics of relativistic charged particles is built. LCCSA possesses Lorentz invariance as well as long-term numerical accuracy and stability, due to the preservation of a discrete symplectic structure and the Lorentz symmetry of the system. For situations with time-dependent electromagnetic fields, which are difficult to handle in traditional construction procedures of symplectic algorithms, LCCSA provides a perfect explicit canonical symplectic solution by implementing the discretization in 4-spacetime. We also show that LCCSA has built-in energy-based adaptive time steps, which can optimize the computation performance when the Lorentz factor varies.

  15. Structural Covariance of the Prefrontal-Amygdala Pathways Associated with Heart Rate Variability.

    Science.gov (United States)

    Wei, Luqing; Chen, Hong; Wu, Guo-Rong

    2018-01-01

    The neurovisceral integration model has shown a key role of the amygdala in neural circuits underlying heart rate variability (HRV) modulation, and suggested that reciprocal connections from amygdala to brain regions centered on the central autonomic network (CAN) are associated with HRV. To provide neuroanatomical evidence for these theoretical perspectives, the current study used covariance analysis of MRI-based gray matter volume (GMV) to map structural covariance network of the amygdala, and then determined whether the interregional structural correlations related to individual differences in HRV. The results showed that covariance patterns of the amygdala encompassed large portions of cortical (e.g., prefrontal, cingulate, and insula) and subcortical (e.g., striatum, hippocampus, and midbrain) regions, lending evidence from structural covariance analysis to the notion that the amygdala was a pivotal node in neural pathways for HRV modulation. Importantly, participants with higher resting HRV showed increased covariance of amygdala to dorsal medial prefrontal cortex and anterior cingulate cortex (dmPFC/dACC) extending into adjacent medial motor regions [i.e., pre-supplementary motor area (pre-SMA)/SMA], demonstrating structural covariance of the prefrontal-amygdala pathways implicated in HRV, and also implying that resting HRV may reflect the function of neural circuits underlying cognitive regulation of emotion as well as facilitation of adaptive behaviors to emotion. Our results, thus, provide anatomical substrates for the neurovisceral integration model that resting HRV may index an integrative neural network which effectively organizes emotional, cognitive, physiological and behavioral responses in the service of goal-directed behavior and adaptability.

  16. Covariances for neutron cross sections calculated using a regional model based on local-model fits to experimental data

    Energy Technology Data Exchange (ETDEWEB)

    Smith, D.L.; Guenther, P.T.

    1983-11-01

    We suggest a procedure for estimating uncertainties in neutron cross sections calculated with a nuclear model descriptive of a specific mass region. It applies standard error propagation techniques, using a model-parameter covariance matrix. Generally, available codes do not generate covariance information in conjunction with their fitting algorithms. Therefore, we resort to estimating a relative covariance matrix a posteriori from a statistical examination of the scatter of elemental parameter values about the regional representation. We numerically demonstrate our method by considering an optical-statistical model analysis of a body of total and elastic scattering data for the light fission-fragment mass region. In this example, strong uncertainty correlations emerge and they conspire to reduce estimated errors to some 50% of those obtained from a naive uncorrelated summation in quadrature. 37 references.

  17. Covariances for neutron cross sections calculated using a regional model based on local-model fits to experimental data

    International Nuclear Information System (INIS)

    Smith, D.L.; Guenther, P.T.

    1983-11-01

    We suggest a procedure for estimating uncertainties in neutron cross sections calculated with a nuclear model descriptive of a specific mass region. It applies standard error propagation techniques, using a model-parameter covariance matrix. Generally, available codes do not generate covariance information in conjunction with their fitting algorithms. Therefore, we resort to estimating a relative covariance matrix a posteriori from a statistical examination of the scatter of elemental parameter values about the regional representation. We numerically demonstrate our method by considering an optical-statistical model analysis of a body of total and elastic scattering data for the light fission-fragment mass region. In this example, strong uncertainty correlations emerge and they conspire to reduce estimated errors to some 50% of those obtained from a naive uncorrelated summation in quadrature. 37 references

  18. Covariance methodology applied to uncertainties in I-126 disintegration rate measurements

    International Nuclear Information System (INIS)

    Fonseca, K.A.; Koskinas, M.F.; Dias, M.S.

    1996-01-01

    The covariance methodology applied to uncertainties in 126 I disintegration rate measurements is described. Two different coincidence systems were used due to the complex decay scheme of this radionuclide. The parameters involved in the determination of the disintegration rate in each experimental system present correlated components. In this case, the conventional statistical methods to determine the uncertainties (law of propagation) result in wrong values for the final uncertainty. Therefore, use of the methodology of the covariance matrix is necessary. The data from both systems were combined taking into account all possible correlations between the partial uncertainties. (orig.)

  19. Classification in medical images using adaptive metric k-NN

    Science.gov (United States)

    Chen, C.; Chernoff, K.; Karemore, G.; Lo, P.; Nielsen, M.; Lauze, F.

    2010-03-01

    The performance of the k-nearest neighborhoods (k-NN) classifier is highly dependent on the distance metric used to identify the k nearest neighbors of the query points. The standard Euclidean distance is commonly used in practice. This paper investigates the performance of k-NN classifier with respect to different adaptive metrics in the context of medical imaging. We propose using adaptive metrics such that the structure of the data is better described, introducing some unsupervised learning knowledge in k-NN. We investigated four different metrics are estimated: a theoretical metric based on the assumption that images are drawn from Brownian Image Model (BIM), the normalized metric based on variance of the data, the empirical metric is based on the empirical covariance matrix of the unlabeled data, and an optimized metric obtained by minimizing the classification error. The spectral structure of the empirical covariance also leads to Principal Component Analysis (PCA) performed on it which results the subspace metrics. The metrics are evaluated on two data sets: lateral X-rays of the lumbar aortic/spine region, where we use k-NN for performing abdominal aorta calcification detection; and mammograms, where we use k-NN for breast cancer risk assessment. The results show that appropriate choice of metric can improve classification.

  20. Covariant holography of a tachyonic accelerating universe

    Energy Technology Data Exchange (ETDEWEB)

    Rozas-Fernandez, Alberto [Consejo Superior de Investigaciones Cientificas, Instituto de Fisica Fundamental, Madrid (Spain); University of Portsmouth, Institute of Cosmology and Gravitation, Portsmouth (United Kingdom)

    2014-08-15

    We apply the holographic principle to a flat dark energy dominated Friedmann-Robertson-Walker spacetime filled with a tachyon scalar field with constant equation of state w = p/ρ, both for w > -1 and w < -1. By using a geometrical covariant procedure, which allows the construction of holographic hypersurfaces, we have obtained for each case the position of the preferred screen and have then compared these with those obtained by using the holographic dark energy model with the future event horizon as the infrared cutoff. In the phantom scenario, one of the two obtained holographic screens is placed on the big rip hypersurface, both for the covariant holographic formalism and the holographic phantom model. It is also analyzed whether the existence of these preferred screens allows a mathematically consistent formulation of fundamental theories based on the existence of an S-matrix at infinite distances. (orig.)

  1. AFCI-2.0 Neutron Cross Section Covariance Library

    Energy Technology Data Exchange (ETDEWEB)

    Herman, M.; Herman, M; Oblozinsky, P.; Mattoon, C.M.; Pigni, M.; Hoblit, S.; Mughabghab, S.F.; Sonzogni, A.; Talou, P.; Chadwick, M.B.; Hale, G.M.; Kahler, A.C.; Kawano, T.; Little, R.C.; Yount, P.G.

    2011-03-01

    The cross section covariance library has been under development by BNL-LANL collaborative effort over the last three years. The project builds on two covariance libraries developed earlier, with considerable input from BNL and LANL. In 2006, international effort under WPEC Subgroup 26 produced BOLNA covariance library by putting together data, often preliminary, from various sources for most important materials for nuclear reactor technology. This was followed in 2007 by collaborative effort of four US national laboratories to produce covariances, often of modest quality - hence the name low-fidelity, for virtually complete set of materials included in ENDF/B-VII.0. The present project is focusing on covariances of 4-5 major reaction channels for 110 materials of importance for power reactors. The work started under Global Nuclear Energy Partnership (GNEP) in 2008, which changed to Advanced Fuel Cycle Initiative (AFCI) in 2009. With the 2011 release the name has changed to the Covariance Multigroup Matrix for Advanced Reactor Applications (COMMARA) version 2.0. The primary purpose of the library is to provide covariances for AFCI data adjustment project, which is focusing on the needs of fast advanced burner reactors. Responsibility of BNL was defined as developing covariances for structural materials and fission products, management of the library and coordination of the work; LANL responsibility was defined as covariances for light nuclei and actinides. The COMMARA-2.0 covariance library has been developed by BNL-LANL collaboration for Advanced Fuel Cycle Initiative applications over the period of three years, 2008-2010. It contains covariances for 110 materials relevant to fast reactor R&D. The library is to be used together with the ENDF/B-VII.0 central values of the latest official release of US files of evaluated neutron cross sections. COMMARA-2.0 library contains neutron cross section covariances for 12 light nuclei (coolants and moderators), 78 structural

  2. AFCI-2.0 Neutron Cross Section Covariance Library

    International Nuclear Information System (INIS)

    Herman, M.; Oblozinsky, P.; Mattoon, C.M.; Pigni, M.; Hoblit, S.; Mughabghab, S.F.; Sonzogni, A.; Talou, P.; Chadwick, M.B.; Hale, G.M.; Kahler, A.C.; Kawano, T.; Little, R.C.; Yount, P.G.

    2011-01-01

    The cross section covariance library has been under development by BNL-LANL collaborative effort over the last three years. The project builds on two covariance libraries developed earlier, with considerable input from BNL and LANL. In 2006, international effort under WPEC Subgroup 26 produced BOLNA covariance library by putting together data, often preliminary, from various sources for most important materials for nuclear reactor technology. This was followed in 2007 by collaborative effort of four US national laboratories to produce covariances, often of modest quality - hence the name low-fidelity, for virtually complete set of materials included in ENDF/B-VII.0. The present project is focusing on covariances of 4-5 major reaction channels for 110 materials of importance for power reactors. The work started under Global Nuclear Energy Partnership (GNEP) in 2008, which changed to Advanced Fuel Cycle Initiative (AFCI) in 2009. With the 2011 release the name has changed to the Covariance Multigroup Matrix for Advanced Reactor Applications (COMMARA) version 2.0. The primary purpose of the library is to provide covariances for AFCI data adjustment project, which is focusing on the needs of fast advanced burner reactors. Responsibility of BNL was defined as developing covariances for structural materials and fission products, management of the library and coordination of the work; LANL responsibility was defined as covariances for light nuclei and actinides. The COMMARA-2.0 covariance library has been developed by BNL-LANL collaboration for Advanced Fuel Cycle Initiative applications over the period of three years, 2008-2010. It contains covariances for 110 materials relevant to fast reactor R and D. The library is to be used together with the ENDF/B-VII.0 central values of the latest official release of US files of evaluated neutron cross sections. COMMARA-2.0 library contains neutron cross section covariances for 12 light nuclei (coolants and moderators), 78

  3. Evaluation of covariances for resolved resonance parameters of 235U, 238U, and 239Pu in JENDL-3.2

    International Nuclear Information System (INIS)

    Kawano, Toshihiko; Shibata, Keiichi

    2003-02-01

    Evaluation of covariances for resolved resonance parameters of 235 U, 238 U, and 239 Pu was carried out. Although a large number of resolved resonances are observed for major actinides, uncertainties in averaged cross sections are more important than those in resonance parameters in reactor calculations. We developed a simple method which derives a covariance matrix for the resolved resonance parameters from uncertainties in the averaged cross sections. The method was adopted to evaluate the covariance data for some important actinides, and the results were compiled in the JENDL-3.2 covariance file. (author)

  4. Emergent gravity on covariant quantum spaces in the IKKT model

    Energy Technology Data Exchange (ETDEWEB)

    Steinacker, Harold C. [Faculty of Physics, University of Vienna,Boltzmanngasse 5, A-1090 Vienna (Austria)

    2016-12-30

    We study perturbations of 4-dimensional fuzzy spheres as backgrounds in the IKKT or IIB matrix model. Gauge fields and metric fluctuations are identified among the excitation modes with lowest spin, supplemented by a tower of higher-spin fields. They arise from an internal structure which can be viewed as a twisted bundle over S{sup 4}, leading to a covariant noncommutative geometry. The linearized 4-dimensional Einstein equations are obtained from the classical matrix model action under certain conditions, modified by an IR cutoff. Some one-loop contributions to the effective action are computed using the formalism of string states.

  5. Random matrix theory filters in portfolio optimisation: A stability and risk assessment

    Science.gov (United States)

    Daly, J.; Crane, M.; Ruskin, H. J.

    2008-07-01

    Random matrix theory (RMT) filters, applied to covariance matrices of financial returns, have recently been shown to offer improvements to the optimisation of stock portfolios. This paper studies the effect of three RMT filters on the realised portfolio risk, and on the stability of the filtered covariance matrix, using bootstrap analysis and out-of-sample testing. We propose an extension to an existing RMT filter, (based on Krzanowski stability), which is observed to reduce risk and increase stability, when compared to other RMT filters tested. We also study a scheme for filtering the covariance matrix directly, as opposed to the standard method of filtering correlation, where the latter is found to lower the realised risk, on average, by up to 6.7%. We consider both equally and exponentially weighted covariance matrices in our analysis, and observe that the overall best method out-of-sample was that of the exponentially weighted covariance, with our Krzanowski stability-based filter applied to the correlation matrix. We also find that the optimal out-of-sample decay factors, for both filtered and unfiltered forecasts, were higher than those suggested by Riskmetrics [J.P. Morgan, Reuters, Riskmetrics technical document, Technical Report, 1996. http://www.riskmetrics.com/techdoc.html], with those for the latter approaching a value of α=1. In conclusion, RMT filtering reduced the realised risk, on average, and in the majority of cases when tested out-of-sample, but increased the realised risk on a marked number of individual days-in some cases more than doubling it.

  6. SG39 Deliverables. Comments on Covariance Data

    International Nuclear Information System (INIS)

    Yokoyama, Kenji

    2015-01-01

    The covariance matrix of a scattered data set, x_i (i=1,n), must be symmetric and positive-definite. As one of WPEC/SG39 contributions to the SG40/CIELO project, several comments or recommendations on the covariance data are described here from the viewpoint of nuclear-data users. To make the comments concrete and useful for nuclear-data evaluators, the covariance data of the latest evaluated nuclear data library, JENDL-4.0 and ENDF/B-VII.1 are treated here as the representative materials. The surveyed nuclides are five isotopes that are most important for fast reactor application. The nuclides, reactions and energy regions dealt with are followings: Pu-239: fission (2.5∼10 keV) and capture (2.5∼10 keV), U-235: fission (500 eV∼10 keV) and capture (500 eV∼30 keV), U-238: fission (1∼10 MeV), capture (below 20 keV, 20∼150 keV), inelastic (above 100 keV) and elastic (above 20 keV), Fe-56: elastic (below 850 keV) and average scattering cosine (above 10 keV), and, Na-23: capture (600 eV∼600 keV), inelastic (above 1 MeV) and elastic (around 2 keV)

  7. Modified Covariance Matrix Adaptation – Evolution Strategy algorithm for constrained optimization under uncertainty, application to rocket design

    Directory of Open Access Journals (Sweden)

    Chocat Rudy

    2015-01-01

    Full Text Available The design of complex systems often induces a constrained optimization problem under uncertainty. An adaptation of CMA-ES(λ, μ optimization algorithm is proposed in order to efficiently handle the constraints in the presence of noise. The update mechanisms of the parametrized distribution used to generate the candidate solutions are modified. The constraint handling method allows to reduce the semi-principal axes of the probable research ellipsoid in the directions violating the constraints. The proposed approach is compared to existing approaches on three analytic optimization problems to highlight the efficiency and the robustness of the algorithm. The proposed method is used to design a two stage solid propulsion launch vehicle.

  8. Covariance Between Genotypic Effects and its Use for Genomic Inference in Half-Sib Families

    Science.gov (United States)

    Wittenburg, Dörte; Teuscher, Friedrich; Klosa, Jan; Reinsch, Norbert

    2016-01-01

    In livestock, current statistical approaches utilize extensive molecular data, e.g., single nucleotide polymorphisms (SNPs), to improve the genetic evaluation of individuals. The number of model parameters increases with the number of SNPs, so the multicollinearity between covariates can affect the results obtained using whole genome regression methods. In this study, dependencies between SNPs due to linkage and linkage disequilibrium among the chromosome segments were explicitly considered in methods used to estimate the effects of SNPs. The population structure affects the extent of such dependencies, so the covariance among SNP genotypes was derived for half-sib families, which are typical in livestock populations. Conditional on the SNP haplotypes of the common parent (sire), the theoretical covariance was determined using the haplotype frequencies of the population from which the individual parent (dam) was derived. The resulting covariance matrix was included in a statistical model for a trait of interest, and this covariance matrix was then used to specify prior assumptions for SNP effects in a Bayesian framework. The approach was applied to one family in simulated scenarios (few and many quantitative trait loci) and using semireal data obtained from dairy cattle to identify genome segments that affect performance traits, as well as to investigate the impact on predictive ability. Compared with a method that does not explicitly consider any of the relationship among predictor variables, the accuracy of genetic value prediction was improved by 10–22%. The results show that the inclusion of dependence is particularly important for genomic inference based on small sample sizes. PMID:27402363

  9. Fast Component Pursuit for Large-Scale Inverse Covariance Estimation.

    Science.gov (United States)

    Han, Lei; Zhang, Yu; Zhang, Tong

    2016-08-01

    The maximum likelihood estimation (MLE) for the Gaussian graphical model, which is also known as the inverse covariance estimation problem, has gained increasing interest recently. Most existing works assume that inverse covariance estimators contain sparse structure and then construct models with the ℓ 1 regularization. In this paper, different from existing works, we study the inverse covariance estimation problem from another perspective by efficiently modeling the low-rank structure in the inverse covariance, which is assumed to be a combination of a low-rank part and a diagonal matrix. One motivation for this assumption is that the low-rank structure is common in many applications including the climate and financial analysis, and another one is that such assumption can reduce the computational complexity when computing its inverse. Specifically, we propose an efficient COmponent Pursuit (COP) method to obtain the low-rank part, where each component can be sparse. For optimization, the COP method greedily learns a rank-one component in each iteration by maximizing the log-likelihood. Moreover, the COP algorithm enjoys several appealing properties including the existence of an efficient solution in each iteration and the theoretical guarantee on the convergence of this greedy approach. Experiments on large-scale synthetic and real-world datasets including thousands of millions variables show that the COP method is faster than the state-of-the-art techniques for the inverse covariance estimation problem when achieving comparable log-likelihood on test data.

  10. Directional selection effects on patterns of phenotypic (co)variation in wild populations.

    Science.gov (United States)

    Assis, A P A; Patton, J L; Hubbe, A; Marroig, G

    2016-11-30

    Phenotypic (co)variation is a prerequisite for evolutionary change, and understanding how (co)variation evolves is of crucial importance to the biological sciences. Theoretical models predict that under directional selection, phenotypic (co)variation should evolve in step with the underlying adaptive landscape, increasing the degree of correlation among co-selected traits as well as the amount of genetic variance in the direction of selection. Whether either of these outcomes occurs in natural populations is an open question and thus an important gap in evolutionary theory. Here, we documented changes in the phenotypic (co)variation structure in two separate natural populations in each of two chipmunk species (Tamias alpinus and T. speciosus) undergoing directional selection. In populations where selection was strongest (those of T. alpinus), we observed changes, at least for one population, in phenotypic (co)variation that matched theoretical expectations, namely an increase of both phenotypic integration and (co)variance in the direction of selection and a re-alignment of the major axis of variation with the selection gradient. © 2016 The Author(s).

  11. Parametric Adaptive Radar Detector with Enhanced Mismatched Signals Rejection Capabilities

    Directory of Open Access Journals (Sweden)

    Liu Bin

    2010-01-01

    Full Text Available We consider the problem of adaptive signal detection in the presence of Gaussian noise with unknown covariance matrix. We propose a parametric radar detector by introducing a design parameter to trade off the target sensitivity with sidelobes energy rejection. The resulting detector merges the statistics of Kelly's GLRT and of the Rao test and so covers Kelly's GLRT and the Rao test as special cases. Both invariance properties and constant false alarm rate (CFAR behavior for this detector are studied. At the analysis stage, the performance of the new receiver is assessed and compared with several traditional adaptive detectors. The results highlight better rejection capabilities of this proposed detector for mismatched signals. Further, we develop two two-stage detectors, one of which consists of an adaptive matched filter (AMF followed by the aforementioned detector, and the other is obtained by cascading a GLRT-based Subspace Detector (SD and the proposed adaptive detector. We show that the former two-stage detector outperforms traditional two-stage detectors in terms of selectivity, and the latter yields more robustness.

  12. Non-evaluation applications for covariance matrices

    Energy Technology Data Exchange (ETDEWEB)

    Smith, D.L.

    1982-05-01

    The possibility for application of covariance matrix techniques to a variety of common research problems other than formal data evaluation are demonstrated by means of several examples. These examples deal with such matters as fitting spectral data, deriving uncertainty estimates for results calculated from experimental data, obtaining the best values for plurally-measured quantities, and methods for analysis of cross section errors based on properties of the experiment. The examples deal with realistic situations encountered in the laboratory, and they are treated in sufficient detail to enable a careful reader to extrapolate the methods to related problems.

  13. Examination of various roles for covariance matrices in the development, evaluation, and application of nuclear data

    International Nuclear Information System (INIS)

    Smith, D.L.

    1988-01-01

    The last decade has been a period of rapid development in the implementation of covariance-matrix methodology in nuclear data research. This paper offers some perspective on the progress which has been made, on some of the unresolved problems, and on the potential yet to be realized. These discussions address a variety of issues related to the development of nuclear data. Topics examined are: the importance of designing and conducting experiments so that error information is conveniently generated; the procedures for identifying error sources and quantifying their magnitudes and correlations; the combination of errors; the importance of consistent and well-characterized measurement standards; the role of covariances in data parameterization (fitting); the estimation of covariances for values calculated from mathematical models; the identification of abnormalities in covariance matrices and the analysis of their consequences; the problems encountered in representing covariance information in evaluated files; the role of covariances in the weighting of diverse data sets; the comparison of various evaluations; the influence of primary-data covariance in the analysis of covariances for derived quantities (sensitivity); and the role of covariances in the merging of the diverse nuclear data information. 226 refs., 2 tabs

  14. Do Time-Varying Covariances, Volatility Comovement and Spillover Matter?

    OpenAIRE

    Lakshmi Balasubramanyan

    2005-01-01

    Financial markets and their respective assets are so intertwined; analyzing any single market in isolation ignores important information. We investigate whether time varying volatility comovement and spillover impact the true variance-covariance matrix under a time-varying correlation set up. Statistically significant volatility spillover and comovement between US, UK and Japan is found. To demonstrate the importance of modelling volatility comovement and spillover, we look at a simple portfo...

  15. A full scale approximation of covariance functions for large spatial data sets

    KAUST Repository

    Sang, Huiyan

    2011-10-10

    Gaussian process models have been widely used in spatial statistics but face tremendous computational challenges for very large data sets. The model fitting and spatial prediction of such models typically require O(n 3) operations for a data set of size n. Various approximations of the covariance functions have been introduced to reduce the computational cost. However, most existing approximations cannot simultaneously capture both the large- and the small-scale spatial dependence. A new approximation scheme is developed to provide a high quality approximation to the covariance function at both the large and the small spatial scales. The new approximation is the summation of two parts: a reduced rank covariance and a compactly supported covariance obtained by tapering the covariance of the residual of the reduced rank approximation. Whereas the former part mainly captures the large-scale spatial variation, the latter part captures the small-scale, local variation that is unexplained by the former part. By combining the reduced rank representation and sparse matrix techniques, our approach allows for efficient computation for maximum likelihood estimation, spatial prediction and Bayesian inference. We illustrate the new approach with simulated and real data sets. © 2011 Royal Statistical Society.

  16. A full scale approximation of covariance functions for large spatial data sets

    KAUST Repository

    Sang, Huiyan; Huang, Jianhua Z.

    2011-01-01

    Gaussian process models have been widely used in spatial statistics but face tremendous computational challenges for very large data sets. The model fitting and spatial prediction of such models typically require O(n 3) operations for a data set of size n. Various approximations of the covariance functions have been introduced to reduce the computational cost. However, most existing approximations cannot simultaneously capture both the large- and the small-scale spatial dependence. A new approximation scheme is developed to provide a high quality approximation to the covariance function at both the large and the small spatial scales. The new approximation is the summation of two parts: a reduced rank covariance and a compactly supported covariance obtained by tapering the covariance of the residual of the reduced rank approximation. Whereas the former part mainly captures the large-scale spatial variation, the latter part captures the small-scale, local variation that is unexplained by the former part. By combining the reduced rank representation and sparse matrix techniques, our approach allows for efficient computation for maximum likelihood estimation, spatial prediction and Bayesian inference. We illustrate the new approach with simulated and real data sets. © 2011 Royal Statistical Society.

  17. Exploiting Data Sparsity In Covariance Matrix Computations on Heterogeneous Systems

    KAUST Repository

    Charara, Ali

    2018-01-01

    the thesis leverage recursive formulations for dense Cholesky-based matrix al- gorithms, but it also implements a novel TLR-Cholesky factorization using batched linear algebra operations to increase hardware occupancy and reduce the overhead of the API

  18. Histogram Equalization to Model Adaptation for Robust Speech Recognition

    Directory of Open Access Journals (Sweden)

    Suh Youngjoo

    2010-01-01

    Full Text Available We propose a new model adaptation method based on the histogram equalization technique for providing robustness in noisy environments. The trained acoustic mean models of a speech recognizer are adapted into environmentally matched conditions by using the histogram equalization algorithm on a single utterance basis. For more robust speech recognition in the heavily noisy conditions, trained acoustic covariance models are efficiently adapted by the signal-to-noise ratio-dependent linear interpolation between trained covariance models and utterance-level sample covariance models. Speech recognition experiments on both the digit-based Aurora2 task and the large vocabulary-based task showed that the proposed model adaptation approach provides significant performance improvements compared to the baseline speech recognizer trained on the clean speech data.

  19. Adaptive Unscented Kalman Filter using Maximum Likelihood Estimation

    DEFF Research Database (Denmark)

    Mahmoudi, Zeinab; Poulsen, Niels Kjølstad; Madsen, Henrik

    2017-01-01

    The purpose of this study is to develop an adaptive unscented Kalman filter (UKF) by tuning the measurement noise covariance. We use the maximum likelihood estimation (MLE) and the covariance matching (CM) method to estimate the noise covariance. The multi-step prediction errors generated...

  20. Anomaly detection in OECD Benchmark data using co-variance methods

    International Nuclear Information System (INIS)

    Srinivasan, G.S.; Krinizs, K.; Por, G.

    1993-02-01

    OECD Benchmark data distributed for the SMORN VI Specialists Meeting in Reactor Noise were investigated for anomaly detection in artificially generated reactor noise benchmark analysis. It was observed that statistical features extracted from covariance matrix of frequency components are very sensitive in terms of the anomaly detection level. It is possible to create well defined alarm levels. (R.P.) 5 refs.; 23 figs.; 1 tab

  1. Covariant Transform

    OpenAIRE

    Kisil, Vladimir V.

    2010-01-01

    The paper develops theory of covariant transform, which is inspired by the wavelet construction. It was observed that many interesting types of wavelets (or coherent states) arise from group representations which are not square integrable or vacuum vectors which are not admissible. Covariant transform extends an applicability of the popular wavelets construction to classic examples like the Hardy space H_2, Banach spaces, covariant functional calculus and many others. Keywords: Wavelets, cohe...

  2. Metagenomic covariation along densely sampled environmental gradients in the Red Sea

    KAUST Repository

    Thompson, Luke R

    2016-07-15

    Oceanic microbial diversity covaries with physicochemical parameters. Temperature, for example, explains approximately half of global variation in surface taxonomic abundance. It is unknown, however, whether covariation patterns hold over narrower parameter gradients and spatial scales, and extending to mesopelagic depths. We collected and sequenced 45 epipelagic and mesopelagic microbial metagenomes on a meridional transect through the eastern Red Sea. We asked which environmental parameters explain the most variation in relative abundances of taxonomic groups, gene ortholog groups, and pathways—at a spatial scale of <2000 km, along narrow but well-defined latitudinal and depth-dependent gradients. We also asked how microbes are adapted to gradients and extremes in irradiance, temperature, salinity, and nutrients, examining the responses of individual gene ortholog groups to these parameters. Functional and taxonomic metrics were equally well explained (75–79%) by environmental parameters. However, only functional and not taxonomic covariation patterns were conserved when comparing with an intruding water mass with different physicochemical properties. Temperature explained the most variation in each metric, followed by nitrate, chlorophyll, phosphate, and salinity. That nitrate explained more variation than phosphate suggested nitrogen limitation, consistent with low surface N:P ratios. Covariation of gene ortholog groups with environmental parameters revealed patterns of functional adaptation to the challenging Red Sea environment: high irradiance, temperature, salinity, and low nutrients. Nutrient-acquisition gene ortholog groups were anti-correlated with concentrations of their respective nutrient species, recapturing trends previously observed across much larger distances and environmental gradients. This dataset of metagenomic covariation along densely sampled environmental gradients includes online data exploration supplements, serving as a community

  3. Vast Volatility Matrix Estimation using High Frequency Data for Portfolio Selection*

    Science.gov (United States)

    Fan, Jianqing; Li, Yingying; Yu, Ke

    2012-01-01

    Portfolio allocation with gross-exposure constraint is an effective method to increase the efficiency and stability of portfolios selection among a vast pool of assets, as demonstrated in Fan et al. (2011). The required high-dimensional volatility matrix can be estimated by using high frequency financial data. This enables us to better adapt to the local volatilities and local correlations among vast number of assets and to increase significantly the sample size for estimating the volatility matrix. This paper studies the volatility matrix estimation using high-dimensional high-frequency data from the perspective of portfolio selection. Specifically, we propose the use of “pairwise-refresh time” and “all-refresh time” methods based on the concept of “refresh time” proposed by Barndorff-Nielsen et al. (2008) for estimation of vast covariance matrix and compare their merits in the portfolio selection. We establish the concentration inequalities of the estimates, which guarantee desirable properties of the estimated volatility matrix in vast asset allocation with gross exposure constraints. Extensive numerical studies are made via carefully designed simulations. Comparing with the methods based on low frequency daily data, our methods can capture the most recent trend of the time varying volatility and correlation, hence provide more accurate guidance for the portfolio allocation in the next time period. The advantage of using high-frequency data is significant in our simulation and empirical studies, which consist of 50 simulated assets and 30 constituent stocks of Dow Jones Industrial Average index. PMID:23264708

  4. Vast Volatility Matrix Estimation using High Frequency Data for Portfolio Selection.

    Science.gov (United States)

    Fan, Jianqing; Li, Yingying; Yu, Ke

    2012-01-01

    Portfolio allocation with gross-exposure constraint is an effective method to increase the efficiency and stability of portfolios selection among a vast pool of assets, as demonstrated in Fan et al. (2011). The required high-dimensional volatility matrix can be estimated by using high frequency financial data. This enables us to better adapt to the local volatilities and local correlations among vast number of assets and to increase significantly the sample size for estimating the volatility matrix. This paper studies the volatility matrix estimation using high-dimensional high-frequency data from the perspective of portfolio selection. Specifically, we propose the use of "pairwise-refresh time" and "all-refresh time" methods based on the concept of "refresh time" proposed by Barndorff-Nielsen et al. (2008) for estimation of vast covariance matrix and compare their merits in the portfolio selection. We establish the concentration inequalities of the estimates, which guarantee desirable properties of the estimated volatility matrix in vast asset allocation with gross exposure constraints. Extensive numerical studies are made via carefully designed simulations. Comparing with the methods based on low frequency daily data, our methods can capture the most recent trend of the time varying volatility and correlation, hence provide more accurate guidance for the portfolio allocation in the next time period. The advantage of using high-frequency data is significant in our simulation and empirical studies, which consist of 50 simulated assets and 30 constituent stocks of Dow Jones Industrial Average index.

  5. Inverse modeling of the terrestrial carbon flux in China with flux covariance among inverted regions

    Science.gov (United States)

    Wang, H.; Jiang, F.; Chen, J. M.; Ju, W.; Wang, H.

    2011-12-01

    Quantitative understanding of the role of ocean and terrestrial biosphere in the global carbon cycle, their response and feedback to climate change is required for the future projection of the global climate. China has the largest amount of anthropogenic CO2 emission, diverse terrestrial ecosystems and an unprecedented rate of urbanization. Thus information on spatial and temporal distributions of the terrestrial carbon flux in China is of great importance in understanding the global carbon cycle. We developed a nested inversion with focus in China. Based on Transcom 22 regions for the globe, we divide China and its neighboring countries into 17 regions, making 39 regions in total for the globe. A Bayesian synthesis inversion is made to estimate the terrestrial carbon flux based on GlobalView CO2 data. In the inversion, GEOS-Chem is used as the transport model to develop the transport matrix. A terrestrial ecosystem model named BEPS is used to produce the prior surface flux to constrain the inversion. However, the sparseness of available observation stations in Asia poses a challenge to the inversion for the 17 small regions. To obtain additional constraint on the inversion, a prior flux covariance matrix is constructed using the BEPS model through analyzing the correlation in the net carbon flux among regions under variable climate conditions. The use of the covariance among different regions in the inversion effectively extends the information content of CO2 observations to more regions. The carbon flux over the 39 land and ocean regions are inverted for the period from 2004 to 2009. In order to investigate the impact of introducing the covariance matrix with non-zero off-diagonal values to the inversion, the inverted terrestrial carbon flux over China is evaluated against ChinaFlux eddy-covariance observations after applying an upscaling methodology.

  6. Criteria of the validation of experimental and evaluated covariance data

    International Nuclear Information System (INIS)

    Badikov, S.

    2008-01-01

    The criteria of the validation of experimental and evaluated covariance data are reviewed. In particular: a) the criterion of the positive definiteness for covariance matrices, b) the relationship between the 'integral' experimental and estimated uncertainties, c) the validity of the statistical invariants, d) the restrictions imposed to correlations between experimental errors, are described. Applying these criteria in nuclear data evaluation was considered and 4 particular points have been examined. First preserving positive definiteness of covariance matrices in case of arbitrary transformation of a random vector was considered, properties of the covariance matrices in operations widely used in neutron and reactor physics (splitting and collapsing energy groups, averaging the physical values over energy groups, estimation parameters on the basis of measurements by means of generalized least squares method) were studied. Secondly, an algorithm for comparison of experimental and estimated 'integral' uncertainties was developed, square root of determinant of a covariance matrix is recommended for use in nuclear data evaluation as a measure of 'integral' uncertainty for vectors of experimental and estimated values. Thirdly, a set of statistical invariants-values which are preserved in statistical processing was presented. And fourthly, the inequality that signals a correlation between experimental errors that leads to unphysical values is given. An application is given concerning the cross-section of the (n,t) reaction on Li 6 with a neutron incident energy comprised between 1 and 100 keV

  7. Statistical mechanics of learning orthogonal signals for general covariance models

    International Nuclear Information System (INIS)

    Hoyle, David C

    2010-01-01

    Statistical mechanics techniques have proved to be useful tools in quantifying the accuracy with which signal vectors are extracted from experimental data. However, analysis has previously been limited to specific model forms for the population covariance C, which may be inappropriate for real world data sets. In this paper we obtain new statistical mechanical results for a general population covariance matrix C. For data sets consisting of p sample points in R N we use the replica method to study the accuracy of orthogonal signal vectors estimated from the sample data. In the asymptotic limit of N,p→∞ at fixed α = p/N, we derive analytical results for the signal direction learning curves. In the asymptotic limit the learning curves follow a single universal form, each displaying a retarded learning transition. An explicit formula for the location of the retarded learning transition is obtained and we find marked variation in the location of the retarded learning transition dependent on the distribution of population covariance eigenvalues. The results of the replica analysis are confirmed against simulation

  8. Super-sample covariance approximations and partial sky coverage

    Science.gov (United States)

    Lacasa, Fabien; Lima, Marcos; Aguena, Michel

    2018-04-01

    Super-sample covariance (SSC) is the dominant source of statistical error on large scale structure (LSS) observables for both current and future galaxy surveys. In this work, we concentrate on the SSC of cluster counts, also known as sample variance, which is particularly useful for the self-calibration of the cluster observable-mass relation; our approach can similarly be applied to other observables, such as galaxy clustering and lensing shear. We first examined the accuracy of two analytical approximations proposed in the literature for the flat sky limit, finding that they are accurate at the 15% and 30-35% level, respectively, for covariances of counts in the same redshift bin. We then developed a harmonic expansion formalism that allows for the prediction of SSC in an arbitrary survey mask geometry, such as large sky areas of current and future surveys. We show analytically and numerically that this formalism recovers the full sky and flat sky limits present in the literature. We then present an efficient numerical implementation of the formalism, which allows fast and easy runs of covariance predictions when the survey mask is modified. We applied our method to a mask that is broadly similar to the Dark Energy Survey footprint, finding a non-negligible negative cross-z covariance, i.e. redshift bins are anti-correlated. We also examined the case of data removal from holes due to, for example bright stars, quality cuts, or systematic removals, and find that this does not have noticeable effects on the structure of the SSC matrix, only rescaling its amplitude by the effective survey area. These advances enable analytical covariances of LSS observables to be computed for current and future galaxy surveys, which cover large areas of the sky where the flat sky approximation fails.

  9. Are your covariates under control? How normalization can re-introduce covariate effects.

    Science.gov (United States)

    Pain, Oliver; Dudbridge, Frank; Ronald, Angelica

    2018-04-30

    Many statistical tests rely on the assumption that the residuals of a model are normally distributed. Rank-based inverse normal transformation (INT) of the dependent variable is one of the most popular approaches to satisfy the normality assumption. When covariates are included in the analysis, a common approach is to first adjust for the covariates and then normalize the residuals. This study investigated the effect of regressing covariates against the dependent variable and then applying rank-based INT to the residuals. The correlation between the dependent variable and covariates at each stage of processing was assessed. An alternative approach was tested in which rank-based INT was applied to the dependent variable before regressing covariates. Analyses based on both simulated and real data examples demonstrated that applying rank-based INT to the dependent variable residuals after regressing out covariates re-introduces a linear correlation between the dependent variable and covariates, increasing type-I errors and reducing power. On the other hand, when rank-based INT was applied prior to controlling for covariate effects, residuals were normally distributed and linearly uncorrelated with covariates. This latter approach is therefore recommended in situations were normality of the dependent variable is required.

  10. Spectral correlation functions of the sum of two independent complex Wishart matrices with unequal covariances

    International Nuclear Information System (INIS)

    Akemann, Gernot; Checinski, Tomasz; Kieburg, Mario

    2016-01-01

    We compute the spectral statistics of the sum H of two independent complex Wishart matrices, each of which is correlated with a different covariance matrix. Random matrix theory enjoys many applications including sums and products of random matrices. Typically ensembles with correlations among the matrix elements are much more difficult to solve. Using a combination of supersymmetry, superbosonisation and bi-orthogonal functions we are able to determine all spectral k -point density correlation functions of H for arbitrary matrix size N . In the half-degenerate case, when one of the covariance matrices is proportional to the identity, the recent results by Kumar for the joint eigenvalue distribution of H serve as our starting point. In this case the ensemble has a bi-orthogonal structure and we explicitly determine its kernel, providing its exact solution for finite N . The kernel follows from computing the expectation value of a single characteristic polynomial. In the general non-degenerate case the generating function for the k -point resolvent is determined from a supersymmetric evaluation of the expectation value of k ratios of characteristic polynomials. Numerical simulations illustrate our findings for the spectral density at finite N and we also give indications how to do the asymptotic large- N analysis. (paper)

  11. The effects of stress and sex on selection, genetic covariance, and the evolutionary response.

    Science.gov (United States)

    Holman, L; Jacomb, F

    2017-10-01

    The capacity of a population to adapt to selection (evolvability) depends on whether the structure of genetic variation permits the evolution of fitter trait combinations. Selection, genetic variance and genetic covariance can change under environmental stress, and males and females are not genetically independent, yet the combined effects of stress and dioecy on evolvability are not well understood. Here, we estimate selection, genetic (co)variance and evolvability in both sexes of Tribolium castaneum flour beetles under stressful and benign conditions, using a half-sib breeding design. Although stress uncovered substantial latent heritability, stress also affected genetic covariance, such that evolvability remained low under stress. Sexual selection on males and natural selection on females favoured a similar phenotype, and there was positive intersex genetic covariance. Consequently, sexual selection on males augmented adaptation in females, and intralocus sexual conflict was weak or absent. This study highlights that increased heritability does not necessarily increase evolvability, suggests that selection can deplete genetic variance for multivariate trait combinations with strong effects on fitness, and tests the recent hypothesis that sexual conflict is weaker in stressful or novel environments. © 2017 European Society For Evolutionary Biology. Journal of Evolutionary Biology © 2017 European Society For Evolutionary Biology.

  12. Covariance evaluation system

    International Nuclear Information System (INIS)

    Kawano, Toshihiko; Shibata, Keiichi.

    1997-09-01

    A covariance evaluation system for the evaluated nuclear data library was established. The parameter estimation method and the least squares method with a spline function are used to generate the covariance data. Uncertainties of nuclear reaction model parameters are estimated from experimental data uncertainties, then the covariance of the evaluated cross sections is calculated by means of error propagation. Computer programs ELIESE-3, EGNASH4, ECIS, and CASTHY are used. Covariances of 238 U reaction cross sections were calculated with this system. (author)

  13. Instantaneous Attitude Determination Based on Original Multi-antenna Observations Using Adaptively Robust Kalman Filtering

    Directory of Open Access Journals (Sweden)

    GAN Yu

    2015-09-01

    Full Text Available Attitude determination directly by carrier phase observation makes optimal use of observation and attitude constraints. The phase models based on misalignment angle and multiplicative quaternion error are derived. The state models for attitude estimation with and without external angular rate sensors are both erected. The attitude errors are estimated by adaptively robust filtering, in which the adaptive factors of ambiguity and attitude error are decided respectively following the idea of multi adaptive factor filtering. The factor of attitude is determined by a three-section function containing Ratio. Adaptively robust filtering makes the best use of constraint and historical information, fusing them in the calculation of float solution. As the accuracy of float solution and the structure of covariance matrix are improved greatly, the fix solution can be searched efficiently using LAMBDA (least-squares ambiguity decorrelation adjustment method merely, perfectly fulfilling the real-time requirement. Field test of a ship-based three-antenna attitude system is used to validate the proposed method. It is showed that direct attitude determination based on adaptively robust filtering has obvious advantages in efficiency and reliability.

  14. Directional variance adjustment: bias reduction in covariance matrices based on factor analysis with an application to portfolio optimization.

    Science.gov (United States)

    Bartz, Daniel; Hatrick, Kerr; Hesse, Christian W; Müller, Klaus-Robert; Lemm, Steven

    2013-01-01

    Robust and reliable covariance estimates play a decisive role in financial and many other applications. An important class of estimators is based on factor models. Here, we show by extensive Monte Carlo simulations that covariance matrices derived from the statistical Factor Analysis model exhibit a systematic error, which is similar to the well-known systematic error of the spectrum of the sample covariance matrix. Moreover, we introduce the Directional Variance Adjustment (DVA) algorithm, which diminishes the systematic error. In a thorough empirical study for the US, European, and Hong Kong stock market we show that our proposed method leads to improved portfolio allocation.

  15. Sparse inverse covariance estimation with the graphical lasso.

    Science.gov (United States)

    Friedman, Jerome; Hastie, Trevor; Tibshirani, Robert

    2008-07-01

    We consider the problem of estimating sparse graphs by a lasso penalty applied to the inverse covariance matrix. Using a coordinate descent procedure for the lasso, we develop a simple algorithm--the graphical lasso--that is remarkably fast: It solves a 1000-node problem ( approximately 500,000 parameters) in at most a minute and is 30-4000 times faster than competing methods. It also provides a conceptual link between the exact problem and the approximation suggested by Meinshausen and Bühlmann (2006). We illustrate the method on some cell-signaling data from proteomics.

  16. On the algebraic structure of covariant anomalies and covariant Schwinger terms

    International Nuclear Information System (INIS)

    Kelnhofer, G.

    1992-01-01

    A cohomological characterization of covariant anomalies and covariant Schwinger terms in an anomalous Yang-Mills theory is formulated and w ill be geometrically interpreted. The BRS and anti-BRS transformations are defined as purely differential geometric objects. Finally the covariant descent equations are formulated within this context. (author)

  17. Generation of phase-covariant quantum cloning

    International Nuclear Information System (INIS)

    Karimipour, V.; Rezakhani, A.T.

    2002-01-01

    It is known that in phase-covariant quantum cloning, the equatorial states on the Bloch sphere can be cloned with a fidelity higher than the optimal bound established for universal quantum cloning. We generalize this concept to include other states on the Bloch sphere with a definite z component of spin. It is shown that once we know the z component, we can always clone a state with a fidelity higher than the universal value and that of equatorial states. We also make a detailed study of the entanglement properties of the output copies and show that the equatorial states are the only states that give rise to a separable density matrix for the outputs

  18. Robust Covariance Estimators Based on Information Divergences and Riemannian Manifold

    Directory of Open Access Journals (Sweden)

    Xiaoqiang Hua

    2018-03-01

    Full Text Available This paper proposes a class of covariance estimators based on information divergences in heterogeneous environments. In particular, the problem of covariance estimation is reformulated on the Riemannian manifold of Hermitian positive-definite (HPD matrices. The means associated with information divergences are derived and used as the estimators. Without resorting to the complete knowledge of the probability distribution of the sample data, the geometry of the Riemannian manifold of HPD matrices is considered in mean estimators. Moreover, the robustness of mean estimators is analyzed using the influence function. Simulation results indicate the robustness and superiority of an adaptive normalized matched filter with our proposed estimators compared with the existing alternatives.

  19. A class of covariate-dependent spatiotemporal covariance functions

    Science.gov (United States)

    Reich, Brian J; Eidsvik, Jo; Guindani, Michele; Nail, Amy J; Schmidt, Alexandra M.

    2014-01-01

    In geostatistics, it is common to model spatially distributed phenomena through an underlying stationary and isotropic spatial process. However, these assumptions are often untenable in practice because of the influence of local effects in the correlation structure. Therefore, it has been of prolonged interest in the literature to provide flexible and effective ways to model non-stationarity in the spatial effects. Arguably, due to the local nature of the problem, we might envision that the correlation structure would be highly dependent on local characteristics of the domain of study, namely the latitude, longitude and altitude of the observation sites, as well as other locally defined covariate information. In this work, we provide a flexible and computationally feasible way for allowing the correlation structure of the underlying processes to depend on local covariate information. We discuss the properties of the induced covariance functions and discuss methods to assess its dependence on local covariate information by means of a simulation study and the analysis of data observed at ozone-monitoring stations in the Southeast United States. PMID:24772199

  20. Adaptive evolution of the matrix extracellular phosphoglycoprotein in mammals

    Directory of Open Access Journals (Sweden)

    Machado João

    2011-11-01

    Full Text Available Abstract Background Matrix extracellular phosphoglycoprotein (MEPE belongs to a family of small integrin-binding ligand N-linked glycoproteins (SIBLINGs that play a key role in skeleton development, particularly in mineralization, phosphate regulation and osteogenesis. MEPE associated disorders cause various physiological effects, such as loss of bone mass, tumors and disruption of renal function (hypophosphatemia. The study of this developmental gene from an evolutionary perspective could provide valuable insights on the adaptive diversification of morphological phenotypes in vertebrates. Results Here we studied the adaptive evolution of the MEPE gene in 26 Eutherian mammals and three birds. The comparative genomic analyses revealed a high degree of evolutionary conservation of some coding and non-coding regions of the MEPE gene across mammals indicating a possible regulatory or functional role likely related with mineralization and/or phosphate regulation. However, the majority of the coding region had a fast evolutionary rate, particularly within the largest exon (1467 bp. Rodentia and Scandentia had distinct substitution rates with an increased accumulation of both synonymous and non-synonymous mutations compared with other mammalian lineages. Characteristics of the gene (e.g. biochemical, evolutionary rate, and intronic conservation differed greatly among lineages of the eight mammalian orders. We identified 20 sites with significant positive selection signatures (codon and protein level outside the main regulatory motifs (dentonin and ASARM suggestive of an adaptive role. Conversely, we find three sites under selection in the signal peptide and one in the ASARM motif that were supported by at least one selection model. The MEPE protein tends to accumulate amino acids promoting disorder and potential phosphorylation targets. Conclusion MEPE shows a high number of selection signatures, revealing the crucial role of positive selection in the

  1. Tensor operators in R-matrix approach

    International Nuclear Information System (INIS)

    Bytsko, A.G.; Rossijskaya Akademiya Nauk, St. Petersburg

    1995-12-01

    The definitions and some properties (e.g. the Wigner-Eckart theorem, the fusion procedure) of covariant and contravariant q-tensor operators for quasitriangular quantum Lie algebras are formulated in the R-matrix language. The case of U q (sl(n)) (in particular, for n=2) is discussed in more detail. (orig.)

  2. Might "Unique" Factors Be "Common"? On the Possibility of Indeterminate Common-Unique Covariances

    Science.gov (United States)

    Grayson, Dave

    2006-01-01

    The present paper shows that the usual factor analytic structured data dispersion matrix lambda psi lambda' + delta can readily arise from a set of scores y = lambda eta + epsilon, shere the "common" (eta) and "unique" (epsilon) factors have nonzero covariance: gamma = Cov epsilon,eta) is not equal to 0. Implications of this finding are discussed…

  3. Covariance matrices for nuclear cross sections derived from nuclear model calculations

    International Nuclear Information System (INIS)

    Smith, D. L.

    2005-01-01

    The growing need for covariance information to accompany the evaluated cross section data libraries utilized in contemporary nuclear applications is spurring the development of new methods to provide this information. Many of the current general purpose libraries of evaluated nuclear data used in applications are derived either almost entirely from nuclear model calculations or from nuclear model calculations benchmarked by available experimental data. Consequently, a consistent method for generating covariance information under these circumstances is required. This report discusses a new approach to producing covariance matrices for cross sections calculated using nuclear models. The present method involves establishing uncertainty information for the underlying parameters of nuclear models used in the calculations and then propagating these uncertainties through to the derived cross sections and related nuclear quantities by means of a Monte Carlo technique rather than the more conventional matrix error propagation approach used in some alternative methods. The formalism to be used in such analyses is discussed in this report along with various issues and caveats that need to be considered in order to proceed with a practical implementation of the methodology

  4. ERRORJ, Multigroup covariance matrices generation from ENDF-6 format

    International Nuclear Information System (INIS)

    Chiba, Go

    2007-01-01

    1 - Description of program or function: ERRORJ produces multigroup covariance matrices from ENDF-6 format following mainly the methods of the ERRORR module in NJOY94.105. New version differs from previous version in the following features: Additional features in ERRORJ with respect to the NJOY94.105/ERRORR module: - expands processing for the covariance matrices of resolved and unresolved resonance parameters; - processes average cosine of scattering angle and fission spectrum; - treats cross-correlation between different materials and reactions; - accepts input of multigroup constants with various forms (user input, GENDF, etc.); - outputs files with various formats through utility NJOYCOVX (COVERX format, correlation matrix, relative error and standard deviation); - uses a 1% sensitivity method for processing of resonance parameters; - ERRORJ can process the JENDL-3.2 and 3.3 covariance matrices. Additional features of the version 2 with respect to the previous version of ERRORJ: - Since the release of version 2, ERRORJ has been modified to increase its reliability and stability, - calculation of the correlation coefficients in the resonance region, - Option for high-speed calculation is implemented, - Perturbation amount is optimised in a sensitivity calculation, - Effect of the resonance self-shielding can be considered, - a compact covariance format (LCOMP=2) proposed by N. M. Larson can be read. Additional features of the version 2.2.1 with respect to the previous version of ERRORJ: - Several routines were modified to reduce calculation time. The new one needs shorter calculation time (50-70%) than the old version without changing results. - In the U-233 and Pu-241 files of JENDL-3.3 an inconsistency between resonance parameters in MF=32 and those in MF=2 was corrected. NEA-1676/06: This version differs from the previous one (NEA-1676/05) in the following: ERRORJ2.2.1 was modified to treat the self-shielding effect accurately. NEA-1676/07: This version

  5. Robust and sparse correlation matrix estimation for the analysis of high-dimensional genomics data.

    Science.gov (United States)

    Serra, Angela; Coretto, Pietro; Fratello, Michele; Tagliaferri, Roberto; Stegle, Oliver

    2018-02-15

    Microarray technology can be used to study the expression of thousands of genes across a number of different experimental conditions, usually hundreds. The underlying principle is that genes sharing similar expression patterns, across different samples, can be part of the same co-expression system, or they may share the same biological functions. Groups of genes are usually identified based on cluster analysis. Clustering methods rely on the similarity matrix between genes. A common choice to measure similarity is to compute the sample correlation matrix. Dimensionality reduction is another popular data analysis task which is also based on covariance/correlation matrix estimates. Unfortunately, covariance/correlation matrix estimation suffers from the intrinsic noise present in high-dimensional data. Sources of noise are: sampling variations, presents of outlying sample units, and the fact that in most cases the number of units is much larger than the number of genes. In this paper, we propose a robust correlation matrix estimator that is regularized based on adaptive thresholding. The resulting method jointly tames the effects of the high-dimensionality, and data contamination. Computations are easy to implement and do not require hand tunings. Both simulated and real data are analyzed. A Monte Carlo experiment shows that the proposed method is capable of remarkable performances. Our correlation metric is more robust to outliers compared with the existing alternatives in two gene expression datasets. It is also shown how the regularization allows to automatically detect and filter spurious correlations. The same regularization is also extended to other less robust correlation measures. Finally, we apply the ARACNE algorithm on the SyNTreN gene expression data. Sensitivity and specificity of the reconstructed network is compared with the gold standard. We show that ARACNE performs better when it takes the proposed correlation matrix estimator as input. The R

  6. High-dimensional statistical inference: From vector to matrix

    Science.gov (United States)

    Zhang, Anru

    Statistical inference for sparse signals or low-rank matrices in high-dimensional settings is of significant interest in a range of contemporary applications. It has attracted significant recent attention in many fields including statistics, applied mathematics and electrical engineering. In this thesis, we consider several problems in including sparse signal recovery (compressed sensing under restricted isometry) and low-rank matrix recovery (matrix recovery via rank-one projections and structured matrix completion). The first part of the thesis discusses compressed sensing and affine rank minimization in both noiseless and noisy cases and establishes sharp restricted isometry conditions for sparse signal and low-rank matrix recovery. The analysis relies on a key technical tool which represents points in a polytope by convex combinations of sparse vectors. The technique is elementary while leads to sharp results. It is shown that, in compressed sensing, delta kA 0, delta kA < 1/3 + epsilon, deltak A + thetak,kA < 1 + epsilon, or deltatkA< √(t - 1) / t + epsilon are not sufficient to guarantee the exact recovery of all k-sparse signals for large k. Similar result also holds for matrix recovery. In addition, the conditions delta kA<1/3, deltak A+ thetak,kA<1, delta tkA < √(t - 1)/t and deltarM<1/3, delta rM+ thetar,rM<1, delta trM< √(t - 1)/ t are also shown to be sufficient respectively for stable recovery of approximately sparse signals and low-rank matrices in the noisy case. For the second part of the thesis, we introduce a rank-one projection model for low-rank matrix recovery and propose a constrained nuclear norm minimization method for stable recovery of low-rank matrices in the noisy case. The procedure is adaptive to the rank and robust against small perturbations. Both upper and lower bounds for the estimation accuracy under the Frobenius norm loss are obtained. The proposed estimator is shown to be rate-optimal under certain conditions. The

  7. Brownian distance covariance

    OpenAIRE

    Székely, Gábor J.; Rizzo, Maria L.

    2010-01-01

    Distance correlation is a new class of multivariate dependence coefficients applicable to random vectors of arbitrary and not necessarily equal dimension. Distance covariance and distance correlation are analogous to product-moment covariance and correlation, but generalize and extend these classical bivariate measures of dependence. Distance correlation characterizes independence: it is zero if and only if the random vectors are independent. The notion of covariance with...

  8. Directional Variance Adjustment: Bias Reduction in Covariance Matrices Based on Factor Analysis with an Application to Portfolio Optimization

    Science.gov (United States)

    Bartz, Daniel; Hatrick, Kerr; Hesse, Christian W.; Müller, Klaus-Robert; Lemm, Steven

    2013-01-01

    Robust and reliable covariance estimates play a decisive role in financial and many other applications. An important class of estimators is based on factor models. Here, we show by extensive Monte Carlo simulations that covariance matrices derived from the statistical Factor Analysis model exhibit a systematic error, which is similar to the well-known systematic error of the spectrum of the sample covariance matrix. Moreover, we introduce the Directional Variance Adjustment (DVA) algorithm, which diminishes the systematic error. In a thorough empirical study for the US, European, and Hong Kong stock market we show that our proposed method leads to improved portfolio allocation. PMID:23844016

  9. Directional variance adjustment: bias reduction in covariance matrices based on factor analysis with an application to portfolio optimization.

    Directory of Open Access Journals (Sweden)

    Daniel Bartz

    Full Text Available Robust and reliable covariance estimates play a decisive role in financial and many other applications. An important class of estimators is based on factor models. Here, we show by extensive Monte Carlo simulations that covariance matrices derived from the statistical Factor Analysis model exhibit a systematic error, which is similar to the well-known systematic error of the spectrum of the sample covariance matrix. Moreover, we introduce the Directional Variance Adjustment (DVA algorithm, which diminishes the systematic error. In a thorough empirical study for the US, European, and Hong Kong stock market we show that our proposed method leads to improved portfolio allocation.

  10. The genetic variance but not the genetic covariance of life-history traits changes towards the north in a time-constrained insect.

    Science.gov (United States)

    Sniegula, Szymon; Golab, Maria J; Drobniak, Szymon M; Johansson, Frank

    2018-03-22

    Seasonal time constraints are usually stronger at higher than lower latitudes and can exert strong selection on life-history traits and the correlations among these traits. To predict the response of life-history traits to environmental change along a latitudinal gradient, information must be obtained about genetic variance in traits and also genetic correlation between traits, that is the genetic variance-covariance matrix, G. Here, we estimated G for key life-history traits in an obligate univoltine damselfly that faces seasonal time constraints. We exposed populations to simulated native temperatures and photoperiods and common garden environmental conditions in a laboratory set-up. Despite differences in genetic variance in these traits between populations (lower variance at northern latitudes), there was no evidence for latitude-specific covariance of the life-history traits. At simulated native conditions, all populations showed strong genetic and phenotypic correlations between traits that shaped growth and development. The variance-covariance matrix changed considerably when populations were exposed to common garden conditions compared with the simulated natural conditions, showing the importance of environmentally induced changes in multivariate genetic structure. Our results highlight the importance of estimating variance-covariance matrixes in environments that mimic selection pressures and not only trait variances or mean trait values in common garden conditions for understanding the trait evolution across populations and environments. © 2018 European Society For Evolutionary Biology. Journal of Evolutionary Biology © 2018 European Society For Evolutionary Biology.

  11. Covariance approximation for large multivariate spatial data sets with an application to multiple climate model errors

    KAUST Repository

    Sang, Huiyan

    2011-12-01

    This paper investigates the cross-correlations across multiple climate model errors. We build a Bayesian hierarchical model that accounts for the spatial dependence of individual models as well as cross-covariances across different climate models. Our method allows for a nonseparable and nonstationary cross-covariance structure. We also present a covariance approximation approach to facilitate the computation in the modeling and analysis of very large multivariate spatial data sets. The covariance approximation consists of two parts: a reduced-rank part to capture the large-scale spatial dependence, and a sparse covariance matrix to correct the small-scale dependence error induced by the reduced rank approximation. We pay special attention to the case that the second part of the approximation has a block-diagonal structure. Simulation results of model fitting and prediction show substantial improvement of the proposed approximation over the predictive process approximation and the independent blocks analysis. We then apply our computational approach to the joint statistical modeling of multiple climate model errors. © 2012 Institute of Mathematical Statistics.

  12. A Charrelation Matrix-Based Blind Adaptive Detector for DS-CDMA Systems

    Directory of Open Access Journals (Sweden)

    Zhongqiang Luo

    2015-08-01

    Full Text Available In this paper, a blind adaptive detector is proposed for blind separation of user signals and blind estimation of spreading sequences in DS-CDMA systems. The blind separation scheme exploits a charrelation matrix for simple computation and effective extraction of information from observation signal samples. The system model of DS-CDMA signals is modeled as a blind separation framework. The unknown user information and spreading sequence of DS-CDMA systems can be estimated only from the sampled observation signals. Theoretical analysis and simulation results show that the improved performance of the proposed algorithm in comparison with the existing conventional algorithms used in DS-CDMA systems. Especially, the proposed scheme is suitable for when the number of observation samples is less and the signal to noise ratio (SNR is low.

  13. A Charrelation Matrix-Based Blind Adaptive Detector for DS-CDMA Systems.

    Science.gov (United States)

    Luo, Zhongqiang; Zhu, Lidong

    2015-08-14

    In this paper, a blind adaptive detector is proposed for blind separation of user signals and blind estimation of spreading sequences in DS-CDMA systems. The blind separation scheme exploits a charrelation matrix for simple computation and effective extraction of information from observation signal samples. The system model of DS-CDMA signals is modeled as a blind separation framework. The unknown user information and spreading sequence of DS-CDMA systems can be estimated only from the sampled observation signals. Theoretical analysis and simulation results show that the improved performance of the proposed algorithm in comparison with the existing conventional algorithms used in DS-CDMA systems. Especially, the proposed scheme is suitable for when the number of observation samples is less and the signal to noise ratio (SNR) is low.

  14. Covariance-based synaptic plasticity in an attractor network model accounts for fast adaptation in free operant learning.

    Science.gov (United States)

    Neiman, Tal; Loewenstein, Yonatan

    2013-01-23

    In free operant experiments, subjects alternate at will between targets that yield rewards stochastically. Behavior in these experiments is typically characterized by (1) an exponential distribution of stay durations, (2) matching of the relative time spent at a target to its relative share of the total number of rewards, and (3) adaptation after a change in the reward rates that can be very fast. The neural mechanism underlying these regularities is largely unknown. Moreover, current decision-making neural network models typically aim at explaining behavior in discrete-time experiments in which a single decision is made once in every trial, making these models hard to extend to the more natural case of free operant decisions. Here we show that a model based on attractor dynamics, in which transitions are induced by noise and preference is formed via covariance-based synaptic plasticity, can account for the characteristics of behavior in free operant experiments. We compare a specific instance of such a model, in which two recurrently excited populations of neurons compete for higher activity, to the behavior of rats responding on two levers for rewarding brain stimulation on a concurrent variable interval reward schedule (Gallistel et al., 2001). We show that the model is consistent with the rats' behavior, and in particular, with the observed fast adaptation to matching behavior. Further, we show that the neural model can be reduced to a behavioral model, and we use this model to deduce a novel "conservation law," which is consistent with the behavior of the rats.

  15. Covariant representations of nuclear *-algebras

    International Nuclear Information System (INIS)

    Moore, S.M.

    1978-01-01

    Extensions of the Csup(*)-algebra theory for covariant representations to nuclear *-algebra are considered. Irreducible covariant representations are essentially unique, an invariant state produces a covariant representation with stable vacuum, and the usual relation between ergodic states and covariant representations holds. There exist construction and decomposition theorems and a possible relation between derivations and covariant representations

  16. Earth Observing System Covariance Realism

    Science.gov (United States)

    Zaidi, Waqar H.; Hejduk, Matthew D.

    2016-01-01

    The purpose of covariance realism is to properly size a primary object's covariance in order to add validity to the calculation of the probability of collision. The covariance realism technique in this paper consists of three parts: collection/calculation of definitive state estimates through orbit determination, calculation of covariance realism test statistics at each covariance propagation point, and proper assessment of those test statistics. An empirical cumulative distribution function (ECDF) Goodness-of-Fit (GOF) method is employed to determine if a covariance is properly sized by comparing the empirical distribution of Mahalanobis distance calculations to the hypothesized parent 3-DoF chi-squared distribution. To realistically size a covariance for collision probability calculations, this study uses a state noise compensation algorithm that adds process noise to the definitive epoch covariance to account for uncertainty in the force model. Process noise is added until the GOF tests pass a group significance level threshold. The results of this study indicate that when outliers attributed to persistently high or extreme levels of solar activity are removed, the aforementioned covariance realism compensation method produces a tuned covariance with up to 80 to 90% of the covariance propagation timespan passing (against a 60% minimum passing threshold) the GOF tests-a quite satisfactory and useful result.

  17. Detecting an atomic clock frequency anomaly using an adaptive Kalman filter algorithm

    Science.gov (United States)

    Song, Huijie; Dong, Shaowu; Wu, Wenjun; Jiang, Meng; Wang, Weixiong

    2018-06-01

    The abnormal frequencies of an atomic clock mainly include frequency jump and frequency drift jump. Atomic clock frequency anomaly detection is a key technique in time-keeping. The Kalman filter algorithm, as a linear optimal algorithm, has been widely used in real-time detection for abnormal frequency. In order to obtain an optimal state estimation, the observation model and dynamic model of the Kalman filter algorithm should satisfy Gaussian white noise conditions. The detection performance is degraded if anomalies affect the observation model or dynamic model. The idea of the adaptive Kalman filter algorithm, applied to clock frequency anomaly detection, uses the residuals given by the prediction for building ‘an adaptive factor’ the prediction state covariance matrix is real-time corrected by the adaptive factor. The results show that the model error is reduced and the detection performance is improved. The effectiveness of the algorithm is verified by the frequency jump simulation, the frequency drift jump simulation and the measured data of the atomic clock by using the chi-square test.

  18. Deriving covariant holographic entanglement

    Energy Technology Data Exchange (ETDEWEB)

    Dong, Xi [School of Natural Sciences, Institute for Advanced Study, Princeton, NJ 08540 (United States); Lewkowycz, Aitor [Jadwin Hall, Princeton University, Princeton, NJ 08544 (United States); Rangamani, Mukund [Center for Quantum Mathematics and Physics (QMAP), Department of Physics, University of California, Davis, CA 95616 (United States)

    2016-11-07

    We provide a gravitational argument in favour of the covariant holographic entanglement entropy proposal. In general time-dependent states, the proposal asserts that the entanglement entropy of a region in the boundary field theory is given by a quarter of the area of a bulk extremal surface in Planck units. The main element of our discussion is an implementation of an appropriate Schwinger-Keldysh contour to obtain the reduced density matrix (and its powers) of a given region, as is relevant for the replica construction. We map this contour into the bulk gravitational theory, and argue that the saddle point solutions of these replica geometries lead to a consistent prescription for computing the field theory Rényi entropies. In the limiting case where the replica index is taken to unity, a local analysis suffices to show that these saddles lead to the extremal surfaces of interest. We also comment on various properties of holographic entanglement that follow from this construction.

  19. Uncertainty estimation of core safety parameters using cross-correlations of covariance matrix

    International Nuclear Information System (INIS)

    Yamamoto, Akio; Yasue, Yoshihiro; Endo, Tomohiro; Kodama, Yasuhiro; Ohoka, Yasunori; Tatsumi, Masahiro

    2013-01-01

    An uncertainty reduction method for core safety parameters, for which measurement values are not obtained, is proposed. We empirically recognize that there exist some correlations among the prediction errors of core safety parameters, e.g., a correlation between the control rod worth and the assembly relative power at corresponding position. Correlations of errors among core safety parameters are theoretically estimated using the covariance of cross sections and sensitivity coefficients of core parameters. The estimated correlations of errors among core safety parameters are verified through the direct Monte Carlo sampling method. Once the correlation of errors among core safety parameters is known, we can estimate the uncertainty of a safety parameter for which measurement value is not obtained. (author)

  20. Random matrix theory for heavy-tailed time series

    DEFF Research Database (Denmark)

    Heiny, Johannes

    2017-01-01

    This paper is a review of recent results for large random matrices with heavy-tailed entries. First, we outline the development of and some classical results in random matrix theory. We focus on large sample covariance matrices, their limiting spectral distributions, the asymptotic behavior...

  1. Adaptive covariation between the coat and movement proteins of prunus necrotic ringspot virus.

    Science.gov (United States)

    Codoñer, Francisco M; Fares, Mario A; Elena, Santiago F

    2006-06-01

    The relative functional and/or structural importance of different amino acid sites in a protein can be assessed by evaluating the selective constraints to which they have been subjected during the course of evolution. Here we explore such constraints at the linear and three-dimensional levels for the movement protein (MP) and coat protein (CP) encoded by RNA 3 of prunus necrotic ringspot ilarvirus (PNRSV). By a maximum-parsimony approach, the nucleotide sequences from 46 isolates of PNRSV varying in symptomatology, host tree, and geographic origin have been analyzed and sites under different selective pressures have been identified in both proteins. We have also performed covariation analyses to explore whether changes in certain amino acid sites condition subsequent variation in other sites of the same protein or the other protein. These covariation analyses shed light on which particular amino acids should be involved in the physical and functional interaction between MP and CP. Finally, we discuss these findings in the light of what is already known about the implication of certain sites and domains in structure and protein-protein and RNA-protein interactions.

  2. Fast LCMV-based Methods for Fundamental Frequency Estimation

    DEFF Research Database (Denmark)

    Jensen, Jesper Rindom; Glentis, George-Othon; Christensen, Mads Græsbøll

    2013-01-01

    peaks and require matrix inversions for each point in the search grid. In this paper, we therefore consider fast implementations of LCMV-based fundamental frequency estimators, exploiting the estimators' inherently low displacement rank of the used Toeplitz-like data covariance matrices, using...... with several orders of magnitude, but, as we show, further computational savings can be obtained by the adoption of an approximative IAA-based data covariance matrix estimator, reminiscent of the recently proposed Quasi-Newton IAA technique. Furthermore, it is shown how the considered pitch estimators can...... as such either the classic time domain averaging covariance matrix estimator, or, if aiming for an increased spectral resolution, the covariance matrix resulting from the application of the recent iterative adaptive approach (IAA). The proposed exact implementations reduce the required computational complexity...

  3. The covariant-evolution-operator method in bound-state QED

    International Nuclear Information System (INIS)

    Lindgren, Ingvar; Salomonson, Sten; Aasen, Bjoern

    2004-01-01

    The methods of quantum-electrodynamical (QED) calculations on bound atomic systems are reviewed with emphasis on the newly developed covariant-evolution-operator method. The aim is to compare that method with other available methods and also to point out possibilities to combine that with standard many-body perturbation theory (MBPT) in order to perform accurate numerical QED calculations, including quasi-degeneracy, also for light elements, where the electron correlation is relatively strong. As a background, the time-independent many-body perturbation theory (MBPT) is briefly reviewed, particularly the method with extended model space. Time-dependent perturbation theory is discussed in some detail, introducing the time-evolution operator and the Gell-Mann-Low relation, generalized to an arbitrary model space. Three methods of treating the bound-state QED problem are discussed. The standard S-matrix formulation, which is restricted to a degenerate model space, is discussed only briefly. Two methods applicable also to the quasi-degenerate problem are treated in more detail, the two-times Green's-function and the covariant-evolution-operator techniques. The treatment is concentrated on the latter technique, which has been developed more recently and which has not been discussed in more detail before. A comparison of the two-times Green's-function and the covariant-evolution-operator techniques, which have great similarities, is performed. In the appendix a simple procedure is derived for expressing the evolution-operator diagrams of arbitrary order. The possibilities of merging QED in the covariant evolution-operator formulation with MBPT in a systematic way is indicated. With such a technique it might be feasible to perform accurate QED calculations also on light elements, which is presently not possible with the techniques available

  4. Spatial Pyramid Covariance based Compact Video Code for Robust Face Retrieval in TV-series.

    Science.gov (United States)

    Li, Yan; Wang, Ruiping; Cui, Zhen; Shan, Shiguang; Chen, Xilin

    2016-10-10

    We address the problem of face video retrieval in TV-series which searches video clips based on the presence of specific character, given one face track of his/her. This is tremendously challenging because on one hand, faces in TV-series are captured in largely uncontrolled conditions with complex appearance variations, and on the other hand retrieval task typically needs efficient representation with low time and space complexity. To handle this problem, we propose a compact and discriminative representation for the huge body of video data, named Compact Video Code (CVC). Our method first models the face track by its sample (i.e., frame) covariance matrix to capture the video data variations in a statistical manner. To incorporate discriminative information and obtain more compact video signature suitable for retrieval, the high-dimensional covariance representation is further encoded as a much lower-dimensional binary vector, which finally yields the proposed CVC. Specifically, each bit of the code, i.e., each dimension of the binary vector, is produced via supervised learning in a max margin framework, which aims to make a balance between the discriminability and stability of the code. Besides, we further extend the descriptive granularity of covariance matrix from traditional pixel-level to more general patchlevel, and proceed to propose a novel hierarchical video representation named Spatial Pyramid Covariance (SPC) along with a fast calculation method. Face retrieval experiments on two challenging TV-series video databases, i.e., the Big Bang Theory and Prison Break, demonstrate the competitiveness of the proposed CVC over state-of-the-art retrieval methods. In addition, as a general video matching algorithm, CVC is also evaluated in traditional video face recognition task on a standard Internet database, i.e., YouTube Celebrities, showing its quite promising performance by using an extremely compact code with only 128 bits.

  5. Separation of Correlated Astrophysical Sources Using Multiple-Lag Data Covariance Matrices

    Directory of Open Access Journals (Sweden)

    Baccigalupi C

    2005-01-01

    Full Text Available This paper proposes a new strategy to separate astrophysical sources that are mutually correlated. This strategy is based on second-order statistics and exploits prior information about the possible structure of the mixing matrix. Unlike ICA blind separation approaches, where the sources are assumed mutually independent and no prior knowledge is assumed about the mixing matrix, our strategy allows the independence assumption to be relaxed and performs the separation of even significantly correlated sources. Besides the mixing matrix, our strategy is also capable to evaluate the source covariance functions at several lags. Moreover, once the mixing parameters have been identified, a simple deconvolution can be used to estimate the probability density functions of the source processes. To benchmark our algorithm, we used a database that simulates the one expected from the instruments that will operate onboard ESA's Planck Surveyor Satellite to measure the CMB anisotropies all over the celestial sphere.

  6. Covariant field equations in supergravity

    Energy Technology Data Exchange (ETDEWEB)

    Vanhecke, Bram [KU Leuven, Institute for Theoretical Physics, Leuven (Belgium); Ghent University, Faculty of Physics, Gent (Belgium); Proeyen, Antoine van [KU Leuven, Institute for Theoretical Physics, Leuven (Belgium)

    2017-12-15

    Covariance is a useful property for handling supergravity theories. In this paper, we prove a covariance property of supergravity field equations: under reasonable conditions, field equations of supergravity are covariant modulo other field equations. We prove that for any supergravity there exist such covariant equations of motion, other than the regular equations of motion, that are equivalent to the latter. The relations that we find between field equations and their covariant form can be used to obtain multiplets of field equations. In practice, the covariant field equations are easily found by simply covariantizing the ordinary field equations. (copyright 2017 WILEY-VCH Verlag GmbH and Co. KGaA, Weinheim)

  7. Covariant field equations in supergravity

    International Nuclear Information System (INIS)

    Vanhecke, Bram; Proeyen, Antoine van

    2017-01-01

    Covariance is a useful property for handling supergravity theories. In this paper, we prove a covariance property of supergravity field equations: under reasonable conditions, field equations of supergravity are covariant modulo other field equations. We prove that for any supergravity there exist such covariant equations of motion, other than the regular equations of motion, that are equivalent to the latter. The relations that we find between field equations and their covariant form can be used to obtain multiplets of field equations. In practice, the covariant field equations are easily found by simply covariantizing the ordinary field equations. (copyright 2017 WILEY-VCH Verlag GmbH and Co. KGaA, Weinheim)

  8. Metagenomic covariation along densely sampled environmental gradients in the Red Sea

    Science.gov (United States)

    Thompson, Luke R; Williams, Gareth J; Haroon, Mohamed F; Shibl, Ahmed; Larsen, Peter; Shorenstein, Joshua; Knight, Rob; Stingl, Ulrich

    2017-01-01

    Oceanic microbial diversity covaries with physicochemical parameters. Temperature, for example, explains approximately half of global variation in surface taxonomic abundance. It is unknown, however, whether covariation patterns hold over narrower parameter gradients and spatial scales, and extending to mesopelagic depths. We collected and sequenced 45 epipelagic and mesopelagic microbial metagenomes on a meridional transect through the eastern Red Sea. We asked which environmental parameters explain the most variation in relative abundances of taxonomic groups, gene ortholog groups, and pathways—at a spatial scale of water mass with different physicochemical properties. Temperature explained the most variation in each metric, followed by nitrate, chlorophyll, phosphate, and salinity. That nitrate explained more variation than phosphate suggested nitrogen limitation, consistent with low surface N:P ratios. Covariation of gene ortholog groups with environmental parameters revealed patterns of functional adaptation to the challenging Red Sea environment: high irradiance, temperature, salinity, and low nutrients. Nutrient-acquisition gene ortholog groups were anti-correlated with concentrations of their respective nutrient species, recapturing trends previously observed across much larger distances and environmental gradients. This dataset of metagenomic covariation along densely sampled environmental gradients includes online data exploration supplements, serving as a community resource for marine microbial ecology. PMID:27420030

  9. PUFF-IV, Code System to Generate Multigroup Covariance Matrices from ENDF/B-VI Uncertainty Files

    International Nuclear Information System (INIS)

    2007-01-01

    1 - Description of program or function: The PUFF-IV code system processes ENDF/B-VI formatted nuclear cross section covariance data into multigroup covariance matrices. PUFF-IV is the newest release in this series of codes used to process ENDF uncertainty information and to generate the desired multi-group correlation matrix for the evaluation of interest. This version includes corrections and enhancements over previous versions. It is written in Fortran 90 and allows for a more modular design, thus facilitating future upgrades. PUFF-IV enhances support for resonance parameter covariance formats described in the ENDF standard and now handles almost all resonance parameter covariance information in the resolved region, with the exception of the long range covariance sub-subsections. PUFF-IV is normally used in conjunction with an AMPX master library containing group averaged cross section data. Two utility modules are included in this package to facilitate the data interface. The module SMILER allows one to use NJOY generated GENDF files containing group averaged cross section data in conjunction with PUFF-IV. The module COVCOMP allows one to compare two files written in COVERX format. 2 - Methods: Cross section and flux values on a 'super energy grid,' consisting of the union of the required energy group structure and the energy data points in the ENDF/B-V file, are interpolated from the input cross sections and fluxes. Covariance matrices are calculated for this grid and then collapsed to the required group structure. 3 - Restrictions on the complexity of the problem: PUFF-IV cannot process covariance information for energy and angular distributions of secondary particles. PUFF-IV does not process covariance information in Files 34 and 35; nor does it process covariance information in File 40. These new formats will be addressed in a future version of PUFF

  10. A global bioheat model with self-tuning optimal regulation of body temperature using Hebbian feedback covariance learning.

    Science.gov (United States)

    Ong, M L; Ng, E Y K

    2005-12-01

    In the lower brain, body temperature is continually being regulated almost flawlessly despite huge fluctuations in ambient and physiological conditions that constantly threaten the well-being of the body. The underlying control problem defining thermal homeostasis is one of great enormity: Many systems and sub-systems are involved in temperature regulation and physiological processes are intrinsically complex and intertwined. Thus the defining control system has to take into account the complications of nonlinearities, system uncertainties, delayed feedback loops as well as internal and external disturbances. In this paper, we propose a self-tuning adaptive thermal controller based upon Hebbian feedback covariance learning where the system is to be regulated continually to best suit its environment. This hypothesis is supported in part by postulations of the presence of adaptive optimization behavior in biological systems of certain organisms which face limited resources vital for survival. We demonstrate the use of Hebbian feedback covariance learning as a possible self-adaptive controller in body temperature regulation. The model postulates an important role of Hebbian covariance adaptation as a means of reinforcement learning in the thermal controller. The passive system is based on a simplified 2-node core and shell representation of the body, where global responses are captured. Model predictions are consistent with observed thermoregulatory responses to conditions of exercise and rest, and heat and cold stress. An important implication of the model is that optimal physiological behaviors arising from self-tuning adaptive regulation in the thermal controller may be responsible for the departure from homeostasis in abnormal states, e.g., fever. This was previously unexplained using the conventional "set-point" control theory.

  11. Covariant w∞ gravity

    NARCIS (Netherlands)

    Bergshoeff, E.; Pope, C.N.; Stelle, K.S.

    1990-01-01

    We discuss the notion of higher-spin covariance in w∞ gravity. We show how a recently proposed covariant w∞ gravity action can be obtained from non-chiral w∞ gravity by making field redefinitions that introduce new gauge-field components with corresponding new gauge transformations.

  12. General-Covariant Quantum Mechanics of Dirac Particle in Curved Space-Times

    International Nuclear Information System (INIS)

    Tagirov, Eh.A.

    1994-01-01

    A general covariant analog of the standard non-relativistic Quantum Mechanics with relativistic corrections in normal geodesic frames in the general Riemannian space-time is constructed for the Dirac particle. Not only the Pauli equation with hermitian Hamiltonian and the pre-Hilbert structure of space of its solutions but also the matrix elements of hermitian operators of momentum, (curvilinear) spatial coordinates and spin of the particle are deduced as general-covariant asymptotic approximation in c -2 , c being the velocity of light, to their naturally determined general-relativistic pre images. It is shown that the Hamiltonian in the Pauli equation originated by the Dirac equation is unitary equivalent to the operator of energy, originated by the metric energy-momentum tensor of the spinor field. Commutation and other properties of the observables connected with the considered change of geometrical background of Quantum Mechanics are briefly discussed. 7 refs

  13. Selecting groups of covariates in the elastic net

    DEFF Research Database (Denmark)

    Clemmensen, Line Katrine Harder

    This paper introduces a novel method to select groups of variables in sparse regression and classication settings. The groups are formed based on the correlations between covariates and ensure that for example spatial or spectral relations are preserved without explicitly coding for these....... The preservation of relations gives increased interpretability. The method is based on the elastic net and adaptively selects highly correlated groups of variables and does therefore not waste time in grouping irrelevant variables for the problem at hand. The method is illustrated on a simulated data set...

  14. Uncertainty estimation of core safety parameters using cross-correlations of covariance matrix

    International Nuclear Information System (INIS)

    Yamamoto, A.; Yasue, Y.; Endo, T.; Kodama, Y.; Ohoka, Y.; Tatsumi, M.

    2012-01-01

    An uncertainty estimation method for core safety parameters, for which measurement values are not obtained, is proposed. We empirically recognize the correlations among the prediction errors among core safety parameters, e.g., a correlation between the control rod worth and assembly relative power of corresponding position. Correlations of uncertainties among core safety parameters are theoretically estimated using the covariance of cross sections and sensitivity coefficients for core parameters. The estimated correlations among core safety parameters are verified through the direct Monte-Carlo sampling method. Once the correlation of uncertainties among core safety parameters is known, we can estimate the uncertainty of a safety parameter for which measurement value is not obtained. Furthermore, the correlations can be also used for the reduction of uncertainties of core safety parameters. (authors)

  15. A comparison of phenotypic variation and covariation patterns and the role of phylogeny, ecology, and ontogeny during cranial evolution of new world monkeys.

    Science.gov (United States)

    Marroig, G; Cheverud, J M

    2001-12-01

    Similarity of genetic and phenotypic variation patterns among populations is important for making quantitative inferences about past evolutionary forces acting to differentiate populations and for evaluating the evolution of relationships among traits in response to new functional and developmental relationships. Here, phenotypic co variance and correlation structure is compared among Platyrrhine Neotropical primates. Comparisons range from among species within a genus to the superfamily level. Matrix correlation followed by Mantel's test and vector correlation among responses to random natural selection vectors (random skewers) were used to compare correlation and variance/covariance matrices of 39 skull traits. Sampling errors involved in matrix estimates were taken into account in comparisons using matrix repeatability to set upper limits for each pairwise comparison. Results indicate that covariance structure is not strictly constant but that the amount of variance pattern divergence observed among taxa is generally low and not associated with taxonomic distance. Specific instances of divergence are identified. There is no correlation between the amount of divergence in covariance patterns among the 16 genera and their phylogenetic distance derived from a conjoint analysis of four already published nuclear gene datasets. In contrast, there is a significant correlation between phylogenetic distance and morphological distance (Mahalanobis distance among genus centroids). This result indicates that while the phenotypic means were evolving during the last 30 millions years of New World monkey evolution, phenotypic covariance structures of Neotropical primate skulls have remained relatively consistent. Neotropical primates can be divided into four major groups based on their feeding habits (fruit-leaves, seed-fruits, insect-fruits, and gum-insect-fruits). Differences in phenotypic covariance structure are correlated with differences in feeding habits, indicating

  16. Evaluation and processing of covariance data

    International Nuclear Information System (INIS)

    Wagner, M.

    1993-01-01

    These proceedings of a specialists'meeting on evaluation and processing of covariance data is divided into 4 parts bearing on: part 1- Needs for evaluated covariance data (2 Papers), part 2- generation of covariance data (15 Papers), part 3- Processing of covariance files (2 Papers), part 4-Experience in the use of evaluated covariance data (2 Papers)

  17. Covariance data processing code. ERRORJ

    International Nuclear Information System (INIS)

    Kosako, Kazuaki

    2001-01-01

    The covariance data processing code, ERRORJ, was developed to process the covariance data of JENDL-3.2. ERRORJ has the processing functions of covariance data for cross sections including resonance parameters, angular distribution and energy distribution. (author)

  18. Accounting for Sampling Error in Genetic Eigenvalues Using Random Matrix Theory.

    Science.gov (United States)

    Sztepanacz, Jacqueline L; Blows, Mark W

    2017-07-01

    The distribution of genetic variance in multivariate phenotypes is characterized by the empirical spectral distribution of the eigenvalues of the genetic covariance matrix. Empirical estimates of genetic eigenvalues from random effects linear models are known to be overdispersed by sampling error, where large eigenvalues are biased upward, and small eigenvalues are biased downward. The overdispersion of the leading eigenvalues of sample covariance matrices have been demonstrated to conform to the Tracy-Widom (TW) distribution. Here we show that genetic eigenvalues estimated using restricted maximum likelihood (REML) in a multivariate random effects model with an unconstrained genetic covariance structure will also conform to the TW distribution after empirical scaling and centering. However, where estimation procedures using either REML or MCMC impose boundary constraints, the resulting genetic eigenvalues tend not be TW distributed. We show how using confidence intervals from sampling distributions of genetic eigenvalues without reference to the TW distribution is insufficient protection against mistaking sampling error as genetic variance, particularly when eigenvalues are small. By scaling such sampling distributions to the appropriate TW distribution, the critical value of the TW statistic can be used to determine if the magnitude of a genetic eigenvalue exceeds the sampling error for each eigenvalue in the spectral distribution of a given genetic covariance matrix. Copyright © 2017 by the Genetics Society of America.

  19. Robustness Beamforming Algorithms

    Directory of Open Access Journals (Sweden)

    Sajad Dehghani

    2014-04-01

    Full Text Available Adaptive beamforming methods are known to degrade in the presence of steering vector and covariance matrix uncertinity. In this paper, a new approach is presented to robust adaptive minimum variance distortionless response beamforming make robust against both uncertainties in steering vector and covariance matrix. This method minimize a optimization problem that contains a quadratic objective function and a quadratic constraint. The optimization problem is nonconvex but is converted to a convex optimization problem in this paper. It is solved by the interior-point method and optimum weight vector to robust beamforming is achieved.

  20. DFT-Based Closed-form Covariance Matrix and Direct Waveforms Design for MIMO Radar to Achieve Desired Beampatterns

    KAUST Repository

    Bouchoucha, Taha; Ahmed, Sajid; Al-Naffouri, Tareq Y.; Alouini, Mohamed-Slim

    2017-01-01

    optimization problems. The computational complexity of these algorithms is very high, which makes them difficult to use in practice. In this paper, to achieve the desired beampattern, a low complexity discrete-Fourier-transform based closed-form covariance

  1. The correlation matrix of Higgs rates at the LHC

    CERN Document Server

    Arbey, Alexandre; Mahmoudi, Farvah; Moreau, Grégory

    2016-11-17

    The imperfect knowledge of the Higgs boson LHC cross sections and decay rates constitutes a critical systematic uncertainty in the study of the Higgs boson properties. We show that the full covariance matrix between the Higgs rates can be determined from the most elementary sources of uncertainty by a direct application of probability theory. We evaluate the error magnitudes and full correlation matrix on the set of Higgs cross sections and partial decay widths at $\\sqrt{s}=7$, $8$, $13$ and $14$~TeV, which are provided in ancillary files. The impact of this correlation matrix on the global fits is illustrated with the latest $7$+$8$ TeV Higgs dataset.

  2. Adaptive generalized function matrix projective lag synchronization between fractional-order and integer-order complex networks with delayed coupling and different dimensions

    International Nuclear Information System (INIS)

    Dai, Hao; Si, Gangquan; Jia, Lixin; Zhang, Yanbin

    2013-01-01

    This paper investigates generalized function matrix projective lag synchronization between fractional-order and integer-order complex networks with delayed coupling, non-identical topological structures and different dimensions. Based on Lyapunov stability theory, generalized function matrix projective lag synchronization criteria are derived by using the adaptive control method. In addition, the three-dimensional fractional-order chaotic system and the four-dimensional integer-order hyperchaotic system as the nodes of the drive and the response networks, respectively, are analyzed in detail, and numerical simulation results are presented to illustrate the effectiveness of the theoretical results. (paper)

  3. Covariance Bell inequalities

    Science.gov (United States)

    Pozsgay, Victor; Hirsch, Flavien; Branciard, Cyril; Brunner, Nicolas

    2017-12-01

    We introduce Bell inequalities based on covariance, one of the most common measures of correlation. Explicit examples are discussed, and violations in quantum theory are demonstrated. A crucial feature of these covariance Bell inequalities is their nonlinearity; this has nontrivial consequences for the derivation of their local bound, which is not reached by deterministic local correlations. For our simplest inequality, we derive analytically tight bounds for both local and quantum correlations. An interesting application of covariance Bell inequalities is that they can act as "shared randomness witnesses": specifically, the value of the Bell expression gives device-independent lower bounds on both the dimension and the entropy of the shared random variable in a local model.

  4. Triple collocation-based estimation of spatially correlated observation error covariance in remote sensing soil moisture data assimilation

    Science.gov (United States)

    Wu, Kai; Shu, Hong; Nie, Lei; Jiao, Zhenhang

    2018-01-01

    Spatially correlated errors are typically ignored in data assimilation, thus degenerating the observation error covariance R to a diagonal matrix. We argue that a nondiagonal R carries more observation information making assimilation results more accurate. A method, denoted TC_Cov, was proposed for soil moisture data assimilation to estimate spatially correlated observation error covariance based on triple collocation (TC). Assimilation experiments were carried out to test the performance of TC_Cov. AMSR-E soil moisture was assimilated with a diagonal R matrix computed using the TC and assimilated using a nondiagonal R matrix, as estimated by proposed TC_Cov. The ensemble Kalman filter was considered as the assimilation method. Our assimilation results were validated against climate change initiative data and ground-based soil moisture measurements using the Pearson correlation coefficient and unbiased root mean square difference metrics. These experiments confirmed that deterioration of diagonal R assimilation results occurred when model simulation is more accurate than observation data. Furthermore, nondiagonal R achieved higher correlation coefficient and lower ubRMSD values over diagonal R in experiments and demonstrated the effectiveness of TC_Cov to estimate richly structuralized R in data assimilation. In sum, compared with diagonal R, nondiagonal R may relieve the detrimental effects of assimilation when simulated model results outperform observation data.

  5. Distance covariance for stochastic processes

    DEFF Research Database (Denmark)

    Matsui, Muneya; Mikosch, Thomas Valentin; Samorodnitsky, Gennady

    2017-01-01

    The distance covariance of two random vectors is a measure of their dependence. The empirical distance covariance and correlation can be used as statistical tools for testing whether two random vectors are independent. We propose an analog of the distance covariance for two stochastic processes...

  6. Parallel ICA identifies sub-components of resting state networks that covary with behavioral indices.

    Science.gov (United States)

    Meier, Timothy B; Wildenberg, Joseph C; Liu, Jingyu; Chen, Jiayu; Calhoun, Vince D; Biswal, Bharat B; Meyerand, Mary E; Birn, Rasmus M; Prabhakaran, Vivek

    2012-01-01

    Parallel Independent Component Analysis (para-ICA) is a multivariate method that can identify complex relationships between different data modalities by simultaneously performing Independent Component Analysis on each data set while finding mutual information between the two data sets. We use para-ICA to test the hypothesis that spatial sub-components of common resting state networks (RSNs) covary with specific behavioral measures. Resting state scans and a battery of behavioral indices were collected from 24 younger adults. Group ICA was performed and common RSNs were identified by spatial correlation to publically available templates. Nine RSNs were identified and para-ICA was run on each network with a matrix of behavioral measures serving as the second data type. Five networks had spatial sub-components that significantly correlated with behavioral components. These included a sub-component of the temporo-parietal attention network that differentially covaried with different trial-types of a sustained attention task, sub-components of default mode networks that covaried with attention and working memory tasks, and a sub-component of the bilateral frontal network that split the left inferior frontal gyrus into three clusters according to its cytoarchitecture that differentially covaried with working memory performance. Additionally, we demonstrate the validity of para-ICA in cases with unbalanced dimensions using simulated data.

  7. Covariance Manipulation for Conjunction Assessment

    Science.gov (United States)

    Hejduk, M. D.

    2016-01-01

    The manipulation of space object covariances to try to provide additional or improved information to conjunction risk assessment is not an uncommon practice. Types of manipulation include fabricating a covariance when it is missing or unreliable to force the probability of collision (Pc) to a maximum value ('PcMax'), scaling a covariance to try to improve its realism or see the effect of covariance volatility on the calculated Pc, and constructing the equivalent of an epoch covariance at a convenient future point in the event ('covariance forecasting'). In bringing these methods to bear for Conjunction Assessment (CA) operations, however, some do not remain fully consistent with best practices for conducting risk management, some seem to be of relatively low utility, and some require additional information before they can contribute fully to risk analysis. This study describes some basic principles of modern risk management (following the Kaplan construct) and then examines the PcMax and covariance forecasting paradigms for alignment with these principles; it then further examines the expected utility of these methods in the modern CA framework. Both paradigms are found to be not without utility, but only in situations that are somewhat carefully circumscribed.

  8. Fuzzy adaptive Kalman filter for indoor mobile target positioning with INS/WSN integrated method

    Institute of Scientific and Technical Information of China (English)

    杨海; 李威; 罗成名

    2015-01-01

    Pure inertial navigation system (INS) has divergent localization errors after a long time. In order to compensate the disadvantage, wireless sensor network (WSN) associated with the INS was applied to estimate the mobile target positioning. Taking traditional Kalman filter (KF) as the framework, the system equation of KF was established by the INS and the observation equation of position errors was built by the WSN. Meanwhile, the observation equation of velocity errors was established by the velocity difference between the INS and WSN, then the covariance matrix of Kalman filter measurement noise was adjusted with fuzzy inference system (FIS), and the fuzzy adaptive Kalman filter (FAKF) based on the INS/WSN was proposed. The simulation results show that the FAKF method has better accuracy and robustness than KF and EKF methods and shows good adaptive capacity with time-varying system noise. Finally, experimental results further prove that FAKF has the fast convergence error, in comparison with KF and EKF methods.

  9. A Study of Adaptive Detection of Range-Distributed Targets

    National Research Council Canada - National Science Library

    Gerlach, Karl R

    2000-01-01

    .... The unknown parameters associated with the hypothesis test are the complex amplitudes in range of the desired target and the unknown covariance matrix of the additive interference, which is assumed...

  10. An adaptive ES with a ranking based constraint handling strategy

    Directory of Open Access Journals (Sweden)

    Kusakci Ali Osman

    2014-01-01

    Full Text Available To solve a constrained optimization problem, equality constraints can be used to eliminate a problem variable. If it is not feasible, the relations imposed implicitly by the constraints can still be exploited. Most conventional constraint handling methods in Evolutionary Algorithms (EAs do not consider the correlations between problem variables imposed by the constraints. This paper relies on the idea that a proper search operator, which captures mentioned implicit correlations, can improve performance of evolutionary constrained optimization algorithms. To realize this, an Evolution Strategy (ES along with a simplified Covariance Matrix Adaptation (CMA based mutation operator is used with a ranking based constraint-handling method. The proposed algorithm is tested on 13 benchmark problems as well as on a real life design problem. The outperformance of the algorithm is significant when compared with conventional ES-based methods.

  11. Performance and stochastic stability of the adaptive fading extended Kalman filter with the matrix forgetting factor

    Directory of Open Access Journals (Sweden)

    Biçer Cenker

    2016-01-01

    Full Text Available In this paper, the stability of the adaptive fading extended Kalman filter with the matrix forgetting factor when applied to the state estimation problem with noise terms in the non–linear discrete–time stochastic systems has been analysed. The analysis is conducted in a similar manner to the standard extended Kalman filter’s stability analysis based on stochastic framework. The theoretical results show that under certain conditions on the initial estimation error and the noise terms, the estimation error remains bounded and the state estimation is stable.

  12. One-loop matching and running with covariant derivative expansion

    Science.gov (United States)

    Henning, Brian; Lu, Xiaochuan; Murayama, Hitoshi

    2018-01-01

    We develop tools for performing effective field theory (EFT) calculations in a manifestly gauge-covariant fashion. We clarify how functional methods account for one-loop diagrams resulting from the exchange of both heavy and light fields, as some confusion has recently arisen in the literature. To efficiently evaluate functional traces containing these "mixed" one-loop terms, we develop a new covariant derivative expansion (CDE) technique that is capable of evaluating a much wider class of traces than previous methods. The technique is detailed in an appendix, so that it can be read independently from the rest of this work. We review the well-known matching procedure to one-loop order with functional methods. What we add to this story is showing how to isolate one-loop terms coming from diagrams involving only heavy propagators from diagrams with mixed heavy and light propagators. This is done using a non-local effective action, which physically connects to the notion of "integrating out" heavy fields. Lastly, we show how to use a CDE to do running analyses in EFTs, i.e. to obtain the anomalous dimension matrix. We demonstrate the methodologies by several explicit example calculations.

  13. Orbit covariance propagation via quadratic-order state transition matrix in curvilinear coordinates

    Science.gov (United States)

    Hernando-Ayuso, Javier; Bombardelli, Claudio

    2017-09-01

    In this paper, an analytical second-order state transition matrix (STM) for relative motion in curvilinear coordinates is presented and applied to the problem of orbit uncertainty propagation in nearly circular orbits (eccentricity smaller than 0.1). The matrix is obtained by linearization around a second-order analytical approximation of the relative motion recently proposed by one of the authors and can be seen as a second-order extension of the curvilinear Clohessy-Wiltshire (C-W) solution. The accuracy of the uncertainty propagation is assessed by comparison with numerical results based on Monte Carlo propagation of a high-fidelity model including geopotential and third-body perturbations. Results show that the proposed STM can greatly improve the accuracy of the predicted relative state: the average error is found to be at least one order of magnitude smaller compared to the curvilinear C-W solution. In addition, the effect of environmental perturbations on the uncertainty propagation is shown to be negligible up to several revolutions in the geostationary region and for a few revolutions in low Earth orbit in the worst case.

  14. The correlation matrix of Higgs rates at the LHC

    Energy Technology Data Exchange (ETDEWEB)

    Arbey, Alexandre [Univ Lyon, Univ Lyon 1, ENS de Lyon, CNRS,Centre de Recherche Astrophysique de Lyon UMR5574,F-69230 Saint-Genis-Laval (France); Theoretical Physics Department, CERN,CH-1211 Geneva 23 (Switzerland); Fichet, Sylvain [ICTP-SAIFR & IFT-UNESP,Rua Dr. Bento Teobaldo Ferraz 271, Sao Paulo (Brazil); Mahmoudi, Farvah [Univ Lyon, Univ Lyon 1, ENS de Lyon, CNRS,Centre de Recherche Astrophysique de Lyon UMR5574,F-69230 Saint-Genis-Laval (France); Theoretical Physics Department, CERN,CH-1211 Geneva 23 (Switzerland); Moreau, Grégory [Laboratoire de Physique Théorique, CNRS, Université Paris-Sud 11, Bât. 210, F-91405 Orsay Cedex (France)

    2016-11-17

    The imperfect knowledge of the Higgs boson decay rates and cross sections at the LHC constitutes a critical systematic uncertainty in the study of the Higgs boson properties. We show that the full covariance matrix between the Higgs rates can be determined from the most elementary sources of uncertainty by a direct application of probability theory. We evaluate the error magnitudes and full correlation matrix on the set of Higgs cross sections and branching ratios at √s=7, 8, 13 and 14 TeV, which are provided in ancillary files. The impact of this correlation matrix on the global fits is illustrated with the latest 7+8 TeV Higgs dataset.

  15. Robust Adaptive Signal Processing Methods for Heterogeneous Radar Clutter Scenarios

    National Research Council Canada - National Science Library

    Rangaswamy, Muralidhar; Lin, Freeman C; Gerlach, Karl R

    2004-01-01

    .... Specifically, we present the performance of the normalized matched filter test in a background of disturbance consisting of clutter having a covariance matrix with known structure and unknown scaling...

  16. Limit theorems for linear spectrum statistics of orthogonal polynomial ensembles and their applications in random matrix theory

    Science.gov (United States)

    Pan, Guangming; Wang, Shaochen; Zhou, Wang

    2017-10-01

    In this paper, we consider the asymptotic behavior of Xfn (n )≔∑i=1 nfn(xi ) , where xi,i =1 ,…,n form orthogonal polynomial ensembles and fn is a real-valued, bounded measurable function. Under the condition that Var Xfn (n )→∞ , the Berry-Esseen (BE) bound and Cramér type moderate deviation principle (MDP) for Xfn (n ) are obtained by using the method of cumulants. As two applications, we establish the BE bound and Cramér type MDP for linear spectrum statistics of Wigner matrix and sample covariance matrix in the complex cases. These results show that in the edge case (which means fn has a particular form f (x ) I (x ≥θn ) where θn is close to the right edge of equilibrium measure and f is a smooth function), Xfn (n ) behaves like the eigenvalues counting function of the corresponding Wigner matrix and sample covariance matrix, respectively.

  17. Poincare covariance and κ-Minkowski spacetime

    International Nuclear Information System (INIS)

    Dabrowski, Ludwik; Piacitelli, Gherardo

    2011-01-01

    A fully Poincare covariant model is constructed as an extension of the κ-Minkowski spacetime. Covariance is implemented by a unitary representation of the Poincare group, and thus complies with the original Wigner approach to quantum symmetries. This provides yet another example (besides the DFR model), where Poincare covariance is realised a la Wigner in the presence of two characteristic dimensionful parameters: the light speed and the Planck length. In other words, a Doubly Special Relativity (DSR) framework may well be realised without deforming the meaning of 'Poincare covariance'. -- Highlights: → We construct a 4d model of noncommuting coordinates (quantum spacetime). → The coordinates are fully covariant under the undeformed Poincare group. → Covariance a la Wigner holds in presence of two dimensionful parameters. → Hence we are not forced to deform covariance (e.g. as quantum groups). → The underlying κ-Minkowski model is unphysical; covariantisation does not cure this.

  18. COVARIANCE ESTIMATION USING CONJUGATE GRADIENT FOR 3D CLASSIFICATION IN CRYO-EM.

    Science.gov (United States)

    Andén, Joakim; Katsevich, Eugene; Singer, Amit

    2015-04-01

    Classifying structural variability in noisy projections of biological macromolecules is a central problem in Cryo-EM. In this work, we build on a previous method for estimating the covariance matrix of the three-dimensional structure present in the molecules being imaged. Our proposed method allows for incorporation of contrast transfer function and non-uniform distribution of viewing angles, making it more suitable for real-world data. We evaluate its performance on a synthetic dataset and an experimental dataset obtained by imaging a 70S ribosome complex.

  19. Assessment of the Gaussian Covariance Approximation over an Earth-Asteroid Encounter Period

    Science.gov (United States)

    Mattern, Daniel W.

    2017-01-01

    In assessing the risk an asteroid may pose to the Earth, the asteroids state is often predicted for many years, often decades. Only by accounting for the asteroids initial state uncertainty can a measure of the risk be calculated. With the asteroids state uncertainty growing as a function of the initial velocity uncertainty, orbit velocity at the last state update, and the time from the last update to the epoch of interest, the asteroids position uncertainties can grow to many times the size of the Earth when propagated to the encounter risk corridor. This paper examines the merits of propagating the asteroids state covariance as an analytical matrix. The results of this study help to bound the efficacy of applying different metrics for assessing the risk an asteroid poses to the Earth. Additionally, this work identifies a criterion for when different covariance propagation methods are needed to continue predictions after an Earth-encounter period.

  20. Improving the ensemble optimization method through covariance matrix adaptation (CMA-EnOpt)

    NARCIS (Netherlands)

    Fonseca, R.M.; Leeuwenburgh, O.; Hof, P.M.J. van den; Jansen, J.D.

    2013-01-01

    Ensemble Optimization (EnOpt) is a rapidly emerging method for reservoir model based production optimization. EnOpt uses an ensemble of controls to approximate the gradient of the objective function with respect to the controls. Current implementations of EnOpt use a Gaussian ensemble with a

  1. An adaptive unscented Kalman filter-based adaptive tracking control for wheeled mobile robots with control constrains in the presence of wheel slipping

    Directory of Open Access Journals (Sweden)

    Mingyue Cui

    2016-09-01

    Full Text Available A novel control approach is proposed for trajectory tracking of a wheeled mobile robot with unknown wheels’ slipping. The longitudinal and lateral slipping are considered and processed as three time-varying parameters. The adaptive unscented Kalman filter is then designed to estimate the slipping parameters online, an adaptive adjustment of the noise covariances in the estimation process is implemented using a technique of covariance matching in the adaptive unscented Kalman filter context. Considering the practical physical constrains, a stable tracking control law for this robot system is proposed by the backstepping method. Asymptotic stability is guaranteed by Lyapunov stability theory. Control gains are determined online by applying pole placement method. Simulation and real experiment results show the effectiveness and robustness of the proposed control method.

  2. A covariance correction that accounts for correlation estimation to improve finite-sample inference with generalized estimating equations: A study on its applicability with structured correlation matrices.

    Science.gov (United States)

    Westgate, Philip M

    2016-01-01

    When generalized estimating equations (GEE) incorporate an unstructured working correlation matrix, the variances of regression parameter estimates can inflate due to the estimation of the correlation parameters. In previous work, an approximation for this inflation that results in a corrected version of the sandwich formula for the covariance matrix of regression parameter estimates was derived. Use of this correction for correlation structure selection also reduces the over-selection of the unstructured working correlation matrix. In this manuscript, we conduct a simulation study to demonstrate that an increase in variances of regression parameter estimates can occur when GEE incorporates structured working correlation matrices as well. Correspondingly, we show the ability of the corrected version of the sandwich formula to improve the validity of inference and correlation structure selection. We also study the relative influences of two popular corrections to a different source of bias in the empirical sandwich covariance estimator.

  3. Econometric analysis of realised covariation: high frequency covariance, regression and correlation in financial economics

    OpenAIRE

    Ole E. Barndorff-Nielsen; Neil Shephard

    2002-01-01

    This paper analyses multivariate high frequency financial data using realised covariation. We provide a new asymptotic distribution theory for standard methods such as regression, correlation analysis and covariance. It will be based on a fixed interval of time (e.g. a day or week), allowing the number of high frequency returns during this period to go to infinity. Our analysis allows us to study how high frequency correlations, regressions and covariances change through time. In particular w...

  4. ISSUES IN NEUTRON CROSS SECTION COVARIANCES

    Energy Technology Data Exchange (ETDEWEB)

    Mattoon, C.M.; Oblozinsky,P.

    2010-04-30

    We review neutron cross section covariances in both the resonance and fast neutron regions with the goal to identify existing issues in evaluation methods and their impact on covariances. We also outline ideas for suitable covariance quality assurance procedures.We show that the topic of covariance data remains controversial, the evaluation methodologies are not fully established and covariances produced by different approaches have unacceptable spread. The main controversy is in very low uncertainties generated by rigorous evaluation methods and much larger uncertainties based on simple estimates from experimental data. Since the evaluators tend to trust the former, while the users tend to trust the latter, this controversy has considerable practical implications. Dedicated effort is needed to arrive at covariance evaluation methods that would resolve this issue and produce results accepted internationally both by evaluators and users.

  5. DOA and Pitch Estimation of Audio Sources using IAA-based Filtering

    DEFF Research Database (Denmark)

    Jensen, Jesper Rindom; Christensen, Mads Græsbøll

    2014-01-01

    For decades, it has been investigated how to separately solve the problems of both direction-of-arrival (DOA) and pitch estimation. Recently, it was found that estimating these parameters jointly from multichannel recordings of audio can be extremely beneficial. Many joint estimators are based...... on knowledge of the inverse sample covariance matrix. Typically, this covariance is estimated using the sample covariance matrix, but for this estimate to be full rank, many temporal samples are needed. In cases with non-stationary signals, this is a serious limitation. We therefore investigate how a recent...... joint DOA and pitch filtering-based estimator can be combined with the iterative adaptive approach to circumvent this limitation in joint DOA and pitch estimation of audio sources. Simulations show a clear improvement compared to when using the sample covariance matrix and the considered approach also...

  6. Gyroaveraging operations using adaptive matrix operators

    Science.gov (United States)

    Dominski, Julien; Ku, Seung-Hoe; Chang, Choong-Seock

    2018-05-01

    A new adaptive scheme to be used in particle-in-cell codes for carrying out gyroaveraging operations with matrices is presented. This new scheme uses an intermediate velocity grid whose resolution is adapted to the local thermal Larmor radius. The charge density is computed by projecting marker weights in a field-line following manner while preserving the adiabatic magnetic moment μ. These choices permit to improve the accuracy of the gyroaveraging operations performed with matrices even when strong spatial variation of temperature and magnetic field is present. Accuracy of the scheme in different geometries from simple 2D slab geometry to realistic 3D toroidal equilibrium has been studied. A successful implementation in the gyrokinetic code XGC is presented in the delta-f limit.

  7. Neutron flux uncertainty and covariances for spectrum adjustment and estimation of WWER-1000 pressure vessel fluences

    International Nuclear Information System (INIS)

    Boehmer, Bertram

    2000-01-01

    Results of estimation of the covariance matrix of the neutron spectrum in the WWER-1000 reactor cavity and pressure vessel positions are presented. Two-dimensional calculations with the discrete ordinates transport code DORT in r-theta and r-z-geometry used to determine the neutron group spectrum covariances including gross-correlations between interesting positions. The new Russian ABBN-93 data set and CONSYST code used to supply all transport calculations with group neutron data. All possible sources of uncertainties namely caused by the neutron gross sections, fission sources, geometrical dimensions and material densities considered, whereas the uncertainty of the calculation method was considered negligible in view of the available precision of Monte Carlo simulation used for more precise evaluation of the neutron fluence. (Authors)

  8. Covariant diagrams for one-loop matching

    Energy Technology Data Exchange (ETDEWEB)

    Zhang, Zhengkang [Michigan Center for Theoretical Physics (MCTP), University of Michigan,450 Church Street, Ann Arbor, MI 48109 (United States); Deutsches Elektronen-Synchrotron (DESY),Notkestraße 85, 22607 Hamburg (Germany)

    2017-05-30

    We present a diagrammatic formulation of recently-revived covariant functional approaches to one-loop matching from an ultraviolet (UV) theory to a low-energy effective field theory. Various terms following from a covariant derivative expansion (CDE) are represented by diagrams which, unlike conventional Feynman diagrams, involve gauge-covariant quantities and are thus dubbed “covariant diagrams.” The use of covariant diagrams helps organize and simplify one-loop matching calculations, which we illustrate with examples. Of particular interest is the derivation of UV model-independent universal results, which reduce matching calculations of specific UV models to applications of master formulas. We show how such derivation can be done in a more concise manner than the previous literature, and discuss how additional structures that are not directly captured by existing universal results, including mixed heavy-light loops, open covariant derivatives, and mixed statistics, can be easily accounted for.

  9. Covariant diagrams for one-loop matching

    International Nuclear Information System (INIS)

    Zhang, Zhengkang

    2017-01-01

    We present a diagrammatic formulation of recently-revived covariant functional approaches to one-loop matching from an ultraviolet (UV) theory to a low-energy effective field theory. Various terms following from a covariant derivative expansion (CDE) are represented by diagrams which, unlike conventional Feynman diagrams, involve gauge-covariant quantities and are thus dubbed “covariant diagrams.” The use of covariant diagrams helps organize and simplify one-loop matching calculations, which we illustrate with examples. Of particular interest is the derivation of UV model-independent universal results, which reduce matching calculations of specific UV models to applications of master formulas. We show how such derivation can be done in a more concise manner than the previous literature, and discuss how additional structures that are not directly captured by existing universal results, including mixed heavy-light loops, open covariant derivatives, and mixed statistics, can be easily accounted for.

  10. Fast Computing for Distance Covariance

    OpenAIRE

    Huo, Xiaoming; Szekely, Gabor J.

    2014-01-01

    Distance covariance and distance correlation have been widely adopted in measuring dependence of a pair of random variables or random vectors. If the computation of distance covariance and distance correlation is implemented directly accordingly to its definition then its computational complexity is O($n^2$) which is a disadvantage compared to other faster methods. In this paper we show that the computation of distance covariance and distance correlation of real valued random variables can be...

  11. ERRORJ. Covariance processing code. Version 2.2

    International Nuclear Information System (INIS)

    Chiba, Go

    2004-07-01

    ERRORJ is the covariance processing code that can produce covariance data of multi-group cross sections, which are essential for uncertainty analyses of nuclear parameters, such as neutron multiplication factor. The ERRORJ code can process the covariance data of cross sections including resonance parameters, angular and energy distributions of secondary neutrons. Those covariance data cannot be processed by the other covariance processing codes. ERRORJ has been modified and the version 2.2 has been developed. This document describes the modifications and how to use. The main topics of the modifications are as follows. Non-diagonal elements of covariance matrices are calculated in the resonance energy region. Option for high-speed calculation is implemented. Perturbation amount is optimized in a sensitivity calculation. Effect of the resonance self-shielding on covariance of multi-group cross section can be considered. It is possible to read a compact covariance format proposed by N.M. Larson. (author)

  12. Adaptive Non-Interventional Heuristics for Covariation Detection in Causal Induction: Model Comparison and Rational Analysis

    Science.gov (United States)

    Hattori, Masasi; Oaksford, Mike

    2007-01-01

    In this article, 41 models of covariation detection from 2 x 2 contingency tables were evaluated against past data in the literature and against data from new experiments. A new model was also included based on a limiting case of the normative phi-coefficient under an extreme rarity assumption, which has been shown to be an important factor in…

  13. Feature selection and multi-kernel learning for adaptive graph regularized nonnegative matrix factorization

    KAUST Repository

    Wang, Jim Jing-Yan

    2014-09-20

    Nonnegative matrix factorization (NMF), a popular part-based representation technique, does not capture the intrinsic local geometric structure of the data space. Graph regularized NMF (GNMF) was recently proposed to avoid this limitation by regularizing NMF with a nearest neighbor graph constructed from the input data set. However, GNMF has two main bottlenecks. First, using the original feature space directly to construct the graph is not necessarily optimal because of the noisy and irrelevant features and nonlinear distributions of data samples. Second, one possible way to handle the nonlinear distribution of data samples is by kernel embedding. However, it is often difficult to choose the most suitable kernel. To solve these bottlenecks, we propose two novel graph-regularized NMF methods, AGNMFFS and AGNMFMK, by introducing feature selection and multiple-kernel learning to the graph regularized NMF, respectively. Instead of using a fixed graph as in GNMF, the two proposed methods learn the nearest neighbor graph that is adaptive to the selected features and learned multiple kernels, respectively. For each method, we propose a unified objective function to conduct feature selection/multi-kernel learning, NMF and adaptive graph regularization simultaneously. We further develop two iterative algorithms to solve the two optimization problems. Experimental results on two challenging pattern classification tasks demonstrate that the proposed methods significantly outperform state-of-the-art data representation methods.

  14. General Galilei Covariant Gaussian Maps

    Science.gov (United States)

    Gasbarri, Giulio; Toroš, Marko; Bassi, Angelo

    2017-09-01

    We characterize general non-Markovian Gaussian maps which are covariant under Galilean transformations. In particular, we consider translational and Galilean covariant maps and show that they reduce to the known Holevo result in the Markovian limit. We apply the results to discuss measures of macroscopicity based on classicalization maps, specifically addressing dissipation, Galilean covariance and non-Markovianity. We further suggest a possible generalization of the macroscopicity measure defined by Nimmrichter and Hornberger [Phys. Rev. Lett. 110, 16 (2013)].

  15. Competing risks and time-dependent covariates

    DEFF Research Database (Denmark)

    Cortese, Giuliana; Andersen, Per K

    2010-01-01

    Time-dependent covariates are frequently encountered in regression analysis for event history data and competing risks. They are often essential predictors, which cannot be substituted by time-fixed covariates. This study briefly recalls the different types of time-dependent covariates......, as classified by Kalbfleisch and Prentice [The Statistical Analysis of Failure Time Data, Wiley, New York, 2002] with the intent of clarifying their role and emphasizing the limitations in standard survival models and in the competing risks setting. If random (internal) time-dependent covariates...

  16. Activities of covariance utilization working group

    International Nuclear Information System (INIS)

    Tsujimoto, Kazufumi

    2013-01-01

    During the past decade, there has been a interest in the calculational uncertainties induced by nuclear data uncertainties in the neutronics design of advanced nuclear system. The covariance nuclear data is absolutely essential for the uncertainty analysis. In the latest version of JENDL, JENDL-4.0, the covariance data for many nuclides, especially actinide nuclides, was substantialy enhanced. The growing interest in the uncertainty analysis and the covariance data has led to the organisation of the working group for covariance utilization under the JENDL committee. (author)

  17. Conservative Sample Size Determination for Repeated Measures Analysis of Covariance.

    Science.gov (United States)

    Morgan, Timothy M; Case, L Douglas

    2013-07-05

    In the design of a randomized clinical trial with one pre and multiple post randomized assessments of the outcome variable, one needs to account for the repeated measures in determining the appropriate sample size. Unfortunately, one seldom has a good estimate of the variance of the outcome measure, let alone the correlations among the measurements over time. We show how sample sizes can be calculated by making conservative assumptions regarding the correlations for a variety of covariance structures. The most conservative choice for the correlation depends on the covariance structure and the number of repeated measures. In the absence of good estimates of the correlations, the sample size is often based on a two-sample t-test, making the 'ultra' conservative and unrealistic assumption that there are zero correlations between the baseline and follow-up measures while at the same time assuming there are perfect correlations between the follow-up measures. Compared to the case of taking a single measurement, substantial savings in sample size can be realized by accounting for the repeated measures, even with very conservative assumptions regarding the parameters of the assumed correlation matrix. Assuming compound symmetry, the sample size from the two-sample t-test calculation can be reduced at least 44%, 56%, and 61% for repeated measures analysis of covariance by taking 2, 3, and 4 follow-up measures, respectively. The results offer a rational basis for determining a fairly conservative, yet efficient, sample size for clinical trials with repeated measures and a baseline value.

  18. Dispersion curve estimation via a spatial covariance method with ultrasonic wavefield imaging.

    Science.gov (United States)

    Chong, See Yenn; Todd, Michael D

    2018-05-01

    Numerous Lamb wave dispersion curve estimation methods have been developed to support damage detection and localization strategies in non-destructive evaluation/structural health monitoring (NDE/SHM) applications. In this paper, the covariance matrix is used to extract features from an ultrasonic wavefield imaging (UWI) scan in order to estimate the phase and group velocities of S0 and A0 modes. A laser ultrasonic interrogation method based on a Q-switched laser scanning system was used to interrogate full-field ultrasonic signals in a 2-mm aluminum plate at five different frequencies. These full-field ultrasonic signals were processed in three-dimensional space-time domain. Then, the time-dependent covariance matrices of the UWI were obtained based on the vector variables in Cartesian and polar coordinate spaces for all time samples. A spatial covariance map was constructed to show spatial correlations within the full wavefield. It was observed that the variances may be used as a feature for S0 and A0 mode properties. The phase velocity and the group velocity were found using a variance map and an enveloped variance map, respectively, at five different frequencies. This facilitated the estimation of Lamb wave dispersion curves. The estimated dispersion curves of the S0 and A0 modes showed good agreement with the theoretical dispersion curves. Copyright © 2018 Elsevier B.V. All rights reserved.

  19. Widespread covariation of early environmental exposures and trait-associated polygenic variation.

    Science.gov (United States)

    Krapohl, E; Hannigan, L J; Pingault, J-B; Patel, H; Kadeva, N; Curtis, C; Breen, G; Newhouse, S J; Eley, T C; O'Reilly, P F; Plomin, R

    2017-10-31

    Although gene-environment correlation is recognized and investigated by family studies and recently by SNP-heritability studies, the possibility that genetic effects on traits capture environmental risk factors or protective factors has been neglected by polygenic prediction models. We investigated covariation between trait-associated polygenic variation identified by genome-wide association studies (GWASs) and specific environmental exposures, controlling for overall genetic relatedness using a genomic relatedness matrix restricted maximum-likelihood model. In a UK-representative sample ( n = 6,710), we find widespread covariation between offspring trait-associated polygenic variation and parental behavior and characteristics relevant to children's developmental outcomes-independently of population stratification. For instance, offspring genetic risk for schizophrenia was associated with paternal age ( R 2 = 0.002; P = 1e-04), and offspring education-associated variation was associated with variance in breastfeeding ( R 2 = 0.021; P = 7e-30), maternal smoking during pregnancy ( R 2 = 0.008; P = 5e-13), parental smacking ( R 2 = 0.01; P = 4e-15), household income ( R 2 = 0.032; P = 1e-22), watching television ( R 2 = 0.034; P = 5e-47), and maternal education ( R 2 = 0.065; P = 3e-96). Education-associated polygenic variation also captured covariation between environmental exposures and children's inattention/hyperactivity, conduct problems, and educational achievement. The finding that genetic variation identified by trait GWASs partially captures environmental risk factors or protective factors has direct implications for risk prediction models and the interpretation of GWAS findings.

  20. Improvement of covariance data for fast reactors

    International Nuclear Information System (INIS)

    Shibata, Keiichi; Hasegawa, Akira

    2000-02-01

    We estimated covariances of the JENDL-3.2 data on the nuclides and reactions needed to analyze fast-reactor cores for the past three years, and produced covariance files. The present work was undertaken to re-examine the covariance files and to make some improvements. The covariances improved are the ones for the inelastic scattering cross section of 16 O, the total cross section of 23 Na, the fission cross section of 235 U, the capture cross section of 238 U, and the resolved resonance parameters for 238 U. Moreover, the covariances of 233 U data were newly estimated by the present work. The covariances obtained were compiled in the ENDF-6 format. (author)

  1. Lorentz Covariance of Langevin Equation

    International Nuclear Information System (INIS)

    Koide, T.; Denicol, G.S.; Kodama, T.

    2008-01-01

    Relativistic covariance of a Langevin type equation is discussed. The requirement of Lorentz invariance generates an entanglement between the force and noise terms so that the noise itself should not be a covariant quantity. (author)

  2. Pseudomonas biofilm matrix composition and niche biology

    Science.gov (United States)

    Mann, Ethan E.; Wozniak, Daniel J.

    2014-01-01

    Biofilms are a predominant form of growth for bacteria in the environment and in the clinic. Critical for biofilm development are adherence, proliferation, and dispersion phases. Each of these stages includes reinforcement by, or modulation of, the extracellular matrix. Pseudomonas aeruginosa has been a model organism for the study of biofilm formation. Additionally, other Pseudomonas species utilize biofilm formation during plant colonization and environmental persistence. Pseudomonads produce several biofilm matrix molecules, including polysaccharides, nucleic acids, and proteins. Accessory matrix components shown to aid biofilm formation and adaptability under varying conditions are also produced by pseudomonads. Adaptation facilitated by biofilm formation allows for selection of genetic variants with unique and distinguishable colony morphology. Examples include rugose small-colony variants and wrinkly spreaders (WS), which over produce Psl/Pel or cellulose, respectively, and mucoid bacteria that over produce alginate. The well-documented emergence of these variants suggests that pseudomonads take advantage of matrix-building subpopulations conferring specific benefits for the entire population. This review will focus on various polysaccharides as well as additional Pseudomonas biofilm matrix components. Discussions will center on structure–function relationships, regulation, and the role of individual matrix molecules in niche biology. PMID:22212072

  3. The detection of influential subsets in linear regression using an influence matrix

    OpenAIRE

    Peña, Daniel; Yohai, Víctor J.

    1991-01-01

    This paper presents a new method to identify influential subsets in linear regression problems. The procedure uses the eigenstructure of an influence matrix which is defined as the matrix of uncentered covariance of the effect on the whole data set of deleting each observation, normalized to include the univariate Cook's statistics in the diagonal. It is shown that points in an influential subset will appear with large weight in at least one of the eigenvector linked to the largest eigenvalue...

  4. A random matrix approach to VARMA processes

    International Nuclear Information System (INIS)

    Burda, Zdzislaw; Jarosz, Andrzej; Nowak, Maciej A; Snarska, Malgorzata

    2010-01-01

    We apply random matrix theory to derive the spectral density of large sample covariance matrices generated by multivariate VMA(q), VAR(q) and VARMA(q 1 , q 2 ) processes. In particular, we consider a limit where the number of random variables N and the number of consecutive time measurements T are large but the ratio N/T is fixed. In this regime, the underlying random matrices are asymptotically equivalent to free random variables (FRV). We apply the FRV calculus to calculate the eigenvalue density of the sample covariance for several VARMA-type processes. We explicitly solve the VARMA(1, 1) case and demonstrate perfect agreement between the analytical result and the spectra obtained by Monte Carlo simulations. The proposed method is purely algebraic and can be easily generalized to q 1 >1 and q 2 >1.

  5. Covariance of dynamic strain responses for structural damage detection

    Science.gov (United States)

    Li, X. Y.; Wang, L. X.; Law, S. S.; Nie, Z. H.

    2017-10-01

    A new approach to address the practical problems with condition evaluation/damage detection of structures is proposed based on the distinct features of a new damage index. The covariance of strain response function (CoS) is a function of modal parameters of the structure. A local stiffness reduction in structure would cause monotonous increase in the CoS. Its sensitivity matrix with respect to local damages of structure is negative and narrow-banded. The damage extent can be estimated with an approximation to the sensitivity matrix to decouple the identification equations. The CoS sensitivity can be calibrated in practice from two previous states of measurements to estimate approximately the damage extent of a structure. A seven-storey plane frame structure is numerically studied to illustrate the features of the CoS index and the proposed method. A steel circular arch in the laboratory is tested. Natural frequencies changed due to damage in the arch and the damage occurrence can be judged. However, the proposed CoS method can identify not only damage happening but also location, even damage extent without need of an analytical model. It is promising for structural condition evaluation of selected components.

  6. Covariance descriptor fusion for target detection

    Science.gov (United States)

    Cukur, Huseyin; Binol, Hamidullah; Bal, Abdullah; Yavuz, Fatih

    2016-05-01

    Target detection is one of the most important topics for military or civilian applications. In order to address such detection tasks, hyperspectral imaging sensors provide useful images data containing both spatial and spectral information. Target detection has various challenging scenarios for hyperspectral images. To overcome these challenges, covariance descriptor presents many advantages. Detection capability of the conventional covariance descriptor technique can be improved by fusion methods. In this paper, hyperspectral bands are clustered according to inter-bands correlation. Target detection is then realized by fusion of covariance descriptor results based on the band clusters. The proposed combination technique is denoted Covariance Descriptor Fusion (CDF). The efficiency of the CDF is evaluated by applying to hyperspectral imagery to detect man-made objects. The obtained results show that the CDF presents better performance than the conventional covariance descriptor.

  7. Generalized Linear Covariance Analysis

    Science.gov (United States)

    Carpenter, James R.; Markley, F. Landis

    2014-01-01

    This talk presents a comprehensive approach to filter modeling for generalized covariance analysis of both batch least-squares and sequential estimators. We review and extend in two directions the results of prior work that allowed for partitioning of the state space into solve-for'' and consider'' parameters, accounted for differences between the formal values and the true values of the measurement noise, process noise, and textita priori solve-for and consider covariances, and explicitly partitioned the errors into subspaces containing only the influence of the measurement noise, process noise, and solve-for and consider covariances. In this work, we explicitly add sensitivity analysis to this prior work, and relax an implicit assumption that the batch estimator's epoch time occurs prior to the definitive span. We also apply the method to an integrated orbit and attitude problem, in which gyro and accelerometer errors, though not estimated, influence the orbit determination performance. We illustrate our results using two graphical presentations, which we call the variance sandpile'' and the sensitivity mosaic,'' and we compare the linear covariance results to confidence intervals associated with ensemble statistics from a Monte Carlo analysis.

  8. Econometric analysis of realized covariation: high frequency based covariance, regression, and correlation in financial economics

    DEFF Research Database (Denmark)

    Barndorff-Nielsen, Ole Eiler; Shephard, N.

    2004-01-01

    This paper analyses multivariate high frequency financial data using realized covariation. We provide a new asymptotic distribution theory for standard methods such as regression, correlation analysis, and covariance. It will be based on a fixed interval of time (e.g., a day or week), allowing...... the number of high frequency returns during this period to go to infinity. Our analysis allows us to study how high frequency correlations, regressions, and covariances change through time. In particular we provide confidence intervals for each of these quantities....

  9. Covariant canonical quantization of fields and Bohmian mechanics

    International Nuclear Information System (INIS)

    Nikolic, H.

    2005-01-01

    We propose a manifestly covariant canonical method of field quantization based on the classical De Donder-Weyl covariant canonical formulation of field theory. Owing to covariance, the space and time arguments of fields are treated on an equal footing. To achieve both covariance and consistency with standard non-covariant canonical quantization of fields in Minkowski spacetime, it is necessary to adopt a covariant Bohmian formulation of quantum field theory. A preferred foliation of spacetime emerges dynamically owing to a purely quantum effect. The application to a simple time-reparametrization invariant system and quantum gravity is discussed and compared with the conventional non-covariant Wheeler-DeWitt approach. (orig.)

  10. Proofs of Contracted Length Non-covariance

    International Nuclear Information System (INIS)

    Strel'tsov, V.N.

    1994-01-01

    Different proofs of contracted length non covariance are discussed. The way based on the establishment of interval inconstancy (dependence on velocity) seems to be the most convincing one. It is stressed that the known non covariance of the electromagnetic field energy and momentum of a moving charge ('the problem 4/3') is a direct consequence of contracted length non covariance. 8 refs

  11. A Fuzzy Adaptive Tightly-Coupled Integration Method for Mobile Target Localization Using SINS/WSN

    Directory of Open Access Journals (Sweden)

    Wei Li

    2016-11-01

    Full Text Available In recent years, mobile target localization for enclosed environments has been a growing interest. In this paper, we have proposed a fuzzy adaptive tightly-coupled integration (FATCI method for positioning and tracking applications using strapdown inertial navigation system (SINS and wireless sensor network (WSN. The wireless signal outage and severe multipath propagation of WSN often influence the accuracy of measured distance and lead to difficulties with the WSN positioning. Note also that the SINS are known for their drifted error over time. Using as a base the well-known loosely-coupled integration method, we have built a tightly-coupled integrated positioning system for SINS/WSN based on the measured distances between anchor nodes and mobile node. The measured distance value of WSN is corrected with a least squares regression (LSR algorithm, with the aim of decreasing the systematic error for measured distance. Additionally, the statistical covariance of measured distance value is used to adjust the observation covariance matrix of a Kalman filter using a fuzzy inference system (FIS, based on the statistical characteristics. Then the tightly-coupled integration model can adaptively adjust the confidence level for measurement according to the different measured accuracies of distance measurements. Hence the FATCI system is achieved using SINS/WSN. This innovative approach is verified in real scenarios. Experimental results show that the proposed positioning system has better accuracy and stability compared with the loosely-coupled and traditional tightly-coupled integration model for WSN short-term failure or normal conditions.

  12. Hybrid vs Adaptive Ensemble Kalman Filtering for Storm Surge Forecasting

    Science.gov (United States)

    Altaf, M. U.; Raboudi, N.; Gharamti, M. E.; Dawson, C.; McCabe, M. F.; Hoteit, I.

    2014-12-01

    Recent storm surge events due to Hurricanes in the Gulf of Mexico have motivated the efforts to accurately forecast water levels. Toward this goal, a parallel architecture has been implemented based on a high resolution storm surge model, ADCIRC. However the accuracy of the model notably depends on the quality and the recentness of the input data (mainly winds and bathymetry), model parameters (e.g. wind and bottom drag coefficients), and the resolution of the model grid. Given all these uncertainties in the system, the challenge is to build an efficient prediction system capable of providing accurate forecasts enough ahead of time for the authorities to evacuate the areas at risk. We have developed an ensemble-based data assimilation system to frequently assimilate available data into the ADCIRC model in order to improve the accuracy of the model. In this contribution we study and analyze the performances of different ensemble Kalman filter methodologies for efficient short-range storm surge forecasting, the aim being to produce the most accurate forecasts at the lowest possible computing time. Using Hurricane Ike meteorological data to force the ADCIRC model over a domain including the Gulf of Mexico coastline, we implement and compare the forecasts of the standard EnKF, the hybrid EnKF and an adaptive EnKF. The last two schemes have been introduced as efficient tools for enhancing the behavior of the EnKF when implemented with small ensembles by exploiting information from a static background covariance matrix. Covariance inflation and localization are implemented in all these filters. Our results suggest that both the hybrid and the adaptive approach provide significantly better forecasts than those resulting from the standard EnKF, even when implemented with much smaller ensembles.

  13. Covariant diagrams for one-loop matching

    International Nuclear Information System (INIS)

    Zhang, Zhengkang

    2016-10-01

    We present a diagrammatic formulation of recently-revived covariant functional approaches to one-loop matching from an ultraviolet (UV) theory to a low-energy effective field theory. Various terms following from a covariant derivative expansion (CDE) are represented by diagrams which, unlike conventional Feynman diagrams, involve gaugecovariant quantities and are thus dubbed ''covariant diagrams.'' The use of covariant diagrams helps organize and simplify one-loop matching calculations, which we illustrate with examples. Of particular interest is the derivation of UV model-independent universal results, which reduce matching calculations of specific UV models to applications of master formulas. We show how such derivation can be done in a more concise manner than the previous literature, and discuss how additional structures that are not directly captured by existing universal results, including mixed heavy-light loops, open covariant derivatives, and mixed statistics, can be easily accounted for.

  14. Covariant diagrams for one-loop matching

    Energy Technology Data Exchange (ETDEWEB)

    Zhang, Zhengkang [Michigan Univ., Ann Arbor, MI (United States). Michigan Center for Theoretical Physics; Deutsches Elektronen-Synchrotron (DESY), Hamburg (Germany)

    2016-10-15

    We present a diagrammatic formulation of recently-revived covariant functional approaches to one-loop matching from an ultraviolet (UV) theory to a low-energy effective field theory. Various terms following from a covariant derivative expansion (CDE) are represented by diagrams which, unlike conventional Feynman diagrams, involve gaugecovariant quantities and are thus dubbed ''covariant diagrams.'' The use of covariant diagrams helps organize and simplify one-loop matching calculations, which we illustrate with examples. Of particular interest is the derivation of UV model-independent universal results, which reduce matching calculations of specific UV models to applications of master formulas. We show how such derivation can be done in a more concise manner than the previous literature, and discuss how additional structures that are not directly captured by existing universal results, including mixed heavy-light loops, open covariant derivatives, and mixed statistics, can be easily accounted for.

  15. Robust estimation of the correlation matrix of longitudinal data

    KAUST Repository

    Maadooliat, Mehdi

    2011-09-23

    We propose a double-robust procedure for modeling the correlation matrix of a longitudinal dataset. It is based on an alternative Cholesky decomposition of the form Σ=DLL⊤D where D is a diagonal matrix proportional to the square roots of the diagonal entries of Σ and L is a unit lower-triangular matrix determining solely the correlation matrix. The first robustness is with respect to model misspecification for the innovation variances in D, and the second is robustness to outliers in the data. The latter is handled using heavy-tailed multivariate t-distributions with unknown degrees of freedom. We develop a Fisher scoring algorithm for computing the maximum likelihood estimator of the parameters when the nonredundant and unconstrained entries of (L,D) are modeled parsimoniously using covariates. We compare our results with those based on the modified Cholesky decomposition of the form LD2L⊤ using simulations and a real dataset. © 2011 Springer Science+Business Media, LLC.

  16. Covariance Evaluation Methodology for Neutron Cross Sections

    Energy Technology Data Exchange (ETDEWEB)

    Herman,M.; Arcilla, R.; Mattoon, C.M.; Mughabghab, S.F.; Oblozinsky, P.; Pigni, M.; Pritychenko, b.; Songzoni, A.A.

    2008-09-01

    We present the NNDC-BNL methodology for estimating neutron cross section covariances in thermal, resolved resonance, unresolved resonance and fast neutron regions. The three key elements of the methodology are Atlas of Neutron Resonances, nuclear reaction code EMPIRE, and the Bayesian code implementing Kalman filter concept. The covariance data processing, visualization and distribution capabilities are integral components of the NNDC methodology. We illustrate its application on examples including relatively detailed evaluation of covariances for two individual nuclei and massive production of simple covariance estimates for 307 materials. Certain peculiarities regarding evaluation of covariances for resolved resonances and the consistency between resonance parameter uncertainties and thermal cross section uncertainties are also discussed.

  17. Covariant perturbations of Schwarzschild black holes

    International Nuclear Information System (INIS)

    Clarkson, Chris A; Barrett, Richard K

    2003-01-01

    We present a new covariant and gauge-invariant perturbation formalism for dealing with spacetimes having spherical symmetry (or some preferred spatial direction) in the background, and apply it to the case of gravitational wave propagation in a Schwarzschild black-hole spacetime. The 1 + 3 covariant approach is extended to a '1 + 1 + 2 covariant sheet' formalism by introducing a radial unit vector in addition to the timelike congruence, and decomposing all covariant quantities with respect to this. The background Schwarzschild solution is discussed and a covariant characterization is given. We give the full first-order system of linearized 1 + 1 + 2 covariant equations, and we show how, by introducing (time and spherical) harmonic functions, these may be reduced to a system of first-order ordinary differential equations and algebraic constraints for the 1 + 1 + 2 variables which may be solved straightforwardly. We show how both odd- and even-parity perturbations may be unified by the discovery of a covariant, frame- and gauge-invariant, transverse-traceless tensor describing gravitational waves, which satisfies a covariant wave equation equivalent to the Regge-Wheeler equation for both even- and odd-parity perturbations. We show how the Zerilli equation may be derived from this tensor, and derive a similar transverse-traceless tensor equation equivalent to this equation. The so-called special quasinormal modes with purely imaginary frequency emerge naturally. The significance of the degrees of freedom in the choice of the two frame vectors is discussed, and we demonstrate that, for a certain frame choice, the underlying dynamics is governed purely by the Regge-Wheeler tensor. The two transverse-traceless Weyl tensors which carry the curvature of gravitational waves are discussed, and we give the closed system of four first-order ordinary differential equations describing their propagation. Finally, we consider the extension of this work to the study of

  18. Strong tracking adaptive Kalman filters for underwater vehicle dead reckoning

    Institute of Scientific and Technical Information of China (English)

    XIAO Kun; FANG Shao-ji; PANG Yong-jie

    2007-01-01

    To improve underwater vehicle dead reckoning, a developed strong tracking adaptive kalman filter is proposed. The filter is improved with an additional adaptive factor and an estimator of measurement noise covariance. Since the magnitude of fading factor is changed adaptively, the tracking ability of the filter is still enhanced in low velocity condition of underwater vehicles. The results of simulation tests prove the presented filter effective.

  19. Covariation in Natural Causal Induction.

    Science.gov (United States)

    Cheng, Patricia W.; Novick, Laura R.

    1991-01-01

    Biases and models usually offered by cognitive and social psychology and by philosophy to explain causal induction are evaluated with respect to focal sets (contextually determined sets of events over which covariation is computed). A probabilistic contrast model is proposed as underlying covariation computation in natural causal induction. (SLD)

  20. ZZ RRDF-98, Cross-sections and covariance matrices for 22 neutron induced dosimetry reactions

    International Nuclear Information System (INIS)

    Zolotarev, K.I.; Ignatyuk, A.V.; Mahokhin, V.N.; Pashchenko, A.B.

    2005-01-01

    1 - Description of program or function: Format: ENDF-6 format; Number of groups: Continuous energy; Dosimetry reactions: 6-C-12(n,2n), 8-O-16(n,2n), 9-F-19(n,2n), 12-Mg-24(n,p), 22-Ti-46(n,2n), 22-Ti-46(n,p), 22-Ti-47(n,x), 22-Ti-48(n,p), 22-Ti-48(n,x), 22-Ti-49(n,x), 23-V-51(n,alpha), 26-Fe-54(n,2n), 26-Fe-54(n,alpha), 26-Fe-56(n,p), 27-Co-59(n,alpha), 29-Cu-63(n,alpha), 33-As-75(n,2n), 41-Nb-93(n,2n), 41-Nb-93(n,n'), 45-Rh-103(n,n'), 49-In-115(n,n'), 59-Pr-141(n,2n); Origin: Russian Federation; Weighting spectrum: None. RRDF-98 contains original evaluations of cross section data performed at the Institute of Physics and Power Engineering, Obninsk, for 22 neutron induced dosimetry reactions. The dataset also contains the corresponding covariance matrices. 2 - Methods: The evaluation of excitation functions was performed on the basis of statistical analysis of corrected experimental data in the framework of generalized least squares method and taking into account the results of optical-statistical STAPRE and GNASH calculations. The experimental cross section data including the most recent results were critically reviewed and processed in this study. If necessary, the data were normalized in order to make adjustments in relevant cross sections and decay schemes. The covariance matrices were prepared and the evaluated cross section data are presented in ENDF-6 format (Files 3, 33). For estimation of correlations between experimental data the total uncertainties of measured cross sections have been separated into statistical and systematic parts and correlation coefficients between components of systematic parts were assigned according to information given in the original publications and EXFOR library. Then the correlation matrix of cross sections measured within one experiment was calculated and approximated by matrix with a constant (average) correlation coefficient. The overall correlation matrix was composed of such sub-matrices in the assumption that the cross

  1. Bootstrapping integrated covariance matrix estimators in noisy jump-diffusion models with non-synchronous trading

    DEFF Research Database (Denmark)

    Hounyo, Ulrich

    to a gneral class of estimators of integrated covolatility. We then show the first-order asymptotic validity of this method in the multivariate context with a potential presence of jumps, dependent microsturcture noise, irregularly spaced and non-synchronous data. Due to our focus on non...... covariance estimator. As an application of our results, we also consider the bootstrap for regression coefficients. We show that the wild blocks of bootstrap, appropriately centered, is able to mimic both the dependence and heterogeneity of the scores, thus justifying the construction of bootstrap percentile...... intervals as well as variance estimates in this context. This contrasts with the traditional pairs bootstrap which is not able to mimic the score heterogeneity even in the simple case where no microsturcture noise is present. Our Monte Carlo simulations show that the wild blocks of blocks bootstrap improves...

  2. Noisy covariance matrices and portfolio optimization II

    Science.gov (United States)

    Pafka, Szilárd; Kondor, Imre

    2003-03-01

    Recent studies inspired by results from random matrix theory (Galluccio et al.: Physica A 259 (1998) 449; Laloux et al.: Phys. Rev. Lett. 83 (1999) 1467; Risk 12 (3) (1999) 69; Plerou et al.: Phys. Rev. Lett. 83 (1999) 1471) found that covariance matrices determined from empirical financial time series appear to contain such a high amount of noise that their structure can essentially be regarded as random. This seems, however, to be in contradiction with the fundamental role played by covariance matrices in finance, which constitute the pillars of modern investment theory and have also gained industry-wide applications in risk management. Our paper is an attempt to resolve this embarrassing paradox. The key observation is that the effect of noise strongly depends on the ratio r= n/ T, where n is the size of the portfolio and T the length of the available time series. On the basis of numerical experiments and analytic results for some toy portfolio models we show that for relatively large values of r (e.g. 0.6) noise does, indeed, have the pronounced effect suggested by Galluccio et al. (1998), Laloux et al. (1999) and Plerou et al. (1999) and illustrated later by Laloux et al. (Int. J. Theor. Appl. Finance 3 (2000) 391), Plerou et al. (Phys. Rev. E, e-print cond-mat/0108023) and Rosenow et al. (Europhys. Lett., e-print cond-mat/0111537) in a portfolio optimization context, while for smaller r (around 0.2 or below), the error due to noise drops to acceptable levels. Since the length of available time series is for obvious reasons limited in any practical application, any bound imposed on the noise-induced error translates into a bound on the size of the portfolio. In a related set of experiments we find that the effect of noise depends also on whether the problem arises in asset allocation or in a risk measurement context: if covariance matrices are used simply for measuring the risk of portfolios with a fixed composition rather than as inputs to optimization, the

  3. Heritability, covariation and natural selection on 24 traits of common evening primrose (Oenothera biennis) from a field experiment.

    Science.gov (United States)

    Johnson, M T J; Agrawal, A A; Maron, J L; Salminen, J-P

    2009-06-01

    This study explored genetic variation and co-variation in multiple functional plant traits. Our goal was to characterize selection, heritabilities and genetic correlations among different types of traits to gain insight into the evolutionary ecology of plant populations and their interactions with insect herbivores. In a field experiment, we detected significant heritable variation for each of 24 traits of Oenothera biennis and extensive genetic covariance among traits. Traits with diverse functions formed several distinct groups that exhibited positive genetic covariation with each other. Genetic variation in life-history traits and secondary chemistry together explained a large proportion of variation in herbivory (r(2) = 0.73). At the same time, selection acted on lifetime biomass, life-history traits and two secondary compounds of O. biennis, explaining over 95% of the variation in relative fitness among genotypes. The combination of genetic covariances and directional selection acting on multiple traits suggests that adaptive evolution of particular traits is constrained, and that correlated evolution of groups of traits will occur, which is expected to drive the evolution of increased herbivore susceptibility. As a whole, our study indicates that an examination of genetic variation and covariation among many different types of traits can provide greater insight into the evolutionary ecology of plant populations and plant-herbivore interactions.

  4. Structural Analysis of Covariance and Correlation Matrices.

    Science.gov (United States)

    Joreskog, Karl G.

    1978-01-01

    A general approach to analysis of covariance structures is considered, in which the variances and covariances or correlations of the observed variables are directly expressed in terms of the parameters of interest. The statistical problems of identification, estimation and testing of such covariance or correlation structures are discussed.…

  5. Spatiotemporal noise covariance estimation from limited empirical magnetoencephalographic data

    International Nuclear Information System (INIS)

    Jun, Sung C; Plis, Sergey M; Ranken, Doug M; Schmidt, David M

    2006-01-01

    The performance of parametric magnetoencephalography (MEG) and electroencephalography (EEG) source localization approaches can be degraded by the use of poor background noise covariance estimates. In general, estimation of the noise covariance for spatiotemporal analysis is difficult mainly due to the limited noise information available. Furthermore, its estimation requires a large amount of storage and a one-time but very large (and sometimes intractable) calculation or its inverse. To overcome these difficulties, noise covariance models consisting of one pair or a sum of multi-pairs of Kronecker products of spatial covariance and temporal covariance have been proposed. However, these approaches cannot be applied when the noise information is very limited, i.e., the amount of noise information is less than the degrees of freedom of the noise covariance models. A common example of this is when only averaged noise data are available for a limited prestimulus region (typically at most a few hundred milliseconds duration). For such cases, a diagonal spatiotemporal noise covariance model consisting of sensor variances with no spatial or temporal correlation has been the common choice for spatiotemporal analysis. In this work, we propose a different noise covariance model which consists of diagonal spatial noise covariance and Toeplitz temporal noise covariance. It can easily be estimated from limited noise information, and no time-consuming optimization and data-processing are required. Thus, it can be used as an alternative choice when one-pair or multi-pair noise covariance models cannot be estimated due to lack of noise information. To verify its capability we used Bayesian inference dipole analysis and a number of simulated and empirical datasets. We compared this covariance model with other existing covariance models such as conventional diagonal covariance, one-pair and multi-pair noise covariance models, when noise information is sufficient to estimate them. We

  6. A FORMALISM FOR COVARIANT POLARIZED RADIATIVE TRANSPORT BY RAY TRACING

    International Nuclear Information System (INIS)

    Gammie, Charles F.; Leung, Po Kin

    2012-01-01

    We write down a covariant formalism for polarized radiative transfer appropriate for ray tracing through a turbulent plasma. The polarized radiation field is represented by the polarization tensor (coherency matrix) N αβ ≡ (a α k a* β k ), where a k is a Fourier coefficient for the vector potential. Using Maxwell's equations, the Liouville-Vlasov equation, and the WKB approximation, we show that the transport equation in vacuo is k μ ∇ μ N αβ = 0. We show that this is equivalent to Broderick and Blandford's formalism based on invariant Stokes parameters and a rotation coefficient, and suggest a modification that may reduce truncation error in some situations. Finally, we write down several alternative approaches to integrating the transfer equation.

  7. Modifications of Sp(2) covariant superfield quantization

    Energy Technology Data Exchange (ETDEWEB)

    Gitman, D.M.; Moshin, P.Yu

    2003-12-04

    We propose a modification of the Sp(2) covariant superfield quantization to realize a superalgebra of generating operators isomorphic to the massless limit of the corresponding superalgebra of the osp(1,2) covariant formalism. The modified scheme ensures the compatibility of the superalgebra of generating operators with extended BRST symmetry without imposing restrictions eliminating superfield components from the quantum action. The formalism coincides with the Sp(2) covariant superfield scheme and with the massless limit of the osp(1,2) covariant quantization in particular cases of gauge-fixing and solutions of the quantum master equations.

  8. Eco-evo-devo in the Study of Adaptive Divergence: Examples from Threespine Stickleback (Gasterosteus aculeatus).

    Science.gov (United States)

    Jamniczky, Heather A; Barry, Tegan N; Rogers, Sean M

    2015-07-01

    The tight fit between form and function in organisms suggests the influence of adaptive evolution in biomechanics; however, the prevalence of adaptive traits, the mechanisms by which they arise and the corresponding responses to selection are subjects of extensive debate. We used three-dimensional microcomputed tomography and geometric morphometrics to characterize the structure of phenotypic covariance within the G. aculeatus trophic apparatus and its supporting structures in wild and controlled crosses of fish from two different localities. Our results reveal that while the structure of phenotypic covariance is conserved in marine and freshwater forms, it may be disrupted in the progeny of artificial crosses or during rapid adaptive divergence events. We discuss these results within the context of integrating covariance structure with quantitative genetics, toward establishing predictive links between genes, development, biomechanics, and the environment. © The Author 2015. Published by Oxford University Press on behalf of the Society for Integrative and Comparative Biology. All rights reserved. For permissions please email: journals.permissions@oup.com.

  9. Covariant quantizations in plane and curved spaces

    International Nuclear Information System (INIS)

    Assirati, J.L.M.; Gitman, D.M.

    2017-01-01

    We present covariant quantization rules for nonsingular finite-dimensional classical theories with flat and curved configuration spaces. In the beginning, we construct a family of covariant quantizations in flat spaces and Cartesian coordinates. This family is parametrized by a function ω(θ), θ element of (1,0), which describes an ambiguity of the quantization. We generalize this construction presenting covariant quantizations of theories with flat configuration spaces but already with arbitrary curvilinear coordinates. Then we construct a so-called minimal family of covariant quantizations for theories with curved configuration spaces. This family of quantizations is parametrized by the same function ω(θ). Finally, we describe a more wide family of covariant quantizations in curved spaces. This family is already parametrized by two functions, the previous one ω(θ) and by an additional function Θ(x,ξ). The above mentioned minimal family is a part at Θ = 1 of the wide family of quantizations. We study constructed quantizations in detail, proving their consistency and covariance. As a physical application, we consider a quantization of a non-relativistic particle moving in a curved space, discussing the problem of a quantum potential. Applying the covariant quantizations in flat spaces to an old problem of constructing quantum Hamiltonian in polar coordinates, we directly obtain a correct result. (orig.)

  10. Covariant quantizations in plane and curved spaces

    Energy Technology Data Exchange (ETDEWEB)

    Assirati, J.L.M. [University of Sao Paulo, Institute of Physics, Sao Paulo (Brazil); Gitman, D.M. [Tomsk State University, Department of Physics, Tomsk (Russian Federation); P.N. Lebedev Physical Institute, Moscow (Russian Federation); University of Sao Paulo, Institute of Physics, Sao Paulo (Brazil)

    2017-07-15

    We present covariant quantization rules for nonsingular finite-dimensional classical theories with flat and curved configuration spaces. In the beginning, we construct a family of covariant quantizations in flat spaces and Cartesian coordinates. This family is parametrized by a function ω(θ), θ element of (1,0), which describes an ambiguity of the quantization. We generalize this construction presenting covariant quantizations of theories with flat configuration spaces but already with arbitrary curvilinear coordinates. Then we construct a so-called minimal family of covariant quantizations for theories with curved configuration spaces. This family of quantizations is parametrized by the same function ω(θ). Finally, we describe a more wide family of covariant quantizations in curved spaces. This family is already parametrized by two functions, the previous one ω(θ) and by an additional function Θ(x,ξ). The above mentioned minimal family is a part at Θ = 1 of the wide family of quantizations. We study constructed quantizations in detail, proving their consistency and covariance. As a physical application, we consider a quantization of a non-relativistic particle moving in a curved space, discussing the problem of a quantum potential. Applying the covariant quantizations in flat spaces to an old problem of constructing quantum Hamiltonian in polar coordinates, we directly obtain a correct result. (orig.)

  11. Smooth individual level covariates adjustment in disease mapping.

    Science.gov (United States)

    Huque, Md Hamidul; Anderson, Craig; Walton, Richard; Woolford, Samuel; Ryan, Louise

    2018-05-01

    Spatial models for disease mapping should ideally account for covariates measured both at individual and area levels. The newly available "indiCAR" model fits the popular conditional autoregresssive (CAR) model by accommodating both individual and group level covariates while adjusting for spatial correlation in the disease rates. This algorithm has been shown to be effective but assumes log-linear associations between individual level covariates and outcome. In many studies, the relationship between individual level covariates and the outcome may be non-log-linear, and methods to track such nonlinearity between individual level covariate and outcome in spatial regression modeling are not well developed. In this paper, we propose a new algorithm, smooth-indiCAR, to fit an extension to the popular conditional autoregresssive model that can accommodate both linear and nonlinear individual level covariate effects while adjusting for group level covariates and spatial correlation in the disease rates. In this formulation, the effect of a continuous individual level covariate is accommodated via penalized splines. We describe a two-step estimation procedure to obtain reliable estimates of individual and group level covariate effects where both individual and group level covariate effects are estimated separately. This distributed computing framework enhances its application in the Big Data domain with a large number of individual/group level covariates. We evaluate the performance of smooth-indiCAR through simulation. Our results indicate that the smooth-indiCAR method provides reliable estimates of all regression and random effect parameters. We illustrate our proposed methodology with an analysis of data on neutropenia admissions in New South Wales (NSW), Australia. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  12. Development of covariance date for fast reactor cores. 3

    International Nuclear Information System (INIS)

    Shibata, Keiichi; Hasegawa, Akira

    1999-03-01

    Covariances have been estimated for nuclear data contained in JENDL-3.2. As for Cr and Ni, the physical quantities for which covariances are deduced are cross sections and the first order Legendre-polynomial coefficient for the angular distribution of elastically scattered neutrons. The covariances were estimated by using the same methodology that had been used in the JENDL-3.2 evaluation in order to keep a consistency between mean values and their covariances. In a case where evaluated data were based on experimental data, the covariances were estimated from the same experimental data. For cross section that had been evaluated by nuclear model calculations, the same model was applied to generate the covariances. The covariances obtained were compiled into ENDF-6 format files. The covariances, which had been prepared by the previous fiscal year, were re-examined, and some improvements were performed. Parts of Fe and 235 U covariances were updated. Covariances of nu-p and nu-d for 241 Pu and of fission neutron spectra for 233,235,238 U and 239,240 Pu were newly added to data files. (author)

  13. Multivariate Error Covariance Estimates by Monte-Carlo Simulation for Assimilation Studies in the Pacific Ocean

    Science.gov (United States)

    Borovikov, Anna; Rienecker, Michele M.; Keppenne, Christian; Johnson, Gregory C.

    2004-01-01

    One of the most difficult aspects of ocean state estimation is the prescription of the model forecast error covariances. The paucity of ocean observations limits our ability to estimate the covariance structures from model-observation differences. In most practical applications, simple covariances are usually prescribed. Rarely are cross-covariances between different model variables used. Here a comparison is made between a univariate Optimal Interpolation (UOI) scheme and a multivariate OI algorithm (MvOI) in the assimilation of ocean temperature. In the UOI case only temperature is updated using a Gaussian covariance function and in the MvOI salinity, zonal and meridional velocities as well as temperature, are updated using an empirically estimated multivariate covariance matrix. Earlier studies have shown that a univariate OI has a detrimental effect on the salinity and velocity fields of the model. Apparently, in a sequential framework it is important to analyze temperature and salinity together. For the MvOI an estimation of the model error statistics is made by Monte-Carlo techniques from an ensemble of model integrations. An important advantage of using an ensemble of ocean states is that it provides a natural way to estimate cross-covariances between the fields of different physical variables constituting the model state vector, at the same time incorporating the model's dynamical and thermodynamical constraints as well as the effects of physical boundaries. Only temperature observations from the Tropical Atmosphere-Ocean array have been assimilated in this study. In order to investigate the efficacy of the multivariate scheme two data assimilation experiments are validated with a large independent set of recently published subsurface observations of salinity, zonal velocity and temperature. For reference, a third control run with no data assimilation is used to check how the data assimilation affects systematic model errors. While the performance of the

  14. Robust Improvement in Estimation of a Covariance Matrix in an Elliptically Contoured Distribution Respect to Quadratic Loss Function

    Directory of Open Access Journals (Sweden)

    Z. Khodadadi

    2008-03-01

    Full Text Available Let S be matrix of residual sum of square in linear model Y = Aβ + e where matrix e is distributed as elliptically contoured with unknown scale matrix Σ. In present work, we consider the problem of estimating Σ with respect to squared loss function, L(Σˆ , Σ = tr(ΣΣˆ −1 −I 2 . It is shown that improvement of the estimators were obtained by James, Stein [7], Dey and Srivasan [1] under the normality assumption remains robust under an elliptically contoured distribution respect to squared loss function

  15. Precomputing Process Noise Covariance for Onboard Sequential Filters

    Science.gov (United States)

    Olson, Corwin G.; Russell, Ryan P.; Carpenter, J. Russell

    2017-01-01

    Process noise is often used in estimation filters to account for unmodeled and mismodeled accelerations in the dynamics. The process noise covariance acts to inflate the state covariance over propagation intervals, increasing the uncertainty in the state. In scenarios where the acceleration errors change significantly over time, the standard process noise covariance approach can fail to provide effective representation of the state and its uncertainty. Consider covariance analysis techniques provide a method to precompute a process noise covariance profile along a reference trajectory using known model parameter uncertainties. The process noise covariance profile allows significantly improved state estimation and uncertainty representation over the traditional formulation. As a result, estimation performance on par with the consider filter is achieved for trajectories near the reference trajectory without the additional computational cost of the consider filter. The new formulation also has the potential to significantly reduce the trial-and-error tuning currently required of navigation analysts. A linear estimation problem as described in several previous consider covariance analysis studies is used to demonstrate the effectiveness of the precomputed process noise covariance, as well as a nonlinear descent scenario at the asteroid Bennu with optical navigation.

  16. Rigorous covariance propagation of geoid errors to geodetic MDT estimates

    Science.gov (United States)

    Pail, R.; Albertella, A.; Fecher, T.; Savcenko, R.

    2012-04-01

    The mean dynamic topography (MDT) is defined as the difference between the mean sea surface (MSS) derived from satellite altimetry, averaged over several years, and the static geoid. Assuming geostrophic conditions, from the MDT the ocean surface velocities as important component of global ocean circulation can be derived from it. Due to the availability of GOCE gravity field models, for the very first time MDT can now be derived solely from satellite observations (altimetry and gravity) down to spatial length-scales of 100 km and even below. Global gravity field models, parameterized in terms of spherical harmonic coefficients, are complemented by the full variance-covariance matrix (VCM). Therefore, for the geoid component a realistic statistical error estimate is available, while the error description of the altimetric component is still an open issue and is, if at all, attacked empirically. In this study we make the attempt to perform, based on the full gravity VCM, rigorous error propagation to derived geostrophic surface velocities, thus also considering all correlations. For the definition of the static geoid we use the third release of the time-wise GOCE model, as well as the satellite-only combination model GOCO03S. In detail, we will investigate the velocity errors resulting from the geoid component in dependence of the harmonic degree, and the impact of using/no using covariances on the MDT errors and its correlations. When deriving an MDT, it is spectrally filtered to a certain maximum degree, which is usually driven by the signal content of the geoid model, by applying isotropic or non-isotropic filters. Since this filtering is acting also on the geoid component, the consistent integration of this filter process into the covariance propagation shall be performed, and its impact shall be quantified. The study will be performed for MDT estimates in specific test areas of particular oceanographic interest.

  17. The covariant chiral ring

    Energy Technology Data Exchange (ETDEWEB)

    Bourget, Antoine; Troost, Jan [Laboratoire de Physique Théorique, École Normale Supérieure, 24 rue Lhomond, 75005 Paris (France)

    2016-03-23

    We construct a covariant generating function for the spectrum of chiral primaries of symmetric orbifold conformal field theories with N=(4,4) supersymmetry in two dimensions. For seed target spaces K3 and T{sup 4}, the generating functions capture the SO(21) and SO(5) representation theoretic content of the chiral ring respectively. Via string dualities, we relate the transformation properties of the chiral ring under these isometries of the moduli space to the Lorentz covariance of perturbative string partition functions in flat space.

  18. Two-stage sparse coding of region covariance via Log-Euclidean kernels to detect saliency.

    Science.gov (United States)

    Zhang, Ying-Ying; Yang, Cai; Zhang, Ping

    2017-05-01

    In this paper, we present a novel bottom-up saliency detection algorithm from the perspective of covariance matrices on a Riemannian manifold. Each superpixel is described by a region covariance matrix on Riemannian Manifolds. We carry out a two-stage sparse coding scheme via Log-Euclidean kernels to extract salient objects efficiently. In the first stage, given background dictionary on image borders, sparse coding of each region covariance via Log-Euclidean kernels is performed. The reconstruction error on the background dictionary is regarded as the initial saliency of each superpixel. In the second stage, an improvement of the initial result is achieved by calculating reconstruction errors of the superpixels on foreground dictionary, which is extracted from the first stage saliency map. The sparse coding in the second stage is similar to the first stage, but is able to effectively highlight the salient objects uniformly from the background. Finally, three post-processing methods-highlight-inhibition function, context-based saliency weighting, and the graph cut-are adopted to further refine the saliency map. Experiments on four public benchmark datasets show that the proposed algorithm outperforms the state-of-the-art methods in terms of precision, recall and mean absolute error, and demonstrate the robustness and efficiency of the proposed method. Copyright © 2017 Elsevier Ltd. All rights reserved.

  19. GLq(N)-covariant quantum algebras and covariant differential calculus

    International Nuclear Information System (INIS)

    Isaev, A.P.; Pyatov, P.N.

    1992-01-01

    GL q (N)-covariant quantum algebras with generators satisfying quadratic polynomial relations are considered. It is that, up to some innessential arbitrariness, there are only two kinds of such quantum algebras, namely, the algebras with q-deformed commutation and q-deformed anticommutation relations. 25 refs

  20. Introduction to covariant formulation of superstring (field) theory

    International Nuclear Information System (INIS)

    Anon.

    1987-01-01

    The author discusses covariant formulation of superstring theories based on BRS invariance. New formulation of superstring was constructed by Green and Schwarz in the light-cone gauge first and then a covariant action was discovered. The covariant action has some interesting geometrical interpretation, however, covariant quantizations are difficult to perform because of existence of local supersymmetries. Introducing extra variables into the action, a modified action has been proposed. However, it would be difficult to prescribe constraints to define a physical subspace, or to reproduce the correct physical spectrum. Hence the old formulation, i.e., the Neveu-Schwarz-Ramond (NSR) model for covariant quantization is used. The author begins by quantizing the NSR model in a covariant way using BRS charges. Then the author discusses the field theory of (free) superstring

  1. Graphical representation of covariant-contravariant modal formulae

    Directory of Open Access Journals (Sweden)

    Miguel Palomino

    2011-08-01

    Full Text Available Covariant-contravariant simulation is a combination of standard (covariant simulation, its contravariant counterpart and bisimulation. We have previously studied its logical characterization by means of the covariant-contravariant modal logic. Moreover, we have investigated the relationships between this model and that of modal transition systems, where two kinds of transitions (the so-called may and must transitions were combined in order to obtain a simple framework to express a notion of refinement over state-transition models. In a classic paper, Boudol and Larsen established a precise connection between the graphical approach, by means of modal transition systems, and the logical approach, based on Hennessy-Milner logic without negation, to system specification. They obtained a (graphical representation theorem proving that a formula can be represented by a term if, and only if, it is consistent and prime. We show in this paper that the formulae from the covariant-contravariant modal logic that admit a "graphical" representation by means of processes, modulo the covariant-contravariant simulation preorder, are also the consistent and prime ones. In order to obtain the desired graphical representation result, we first restrict ourselves to the case of covariant-contravariant systems without bivariant actions. Bivariant actions can be incorporated later by means of an encoding that splits each bivariant action into its covariant and its contravariant parts.

  2. Covariant differential calculus on the quantum exterior vector space

    International Nuclear Information System (INIS)

    Parashar, P.; Soni, S.K.

    1992-01-01

    We formulate a differential calculus on the quantum exterior vector space spanned by the generators of a non-anticommutative algebra satisfying r ij = θ i θ j +B kl ij θ k θ l =0 i, j=1, 2, ..., n. and (θ i ) 2 =(θ j ) 2 =...=(θ n ) 2 =0, where B kl ij is the most general matrix defined in terms of complex deformation parameters. Following considerations analogous to those of Wess and Zumino, we are able to exhibit covariance of our calculus under ( 2 n )+1 parameter deformation of GL(n) and explicitly check that the non-anticommutative differential calculus satisfies the general constraints given by them, such as the 'linear' conditions dr ij ≅0 and the 'quadratic' condition r ij x n ≅0 where x n =dθ n are the differentials of the variables. (orig.)

  3. An adaptive recurrent neural-network controller using a stabilization matrix and predictive inputs to solve a tracking problem under disturbances.

    Science.gov (United States)

    Fairbank, Michael; Li, Shuhui; Fu, Xingang; Alonso, Eduardo; Wunsch, Donald

    2014-01-01

    We present a recurrent neural-network (RNN) controller designed to solve the tracking problem for control systems. We demonstrate that a major difficulty in training any RNN is the problem of exploding gradients, and we propose a solution to this in the case of tracking problems, by introducing a stabilization matrix and by using carefully constrained context units. This solution allows us to achieve consistently lower training errors, and hence allows us to more easily introduce adaptive capabilities. The resulting RNN is one that has been trained off-line to be rapidly adaptive to changing plant conditions and changing tracking targets. The case study we use is a renewable-energy generator application; that of producing an efficient controller for a three-phase grid-connected converter. The controller we produce can cope with the random variation of system parameters and fluctuating grid voltages. It produces tracking control with almost instantaneous response to changing reference states, and virtually zero oscillation. This compares very favorably to the classical proportional integrator (PI) controllers, which we show produce a much slower response and settling time. In addition, the RNN we propose exhibits better learning stability and convergence properties, and can exhibit faster adaptation, than has been achieved with adaptive critic designs. Copyright © 2013 Elsevier Ltd. All rights reserved.

  4. Note on dual superconformal symmetry of the N=4 super Yang-Mills S matrix

    International Nuclear Information System (INIS)

    Brandhuber, Andreas; Heslop, Paul; Travaglini, Gabriele

    2008-01-01

    We present a supersymmetric recursion relation for tree-level scattering amplitudes in N=4 super Yang-Mills. Using this recursion relation, we prove that the tree-level S matrix of the maximally supersymmetric theory is covariant under dual superconformal transformations. We further analyze the consequences that the transformation properties of the trees under this symmetry have on those of the loops. In particular, we show that the coefficients of the expansion of generic one-loop amplitudes in a basis of pseudoconformally invariant scalar box functions transform covariantly under dual superconformal symmetry, and in exactly the same way as the corresponding tree-level amplitudes.

  5. Covariant single-hole optical potential

    International Nuclear Information System (INIS)

    Kam, J. de

    1982-01-01

    In this investigation a covariant optical potential model is constructed for scattering processes of mesons from nuclei in which the meson interacts repeatedly with one of the target nucleons. The nuclear binding interactions in the intermediate scattering state are consistently taken into account. In particular for pions and K - projectiles this is important in view of the strong energy dependence of the elementary projectile-nucleon amplitude. Furthermore, this optical potential satisfies unitarity and relativistic covariance. The starting point in our discussion is the three-body model for the optical potential. To obtain a practical covariant theory I formulate the three-body model as a relativistic quasi two-body problem. Expressions for the transition interactions and propagators in the quasi two-body equations are found by imposing the correct s-channel unitarity relations and by using dispersion integrals. This is done in such a way that the correct non-relativistic limit is obtained, avoiding clustering problems. Corrections to the quasi two-body treatment from the Pauli principle and the required ground-state exclusion are taken into account. The covariant equations that we arrive at are amenable to practical calculations. (orig.)

  6. Nuclear data covariances in the Indian context

    International Nuclear Information System (INIS)

    Ganesan, S.

    2014-01-01

    The topic of covariances is recognized as an important part of several ongoing nuclear data science activities, since 2007, in the Nuclear Data Physics Centre of India (NDPCI). A Phase-1 project in collaboration with the Statistics department in Manipal University, Karnataka (Prof. K.M. Prasad and Prof. S. Nair) on nuclear data covariances was executed successfully during 2007-2011 period. In Phase-I, the NDPCI has conducted three national Theme meetings sponsored by the DAE-BRNS in 2008, 2010 and 2013 on nuclear data covariances. In Phase-1, the emphasis was on a thorough basic understanding of the concept of covariances including assigning uncertainties to experimental data in terms of partial errors and micro correlations, through a study and a detailed discussion of open literature. Towards the end of Phase-1, measurements and a first time covariance analysis of cross-sections for 58 Ni (n, p) 58 Co reaction measured in Mumbai Pelletron accelerator using 7 Li (p,n) reactions as neutron source in the MeV energy region were performed under a PhD programme on nuclear data covariances in which enrolled are two students, Shri B.S. Shivashankar and Ms. Shanti Sheela. India is also successfully evolving a team of young researchers to code nuclear data of uncertainties, with the perspectives on covariances, in the IAEA-EXFOR format. A Phase-II DAE-BRNS-NDPCI proposal of project at Manipal has been submitted and the proposal is undergoing a peer-review at this time. In Phase-2, modern nuclear data evaluation techniques that including covariances will be further studied as a research and development effort, as a first time effort. These efforts include the use of techniques such as that of the Kalman filter. Presently, a 48 hours lecture series on treatment of errors and their propagation is being formulated under auspices of the Homi Bhabha National Institute. The talk describes the progress achieved thus far in the learning curve of the above-mentioned and exciting

  7. Exploring multicollinearity using a random matrix theory approach.

    Science.gov (United States)

    Feher, Kristen; Whelan, James; Müller, Samuel

    2012-01-01

    Clustering of gene expression data is often done with the latent aim of dimension reduction, by finding groups of genes that have a common response to potentially unknown stimuli. However, what is poorly understood to date is the behaviour of a low dimensional signal embedded in high dimensions. This paper introduces a multicollinear model which is based on random matrix theory results, and shows potential for the characterisation of a gene cluster's correlation matrix. This model projects a one dimensional signal into many dimensions and is based on the spiked covariance model, but rather characterises the behaviour of the corresponding correlation matrix. The eigenspectrum of the correlation matrix is empirically examined by simulation, under the addition of noise to the original signal. The simulation results are then used to propose a dimension estimation procedure of clusters from data. Moreover, the simulation results warn against considering pairwise correlations in isolation, as the model provides a mechanism whereby a pair of genes with `low' correlation may simply be due to the interaction of high dimension and noise. Instead, collective information about all the variables is given by the eigenspectrum.

  8. Experience in using the covariances of some ENDF/B-V dosimetry cross sections: proposed improvements and addition of cross-reaction covariances

    International Nuclear Information System (INIS)

    Fu, C.Y.; Hetrick, D.M.

    1982-01-01

    Recent ratio data, with carefully evaluated covariances, were combined with eleven of the ENDF/B-V dosimetry cross sections using the generalized least-squares method. The purpose was to improve these evaluated cross sections and covariances, as well as to generate values for the cross-reaction covariances. The results represent improved cross sections as well as realistic and usable covariances. The latter are necessary for meaningful intergral-differential comparisons and for spectrum unfolding

  9. High-dimensional covariance estimation with high-dimensional data

    CERN Document Server

    Pourahmadi, Mohsen

    2013-01-01

    Methods for estimating sparse and large covariance matrices Covariance and correlation matrices play fundamental roles in every aspect of the analysis of multivariate data collected from a variety of fields including business and economics, health care, engineering, and environmental and physical sciences. High-Dimensional Covariance Estimation provides accessible and comprehensive coverage of the classical and modern approaches for estimating covariance matrices as well as their applications to the rapidly developing areas lying at the intersection of statistics and mac

  10. Computation of large covariance matrices by SAMMY on graphical processing units and multicore CPUs

    International Nuclear Information System (INIS)

    Arbanas, G.; Dunn, M.E.; Wiarda, D.

    2011-01-01

    Computational power of Graphical Processing Units and multicore CPUs was harnessed by the nuclear data evaluation code SAMMY to speed up computations of large Resonance Parameter Covariance Matrices (RPCMs). This was accomplished by linking SAMMY to vendor-optimized implementations of the matrix-matrix multiplication subroutine of the Basic Linear Algebra Library to compute the most time-consuming step. The 235 U RPCM computed previously using a triple-nested loop was re-computed using the NVIDIA implementation of the subroutine on a single Tesla Fermi Graphical Processing Unit, and also using the Intel's Math Kernel Library implementation on two different multicore CPU systems. A multiplication of two matrices of dimensions 16,000×20,000 that had previously taken days, took approximately one minute on the GPU. Comparable performance was achieved on a dual six-core CPU system. The magnitude of the speed-up suggests that these, or similar, combinations of hardware and libraries may be useful for large matrix operations in SAMMY. Uniform interfaces of standard linear algebra libraries make them a promising candidate for a programming framework of a new generation of SAMMY for the emerging heterogeneous computing platforms. (author)

  11. Computation of large covariance matrices by SAMMY on graphical processing units and multicore CPUs

    Energy Technology Data Exchange (ETDEWEB)

    Arbanas, G.; Dunn, M.E.; Wiarda, D., E-mail: arbanasg@ornl.gov, E-mail: dunnme@ornl.gov, E-mail: wiardada@ornl.gov [Oak Ridge National Laboratory, Oak Ridge, TN (United States)

    2011-07-01

    Computational power of Graphical Processing Units and multicore CPUs was harnessed by the nuclear data evaluation code SAMMY to speed up computations of large Resonance Parameter Covariance Matrices (RPCMs). This was accomplished by linking SAMMY to vendor-optimized implementations of the matrix-matrix multiplication subroutine of the Basic Linear Algebra Library to compute the most time-consuming step. The {sup 235}U RPCM computed previously using a triple-nested loop was re-computed using the NVIDIA implementation of the subroutine on a single Tesla Fermi Graphical Processing Unit, and also using the Intel's Math Kernel Library implementation on two different multicore CPU systems. A multiplication of two matrices of dimensions 16,000×20,000 that had previously taken days, took approximately one minute on the GPU. Comparable performance was achieved on a dual six-core CPU system. The magnitude of the speed-up suggests that these, or similar, combinations of hardware and libraries may be useful for large matrix operations in SAMMY. Uniform interfaces of standard linear algebra libraries make them a promising candidate for a programming framework of a new generation of SAMMY for the emerging heterogeneous computing platforms. (author)

  12. Covariance problem in two-dimensional quantum chromodynamics

    International Nuclear Information System (INIS)

    Hagen, C.R.

    1979-01-01

    The problem of covariance in the field theory of a two-dimensional non-Abelian gauge field is considered. Since earlier work has shown that covariance fails (in charged sectors) for the Schwinger model, particular attention is given to an evaluation of the role played by the non-Abelian nature of the fields. In contrast to all earlier attempts at this problem, it is found that the potential covariance-breaking terms are identical to those found in the Abelian theory provided that one expresses them in terms of the total (i.e., conserved) current operator. The question of covariance is thus seen to reduce in all cases to a determination as to whether there exists a conserved global charge in the theory. Since the charge operator in the Schwinger model is conserved only in neutral sectors, one is thereby led to infer a probable failure of covariance in the non-Abelian theory, but one which is identical to that found for the U(1) case

  13. Schroedinger covariance states in anisotropic waveguides

    International Nuclear Information System (INIS)

    Angelow, A.; Trifonov, D.

    1995-03-01

    In this paper Squeezed and Covariance States based on Schroedinger inequality and their connection with other nonclassical states are considered for particular case of anisotropic waveguide in LiNiO 3 . Here, the problem of photon creation and generation of squeezed and Schroedinger covariance states in optical waveguides is solved in two steps: 1. Quantization of electromagnetic field is provided in the presence of dielectric waveguide using normal-mode expansion. The photon creation and annihilation operators are introduced, expanding the solution A-vector(r-vector,t) in a series in terms of the Sturm - Liouville mode-functions. 2. In terms of these operators the Hamiltonian of the field in a nonlinear waveguide is derived. For such Hamiltonian we construct the covariance states as stable (with nonzero covariance), which minimize the Schroedinger uncertainty relation. The evolutions of the three second momenta of q-circumflex j and p-circumflex j are calculated. For this Hamiltonian all three momenta are expressed in terms of one real parameters s only. It is found out how covariance, via this parameter s, depends on the waveguide profile n(x,y), on the mode-distributions u-vector j (x,y), and on the waveguide phase mismatching Δβ. (author). 37 refs

  14. Form of the manifestly covariant Lagrangian

    Science.gov (United States)

    Johns, Oliver Davis

    1985-10-01

    The preferred form for the manifestly covariant Lagrangian function of a single, charged particle in a given electromagnetic field is the subject of some disagreement in the textbooks. Some authors use a ``homogeneous'' Lagrangian and others use a ``modified'' form in which the covariant Hamiltonian function is made to be nonzero. We argue in favor of the ``homogeneous'' form. We show that the covariant Lagrangian theories can be understood only if one is careful to distinguish quantities evaluated on the varied (in the sense of the calculus of variations) world lines from quantities evaluated on the unvaried world lines. By making this distinction, we are able to derive the Hamilton-Jacobi and Klein-Gordon equations from the ``homogeneous'' Lagrangian, even though the covariant Hamiltonian function is identically zero on all world lines. The derivation of the Klein-Gordon equation in particular gives Lagrangian theoretical support to the derivations found in standard quantum texts, and is also shown to be consistent with the Feynman path-integral method. We conclude that the ``homogeneous'' Lagrangian is a completely adequate basis for covariant Lagrangian theory both in classical and quantum mechanics. The article also explores the analogy with the Fermat theorem of optics, and illustrates a simple invariant notation for the Lagrangian and other four-vector equations.

  15. Determination of covariant Schwinger terms in anomalous gauge theories

    International Nuclear Information System (INIS)

    Kelnhofer, G.

    1991-01-01

    A functional integral method is used to determine equal time commutators between the covariant currents and the covariant Gauss-law operators in theories which are affected by an anomaly. By using a differential geometrical setup we show how the derivation of consistent- and covariant Schwinger terms can be understood on an equal footing. We find a modified consistency condition for the covariant anomaly. As a by-product the Bardeen-Zumino functional, which relates consistent and covariant anomalies, can be interpreted as connection on a certain line bundle over all gauge potentials. Finally the covariant commutator anomalies are calculated for the two- and four dimensional case. (orig.)

  16. Cross-population myelination covariance of human cerebral cortex.

    Science.gov (United States)

    Ma, Zhiwei; Zhang, Nanyin

    2017-09-01

    Cross-population covariance of brain morphometric quantities provides a measure of interareal connectivity, as it is believed to be determined by the coordinated neurodevelopment of connected brain regions. Although useful, structural covariance analysis predominantly employed bulky morphological measures with mixed compartments, whereas studies of the structural covariance of any specific subdivisions such as myelin are rare. Characterizing myelination covariance is of interest, as it will reveal connectivity patterns determined by coordinated development of myeloarchitecture between brain regions. Using myelin content MRI maps from the Human Connectome Project, here we showed that the cortical myelination covariance was highly reproducible, and exhibited a brain organization similar to that previously revealed by other connectivity measures. Additionally, the myelination covariance network shared common topological features of human brain networks such as small-worldness. Furthermore, we found that the correlation between myelination covariance and resting-state functional connectivity (RSFC) was uniform within each resting-state network (RSN), but could considerably vary across RSNs. Interestingly, this myelination covariance-RSFC correlation was appreciably stronger in sensory and motor networks than cognitive and polymodal association networks, possibly due to their different circuitry structures. This study has established a new brain connectivity measure specifically related to axons, and this measure can be valuable to investigating coordinated myeloarchitecture development. Hum Brain Mapp 38:4730-4743, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

  17. R-matrix analysis of the /sup 239/Pu neutron cross sections

    Energy Technology Data Exchange (ETDEWEB)

    Saussure, G. de; Perez, R.B.; Macklin, R.L.

    1986-03-01

    /sup 239/Pu neutron cross-section data in the resolved resonance region were analyzed with the R-Matrix Bayesian Program SAMMY. Below 30 eV the cross sections computed with the multilevel parameters are consistent with recent fission and transmission measurements as well as with older capture and alpha measurements. Above 30 eV no suitable transmission data were available and only fission cross-section measurements were analyzed. However, since the analysis conserves the complete covariance matrix, the analysis can be updated by the Bayes method as transmission measurements become available. To date, the analysis of the fission measurements has been completed up to 300 eV.

  18. Covariant extensions and the nonsymmetric unified field

    International Nuclear Information System (INIS)

    Borchsenius, K.

    1976-01-01

    The problem of generally covariant extension of Lorentz invariant field equations, by means of covariant derivatives extracted from the nonsymmetric unified field, is considered. It is shown that the contracted curvature tensor can be expressed in terms of a covariant gauge derivative which contains the gauge derivative corresponding to minimal coupling, if the universal constant p, characterizing the nonsymmetric theory, is fixed in terms of Planck's constant and the elementary quantum of charge. By this choice the spinor representation of the linear connection becomes closely related to the spinor affinity used by Infeld and Van Der Waerden (Sitzungsber. Preuss. Akad. Wiss. Phys. Math. Kl.; 9:380 (1933)) in their generally covariant formulation of Dirac's equation. (author)

  19. Computing more proper covariances of energy dependent nuclear data

    International Nuclear Information System (INIS)

    Vanhanen, R.

    2016-01-01

    Highlights: • We present conditions for covariances of energy dependent nuclear data to be proper. • We provide methods to detect non-positive and inconsistent covariances in ENDF-6 format. • We propose methods to find nearby more proper covariances. • The methods can be used as a part of a quality assurance program. - Abstract: We present conditions for covariances of energy dependent nuclear data to be proper in the sense that the covariances are positive, i.e., its eigenvalues are non-negative, and consistent with respect to the sum rules of nuclear data. For the ENDF-6 format covariances we present methods to detect non-positive and inconsistent covariances. These methods would be useful as a part of a quality assurance program. We also propose methods that can be used to find nearby more proper energy dependent covariances. These methods can be used to remove unphysical components, while preserving most of the physical components. We consider several different senses in which the nearness can be measured. These methods could be useful if a re-evaluation of improper covariances is not feasible. Two practical examples are processed and analyzed. These demonstrate some of the properties of the methods. We also demonstrate that the ENDF-6 format covariances of linearly dependent nuclear data should usually be encoded with the derivation rules.

  20. Notes on branes in matrix theory

    International Nuclear Information System (INIS)

    Keski-Vakkuri, E.; Kraus, P.

    1998-01-01

    We study the effective actions of various brane configurations in matrix theory. Starting from the 0+1-dimensional quantum mechanics, we replace coordinate matrices by covariant derivatives in the large N limit, thereby obtaining effective field theories on the brane world-volumes. Even for non-compact branes, these effective theories are of Yang-Mills type, with constant background magnetic fields. In the case of a D2-brane, we show explicitly how the effective action equals the large magnetic field limit of the Born-Infeld action, and thus derive from matrix theory the action used by Polchinski and Pouliot to compute M-momentum transfer between membranes. We also consider the effect of compactifying transverse directions. Finally, we analyze a scattering process involving a recently proposed background representing a classically stable D6+D0 brane configuration. We compute the potential between this configuration and a D0-brane, and show that the result agrees with supergravity. (orig.)

  1. Covariance Spectroscopy for Fissile Material Detection

    International Nuclear Information System (INIS)

    Trainham, Rusty; Tinsley, Jim; Hurley, Paul; Keegan, Ray

    2009-01-01

    Nuclear fission produces multiple prompt neutrons and gammas at each fission event. The resulting daughter nuclei continue to emit delayed radiation as neutrons boil off, beta decay occurs, etc. All of the radiations are causally connected, and therefore correlated. The correlations are generally positive, but when different decay channels compete, so that some radiations tend to exclude others, negative correlations could also be observed. A similar problem of reduced complexity is that of cascades radiation, whereby a simple radioactive decay produces two or more correlated gamma rays at each decay. Covariance is the usual means for measuring correlation, and techniques of covariance mapping may be useful to produce distinct signatures of special nuclear materials (SNM). A covariance measurement can also be used to filter data streams because uncorrelated signals are largely rejected. The technique is generally more effective than a coincidence measurement. In this poster, we concentrate on cascades and the covariance filtering problem

  2. Rigorous constraints on the matrix elements of the energy–momentum tensor

    Directory of Open Access Journals (Sweden)

    Peter Lowdon

    2017-11-01

    Full Text Available The structure of the matrix elements of the energy–momentum tensor play an important role in determining the properties of the form factors A(q2, B(q2 and C(q2 which appear in the Lorentz covariant decomposition of the matrix elements. In this paper we apply a rigorous frame-independent distributional-matching approach to the matrix elements of the Poincaré generators in order to derive constraints on these form factors as q→0. In contrast to the literature, we explicitly demonstrate that the vanishing of the anomalous gravitomagnetic moment B(0 and the condition A(0=1 are independent of one another, and that these constraints are not related to the specific properties or conservation of the individual Poincaré generators themselves, but are in fact a consequence of the physical on-shell requirement of the states in the matrix elements and the manner in which these states transform under Poincaré transformations.

  3. Covariate Imbalance and Precision in Measuring Treatment Effects

    Science.gov (United States)

    Liu, Xiaofeng Steven

    2011-01-01

    Covariate adjustment can increase the precision of estimates by removing unexplained variance from the error in randomized experiments, although chance covariate imbalance tends to counteract the improvement in precision. The author develops an easy measure to examine chance covariate imbalance in randomization by standardizing the average…

  4. Covariant description of Hamiltonian form for field dynamics

    International Nuclear Information System (INIS)

    Ozaki, Hiroshi

    2005-01-01

    Hamiltonian form of field dynamics is developed on a space-like hypersurface in space-time. A covariant Poisson bracket on the space-like hypersurface is defined and it plays a key role to describe every algebraic relation into a covariant form. It is shown that the Poisson bracket has the same symplectic structure that was brought in the covariant symplectic approach. An identity invariant under the canonical transformations is obtained. The identity follows a canonical equation in which the interaction Hamiltonian density generates a deformation of the space-like hypersurface. The equation just corresponds to the Yang-Feldman equation in the Heisenberg pictures in quantum field theory. By converting the covariant Poisson bracket on the space-like hypersurface to four-dimensional commutator, we can pass over to quantum field theory in the Heisenberg picture without spoiling the explicit relativistic covariance. As an example the canonical QCD is displayed in a covariant way on a space-like hypersurface

  5. Application of Improved 5th-Cubature Kalman Filter in Initial Strapdown Inertial Navigation System Alignment for Large Misalignment Angles.

    Science.gov (United States)

    Wang, Wei; Chen, Xiyuan

    2018-02-23

    In view of the fact the accuracy of the third-degree Cubature Kalman Filter (CKF) used for initial alignment under large misalignment angle conditions is insufficient, an improved fifth-degree CKF algorithm is proposed in this paper. In order to make full use of the innovation on filtering, the innovation covariance matrix is calculated recursively by an innovative sequence with an exponent fading factor. Then a new adaptive error covariance matrix scaling algorithm is proposed. The Singular Value Decomposition (SVD) method is used for improving the numerical stability of the fifth-degree CKF in this paper. In order to avoid the overshoot caused by excessive scaling of error covariance matrix during the convergence stage, the scaling scheme is terminated when the gradient of azimuth reaches the maximum. The experimental results show that the improved algorithm has better alignment accuracy with large misalignment angles than the traditional algorithm.

  6. Sensitivity analysis for missing dichotomous outcome data in multi-visit randomized clinical trial with randomization-based covariance adjustment.

    Science.gov (United States)

    Li, Siying; Koch, Gary G; Preisser, John S; Lam, Diana; Sanchez-Kam, Matilde

    2017-01-01

    Dichotomous endpoints in clinical trials have only two possible outcomes, either directly or via categorization of an ordinal or continuous observation. It is common to have missing data for one or more visits during a multi-visit study. This paper presents a closed form method for sensitivity analysis of a randomized multi-visit clinical trial that possibly has missing not at random (MNAR) dichotomous data. Counts of missing data are redistributed to the favorable and unfavorable outcomes mathematically to address possibly informative missing data. Adjusted proportion estimates and their closed form covariance matrix estimates are provided. Treatment comparisons over time are addressed with Mantel-Haenszel adjustment for a stratification factor and/or randomization-based adjustment for baseline covariables. The application of such sensitivity analyses is illustrated with an example. An appendix outlines an extension of the methodology to ordinal endpoints.

  7. Parametric Covariance Model for Horizon-Based Optical Navigation

    Science.gov (United States)

    Hikes, Jacob; Liounis, Andrew J.; Christian, John A.

    2016-01-01

    This Note presents an entirely parametric version of the covariance for horizon-based optical navigation measurements. The covariance can be written as a function of only the spacecraft position, two sensor design parameters, the illumination direction, the size of the observed planet, the size of the lit arc to be used, and the total number of observed horizon points. As a result, one may now more clearly understand the sensitivity of horizon-based optical navigation performance as a function of these key design parameters, which is insight that was obscured in previous (and nonparametric) versions of the covariance. Finally, the new parametric covariance is shown to agree with both the nonparametric analytic covariance and results from a Monte Carlo analysis.

  8. Duality covariant type-IIB supersymmetry and nonperturbative consequences

    International Nuclear Information System (INIS)

    Bars, I.

    1997-01-01

    Type-IIB supersymmetric theories have an SL(2,Z) invariance, known as U duality, which controls the nonperturbative behavior of the theory. Under SL(2,Z) the supercharges are doublets, implying that the bosonic charges would be singlets or triplets. However, among the bosonic charges there are doublet strings and doublet five-branes which are in conflict with the doublet property of the supercharges. It is shown that the conflict is resolved by structure constants that depend on moduli, such as the tau parameter, which transform under the same SL(2,Z). The resulting superalgebra encodes the nonperturbative duality properties of the theory and is valid for any value of the string coupling constant. The usefulness of the formalism is illustrated by applying it to purely algebraic computations of the tension of (p,q) strings, and the mass and entropy of extremal black holes constructed from D-1-branes and D-5-branes. In the latter case the nonperturbative coupling dependence of the BPS mass and renormalization is computed for the first time in this paper. It is further argued that the moduli dependence of the superalgebra provides hints for four more dimensions beyond ten, such that the superalgebra is embedded in a fundamental theory which would be covariant under SO(11,3). An outline is given for a matrix theory in 14 dimensions that would be consistent with M(atrix) theory as well as with the above observations. copyright 1997 The American Physical Society

  9. Activities on covariance estimation in Japanese Nuclear Data Committee

    Energy Technology Data Exchange (ETDEWEB)

    Shibata, Keiichi [Japan Atomic Energy Research Inst., Tokai, Ibaraki (Japan). Tokai Research Establishment

    1997-03-01

    Described are activities on covariance estimation in the Japanese Nuclear Data Committee. Covariances are obtained from measurements by using the least-squares methods. A simultaneous evaluation was performed to deduce covariances of fission cross sections of U and Pu isotopes. A code system, KALMAN, is used to estimate covariances of nuclear model calculations from uncertainties in model parameters. (author)

  10. Neutron spectrum adjustment. The role of covariances

    International Nuclear Information System (INIS)

    Remec, I.

    1992-01-01

    Neutron spectrum adjustment method is shortly reviewed. Practical example dealing with power reactor pressure vessel exposure rates determination is analysed. Adjusted exposure rates are found only slightly affected by the covariances of measured reaction rates and activation cross sections, while the multigroup spectra covariances were found important. Approximate spectra covariance matrices, as suggested in Astm E944-89, were found useful but care is advised if they are applied in adjustments of spectra at locations without dosimetry. (author) [sl

  11. Multiple Kernel Learning for adaptive graph regularized nonnegative matrix factorization

    KAUST Repository

    Wang, Jim Jing-Yan; AbdulJabbar, Mustafa Abdulmajeed

    2012-01-01

    Nonnegative Matrix Factorization (NMF) has been continuously evolving in several areas like pattern recognition and information retrieval methods. It factorizes a matrix into a product of 2 low-rank non-negative matrices that will define parts-based, and linear representation of non-negative data. Recently, Graph regularized NMF (GrNMF) is proposed to find a compact representation, which uncovers the hidden semantics and simultaneously respects the intrinsic geometric structure. In GNMF, an affinity graph is constructed from the original data space to encode the geometrical information. In this paper, we propose a novel idea which engages a Multiple Kernel Learning approach into refining the graph structure that reflects the factorization of the matrix and the new data space. The GrNMF is improved by utilizing the graph refined by the kernel learning, and then a novel kernel learning method is introduced under the GrNMF framework. Our approach shows encouraging results of the proposed algorithm in comparison to the state-of-the-art clustering algorithms like NMF, GrNMF, SVD etc.

  12. Regularization in Matrix Relevance Learning

    NARCIS (Netherlands)

    Schneider, Petra; Bunte, Kerstin; Stiekema, Han; Hammer, Barbara; Villmann, Thomas; Biehl, Michael

    A In this paper, we present a regularization technique to extend recently proposed matrix learning schemes in learning vector quantization (LVQ). These learning algorithms extend the concept of adaptive distance measures in LVQ to the use of relevance matrices. In general, metric learning can

  13. Conformally covariant massless spin-two field equations

    International Nuclear Information System (INIS)

    Drew, M.S.; Gegenberg, J.D.

    1980-01-01

    An explicit proof is constructed to show that the field equations for a symmetric tensor field hsub(ab) describing massless spin-2 particles in Minkowski space-time are not covariant under the 15-parameter group SOsub(4,2); this group is usually associated with conformal transformations on flat space, and here it will be considered as a global gauge group which acts upon matter fields defined on space-time. Notwithstanding the above noncovariance, the equations governing the rank-4 tensor Ssub(abcd) constructed from hsub(ab) are shown to be covariant provided the contraction Ssub(ab) vanishes. Conformal covariance is proved by demonstrating the covariance of the equations for the equivalent 5-component complex field; in fact, covariance is proved for a general field equation applicable to massless particles of any spin >0. It is shown that the noncovariance of the hsub(ab) equations may be ascribed to the fact that the transformation behaviour of hsub(ab) is not the same as that of a field consisting of a gauge only. Since this is in contradistinction to the situation for the electromagnetic-field equations, the vector form of the electromagnetic equations is cast into a form which can be duplicated for the hsub(ab)-field. This procedure results in an alternative, covariant, field equation for hsub(ab). (author)

  14. Novel Direction Of Arrival Estimation Method Based on Coherent Accumulation Matrix Reconstruction

    Directory of Open Access Journals (Sweden)

    Li Lei

    2015-04-01

    Full Text Available Based on coherent accumulation matrix reconstruction, a novel Direction Of Arrival (DOA estimation decorrelation method of coherent signals is proposed using a small sample. First, the Signal to Noise Ratio (SNR is improved by performing coherent accumulation operation on an array of observed data. Then, according to the structure characteristics of the accumulated snapshot vector, the equivalent covariance matrix, whose rank is the same as the number of array elements, is constructed. The rank of this matrix is proved to be determined just by the number of incident signals, which realize the decorrelation of coherent signals. Compared with spatial smoothing method, the proposed method performs better by effectively avoiding aperture loss with high-resolution characteristics and low computational complexity. Simulation results demonstrate the efficiency of the proposed method.

  15. Evaluation of covariance for 238U cross sections

    International Nuclear Information System (INIS)

    Kawano, Toshihiko; Nakamura, Masahiro; Matsuda, Nobuyuki; Kanda, Yukinori

    1995-01-01

    Covariances of 238 U are generated using analytic functions for representation of the cross sections. The covariances of the (n,2n) and (n,3n) reactions are derived with a spline function, while the covariances of the total and the inelastic scattering cross section are estimated with a linearized nuclear model calculation. (author)

  16. GLq(N)-covariant quantum algebras and covariant differential calculus

    International Nuclear Information System (INIS)

    Isaev, A.P.; Pyatov, P.N.

    1993-01-01

    We consider GL q (N)-covariant quantum algebras with generators satisfying quadratic polynomial relations. We show that, up to some inessential arbitrariness, there are only two kinds of such quantum algebras, namely, the algebras with q-deformed commutation and q-deformed anticommutation relations. The connection with the bicovariant differential calculus on the linear quantum groups is discussed. (orig.)

  17. Adapted Boolean network models for extracellular matrix formation

    Directory of Open Access Journals (Sweden)

    Wollbold Johannes

    2009-07-01

    Full Text Available Abstract Background Due to the rapid data accumulation on pathogenesis and progression of chronic inflammation, there is an increasing demand for approaches to analyse the underlying regulatory networks. For example, rheumatoid arthritis (RA is a chronic inflammatory disease, characterised by joint destruction and perpetuated by activated synovial fibroblasts (SFB. These abnormally express and/or secrete pro-inflammatory cytokines, collagens causing joint fibrosis, or tissue-degrading enzymes resulting in destruction of the extra-cellular matrix (ECM. We applied three methods to analyse ECM regulation: data discretisation to filter out noise and to reduce complexity, Boolean network construction to implement logic relationships, and formal concept analysis (FCA for the formation of minimal, but complete rule sets from the data. Results First, we extracted literature information to develop an interaction network containing 18 genes representing ECM formation and destruction. Subsequently, we constructed an asynchronous Boolean network with biologically plausible time intervals for mRNA and protein production, secretion, and inactivation. Experimental gene expression data was obtained from SFB stimulated by TGFβ1 or by TNFα and discretised thereafter. The Boolean functions of the initial network were improved iteratively by the comparison of the simulation runs to the experimental data and by exploitation of expert knowledge. This resulted in adapted networks for both cytokine stimulation conditions. The simulations were further analysed by the attribute exploration algorithm of FCA, integrating the observed time series in a fine-tuned and automated manner. The resulting temporal rules yielded new contributions to controversially discussed aspects of fibroblast biology (e.g., considerable expression of TNF and MMP9 by fibroblasts stimulation and corroborated previously known facts (e.g., co-expression of collagens and MMPs after TNF

  18. Students’ Covariational Reasoning in Solving Integrals’ Problems

    Science.gov (United States)

    Harini, N. V.; Fuad, Y.; Ekawati, R.

    2018-01-01

    Covariational reasoning plays an important role to indicate quantities vary in learning calculus. This study investigates students’ covariational reasoning during their studies concerning two covarying quantities in integral problem. Six undergraduate students were chosen to solve problems that involved interpreting and representing how quantities change in tandem. Interviews were conducted to reveal the students’ reasoning while solving covariational problems. The result emphasizes that undergraduate students were able to construct the relation of dependent variables that changes in tandem with the independent variable. However, students faced difficulty in forming images of continuously changing rates and could not accurately apply the concept of integrals. These findings suggest that learning calculus should be increased emphasis on coordinating images of two quantities changing in tandem about instantaneously rate of change and to promote conceptual knowledge in integral techniques.

  19. Forecasting Covariance Matrices: A Mixed Frequency Approach

    DEFF Research Database (Denmark)

    Halbleib, Roxana; Voev, Valeri

    This paper proposes a new method for forecasting covariance matrices of financial returns. The model mixes volatility forecasts from a dynamic model of daily realized volatilities estimated with high-frequency data with correlation forecasts based on daily data. This new approach allows for flexi......This paper proposes a new method for forecasting covariance matrices of financial returns. The model mixes volatility forecasts from a dynamic model of daily realized volatilities estimated with high-frequency data with correlation forecasts based on daily data. This new approach allows...... for flexible dependence patterns for volatilities and correlations, and can be applied to covariance matrices of large dimensions. The separate modeling of volatility and correlation forecasts considerably reduces the estimation and measurement error implied by the joint estimation and modeling of covariance...

  20. Covariance upperbound controllers for networked control systems

    International Nuclear Information System (INIS)

    Ko, Sang Ho

    2012-01-01

    This paper deals with designing covariance upperbound controllers for a linear system that can be used in a networked control environment in which control laws are calculated in a remote controller and transmitted through a shared communication link to the plant. In order to compensate for possible packet losses during the transmission, two different techniques are often employed: the zero-input and the hold-input strategy. These use zero input and the latest control input, respectively, when a packet is lost. For each strategy, we synthesize a class of output covariance upperbound controllers for a given covariance upperbound and a packet loss probability. Existence conditions of the covariance upperbound controller are also provided for each strategy. Through numerical examples, performance of the two strategies is compared in terms of feasibility of implementing the controllers

  1. ERRORJ. Covariance processing code system for JENDL. Version 2

    International Nuclear Information System (INIS)

    Chiba, Gou

    2003-09-01

    ERRORJ is the covariance processing code system for Japanese Evaluated Nuclear Data Library (JENDL) that can produce group-averaged covariance data to apply it to the uncertainty analysis of nuclear characteristics. ERRORJ can treat the covariance data for cross sections including resonance parameters as well as angular distributions and energy distributions of secondary neutrons which could not be dealt with by former covariance processing codes. In addition, ERRORJ can treat various forms of multi-group cross section and produce multi-group covariance file with various formats. This document describes an outline of ERRORJ and how to use it. (author)

  2. Recognition of Risk Information - Adaptation of J. Bertin's Orderable Matrix for social communication

    Science.gov (United States)

    Ishida, Keiichi

    2018-05-01

    This paper aims to show capability of the Orderable Matrix of Jacques Bertin which is a visualization method of data analyze and/or a method to recognize data. That matrix can show the data by replacing numbers to visual element. As an example, using a set of data regarding natural hazard rankings for certain metropolitan cities in the world, this paper describes how the Orderable Matrix handles the data set and show characteristic factors of this data to understand it. Not only to see a kind of risk ranking of cities, the Orderable Matrix shows how differently danger concerned cities ones and others are. Furthermore, we will see that the visualized data by Orderable Matrix allows us to see the characteristics of the data set comprehensively and instantaneously.

  3. Covariate-adjusted measures of discrimination for survival data

    DEFF Research Database (Denmark)

    White, Ian R; Rapsomaniki, Eleni; Frikke-Schmidt, Ruth

    2015-01-01

    by the study design (e.g. age and sex) influence discrimination and can make it difficult to compare model discrimination between studies. Although covariate adjustment is a standard procedure for quantifying disease-risk factor associations, there are no covariate adjustment methods for discrimination...... statistics in censored survival data. OBJECTIVE: To develop extensions of the C-index and D-index that describe the prognostic ability of a model adjusted for one or more covariate(s). METHOD: We define a covariate-adjusted C-index and D-index for censored survival data, propose several estimators......, and investigate their performance in simulation studies and in data from a large individual participant data meta-analysis, the Emerging Risk Factors Collaboration. RESULTS: The proposed methods perform well in simulations. In the Emerging Risk Factors Collaboration data, the age-adjusted C-index and D-index were...

  4. Comparative Analyses of Phenotypic Trait Covariation within and among Populations.

    Science.gov (United States)

    Peiman, Kathryn S; Robinson, Beren W

    2017-10-01

    Many morphological, behavioral, physiological, and life-history traits covary across the biological scales of individuals, populations, and species. However, the processes that cause traits to covary also change over these scales, challenging our ability to use patterns of trait covariance to infer process. Trait relationships are also widely assumed to have generic functional relationships with similar evolutionary potentials, and even though many different trait relationships are now identified, there is little appreciation that these may influence trait covariation and evolution in unique ways. We use a trait-performance-fitness framework to classify and organize trait relationships into three general classes, address which ones more likely generate trait covariation among individuals in a population, and review how selection shapes phenotypic covariation. We generate predictions about how trait covariance changes within and among populations as a result of trait relationships and in response to selection and consider how these can be tested with comparative data. Careful comparisons of covariation patterns can narrow the set of hypothesized processes that cause trait covariation when the form of the trait relationship and how it responds to selection yield clear predictions about patterns of trait covariation. We discuss the opportunities and limitations of comparative approaches to evaluate hypotheses about the evolutionary causes and consequences of trait covariation and highlight the importance of evaluating patterns within populations replicated in the same and in different selective environments. Explicit hypotheses about trait relationships are key to generating effective predictions about phenotype and its evolution using covariance data.

  5. Earth Observation System Flight Dynamics System Covariance Realism

    Science.gov (United States)

    Zaidi, Waqar H.; Tracewell, David

    2016-01-01

    This presentation applies a covariance realism technique to the National Aeronautics and Space Administration (NASA) Earth Observation System (EOS) Aqua and Aura spacecraft based on inferential statistics. The technique consists of three parts: collection calculation of definitive state estimates through orbit determination, calculation of covariance realism test statistics at each covariance propagation point, and proper assessment of those test statistics.

  6. Frequency Adaptability and Waveform Design for OFDM Radar Space-Time Adaptive Processing

    Energy Technology Data Exchange (ETDEWEB)

    Sen, Satyabrata [ORNL; Glover, Charles Wayne [ORNL

    2012-01-01

    We propose an adaptive waveform design technique for an orthogonal frequency division multiplexing (OFDM) radar signal employing a space-time adaptive processing (STAP) technique. We observe that there are inherent variabilities of the target and interference responses in the frequency domain. Therefore, the use of an OFDM signal can not only increase the frequency diversity of our system, but also improve the target detectability by adaptively modifying the OFDM coefficients in order to exploit the frequency-variabilities of the scenario. First, we formulate a realistic OFDM-STAP measurement model considering the sparse nature of the target and interference spectra in the spatio-temporal domain. Then, we show that the optimal STAP-filter weight-vector is equal to the generalized eigenvector corresponding to the minimum generalized eigenvalue of the interference and target covariance matrices. With numerical examples we demonstrate that the resultant OFDM-STAP filter-weights are adaptable to the frequency-variabilities of the target and interference responses, in addition to the spatio-temporal variabilities. Hence, by better utilizing the frequency variabilities, we propose an adaptive OFDM-waveform design technique, and consequently gain a significant amount of STAP-performance improvement.

  7. Adaptive processes in economic systems

    CERN Document Server

    Murphy, Roy E

    1965-01-01

    In this book, we study theoretical and practical aspects of computing methods for mathematical modelling of nonlinear systems. A number of computing techniques are considered, such as methods of operator approximation with any given accuracy; operator interpolation techniques including a non-Lagrange interpolation; methods of system representation subject to constraints associated with concepts of causality, memory and stationarity; methods of system representation with an accuracy that is the best within a given class of models; methods of covariance matrix estimation;. methods for low-rank m

  8. Determination of covariant Schwinger terms in anomalous gauge theories

    International Nuclear Information System (INIS)

    Kelnhofer, G.

    1991-01-01

    A functional integral method is used to determine equal time commutators between the covariant currents and the covariant Gauss-law operators in theories which are affected by an anomaly. By using a differential geometrical setup we show how the derivation of consistent- and covariant Schwinger terms can be understood on an equal footing. We find a modified consistency condition for the covariant anomaly. As a by-product the Bardeen-Zumino functional, which relates consistent and covariant anomalies, can be interpreted as connection on a certain line bundle over all gauge potentials. Finally the commutator anomalies are calculated for the two- and four dimensional case. (Author) 13 refs

  9. Coherent states and covariant semi-spectral measures

    International Nuclear Information System (INIS)

    Scutaru, H.

    1976-01-01

    The close connection between Mackey's theory of imprimitivity systems and the so called generalized coherent states introduced by Perelomov is established. Coherent states give a covariant description of the ''localization'' of a quantum system in the phase space in a similar way as the imprimitivity systems give a covariant description of the localization of a quantum system in the configuration space. The observation that for any system of coherent states one can define a covariant semi-spectral measure made possible a rigurous formulation of this idea. A generalization of the notion of coherent states is given. Covariant semi-spectral measures associated with systems of coherent states are defined and characterized. Necessary and sufficient conditions for a unitary representation of a Lie group to be i) a subrepresentation of an induced one and ii) a representation with coherent states are given (author)

  10. Covariance NMR Processing and Analysis for Protein Assignment.

    Science.gov (United States)

    Harden, Bradley J; Frueh, Dominique P

    2018-01-01

    During NMR resonance assignment it is often necessary to relate nuclei to one another indirectly, through their common correlations to other nuclei. Covariance NMR has emerged as a powerful technique to correlate such nuclei without relying on error-prone peak peaking. However, false-positive artifacts in covariance spectra have impeded a general application to proteins. We recently introduced pre- and postprocessing steps to reduce the prevalence of artifacts in covariance spectra, allowing for the calculation of a variety of 4D covariance maps obtained from diverse combinations of pairs of 3D spectra, and we have employed them to assign backbone and sidechain resonances in two large and challenging proteins. In this chapter, we present a detailed protocol describing how to (1) properly prepare existing 3D spectra for covariance, (2) understand and apply our processing script, and (3) navigate and interpret the resulting 4D spectra. We also provide solutions to a number of errors that may occur when using our script, and we offer practical advice when assigning difficult signals. We believe such 4D spectra, and covariance NMR in general, can play an integral role in the assignment of NMR signals.

  11. The Matrix exponential, Dynamic Systems and Control

    DEFF Research Database (Denmark)

    Poulsen, Niels Kjølstad

    The matrix exponential can be found in various connections in analysis and control of dynamic systems. In this short note we are going to list a few examples. The matrix exponential usably pops up in connection to the sampling process, whatever it is in a deterministic or a stochastic setting...... or it is a tool for determining a Gramian matrix. This note is intended to be used in connection to the teaching post the course in Stochastic Adaptive Control (02421) given at Informatics and Mathematical Modelling (IMM), The Technical University of Denmark. This work is a result of a study of the litterature....

  12. Some remarks on general covariance of quantum theory

    International Nuclear Information System (INIS)

    Schmutzer, E.

    1977-01-01

    If one accepts Einstein's general principle of relativity (covariance principle) also for the sphere of microphysics (quantum, mechanics, quantum field theory, theory of elemtary particles), one has to ask how far the fundamental laws of traditional quantum physics fulfil this principle. Attention is here drawn to a series of papers that have appeared during the last years, in which the author criticized the usual scheme of quantum theory (Heisenberg picture, Schroedinger picture etc.) and presented a new foundation of the basic laws of quantum physics, obeying the 'principle of fundamental covariance' (Einstein's covariance principle in space-time and covariance principle in Hilbert space of quantum operators and states). (author)

  13. Reprint of "Two-stage sparse coding of region covariance via Log-Euclidean kernels to detect saliency".

    Science.gov (United States)

    Zhang, Ying-Ying; Yang, Cai; Zhang, Ping

    2017-08-01

    In this paper, we present a novel bottom-up saliency detection algorithm from the perspective of covariance matrices on a Riemannian manifold. Each superpixel is described by a region covariance matrix on Riemannian Manifolds. We carry out a two-stage sparse coding scheme via Log-Euclidean kernels to extract salient objects efficiently. In the first stage, given background dictionary on image borders, sparse coding of each region covariance via Log-Euclidean kernels is performed. The reconstruction error on the background dictionary is regarded as the initial saliency of each superpixel. In the second stage, an improvement of the initial result is achieved by calculating reconstruction errors of the superpixels on foreground dictionary, which is extracted from the first stage saliency map. The sparse coding in the second stage is similar to the first stage, but is able to effectively highlight the salient objects uniformly from the background. Finally, three post-processing methods-highlight-inhibition function, context-based saliency weighting, and the graph cut-are adopted to further refine the saliency map. Experiments on four public benchmark datasets show that the proposed algorithm outperforms the state-of-the-art methods in terms of precision, recall and mean absolute error, and demonstrate the robustness and efficiency of the proposed method. Copyright © 2017 Elsevier Ltd. All rights reserved.

  14. Covariance-Based Measurement Selection Criterion for Gaussian-Based Algorithms

    Directory of Open Access Journals (Sweden)

    Fernando A. Auat Cheein

    2013-01-01

    Full Text Available Process modeling by means of Gaussian-based algorithms often suffers from redundant information which usually increases the estimation computational complexity without significantly improving the estimation performance. In this article, a non-arbitrary measurement selection criterion for Gaussian-based algorithms is proposed. The measurement selection criterion is based on the determination of the most significant measurement from both an estimation convergence perspective and the covariance matrix associated with the measurement. The selection criterion is independent from the nature of the measured variable. This criterion is used in conjunction with three Gaussian-based algorithms: the EIF (Extended Information Filter, the EKF (Extended Kalman Filter and the UKF (Unscented Kalman Filter. Nevertheless, the measurement selection criterion shown herein can also be applied to other Gaussian-based algorithms. Although this work is focused on environment modeling, the results shown herein can be applied to other Gaussian-based algorithm implementations. Mathematical descriptions and implementation results that validate the proposal are also included in this work.

  15. The impact of covariance localization on the performance of an ocean EnKF system assimilating glider data in the Ligurian Sea

    Science.gov (United States)

    Falchetti, Silvia; Alvarez, Alberto

    2018-04-01

    Data assimilation through an ensemble Kalman filter (EnKF) is not exempt from deficiencies, including the generation of long-range unphysical correlations that degrade its performance. The covariance localization technique has been proposed and used in previous research to mitigate this effect. However, an evaluation of its performance is usually hindered by the sparseness and unsustained collection of independent observations. This article assesses the performance of an ocean prediction system composed of a multivariate EnKF coupled with a regional configuration of the Regional Ocean Model System (ROMS) with a covariance localization solution and data assimilation from an ocean glider that operated over a limited region of the Ligurian Sea. Simultaneous with the operation of the forecast system, a high-quality data set was repeatedly collected with a CTD sensor, i.e., every day during the period from 5 to 20 August 2013 (approximately 4 to 5 times the synoptic time scale of the area), located on board the NR/V Alliance for model validation. Comparisons between the validation data set and the forecasts provide evidence that the performance of the prediction system with covariance localization is superior to that observed using only EnKF assimilation without localization or using a free run ensemble. Furthermore, it is shown that covariance localization also increases the robustness of the model to the location of the assimilated data. Our analysis reveals that improvements are detected with regard to not only preventing the occurrence of spurious correlations but also preserving the spatial coherence in the updated covariance matrix. Covariance localization has been shown to be relevant in operational frameworks where short-term forecasts (on the order of days) are required.

  16. Determination of neutron resonance parameters of Neptunium 237 between 0 and 500 eV. The covariance matrices of statistical and of systematic origin, relating the resonance parameters, are also given; Determination des parametres des resonances neutroniques du neptunium 237, en dessous de 500eV, et obtention des matrices de covariances statistiques et systematiques entre les parametres de ces resonances

    Energy Technology Data Exchange (ETDEWEB)

    Lepretre, A.; Herault, N. [CEA Saclay, Dept. d' Astrophysique de Physique des Particules, de Physique Nucleaire et de l' Instrumentation Associee, 91- Gif sur Yvette (France); Brusegan, A.; Noguere, G.; Siegler, P. [Institut des Materiaux et des Metrologies - IRMM, Joint Research Centre, Gell (Belgium)

    2002-12-01

    This report is a follow up of the report CEA DAPNIA/SPHN-99-04T of Vincent Gressier. In the frame of a collaboration between the 'Commissariat a l'Energie Atomique (CEA)' and the Institute for Reference Materials and Measurement (IRMM, Geel, Belgique), the resonance parameters of neptunium 237 have been determined in the energy interval between 0 and 500 eV. These parameters have been obtained by using the Refit code in analysing simultaneously three transmission experiments. The covariance matrix of statistical origin is provided. A new method, based on various sensitivity studies is proposed for determining also the covariance matrix of systematic origin, relating the resonance parameters. From an experimental viewpoint, the study indicated that, with a large probability, the background spectrum has structure. A two dimensional profiler for the neutron density has been proved feasible. Such a profiler could, among others, demonstrate the existence of the structured background. (authors)

  17. Treatment Effects with Many Covariates and Heteroskedasticity

    DEFF Research Database (Denmark)

    Cattaneo, Matias D.; Jansson, Michael; Newey, Whitney K.

    The linear regression model is widely used in empirical work in Economics. Researchers often include many covariates in their linear model specification in an attempt to control for confounders. We give inference methods that allow for many covariates and heteroskedasticity. Our results...

  18. Contribution to high voltage matrix switches reliability

    International Nuclear Information System (INIS)

    Lausenaz, Yvan

    2000-01-01

    Nowadays, power electronic equipment requirements are important, concerning performances, quality and reliability. On the other hand, costs have to be reduced in order to satisfy the market rules. To provide cheap, reliability and performances, many standard components with mass production are developed. But the construction of specific products must be considered following these two different points: in one band you can produce specific components, with delay, over-cost problems and eventuality quality and reliability problems, in the other and you can use standard components in a adapted topologies. The CEA of Pierrelatte has adopted this last technique of power electronic conception for the development of these high voltage pulsed power converters. The technique consists in using standard components and to associate them in series and in parallel. The matrix constitutes high voltage macro-switch where electrical parameters are distributed between the synchronized components. This study deals with the reliability of these structures. It brings up the high reliability aspect of MOSFETs matrix associations. Thanks to several homemade test facilities, we obtained lots of data concerning the components we use. The understanding of defects propagation mechanisms in matrix structures has allowed us to put forwards the necessity of robust drive system, adapted clamping voltage protection, and careful geometrical construction. All these reliability considerations in matrix associations have notably allowed the construction of a new matrix structure regrouping all solutions insuring reliability. Reliable and robust, this product has already reaches the industrial stage. (author) [fr

  19. Empirical Likelihood in Nonignorable Covariate-Missing Data Problems.

    Science.gov (United States)

    Xie, Yanmei; Zhang, Biao

    2017-04-20

    Missing covariate data occurs often in regression analysis, which frequently arises in the health and social sciences as well as in survey sampling. We study methods for the analysis of a nonignorable covariate-missing data problem in an assumed conditional mean function when some covariates are completely observed but other covariates are missing for some subjects. We adopt the semiparametric perspective of Bartlett et al. (Improving upon the efficiency of complete case analysis when covariates are MNAR. Biostatistics 2014;15:719-30) on regression analyses with nonignorable missing covariates, in which they have introduced the use of two working models, the working probability model of missingness and the working conditional score model. In this paper, we study an empirical likelihood approach to nonignorable covariate-missing data problems with the objective of effectively utilizing the two working models in the analysis of covariate-missing data. We propose a unified approach to constructing a system of unbiased estimating equations, where there are more equations than unknown parameters of interest. One useful feature of these unbiased estimating equations is that they naturally incorporate the incomplete data into the data analysis, making it possible to seek efficient estimation of the parameter of interest even when the working regression function is not specified to be the optimal regression function. We apply the general methodology of empirical likelihood to optimally combine these unbiased estimating equations. We propose three maximum empirical likelihood estimators of the underlying regression parameters and compare their efficiencies with other existing competitors. We present a simulation study to compare the finite-sample performance of various methods with respect to bias, efficiency, and robustness to model misspecification. The proposed empirical likelihood method is also illustrated by an analysis of a data set from the US National Health and

  20. Extensions of linear-quadratic control, optimization and matrix theory

    CERN Document Server

    Jacobson, David H

    1977-01-01

    In this book, we study theoretical and practical aspects of computing methods for mathematical modelling of nonlinear systems. A number of computing techniques are considered, such as methods of operator approximation with any given accuracy; operator interpolation techniques including a non-Lagrange interpolation; methods of system representation subject to constraints associated with concepts of causality, memory and stationarity; methods of system representation with an accuracy that is the best within a given class of models; methods of covariance matrix estimation;methods for low-rank mat