Covariance matrix adaptation for multi-objective optimization.
Igel, Christian; Hansen, Nikolaus; Roth, Stefan
2007-01-01
The covariance matrix adaptation evolution strategy (CMA-ES) is one of the most powerful evolutionary algorithms for real-valued single-objective optimization. In this paper, we develop a variant of the CMA-ES for multi-objective optimization (MOO). We first introduce a single-objective, elitist CMA-ES using plus-selection and step size control based on a success rule. This algorithm is compared to the standard CMA-ES. The elitist CMA-ES turns out to be slightly faster on unimodal functions, but is more prone to getting stuck in sub-optimal local minima. In the new multi-objective CMAES (MO-CMA-ES) a population of individuals that adapt their search strategy as in the elitist CMA-ES is maintained. These are subject to multi-objective selection. The selection is based on non-dominated sorting using either the crowding-distance or the contributing hypervolume as second sorting criterion. Both the elitist single-objective CMA-ES and the MO-CMA-ES inherit important invariance properties, in particular invariance against rotation of the search space, from the original CMA-ES. The benefits of the new MO-CMA-ES in comparison to the well-known NSGA-II and to NSDE, a multi-objective differential evolution algorithm, are experimentally shown.
CSIR Research Space (South Africa)
Herselman, PL
2008-09-01
Full Text Available Asymptotically optimal coherent detection techniques yield sub-clutter visibility in heavy-tailed sea clutter. The adaptive linear quadratic detector inherently assumes spectral homogeneity for the reference window of the covariance matrix estimator...
A low-complexity adaptive beamformer for ultrasound imaging using structured covariance matrix.
Asl, Babak Mohammadzadeh; Mahloojifar, Ali
2012-04-01
In recent years, adaptive beamforming methods have been successfully applied to medical ultrasound imaging, resulting in simultaneous improvement in imaging resolution and contrast. These improvements have been achieved at the expense of higher computational complexity, with respect to the conventional non-adaptive delay-and-sum (DAS) beamformer, in which computational complexity is proportional to the number of elements, O(M). The computational overhead results from the covariance matrix inversion needed for computation of the adaptive weights, the complexity of which is cubic with the subarray size, O(L(3)). This is a computationally intensive procedure, which makes the implementation of adaptive beamformers less attractive in spite of their advantages. Considering that, in medical ultrasound applications, most of the energy is scattered from angles close to the steering angle, assuming spatial stationarity is a good approximation, allowing us to assume the Toeplitz structure for the estimated covariance matrix. Based on this idea, in this paper, we have applied the Toeplitz structure to the spatially smoothed covariance matrix by averaging the entries along all subdiagonals. Because the inverse of the resulting Toeplitz covariance matrix can be computed in O(L(2)) operations, this technique results in a greatly reduced computational complexity. By using simulated and experimental RF data-point targets as well as cyst phantoms-we show that the proposed low-complexity adaptive beamformer significantly outperforms the DAS and its performance is comparable to that of the minimum variance beamformer, with reduced computational complexity.
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.
Hansen, Nikolaus; Müller, Sibylle D; Koumoutsakos, Petros
2003-01-01
This paper presents a novel evolutionary optimization strategy based on the derandomized evolution strategy with covariance matrix adaptation (CMA-ES). This new approach is intended to reduce the number of generations required for convergence to the optimum. Reducing the number of generations, i.e., the time complexity of the algorithm, is important if a large population size is desired: (1) to reduce the effect of noise; (2) to improve global search properties; and (3) to implement the algorithm on (highly) parallel machines. Our method results in a highly parallel algorithm which scales favorably with large numbers of processors. This is accomplished by efficiently incorporating the available information from a large population, thus significantly reducing the number of generations needed to adapt the covariance matrix. The original version of the CMA-ES was designed to reliably adapt the covariance matrix in small populations but it cannot exploit large populations efficiently. Our modifications scale up the efficiency to population sizes of up to 10n, where n is the problem dimension. This method has been applied to a large number of test problems, demonstrating that in many cases the CMA-ES can be advanced from quadratic to linear time complexity.
Directory of Open Access Journals (Sweden)
Sivananaithaperumal Sudalaiandi
2014-06-01
Full Text Available This paper presents an automatic tuning of multivariable Fractional-Order Proportional, Integral and Derivative controller (FO-PID parameters using Covariance Matrix Adaptation Evolution Strategy (CMAES algorithm. Decoupled multivariable FO-PI and FO-PID controller structures are considered. Oustaloup integer order approximation is used for the fractional integrals and derivatives. For validation, two Multi-Input Multi- Output (MIMO distillation columns described byWood and Berry and Ogunnaike and Ray are considered for the design of multivariable FO-PID controller. Optimal FO-PID controller is designed by minimizing Integral Absolute Error (IAE as objective function. The results of previously reported PI/PID controller are considered for comparison purposes. Simulation results reveal that the performance of FOPI and FO-PID controller is better than integer order PI/PID controller in terms of IAE. Also, CMAES algorithm is suitable for the design of FO-PI / FO-PID controller.
Ahrari, Ali; Deb, Kalyanmoy; Preuss, Mike
2017-01-01
During the recent decades, many niching methods have been proposed and empirically verified on some available test problems. They often rely on some particular assumptions associated with the distribution, shape, and size of the basins, which can seldom be made in practical optimization problems. This study utilizes several existing concepts and techniques, such as taboo points, normalized Mahalanobis distance, and the Ursem's hill-valley function in order to develop a new tool for multimodal optimization, which does not make any of these assumptions. In the proposed method, several subpopulations explore the search space in parallel. Offspring of a subpopulation are forced to maintain a sufficient distance to the center of fitter subpopulations and the previously identified basins, which are marked as taboo points. The taboo points repel the subpopulation to prevent convergence to the same basin. A strategy to update the repelling power of the taboo points is proposed to address the challenge of basins of dissimilar size. The local shape of a basin is also approximated by the distribution of the subpopulation members converging to that basin. The proposed niching strategy is incorporated into the covariance matrix self-adaptation evolution strategy (CMSA-ES), a potent global optimization method. The resultant method, called the covariance matrix self-adaptation with repelling subpopulations (RS-CMSA), is assessed and compared to several state-of-the-art niching methods on a standard test suite for multimodal optimization. An organized procedure for parameter setting is followed which assumes a rough estimation of the desired/expected number of minima available. Performance sensitivity to the accuracy of this estimation is also studied by introducing the concept of robust mean peak ratio. Based on the numerical results using the available and the introduced performance measures, RS-CMSA emerges as the most successful method when robustness and efficiency are
Energy Technology Data Exchange (ETDEWEB)
Ribes, Aurelien; Planton, Serge [CNRM-GAME, Meteo France-CNRS, Toulouse (France); Azais, Jean-Marc [Universite de Toulouse, UPS, IMT, LSP, Toulouse (France)
2009-10-15
The ''optimal fingerprint'' method, usually used for detection and attribution studies, requires to know, or, in practice, to estimate the covariance matrix of the internal climate variability. In this work, a new adaptation of the ''optimal fingerprints'' method is presented. The main goal is to allow the use of a covariance matrix estimate based on an observation dataset in which the number of years used for covariance estimation is close to the number of observed time series. Our adaptation is based on the use of a regularized estimate of the covariance matrix, that is well-conditioned, and asymptotically more precise, in the sense of the mean square error. This method is shown to be more powerful than the basic ''guess pattern fingerprint'', and than the classical use of a pseudo-inverted truncation of the empirical covariance matrix. The construction of the detection test is achieved by using a bootstrap technique particularly well-suited to estimate the internal climate variability in real world observations. In order to validate the efficiency of the detection algorithm with climate data, the methodology presented here is first applied with pseudo-observations derived from transient regional climate change scenarios covering the 1960-2099 period. It is then used to perform a formal detection study of climate change over France, analyzing homogenized observed temperature series from 1900 to 2006. In this case, the estimation of the covariance matrix is only based on a part of the observation dataset. This new approach allows the confirmation and extension of previous results regarding the detection of an anthropogenic climate change signal over the country. (orig.)
Bayes linear covariance matrix adjustment
Wilkinson, Darren J
1995-01-01
In this thesis, a Bayes linear methodology for the adjustment of covariance matrices is presented and discussed. A geometric framework for quantifying uncertainties about covariance matrices is set up, and an inner-product for spaces of random matrices is motivated and constructed. The inner-product on this space captures aspects of our beliefs about the relationship between covariance matrices of interest to us, providing a structure rich enough for us to adjust beliefs about unknown matrices in the light of data such as sample covariance matrices, exploiting second-order exchangeability and related specifications to obtain representations allowing analysis. Adjustment is associated with orthogonal projection, and illustrated with examples of adjustments for some common problems. The problem of adjusting the covariance matrices underlying exchangeable random vectors is tackled and discussed. Learning about the covariance matrices associated with multivariate time series dynamic linear models is shown to be a...
HIGH DIMENSIONAL COVARIANCE MATRIX ESTIMATION IN APPROXIMATE FACTOR MODELS.
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.
High-dimensional covariance matrix estimation in approximate factor models
Fan, Jianqing; Mincheva, Martina; 10.1214/11-AOS944
2012-01-01
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 [J. Amer. Statist. Assoc. 106 (2011) 672--684], 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 studi...
Hierarchical matrix approximation of large covariance matrices
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
Hierarchical matrix approximation of large covariance matrices
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.
Hierarchical matrix approximation of large covariance matrices
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.
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.
Risk evaluation with enhaced covariance matrix
Urbanowicz, K; Richmond, P; Holyst, Janusz A.; Richmond, Peter; Urbanowicz, Krzysztof
2006-01-01
We propose a route for the evaluation of risk based on a transformation of the covariance matrix. The approach uses a `potential' or `objective' function. This allows us to rescale data from diferent assets (or sources) such that each set then has similar statistical properties in terms of their probability distributions. The method is tested using historical data from both the New York and Warsaw Stock Exchanges.
ANL Critical Assembly Covariance Matrix Generation
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)
2014-01-15
This report discusses the generation of a covariance matrix for selected critical assemblies that were carried out by Argonne National Laboratory (ANL) using four critical facilities-all of which are now decommissioned. The four different ANL critical facilities are: ZPR-3 located at ANL-West (now Idaho National Laboratory- INL), ZPR-6 and ZPR-9 located at ANL-East (Illinois) and ZPPr located at ANL-West.
Institute of Scientific and Technical Information of China (English)
ZHENG Xiaogu; WU Guocan; ZHANG Shupeng; LIANG Xiao; DAI Yongjiu; LI Yong
2013-01-01
Correctly estimating the forecast error covariance matrix is a key step in any data assimilation scheme.If it is not correctly estimated,the assimilated states could be far from the true states.A popular method to address this problem is error covariance matrix inflation.That is,to multiply the forecast error covariance matrix by an appropriate factor.In this paper,analysis states are used to construct the forecast error covariance matrix and an adaptive estimation procedure associated with the error covariance matrix inflation technique is developed.The proposed assimilation scheme was tested on the Lorenz-96 model and 2D Shallow Water Equation model,both of which are associated with spatially correlated observational systems.The experiments showed that by introducing the proposed structure of the forecast error covariance matrix and applying its adaptive estimation procedure,the assimilation results were further improved.
Covariance-Adaptive Slice Sampling
Thompson, Madeleine; Neal, Radford M.
2010-01-01
We describe two slice sampling methods for taking multivariate steps using the crumb framework. These methods use the gradients at rejected proposals to adapt to the local curvature of the log-density surface, a technique that can produce much better proposals when parameters are highly correlated. We evaluate our methods on four distributions and compare their performance to that of a non-adaptive slice sampling method and a Metropolis method. The adaptive methods perform favorably on low-di...
Efficient retrieval of landscape Hessian: forced optimal covariance adaptive learning.
Shir, Ofer M; Roslund, Jonathan; Whitley, Darrell; Rabitz, Herschel
2014-06-01
Knowledge of the Hessian matrix at the landscape optimum of a controlled physical observable offers valuable information about the system robustness to control noise. The Hessian can also assist in physical landscape characterization, which is of particular interest in quantum system control experiments. The recently developed landscape theoretical analysis motivated the compilation of an automated method to learn the Hessian matrix about the global optimum without derivative measurements from noisy data. The current study introduces the forced optimal covariance adaptive learning (FOCAL) technique for this purpose. FOCAL relies on the covariance matrix adaptation evolution strategy (CMA-ES) that exploits covariance information amongst the control variables by means of principal component analysis. The FOCAL technique is designed to operate with experimental optimization, generally involving continuous high-dimensional search landscapes (≳30) with large Hessian condition numbers (≳10^{4}). This paper introduces the theoretical foundations of the inverse relationship between the covariance learned by the evolution strategy and the actual Hessian matrix of the landscape. FOCAL is presented and demonstrated to retrieve the Hessian matrix with high fidelity on both model landscapes and quantum control experiments, which are observed to possess nonseparable, nonquadratic search landscapes. The recovered Hessian forms were corroborated by physical knowledge of the systems. The implications of FOCAL extend beyond the investigated studies to potentially cover other physically motivated multivariate landscapes.
Shrinkage covariance matrix approach for microarray data
Karjanto, Suryaefiza; Aripin, Rasimah
2013-04-01
Microarray technology was developed for the purpose of monitoring the expression levels of thousands of genes. A microarray data set typically consists of tens of thousands of genes (variables) from just dozens of samples due to various constraints including the high cost of producing microarray chips. As a result, the widely used standard covariance estimator is not appropriate for this purpose. One such technique is the Hotelling's T2 statistic which is a multivariate test statistic for comparing means between two groups. It requires that the number of observations (n) exceeds the number of genes (p) in the set but in microarray studies it is common that n Hotelling's T2 statistic with the shrinkage approach is proposed to estimate the covariance matrix for testing differential gene expression. The performance of this approach is then compared with other commonly used multivariate tests using a widely analysed diabetes data set as illustrations. The results across the methods are consistent, implying that this approach provides an alternative to existing techniques.
Wang, Yasen; Bao, Qinglong; Chen, Zengping
2016-07-01
In order to suppress multiple mainlobe interferences and sidelobe interferences simultaneously, a mainlobe interference suppression algorithm is proposed. In this algorithm, the number of mainlobe interferences is estimated through a matrix filter at first. Then, the eigenvectors associated with mainlobe interference are determined and the eigen-projection matrix can be calculated. Next, the sidelobe-interference-plus-noise covariance matrix is reconstructed through eigenvalue replacement procedure. Finally, we can get the adaptive weight vector. Simulation results demonstrate the effectiveness of the proposed method when multiple mainlobe interferences exist.
Perturbative approach to covariance matrix of the matter power spectrum
Energy Technology Data Exchange (ETDEWEB)
Mohammed, Irshad [Fermilab; Seljak, Uros [UC, Berkeley, Astron. Dept.; Vlah, Zvonimir [Stanford U., ITP
2016-06-30
We evaluate the covariance matrix of the matter power spectrum using perturbation theory up to dominant terms at 1-loop order and compare it to numerical simulations. We decompose the covariance matrix into the disconnected (Gaussian) part, trispectrum from the modes outside the survey (beat coupling or super-sample variance), and trispectrum from the modes inside the survey, and show how the different components contribute to the overall covariance matrix. We find the agreement with the simulations is at a 10\\% level up to $k \\sim 1 h {\\rm Mpc^{-1}}$. We show that all the connected components are dominated by the large-scale modes ($k<0.1 h {\\rm Mpc^{-1}}$), regardless of the value of the wavevectors $k,\\, k'$ of the covariance matrix, suggesting that one must be careful in applying the jackknife or bootstrap methods to the covariance matrix. We perform an eigenmode decomposition of the connected part of the covariance matrix, showing that at higher $k$ it is dominated by a single eigenmode. The full covariance matrix can be approximated as the disconnected part only, with the connected part being treated as an external nuisance parameter with a known scale dependence, and a known prior on its variance for a given survey volume. Finally, we provide a prescription for how to evaluate the covariance matrix from small box simulations without the need to simulate large volumes.
Perturbative approach to covariance matrix of the matter power spectrum
Mohammed, Irshad; Seljak, Uroš; Vlah, Zvonimir
2017-04-01
We evaluate the covariance matrix of the matter power spectrum using perturbation theory up to dominant terms at 1-loop order and compare it to numerical simulations. We decompose the covariance matrix into the disconnected (Gaussian) part, trispectrum from the modes outside the survey (supersample variance) and trispectrum from the modes inside the survey, and show how the different components contribute to the overall covariance matrix. We find the agreement with the simulations is at a 10 per cent level up to k ˜ 1 h Mpc-1. We show that all the connected components are dominated by the large-scale modes (k covariance matrix, suggesting that one must be careful in applying the jackknife or bootstrap methods to the covariance matrix. We perform an eigenmode decomposition of the connected part of the covariance matrix, showing that at higher k, it is dominated by a single eigenmode. The full covariance matrix can be approximated as the disconnected part only, with the connected part being treated as an external nuisance parameter with a known scale dependence, and a known prior on its variance for a given survey volume. Finally, we provide a prescription for how to evaluate the covariance matrix from small box simulations without the need to simulate large volumes.
Adaptive Covariance Estimation with model selection
Biscay, Rolando; Loubes, Jean-Michel
2012-01-01
We provide in this paper a fully adaptive penalized procedure to select a covariance among a collection of models observing i.i.d replications of the process at fixed observation points. For this we generalize previous results of Bigot and al. and propose to use a data driven penalty to obtain an oracle inequality for the estimator. We prove that this method is an extension to the matricial regression model of the work by Baraud.
Estimating the power spectrum covariance matrix with fewer mock samples
Pearson, David W
2015-01-01
The covariance matrices of power-spectrum (P(k)) measurements from galaxy surveys are difficult to compute theoretically. The current best practice is to estimate covariance matrices by computing a sample covariance of a large number of mock catalogues. The next generation of galaxy surveys will require thousands of large volume mocks to determine the covariance matrices to desired accuracy. The errors in the inverse covariance matrix are larger and scale with the number of P(k) bins, making the problem even more acute. We develop a method of estimating covariance matrices using a theoretically justified, few-parameter model, calibrated with mock catalogues. Using a set of 600 BOSS DR11 mock catalogues, we show that a seven parameter model is sufficient to fit the covariance matrix of BOSS DR11 P(k) measurements. The covariance computed with this method is better than the sample covariance at any number of mocks and only ~100 mocks are required for it to fully converge and the inverse covariance matrix conver...
Sparse Inverse Covariance Estimation via an Adaptive Gradient-Based Method
Sra, Suvrit; Kim, Dongmin
2011-01-01
We study the problem of estimating from data, a sparse approximation to the inverse covariance matrix. Estimating a sparsity constrained inverse covariance matrix is a key component in Gaussian graphical model learning, but one that is numerically very challenging. We address this challenge by developing a new adaptive gradient-based method that carefully combines gradient information with an adaptive step-scaling strategy, which results in a scalable, highly competitive method. Our algorithm...
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
to an amplitude and phase estimation (APES) based filter. For a fixed number of samples, the performance in terms of signal-to-noise ratio can be increased by using the IAA method, whereas if the filter size is fixed and the number of samples in the APES based filter is increased, the APES based filter performs......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...
Energy Technology Data Exchange (ETDEWEB)
Theiler, James P [Los Alamos National Laboratory; Cao, Guangzhi [PURDUE UNIV; Bouman, Charles A [PURDUE UNIV
2009-01-01
Many detection algorithms in hyperspectral image analysis, from well-characterized gaseous and solid targets to deliberately uncharacterized anomalies and anomlous changes, depend on accurately estimating the covariance matrix of the background. In practice, the background covariance is estimated from samples in the image, and imprecision in this estimate can lead to a loss of detection power. In this paper, we describe the sparse matrix transform (SMT) and investigate its utility for estimating the covariance matrix from a limited number of samples. The SMT is formed by a product of pairwise coordinate (Givens) rotations, which can be efficiently estimated using greedy optimization. Experiments on hyperspectral data show that the estimate accurately reproduces even small eigenvalues and eigenvectors. In particular, we find that using the SMT to estimate the covariance matrix used in the adaptive matched filter leads to consistently higher signal-to-noise ratios.
High-dimensional covariance matrix estimation with missing observations
Lounici, Karim
2014-01-01
In this paper, we study the problem of high-dimensional covariance matrix estimation with missing observations. We propose a simple procedure computationally tractable in high-dimension and that does not require imputation of the missing data. We establish non-asymptotic sparsity oracle inequalities for the estimation of the covariance matrix involving the Frobenius and the spectral norms which are valid for any setting of the sample size, probability of a missing observation and the dimensio...
Perturbative approach to covariance matrix of the matter power spectrum
Mohammed, Irshad; Vlah, Zvonimir
2016-01-01
We evaluate the covariance matrix of the matter power spectrum using perturbation theory up to dominant terms at 1-loop order and compare it to numerical simulations. We decompose the covariance matrix into the disconnected (Gaussian) part, trispectrum from the modes outside the survey (beat coupling or super-sample variance), and trispectrum from the modes inside the survey, and show how the different components contribute to the overall covariance matrix. We find the agreement with the simulations is at a 10\\% level up to $k \\sim 1 h {\\rm Mpc^{-1}}$. We show that all the connected components are dominated by the large-scale modes ($k<0.1 h {\\rm Mpc^{-1}}$), regardless of the value of the wavevectors $k,\\, k'$ of the covariance matrix, suggesting that one must be careful in applying the jackknife or bootstrap methods to the covariance matrix. We perform an eigenmode decomposition of the connected part of the covariance matrix, showing that at higher $k$ it is dominated by a single eigenmode. The full cova...
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.
Covariance, correlation matrix, and the multiscale community structure of networks.
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.
A New Test for a Normal Covariance Matrix
Institute of Scientific and Technical Information of China (English)
禹建奇
2015-01-01
The problem of testing the normal covariance matrix equal to a specified matrix is considered.A new Chi-Square test statistic is derived for multivariate normal population.Unlike the likelihood ratio test,the new test is an exact one.
Hui, Yi; Law, Siu Seong; Ku, Chiu Jen
2017-02-01
Covariance of the auto/cross-covariance matrix based method is studied for the damage identification of a structure with illustrations on its advantages and limitations. The original method is extended for structures under direct white noise excitations. The auto/cross-covariance function of the measured acceleration and its corresponding derivatives are formulated analytically, and the method is modified in two new strategies to enable successful identification with much fewer sensors. Numerical examples are adopted to illustrate the improved method, and the effects of sampling frequency and sampling duration are discussed. Results show that the covariance of covariance calculated from responses of higher order modes of a structure play an important role to the accurate identification of local damage in a structure.
An Empirical State Error Covariance Matrix for Batch State Estimation
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
Web Tool for Constructing a Covariance Matrix from EXFOR Uncertainties
Directory of Open Access Journals (Sweden)
Zerkin V.
2012-05-01
Full Text Available The experimental nuclear reaction database EXFOR contains almost no covariance data because most experimentalists provide experimental data only with uncertainties. With the tool described here a user can construct an experimental covariance matrix from uncertainties using general assumptions when uncertainty information given in EXFOR is poor (or even absent. The tool is publically available in the IAEA EXFOR Web retrieval system [1].
Bayes linear covariance matrix adjustment for multivariate dynamic linear models
Wilkinson, Darren J
2008-01-01
A methodology is developed for the adjustment of the covariance matrices underlying a multivariate constant time series dynamic linear model. The covariance matrices are embedded in a distribution-free inner-product space of matrix objects which facilitates such adjustment. This approach helps to make the analysis simple, tractable and robust. To illustrate the methods, a simple model is developed for a time series representing sales of certain brands of a product from a cash-and-carry depot. The covariance structure underlying the model is revised, and the benefits of this revision on first order inferences are then examined.
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.
Neutron Resonance Parameters and Covariance Matrix of 239Pu
Energy Technology Data Exchange (ETDEWEB)
Derrien, Herve [ORNL; Leal, Luiz C [ORNL; Larson, Nancy M [ORNL
2008-08-01
In order to obtain the resonance parameters in a single energy range and the corresponding covariance matrix, a reevaluation of 239Pu was performed with the code SAMMY. The most recent experimental data were analyzed or reanalyzed in the energy range thermal to 2.5 keV. The normalization of the fission cross section data was reconsidered by taking into account the most recent measurements of Weston et al. and Wagemans et al. A full resonance parameter covariance matrix was generated. The method used to obtain realistic uncertainties on the average cross section calculated by SAMMY or other processing codes was examined.
High-dimensional covariance matrix estimation with missing observations
Lounici, Karim
2012-01-01
In this paper, we study the problem of high-dimensional approximately low-rank covariance matrix estimation with missing observations. We propose a simple procedure computationally tractable in high-dimension and that does not require imputation of the missing data. We establish non-asymptotic sparsity oracle inequalities for the estimation of the covariance matrix with the Frobenius and spectral norms, valid for any setting of the sample size and the dimension of the observations. We further establish minimax lower bounds showing that our rates are minimax optimal up to a logarithmic factor.
DEFF Research Database (Denmark)
Hounyo, Ulrich
We propose a bootstrap mehtod for estimating the distribution (and functionals of it such as the variance) of various integrated covariance matrix estimators. In particular, we first adapt the wild blocks of blocks bootsratp method suggested for the pre-averaged realized volatility estimator......-studentized statistics, our results justify using the bootstrap to esitmate the covariance matrix of a broad class of covolatility estimators. The bootstrap variance estimator is positive semi-definite by construction, an appealing feature that is not always shared by existing variance estimators of the integrated...
Data Covariances from R-Matrix Analyses of Light Nuclei
Energy Technology Data Exchange (ETDEWEB)
Hale, G.M., E-mail: ghale@lanl.gov; Paris, M.W.
2015-01-15
After first reviewing the parametric description of light-element reactions in multichannel systems using R-matrix theory and features of the general LANL R-matrix analysis code EDA, we describe how its chi-square minimization procedure gives parameter covariances. This information is used, together with analytically calculated sensitivity derivatives, to obtain cross section covariances for all reactions included in the analysis by first-order error propagation. Examples are given of the covariances obtained for systems with few resonances ({sup 5}He) and with many resonances ({sup 13}C ). We discuss the prevalent problem of this method leading to cross section uncertainty estimates that are unreasonably small for large data sets. The answer to this problem appears to be using parameter confidence intervals in place of standard errors.
An Empirical State Error Covariance Matrix Orbit Determination Example
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
Neutron Multiplicity: LANL W Covariance Matrix for Curve Fitting
Energy Technology Data Exchange (ETDEWEB)
Wendelberger, James G. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
2016-12-08
In neutron multiplicity counting one may fit a curve by minimizing an objective function, χ$2\\atop{n}$. The objective function includes the inverse of an n by n matrix of covariances, W. The inverse of the W matrix has a closed form solution. In addition W^{-1} is a tri-diagonal matrix. The closed form and tridiagonal nature allows for a simpler expression of the objective function χ$2\\atop{n}$. Minimization of this simpler expression will provide the optimal parameters for the fitted curve.
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.
MIMO Radar Transmit Beampattern Design Without Synthesising the Covariance Matrix
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.
A covariance matrix test for high-dimensional data
Directory of Open Access Journals (Sweden)
Saowapha Chaipitak
2016-10-01
Full Text Available For the multivariate normally distributed data with the dimension larger than or equal to the number of observations, or the sample size, called high-dimensional normal data, we proposed a test for testing the null hypothesis that the covariance matrix of a normal population is proportional to a given matrix on some conditions when the dimension goes to infinity. We showed that this test statistic is consistent. The asymptotic null and non-null distribution of the test statistic is also given. The performance of the proposed test is evaluated via simulation study and its application.
Large-scale portfolios using realized covariance matrix: evidence from the Japanese stock market
Masato Ubukata
2009-01-01
The objective of this paper is to examine effects of realized covariance matrix estimators based on intraday returns on large-scale minimum-variance equity portfolio optimization. We empirically assess out-of-sample performance of portfolios with different covariance matrix estimators: the realized covariance matrix estimators and Bayesian shrinkage estimators based on the past monthly and daily returns. The main results are: (1) the realized covariance matrix estimators using the past intrad...
Eigenvectors of some large sample covariance matrix ensembles
Ledoit, Olivier
2009-01-01
We consider sample covariance matrices $S_N=\\frac{1}{p}\\Sigma_N^{1/2}X_NX_N^* \\Sigma_N^{1/2}$ where $X_N$ is a $N \\times p$ real or complex matrix with i.i.d. entries with finite $12^{\\rm th}$ moment and $\\Sigma_N$ is a $N \\times N$ positive definite matrix. In addition we assume that the spectral measure of $\\Sigma_N$ almost surely converges to some limiting probability distribution as $N \\to \\infty$ and $p/N \\to \\gamma >0.$ We quantify the relationship between sample and population eigenvectors by studying the asymptotics of functionals of the type $\\frac{1}{N} \\text{Tr} (g(\\Sigma_N) (S_N-zI)^{-1})),$ where $I$ is the identity matrix, $g$ is a bounded function and $z$ is a complex number. This is then used to compute the asymptotically optimal bias correction for sample eigenvalues, paving the way for a new generation of improved estimators of the covariance matrix and its inverse.
Improving on the empirical covariance matrix using truncated PCA with white noise residuals
Jewson, S
2005-01-01
The empirical covariance matrix is not necessarily the best estimator for the population covariance matrix: we describe a simple method which gives better estimates in two examples. The method models the covariance matrix using truncated PCA with white noise residuals. Jack-knife cross-validation is used to find the truncation that maximises the out-of-sample likelihood score.
Random matrix theory and robust covariance matrix estimation for financial data
Frahm, G; Frahm, Gabriel; Jaekel, Uwe
2005-01-01
The traditional class of elliptical distributions is extended to allow for asymmetries. A completely robust dispersion matrix estimator (the `spectral estimator') for the new class of `generalized elliptical distributions' is presented. It is shown that the spectral estimator corresponds to an M-estimator proposed by Tyler (1983) in the context of elliptical distributions. Both the generalization of elliptical distributions and the development of a robust dispersion matrix estimator are motivated by the stylized facts of empirical finance. Random matrix theory is used for analyzing the linear dependence structure of high-dimensional data. It is shown that the Marcenko-Pastur law fails if the sample covariance matrix is considered as a random matrix in the context of elliptically distributed and heavy tailed data. But substituting the sample covariance matrix by the spectral estimator resolves the problem and the Marcenko-Pastur law remains valid.
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.
USING COVARIANCE MATRIX FOR CHANGE DETECTION OF POLARIMETRIC SAR DATA
Directory of Open Access Journals (Sweden)
M. Esmaeilzade
2017-09-01
Full Text Available Nowadays change detection is an important role in civil and military fields. The Synthetic Aperture Radar (SAR images due to its independent of atmospheric conditions and cloud cover, have attracted much attention in the change detection applications. When the SAR data are used, one of the appropriate ways to display the backscattered signal is using covariance matrix that follows the Wishart distribution. Based on this distribution a statistical test for equality of two complex variance-covariance matrices can be used. In this study, two full polarization data in band L from UAVSAR are used for change detection in agricultural fields and urban areas in the region of United States which the first image belong to 2014 and the second one is from 2017. To investigate the effect of polarization on the rate of change, full polarization data and dual polarization data were used and the results were compared. According to the results, full polarization shows more changes than dual polarization.
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.
Analysis of gene set using shrinkage covariance matrix approach
Karjanto, Suryaefiza; Aripin, Rasimah
2013-09-01
Microarray methodology has been exploited for different applications such as gene discovery and disease diagnosis. This technology is also used for quantitative and highly parallel measurements of gene expression. Recently, microarrays have been one of main interests of statisticians because they provide a perfect example of the paradigms of modern statistics. In this study, the alternative approach to estimate the covariance matrix has been proposed to solve the high dimensionality problem in microarrays. The extension of traditional Hotelling's T2 statistic is constructed for determining the significant gene sets across experimental conditions using shrinkage approach. Real data sets were used as illustrations to compare the performance of the proposed methods with other methods. The results across the methods are consistent, implying that this approach provides an alternative to existing techniques.
Large-scale portfolios using realized covariance matrix: evidence from the Japanese stock market
Masato Ubukata
2010-01-01
This paper examines effects of realized covariance matrix estimators based on high-frequency data on large-scale minimum-variance equity portfolio optimization. The main results are: (i) the realized covariance matrix estimators yield a lower standard deviation of large-scale portfolio returns than Bayesian shrinkage estimators based on monthly and daily historical returns; (ii) gains to switching to strategies using the realized covariance matrix estimators are higher for an investor with hi...
Ocean spectral data assimilation without background error covariance matrix
Chu, Peter C.; Fan, Chenwu; Margolina, Tetyana
2016-09-01
Predetermination of background error covariance matrix B is challenging in existing ocean data assimilation schemes such as the optimal interpolation (OI). An optimal spectral decomposition (OSD) has been developed to overcome such difficulty without using the B matrix. The basis functions are eigenvectors of the horizontal Laplacian operator, pre-calculated on the base of ocean topography, and independent on any observational data and background fields. Minimization of analysis error variance is achieved by optimal selection of the spectral coefficients. Optimal mode truncation is dependent on the observational data and observational error variance and determined using the steep-descending method. Analytical 2D fields of large and small mesoscale eddies with white Gaussian noises inside a domain with four rigid and curved boundaries are used to demonstrate the capability of the OSD method. The overall error reduction using the OSD is evident in comparison to the OI scheme. Synoptic monthly gridded world ocean temperature, salinity, and absolute geostrophic velocity datasets produced with the OSD method and quality controlled by the NOAA National Centers for Environmental Information (NCEI) are also presented.
Accuracy of Pseudo-Inverse Covariance Learning--A Random Matrix Theory Analysis.
Hoyle, David C
2011-07-01
For many learning problems, estimates of the inverse population covariance are required and often obtained by inverting the sample covariance matrix. Increasingly for modern scientific data sets, the number of sample points is less than the number of features and so the sample covariance is not invertible. In such circumstances, the Moore-Penrose pseudo-inverse sample covariance matrix, constructed from the eigenvectors corresponding to nonzero sample covariance eigenvalues, is often used as an approximation to the inverse population covariance matrix. The reconstruction error of the pseudo-inverse sample covariance matrix in estimating the true inverse covariance can be quantified via the Frobenius norm of the difference between the two. The reconstruction error is dominated by the smallest nonzero sample covariance eigenvalues and diverges as the sample size becomes comparable to the number of features. For high-dimensional data, we use random matrix theory techniques and results to study the reconstruction error for a wide class of population covariance matrices. We also show how bagging and random subspace methods can result in a reduction in the reconstruction error and can be combined to improve the accuracy of classifiers that utilize the pseudo-inverse sample covariance matrix. We test our analysis on both simulated and benchmark data sets.
Adaptive Covariance Inflation in a Multi-Resolution Assimilation Scheme
Hickmann, K. S.; Godinez, H. C.
2015-12-01
When forecasts are performed using modern data assimilation methods observation and model error can be scaledependent. During data assimilation the blending of error across scales can result in model divergence since largeerrors at one scale can be propagated across scales during the analysis step. Wavelet based multi-resolution analysiscan be used to separate scales in model and observations during the application of an ensemble Kalman filter. However,this separation is done at the cost of implementing an ensemble Kalman filter at each scale. This presents problemswhen tuning the covariance inflation parameter at each scale. We present a method to adaptively tune a scale dependentcovariance inflation vector based on balancing the covariance of the innovation and the covariance of observations ofthe ensemble. Our methods are demonstrated on a one dimensional Kuramoto-Sivashinsky (K-S) model known todemonstrate non-linear interactions between scales.
On the regularity of the covariance matrix of a discretized scalar field on the sphere
Bilbao-Ahedo, J. D.; Barreiro, R. B.; Herranz, D.; Vielva, P.; Martínez-González, E.
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.
An Adaptive Approach to Mitigate Background Covariance Limitations in the Ensemble Kalman Filter
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.
Rapid Divergence of Genetic Variance-Covariance Matrix within a Natural Population
Doroszuk, A.; Wojewodzic, M.W.; Gort, G.; Kammenga, J.E.
2008-01-01
The matrix of genetic variances and covariances (G matrix) represents the genetic architecture of multiple traits sharing developmental and genetic processes and is central for predicting phenotypic evolution. These predictions require that the G matrix be stable. Yet the timescale and conditions pr
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...
National Research Council Canada - National Science Library
Aoki, Yasunori; Nordgren, Rikard; Hooker, Andrew C
2016-01-01
... a bottleneck in the analysis. We propose a preconditioning method for non-linear mixed effects models used in pharmacometric analyses to stabilise the computation of the variance-covariance matrix...
ℋ-matrix techniques for approximating large covariance matrices and estimating its parameters
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)
Exploring Eddy-Covariance Measurements Using a Spatial Approach: The Eddy Matrix
Engelmann, Christian; Bernhofer, Christian
2016-10-01
Taylor's frozen turbulence hypothesis states that "standard" eddy-covariance measurements of fluxes at a fixed location can replace a spatial ensemble of instantaneous values at multiple locations. For testing this hypothesis, a unique turbulence measurement set-up was used for two measurement campaigns over desert (Namibia) and grassland (Germany) in 2012. This "Eddy Matrix" combined nine ultrasonic anemometer-thermometers and 17 thermocouples in a 10 m × 10 m regular grid with 2.5-m grid distance. The instantaneous buoyancy flux derived from the spatial eddy covariance of the Eddy Matrix was highly variable in time (from -0.3 to 1 m K s^{-1}). However, the 10-min average reflected 83 % of the reference eddy-covariance flux with a good correlation. By introducing a combined eddy-covariance method (the spatial eddy covariance plus the additional flux of the temporal eddy covariance of the spatial mean values), the mean flux increases by 9 % relative to the eddy-covariance reference. Considering the typical underestimation of fluxes by the standard eddy-covariance method, this is seen as an improvement. Within the limits of the Eddy Matrix, Taylor's hypothesis is supported by the results.
Adaptive automatic sleep stage classification under covariate shift.
Khalighi, Sirvan; Sousa, Teresa; Nunes, Urbano
2012-01-01
Current automatic sleep stage classification (ASSC) methods that rely on polysomnographic (PSG) signals suffer from inter-subject differences that make them unreliable in facing with new and different subjects. A novel adaptive sleep scoring method based on unsupervised domain adaptation, aiming to be robust to inter-subject variability, is proposed. We assume that the sleep quality variants follow a covariate shift model, where only the sleep features distribution change in the training and test phases. The maximum overlap discrete wavelet transform (MODWT) is applied to extract relevant features from EEG, EOG and EMG signals. A set of significant features are selected by minimum-redundancy maximum-relevance (mRMR) which is a powerful feature selection method. Finally, an instance-weighting method, namely the importance weighted kernel logistic regression (IWKLR) is applied for the purpose of obtaining adaptation in classification. The classification results using leave one out cross-validation (LOOCV), show that the proposed method performs at the state-of-the art in the field of ASSC.
Covariant 4-dimensional fuzzy spheres, matrix models and higher spin
Sperling, Marcus; Steinacker, Harold C.
2017-09-01
We study in detail generalized 4-dimensional fuzzy spheres with twisted extra dimensions. These spheres can be viewed as SO(5) -equivariant projections of quantized coadjoint orbits of SO(6) . We show that they arise as solutions in Yang-Mills matrix models, which naturally leads to higher-spin gauge theories on S 4. Several types of embeddings in matrix models are found, including one with self-intersecting fuzzy extra dimensions \
An Alternative Method for Computing Mean and Covariance Matrix of Some Multivariate Distributions
Radhakrishnan, R.; Choudhury, Askar
2009-01-01
Computing the mean and covariance matrix of some multivariate distributions, in particular, multivariate normal distribution and Wishart distribution are considered in this article. It involves a matrix transformation of the normal random vector into a random vector whose components are independent normal random variables, and then integrating…
Empirical data and the variance-covariance matrix for the 1969 Smithsonian Standard Earth (2)
Gaposchkin, E. M.
1972-01-01
The empirical data used in the 1969 Smithsonian Standard Earth (2) are presented. The variance-covariance matrix, or the normal equations, used for correlation analysis, are considered. The format and contents of the matrix, available on magnetic tape, are described and a sample printout is given.
A synthetic covariance matrix for monitoring by terrestrial laser scanning
Kauker, Stephanie; Schwieger, Volker
2017-06-01
Modelling correlations within laser scanning point clouds can be achieved by using synthetic covariance matrices. These are based on the elementary error model which contains different groups of correlations: non-correlating, functional correlating and stochastic correlating. By applying the elementary error model on terrestrial laser scanning several groups of error sources should be considered: instrumental, atmospheric and object based. This contribution presents calculations for the Leica HDS 7000. The determined variances and the spatial correlations of the points are estimated and discussed. Hereby, the mean standard deviation of the point cloud is up to 0.6 mm and the mean correlation is about 0.6 with respect to 5 m scanning range. The change of these numerical values compared to previous publications as Kauker and Schwieger [17] is mainly caused by the complete consideration of the object related error sources.
Speaker Adaptation with Transformation Matrix Linear Interpolation
Institute of Scientific and Technical Information of China (English)
XU Xiang-hua; ZHU Jie
2004-01-01
A transformation matrix linear interpolation (TMLI) approach for speaker adaptation is proposed. TMLI uses the transformation matrixes produced by MLLR from selected training speakers and the testing speaker. With only 3 adaptation sentences, the performance shows a 12.12% word error rate reduction. As the number of adaptation sentences increases, the performance saturates quickly. To improve the behavior of TMLI for large amounts of adaptation data, the TMLI+MAP method which combines TMLI with MAP technique is proposed. Experimental results show TMLI+MAP achieved better recognition accuracy than MAP and MLLR+MAP for both small and large amounts of adaptation data.
A new family of covariate-adjusted response adaptive designs and their properties
Institute of Scientific and Technical Information of China (English)
ZHANG Li-Xin; HU Fei-fang
2009-01-01
It is often important to incorporate covariate information in the design of clinical trials. In literature there are many designs of using stratification and covariate-adaptive randomization to balance certain known covaxiate. Recently, some covariate-adjusted response-adaptive (CARA) designs have been proposed and their asymptotic properties have been studied (Ann.Statist. 2007). However, these CARA designs usually have high variabilities. In this paper, a new family of covariate-adjusted response-adaptive (CARA) designs is presented. It is shown that the new designs have less variables and therefore are more efficient.
DEFF Research Database (Denmark)
Janssen, Anja; Mikosch, Thomas Valentin; Rezapour, Mohsen
2017-01-01
We consider a multivariate heavy-tailed stochastic volatility model and analyze the large-sample behavior of its sample covariance matrix. We study the limiting behavior of its entries in the infinite-variance case and derive results for the ordered eigenvalues and corresponding eigenvectors...... of the sample covariance matrix. While we show that in the case of heavy-tailed innovations the limiting behavior resembles that of completely independent observations, we also derive that in the case of a heavy-tailed volatility sequence the possible limiting behavior is more diverse, i.e. allowing...
Shrinkage covariance matrix approach based on robust trimmed mean in gene sets detection
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.
A Note on the Eigensystem of the Covariance Matrix of Dichotomous Guttman Items.
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).
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.
Aoki, Yasunori; Nordgren, Rikard; Hooker, Andrew C
2016-03-01
As the importance of pharmacometric analysis increases, more and more complex mathematical models are introduced and computational error resulting from computational instability starts to become a bottleneck in the analysis. We propose a preconditioning method for non-linear mixed effects models used in pharmacometric analyses to stabilise the computation of the variance-covariance matrix. Roughly speaking, the method reparameterises the model with a linear combination of the original model parameters so that the Hessian matrix of the likelihood of the reparameterised model becomes close to an identity matrix. This approach will reduce the influence of computational error, for example rounding error, to the final computational result. We present numerical experiments demonstrating that the stabilisation of the computation using the proposed method can recover failed variance-covariance matrix computations, and reveal non-identifiability of the model parameters.
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.
Asymptotic properties of eigenmatrices of a large sample covariance matrix
Bai, Z D; Wong, W K; 10.1214/10-AAP748
2012-01-01
Let $S_n=\\frac{1}{n}X_nX_n^*$ where $X_n=\\{X_{ij}\\}$ is a $p\\times n$ matrix with i.i.d. complex standardized entries having finite fourth moments. Let $Y_n(\\mathbf {t}_1,\\mathbf {t}_2,\\sigma)=\\sqrt{p}({\\mathbf {x}}_n(\\mathbf {t}_1)^*(S_n+\\sigma I)^{-1}{\\mathbf {x}}_n(\\mathbf {t}_2)-{\\mathbf {x}}_n(\\mathbf {t}_1)^*{\\mathbf {x}}_n(\\mathbf {t}_2)m_n(\\sigma))$ in which $\\sigma>0$ and $m_n(\\sigma)=\\int\\frac{dF_{y_n}(x)}{x+\\sigma}$ where $F_{y_n}(x)$ is the Mar\\v{c}enko--Pastur law with parameter $y_n=p/n$; which converges to a positive constant as $n\\to\\infty$, and ${\\mathbf {x}}_n(\\mathbf {t}_1)$ and ${\\mathbf {x}}_n(\\mathbf {t}_2)$ are unit vectors in ${\\Bbb{C}}^p$, having indices $\\mathbf {t}_1$ and $\\mathbf {t}_2$, ranging in a compact subset of a finite-dimensional Euclidean space. In this paper, we prove that the sequence $Y_n(\\mathbf {t}_1,\\mathbf {t}_2,\\sigma)$ converges weakly to a $(2m+1)$-dimensional Gaussian process. This result provides further evidence in support of the conjecture that the distribut...
Genome-Wide Scan for Adaptive Divergence and Association with Population-Specific Covariates.
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.inra.fr/CBGP/software/baypass/.
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
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...
Quantifying lost information due to covariance matrix estimation in parameter inference
Sellentin, Elena; Heavens, Alan F.
2017-02-01
Parameter inference with an estimated covariance matrix systematically loses information due to the remaining uncertainty of the covariance matrix. Here, we quantify this loss of precision and develop a framework to hypothetically restore it, which allows to judge how far away a given analysis is from the ideal case of a known covariance matrix. We point out that it is insufficient to estimate this loss by debiasing the Fisher matrix as previously done, due to a fundamental inequality that describes how biases arise in non-linear functions. We therefore develop direct estimators for parameter credibility contours and the figure of merit, finding that significantly fewer simulations than previously thought are sufficient to reach satisfactory precisions. We apply our results to DES Science Verification weak lensing data, detecting a 10 per cent loss of information that increases their credibility contours. No significant loss of information is found for KiDS. For a Euclid-like survey, with about 10 nuisance parameters we find that 2900 simulations are sufficient to limit the systematically lost information to 1 per cent, with an additional uncertainty of about 2 per cent. Without any nuisance parameters, 1900 simulations are sufficient to only lose 1 per cent of information. We further derive estimators for all quantities needed for forecasting with estimated covariance matrices. Our formalism allows to determine the sweetspot between running sophisticated simulations to reduce the number of nuisance parameters, and running as many fast simulations as possible.
A numerical approach to the approximate and the exact minimum rank of a covariance matrix
ten Berge, Jos M.F.; Kiers, Henk A.L.
A concept of approximate minimum rank for a covariance matrix is defined, which contains the (exact) minimum rank as a special case. A computational procedure to evaluate the approximate minimum rank is offered. The procedure yields those proper communalities for which the unexplained common
Institute of Scientific and Technical Information of China (English)
Yee LEUNG; WU Kefa; DONG Tianxin
2001-01-01
In this paper, a multivariate linear functional relationship model, where the covariance matrix of the observational errors is not restricted, is considered. The parameter estimation of this model is discussed. The estimators are shown to be a strongly consistent estimation under some mild conditions on the incidental parameters.
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.
Lee, Wonyul; Liu, Yufeng
2012-10-01
Multivariate regression is a common statistical tool for practical problems. Many multivariate regression techniques are designed for univariate response cases. For problems with multiple response variables available, one common approach is to apply the univariate response regression technique separately on each response variable. Although it is simple and popular, the univariate response approach ignores the joint information among response variables. In this paper, we propose three new methods for utilizing joint information among response variables. All methods are in a penalized likelihood framework with weighted L(1) regularization. The proposed methods provide sparse estimators of conditional inverse co-variance matrix of response vector given explanatory variables as well as sparse estimators of regression parameters. Our first approach is to estimate the regression coefficients with plug-in estimated inverse covariance matrices, and our second approach is to estimate the inverse covariance matrix with plug-in estimated regression parameters. Our third approach is to estimate both simultaneously. Asymptotic properties of these methods are explored. Our numerical examples demonstrate that the proposed methods perform competitively in terms of prediction, variable selection, as well as inverse covariance matrix estimation.
Self-Calibration of Radio Astronomical Arrays With Non-Diagonal Noise Covariance Matrix
Wijnholds, Stefan J
2010-01-01
The radio astronomy community is currently building a number of phased array telescopes. The calibration of these telescopes is hampered by the fact that covariances of signals from closely spaced antennas are sensitive to noise coupling and to variations in sky brightness on large spatial scales. These effects are difficult and computationally expensive to model. We propose to model them phenomenologically using a non-diagonal noise covariance matrix. The parameters can be estimated using a weighted alternating least squares (WALS) algorithm iterating between the calibration parameters and the additive nuisance parameters. We demonstrate the effectiveness of our method using data from the low frequency array (LOFAR) prototype station.
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
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).
Quantifying lost information due to covariance matrix estimation in parameter inference
Sellentin, Elena
2016-01-01
Parameter inference with an estimated covariance matrix systematically loses information due to the remaining uncertainty of the covariance matrix. Here, we quantify this loss of precision and develop a framework to hypothetically restore it, which allows to judge how far away a given analysis is from the ideal case of a known covariance matrix. We point out that it is insufficient to estimate this loss by debiasing a Fisher matrix as previously done, due to a fundamental inequality that describes how biases arise in non-linear functions. We therefore develop direct estimators for parameter credibility contours and the figure of merit. We apply our results to DES Science Verification weak lensing data, detecting a 10% loss of information that increases their credibility contours. No significant loss of information is found for KiDS. For a Euclid-like survey, with about 10 nuisance parameters we find that 2900 simulations are sufficient to limit the systematically lost information to 1%, with an additional unc...
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.
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.
A new procedure to built a model covariance matrix: first results
Barzaghi, R.; Marotta, A. M.; Splendore, R.; Borghi, A.
2012-04-01
In order to validate the results of geophysical models a common procedure is to compare model predictions with observations by means of statistical tests. A limit of this approach is the lack of a covariance matrix associated to model results, that may frustrate the achievement of a confident statistical significance of the results. Trying to overcome this limit, we have implemented a new procedure to build a model covariance matrix that could allow a more reliable statistical analysis. This procedure has been developed in the frame of the thermo-mechanical model described in Splendore et al. (2010), that predicts the present-day crustal velocity field in the Tyrrhenian due to Africa-Eurasia convergence and to lateral rheological heterogeneities of the lithosphere. Modelled tectonic velocity field has been compared to the available surface velocity field based on GPS observation, determining the best fit model and the degree of fitting, through the use of a χ2 test. Once we have identified the key models parameters and defined their appropriate ranges of variability, we have run 100 different models for 100 sets of randomly values of the parameters extracted within the corresponding interval, obtaining a stack of 100 velocity fields. Then, we calculated variance and empirical covariance for the stack of results, taking into account also cross-correlation, obtaining a positive defined, diagonal matrix that represents the covariance matrix of the model. This empirical approach allows us to define a more robust statistical analysis with respect the classic approach. Reference Splendore, Marotta, Barzaghi, Borghi and Cannizzaro, 2010. Block model versus thermomechanical model: new insights on the present-day regional deformation in the surroundings of the Calabrian Arc. In: Spalla, Marotta and Gosso (Eds) Advances in Interpretation of Geological Processes: Refinement of Multi scale Data and Integration in Numerical Modelling. Geological Society, London, Special
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.
Genome-Wide Scan for Adaptive Divergence and Association with Population-Specific Covariates
Gautier, Mathieu
2015-01-01
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 (i...
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.
Adaptive error covariances estimation methods for ensemble Kalman filters
Energy Technology Data Exchange (ETDEWEB)
Zhen, Yicun, E-mail: zhen@math.psu.edu [Department of Mathematics, The Pennsylvania State University, University Park, PA 16802 (United States); Harlim, John, E-mail: jharlim@psu.edu [Department of Mathematics and Department of Meteorology, The Pennsylvania State University, University Park, PA 16802 (United States)
2015-08-01
This paper presents a computationally fast algorithm for estimating, both, the system and observation noise covariances of nonlinear dynamics, that can be used in an ensemble Kalman filtering framework. The new method is a modification of Belanger's recursive method, to avoid an expensive computational cost in inverting error covariance matrices of product of innovation processes of different lags when the number of observations becomes large. When we use only product of innovation processes up to one-lag, the computational cost is indeed comparable to a recently proposed method by Berry–Sauer's. However, our method is more flexible since it allows for using information from product of innovation processes of more than one-lag. Extensive numerical comparisons between the proposed method and both the original Belanger's and Berry–Sauer's schemes are shown in various examples, ranging from low-dimensional linear and nonlinear systems of SDEs and 40-dimensional stochastically forced Lorenz-96 model. Our numerical results suggest that the proposed scheme is as accurate as the original Belanger's scheme on low-dimensional problems and has a wider range of more accurate estimates compared to Berry–Sauer's method on L-96 example.
Tanaka, S
2004-01-01
Noncommutative field theory on Yang's quantized space-time algebra (YSTA) is studied. It gives a theoretical framework to reformulate the matrix model as quantum mechanics of $D_0$ branes in a Lorentz-covariant form. The so-called kinetic term ($\\sim {\\hat{P_i}}^2)$ and potential term ($\\sim {[\\hat{X_i},\\hat{X_j}]}^2)$ of $D_0$ branes in the matrix model are described now in terms of Casimir operator of $SO(D,1)$, a subalgebra of the primary algebra $SO(D+1,1)$ which underlies YSTA with two contraction- parameters, $\\lambda$ and $R$. $D$-dimensional noncommutative space-time and momentum operators $\\hat{X_\\mu}$ and $\\hat{P_\\mu}$ in YSTA show a distinctive spectral structure, that is, space-components $\\hat{X_i}$ and $\\hat{P_i}$ have discrete eigenvalues, and time-components $\\hat{X_0}$ and $\\hat{P_0}$ continuous eigenvalues, consistently with Lorentz-covariance. According to the method of Lorentz-covariant Moyal star product proper to YSTA, the field equation of $D_0$ brane on YSTA is derived in a nontrivial ...
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.
Exact distribution of MLE of covariance matrix in a GMANOVA-MANOVA model
Institute of Scientific and Technical Information of China (English)
BAI; Peng
2005-01-01
For a GMANOVA-MANOVA model with normal error: Y = XB1Z1T + B2Z2T +E, E ～ Nq×n(0, In(×) ∑), the present paper is devoted to the study of distribution of MLE,Σ, of covariance matrix ∑. The main results obtained are stated as follows: (1) When rk(Z) -rk(Z2) ≥ q-rk(X), the exact distribution of (Σ) is derived, where Z = (Z1, Z2), rk(A)denotes the rank of matrix A. (2) The exact distribution of |(Σ)| is gained. (3) It is proved that ntr{[∑-1 - ∑-1XM(MTXT∑-1XM)-1MTXT∑-1]Σ} has x2(q-rk(X))(n-rk(Z2)) distribution, where M is the matrix whose columns are the standardized orthogonal eigenvectors corresponding to the nonzero eigenvalues of XT∑-1X.
Pereverzev, Andrey; Sewell, Thomas D.
2015-04-01
We show that for solids the effective Hessian matrix, averaged over the canonical ensemble, can be calculated from the force covariance matrix. This effective Hessian reduces to the standard Hessian as the temperature approaches zero, while at finite temperatures it implicitly includes anharmonic corrections. As a case study, we calculate the effective Hessians and the corresponding normal mode eigenvectors and frequencies for the crystalline organic explosives pentaerythritol tetranitrate and α-1,3,5-trinitro-1,3,5-triazacyclohexane. The resulting normal mode frequencies are compared to those obtained by diagonalizing the standard Hessian matrix of second derivatives in Cartesian displacements about the potential energy minimum. Effects of temperature and statistical noise on the effective Hessians and normal mode frequencies are discussed.
A covariance-adaptive approach for regularized inversion in linear models
Kotsakis, Christopher
2007-11-01
The optimal inversion of a linear model under the presence of additive random noise in the input data is a typical problem in many geodetic and geophysical applications. Various methods have been developed and applied for the solution of this problem, ranging from the classic principle of least-squares (LS) estimation to other more complex inversion techniques such as the Tikhonov-Philips regularization, truncated singular value decomposition, generalized ridge regression, numerical iterative methods (Landweber, conjugate gradient) and others. In this paper, a new type of optimal parameter estimator for the inversion of a linear model is presented. The proposed methodology is based on a linear transformation of the classic LS estimator and it satisfies two basic criteria. First, it provides a solution for the model parameters that is optimally fitted (in an average quadratic sense) to the classic LS parameter solution. Second, it complies with an external user-dependent constraint that specifies a priori the error covariance (CV) matrix of the estimated model parameters. The formulation of this constrained estimator offers a unified framework for the description of many regularization techniques that are systematically used in geodetic inverse problems, particularly for those methods that correspond to an eigenvalue filtering of the ill-conditioned normal matrix in the underlying linear model. Our study lies on the fact that it adds an alternative perspective on the statistical properties and the regularization mechanism of many inversion techniques commonly used in geodesy and geophysics, by interpreting them as a family of `CV-adaptive' parameter estimators that obey a common optimal criterion and differ only on the pre-selected form of their error CV matrix under a fixed model design.
National Research Council Canada - National Science Library
John B Holmes; Ken G Dodds; Michael A Lee
2017-01-01
.... 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...
Ponçot, Angélique; Bouriquet, Bertrand; Erhard, Patrick; Gratton, Serge; Thual, Olivier
2013-01-01
Data assimilation method consists in combining all available pieces of information about a system to obtain optimal estimates of initial states. The different sources of information are weighted according to their accuracy by the means of error covariance matrices. Our purpose here is to evaluate the efficiency of variational data assimilation for the xenon induced oscillations forecasts in nuclear cores. In this paper we focus on the comparison between 3DVAR schemes with optimised background error covariance matrix B and a 4DVAR scheme. Tests were made in twin experiments using a simulation code which implements a mono-dimensional coupled model of xenon dynamics, thermal, and thermal-hydraulic processes. We enlighten the very good efficiency of the 4DVAR scheme as well as good results with the 3DVAR one using a careful multivariate modelling of B.
Employment of the covariance matrix in parameter estimation for stochastic processes in cell biology
Preuss, R.; Dieterich, P.
2013-08-01
The dynamics of movements of biological cells can be described with models from correlated stochastic processes. In order to overcome problems from correlated and insufficient data in the determination of the model parameters of such processes we employ the covariance matrix of the data. Since the covariance suffers itself from statistical uncertainty it is corrected by a renormalization treatment [1]. For the example of normal and fractional Brownian motion, which allows both to access all quantities on full theoretical grounds and to generate data similar to experiment, we discuss our results and those of previous works by Gregory [2] and Sivia [3]. The presented approach has the potential to estimate the aging correlation function of observed cell paths and can be applied to more complicated models.
Directory of Open Access Journals (Sweden)
Filippo Palombi
2017-01-01
Full Text Available We relate the matrix SB of the second moments of a spherically truncated normal multivariate to its full covariance matrix Σ and present an algorithm to invert the relation and reconstruct Σ from SB. While the eigenvectors of Σ are left invariant by the truncation, its eigenvalues are nonuniformly damped. We show that the eigenvalues of Σ can be reconstructed from their truncated counterparts via a fixed point iteration, whose convergence we prove analytically. The procedure requires the computation of multidimensional Gaussian integrals over an Euclidean ball, for which we extend a numerical technique, originally proposed by Ruben in 1962, based on a series expansion in chi-square distributions. In order to study the feasibility of our approach, we examine the convergence rate of some iterative schemes on suitably chosen ensembles of Wishart matrices. We finally discuss the practical difficulties arising in sample space and outline a regularization of the problem based on perturbation theory.
Zhang, Zhe; Erbe, Malena; He, Jinlong; Ober, Ulrike; Gao, Ning; Zhang, Hao; Simianer, Henner; Li, Jiaqi
2015-02-09
Obtaining accurate predictions of unobserved genetic or phenotypic values for complex traits in animal, plant, and human populations is possible through whole-genome prediction (WGP), a combined analysis of genotypic and phenotypic data. Because the underlying genetic architecture of the trait of interest is an important factor affecting model selection, we propose a new strategy, termed BLUP|GA (BLUP-given genetic architecture), which can use genetic architecture information within the dataset at hand rather than from public sources. This is achieved by using a trait-specific covariance matrix ( T: ), which is a weighted sum of a genetic architecture part ( S: matrix) and the realized relationship matrix ( G: ). The algorithm of BLUP|GA (BLUP-given genetic architecture) is provided and illustrated with real and simulated datasets. Predictive ability of BLUP|GA was validated with three model traits in a dairy cattle dataset and 11 traits in three public datasets with a variety of genetic architectures and compared with GBLUP and other approaches. Results show that BLUP|GA outperformed GBLUP in 20 of 21 scenarios in the dairy cattle dataset and outperformed GBLUP, BayesA, and BayesB in 12 of 13 traits in the analyzed public datasets. Further analyses showed that the difference of accuracies for BLUP|GA and GBLUP significantly correlate with the distance between the T: and G: matrices. The new strategy applied in BLUP|GA is a favorable and flexible alternative to the standard GBLUP model, allowing to account for the genetic architecture of the quantitative trait under consideration when necessary. This feature is mainly due to the increased similarity between the trait-specific relationship matrix ( T: matrix) and the genetic relationship matrix at unobserved causal loci. Applying BLUP|GA in WGP would ease the burden of model selection. Copyright © 2015 Zhang et al.
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.
Simon, Richard; Simon, Noah Robin
2011-07-01
We demonstrate that clinical trials using response adaptive randomized treatment assignment rules are subject to substantial bias if there are time trends in unknown prognostic factors and standard methods of analysis are used. We develop a general class of randomization tests based on generating the null distribution of a general test statistic by repeating the adaptive randomized treatment assignment rule holding fixed the sequence of outcome values and covariate vectors actually observed in the trial. We develop broad conditions on the adaptive randomization method and the stochastic mechanism by which outcomes and covariate vectors are sampled that ensure that the type I error is controlled at the level of the randomization test. These conditions ensure that the use of the randomization test protects the type I error against time trends that are independent of the treatment assignments. Under some conditions in which the prognosis of future patients is determined by knowledge of the current randomization weights, the type I error is not strictly protected. We show that response-adaptive randomization can result in substantial reduction in statistical power when the type I error is preserved. Our results also ensure that type I error is controlled at the level of the randomization test for adaptive stratification designs used for balancing covariates.
Efficient covariance matrix algorithm and realization for images on GPU%基于GPU的高效图像协方差矩阵算法与实现
Institute of Scientific and Technical Information of China (English)
陈彬; 陈和平; 李晓卉
2014-01-01
To improve the efficiency of covariance matrix computation for image processing to adapt to real‐time or near real‐time requirements of practical applications ,with the general purpose of GPU technology ,the covariance matrix computation was opti‐mized for parallel computation and a novel parallelization approach for covariance matrix computation based on CUDA program‐ming model was proposed .Image processing applications based on covariance matrices can be processed in real time on general personal PC platforms .Moreover ,as to image processing ,there are many other real‐time requirements based on covariance ma‐trices can also be satisfied .Compared to the original CPU implementation ,the proposed GPU implementation improves the effi‐ciency by thousands of times on average .%为提高图像处理领域协方差矩阵的计算效率，满足其在实时要求下的应用，借助GPU 通用计算技术，结合CU‐DA编程模型，对协方差矩阵的计算进行有针对性的并行化优化，设计并实现一种高效的并行图像协方差矩阵算法。为在通用PC平台上使用协方差矩阵并满足实时性需求的各种图像处理应用提供了一个可行的解决方法，对其它领域涉及到协方差矩阵的实时计算也有良好的借鉴作用。与原有的CPU实现方法相比， GPU的效率有了平均数千倍的提升。
Pre-processing ambient noise cross-correlations with equalizing the covariance matrix eigenspectrum
Seydoux, Léonard; de Rosny, Julien; Shapiro, Nikolai M.
2017-09-01
Passive imaging techniques from ambient seismic noise requires a nearly isotropic distribution of the noise sources in order to ensure reliable traveltime measurements between seismic stations. However, real ambient seismic noise often partially fulfils this condition. It is generated in preferential areas (in deep ocean or near continental shores), and some highly coherent pulse-like signals may be present in the data such as those generated by earthquakes. Several pre-processing techniques have been developed in order to attenuate the directional and deterministic behaviour of this real ambient noise. Most of them are applied to individual seismograms before cross-correlation computation. The most widely used techniques are the spectral whitening and temporal smoothing of the individual seismic traces. We here propose an additional pre-processing to be used together with the classical ones, which is based on the spatial analysis of the seismic wavefield. We compute the cross-spectra between all available stations pairs in spectral domain, leading to the data covariance matrix. We apply a one-bit normalization to the covariance matrix eigenspectrum before extracting the cross-correlations in the time domain. The efficiency of the method is shown with several numerical tests. We apply the method to the data collected by the USArray, when the M8.8 Maule earthquake occurred on 2010 February 27. The method shows a clear improvement compared with the classical equalization to attenuate the highly energetic and coherent waves incoming from the earthquake, and allows to perform reliable traveltime measurement even in the presence of the earthquake.
Finding Imaging Patterns of Structural Covariance via Non-Negative Matrix Factorization
Sotiras, Aristeidis; Resnick, Susan M.; Davatzikos, Christos
2015-01-01
In this paper, we investigate the use of Non-Negative Matrix Factorization (NNMF) for the analysis of structural neuroimaging data. The goal is to identify the brain regions that co-vary across individuals in a consistent way, hence potentially being part of underlying brain networks or otherwise influenced by underlying common mechanisms such as genetics and pathologies. NNMF offers a directly data-driven way of extracting relatively localized co-varying structural regions, thereby transcending limitations of Principal Component Analysis (PCA), Independent Component Analysis (ICA) and other related methods that tend to produce dispersed components of positive and negative loadings. In particular, leveraging upon the well known ability of NNMF to produce parts-based representations of image data, we derive decompositions that partition the brain into regions that vary in consistent ways across individuals. Importantly, these decompositions achieve dimensionality reduction via highly interpretable ways and generalize well to new data as shown via split-sample experiments. We empirically validate NNMF in two data sets: i) a Diffusion Tensor (DT) mouse brain development study, and ii) a structural Magnetic Resonance (sMR) study of human brain aging. We demonstrate the ability of NNMF to produce sparse parts-based representations of the data at various resolutions. These representations seem to follow what we know about the underlying functional organization of the brain and also capture some pathological processes. Moreover, we show that these low dimensional representations favorably compare to descriptions obtained with more commonly used matrix factorization methods like PCA and ICA. PMID:25497684
Pre-Processing Noise Cross-Correlations with Equalizing the Network Covariance Matrix Eigen-Spectrum
Seydoux, L.; de Rosny, J.; Shapiro, N.
2016-12-01
Theoretically, the extraction of Green functions from noise cross-correlation requires the ambient seismic wavefield to be generated by uncorrelated sources evenly distributed in the medium. Yet, this condition is often not verified. Strong events such as earthquakes often produce highly coherent transient signals. Also, the microseismic noise is generated at specific places on the Earth's surface with source regions often very localized in space. Different localized and persistent seismic sources may contaminate the cross-correlations of continuous records resulting in spurious arrivals or asymmetry and, finally, in biased travel-time measurements. Pre-processing techniques therefore must be applied to the seismic data in order to reduce the effect of noise anisotropy and the influence of strong localized events. Here we describe a pre-processing approach that uses the covariance matrix computed from signals recorded by a network of seismographs. We extend the widely used time and spectral equalization pre-processing to the equalization of the covariance matrix spectrum (i.e., its ordered eigenvalues). This approach can be considered as a spatial equalization. This method allows us to correct for the wavefield anisotropy in two ways: (1) the influence of strong directive sources is substantially attenuated, and (2) the weakly excited modes are reinforced, allowing to partially recover the conditions required for the Green's function retrieval. We also present an eigenvector-based spatial filter used to distinguish between surface and body waves. This last filter is used together with the equalization of the eigenvalue spectrum. We simulate two-dimensional wavefield in a heterogeneous medium with strongly dominating source. We show that our method greatly improves the travel-time measurements obtained from the inter-station cross-correlation functions. Also, we apply the developed method to the USArray data and pre-process the continuous records strongly influenced
Parameter inference with estimated covariance matrices
Sellentin, Elena
2015-01-01
When inferring parameters from a Gaussian-distributed data set by computing a likelihood, a covariance matrix is needed that describes the data errors and their correlations. If the covariance matrix is not known a priori, it may be estimated and thereby becomes a random object with some intrinsic uncertainty itself. We show how to infer parameters in the presence of such an estimated covariance matrix, by marginalising over the true covariance matrix, conditioned on its estimated value. This leads to a likelihood function that is no longer Gaussian, but rather an adapted version of a multivariate $t$-distribution, which has the same numerical complexity as the multivariate Gaussian. As expected, marginalisation over the true covariance matrix improves inference when compared with Hartlap et al.'s method, which uses an unbiased estimate of the inverse covariance matrix but still assumes that the likelihood is Gaussian.
The Covariance matrix Analysis Of Log Data%测井资料的协方差阵分析
Institute of Scientific and Technical Information of China (English)
秦海菲; 陈超慧
2012-01-01
协方差阵是多元分析中一个重要的组成部分。协方差分析是方差分析和回归分析结合而产生的一种数据分析方法。本文利用协方差阵对测并资料中油层、水层、干层的数据进行分析，通过准备数据，建立油层、水层、干层的矩阵，分析矩阵并建立相应的协方差阵，计算协方差阵和聚类中心，比较每一个层与聚类中心的距离，判断出所要判断的层是油，水，干层中的哪一层，再与实际层对比，得出符合率。通过证明，说明了用协方差阵对测井数据进行分析是可行的，也是可信的。%Covariance matrix is a essential part of multivariate analysis .Covariance matrix analysis is a method of data analysis that mixed the Variance analysis and Regression analysis.This paper using Covariance matrix analysis Logging data. According to data prepare, build the matrix of layer in oil, water, dry. Analysis matrix and build Covariance matrix, computer Covariance matrix, build the center of cluster. Compare the distance of the layer to the center of cluster, discriminanted the layer is that is oil,water or dry layer. Compare the discriminant layer and actual layer,obtain the coincidence rate.
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.
Covariance and crossover matrix guided differential evolution for global numerical optimization.
Li, YongLi; Feng, JinFu; Hu, JunHua
2016-01-01
Differential evolution (DE) is an efficient and robust evolutionary algorithm and has wide application in various science and engineering fields. DE is sensitive to the selection of mutation and crossover strategies and their associated control parameters. However, the structure and implementation of DEs are becoming more complex because of the diverse mutation and crossover strategies that use distinct parameter settings during the different stages of the evolution. A novel strategy is used in this study to improve the crossover and mutation operations. The crossover matrix, instead of a crossover operator and its control parameter CR, is proposed to implement the function of the crossover operation. Meanwhile, Gaussian distribution centers the best individuals found in each generation based on the proposed covariance matrix, which is generated between the best individual and several better individuals. Improved mutation operator based on the crossover matrix is randomly selected to generate the trial population. This operator is used to generate high-quality solutions to improve the capability of exploitation and enhance the preference of exploration. In addition, the memory population is randomly chosen from previous generation and used to control the search direction in the novel mutation strategy. Accordingly, the diversity of the population is improved. Thus, CCDE, which is a novel efficient and simple DE variant, is presented in this paper. CCDE has been tested on 30 benchmarks and 5 real-world optimization problems from the IEEE Congress on Evolutionary Computation (CEC) 2014 and CEC 2011, respectively. Experimental and statistical results demonstrate the effectiveness of CCDE for global numerical and engineering optimization. CCDE can solve the test benchmark functions and engineering problems more successfully than the other DE variants and algorithms from CEC 2014.
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.
The potential of more accurate InSAR covariance matrix estimation for land cover mapping
Jiang, Mi; Yong, Bin; Tian, Xin; Malhotra, Rakesh; Hu, Rui; Li, Zhiwei; Yu, Zhongbo; Zhang, Xinxin
2017-04-01
Synthetic aperture radar (SAR) and Interferometric SAR (InSAR) provide both structural and electromagnetic information for the ground surface and therefore have been widely used for land cover classification. However, relatively few studies have developed analyses that investigate SAR datasets over richly textured areas where heterogeneous land covers exist and intermingle over short distances. One of main difficulties is that the shapes of the structures in a SAR image cannot be represented in detail as mixed pixels are likely to occur when conventional InSAR parameter estimation methods are used. To solve this problem and further extend previous research into remote monitoring of urban environments, we address the use of accurate InSAR covariance matrix estimation to improve the accuracy of land cover mapping. The standard and updated methods were tested using the HH-polarization TerraSAR-X dataset and compared with each other using the random forest classifier. A detailed accuracy assessment complied for six types of surfaces shows that the updated method outperforms the standard approach by around 9%, with an overall accuracy of 82.46% over areas with rich texture in Zhuhai, China. This paper demonstrates that the accuracy of land cover mapping can benefit from the 3 enhancement of the quality of the observations in addition to classifiers selection and multi-source data ingratiation reported in previous studies.
Analytic model for the matter power spectrum, its covariance matrix, and baryonic effects
Mohammed, Irshad
2014-01-01
We develop a model for the matter power spectrum as the sum of quasi-linear Zeldovich approximation and even powers of $k$, i.e., $A_0 - A_2k^2 + A_4k^4 - ...$, compensated at low $k$. The model can predict the true power spectrum to a few percent accuracy up to $k \\sim 0.7\\ h \\rm{Mpc}^{-1}$, over a wide range of redshifts and models, including massive neutrino models. We write a simple form of the covariance matrix as a sum of Gaussian part and $A_0$ variance and we find that it reproduces well the simulations. We investigate the super-sample variance effect and show it induces a relation between the Zeldovich term and $A_0$ that differs from the amplitude change, allowing it to be modeled as an additional parameter that can be determined from the data. The $A_n$ coefficients contain information about cosmology, in particular the amplitude of fluctuations $\\sigma_8$. We explore their information content, showing that $A_0$ contains the bulk of amplitude information, scaling as $\\sigma_8^{3.9}$, which allows ...
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.
Berner, Daniel; Stutz, William E; Bolnick, Daniel I
2010-08-01
How does natural selection shape the structure of variance and covariance among multiple traits, and how do (co)variances influence trajectories of adaptive diversification? We investigate these pivotal but open questions by comparing phenotypic (co)variances among multiple morphological traits across 18 derived lake-dwelling populations of threespine stickleback, and their marine ancestor. Divergence in (co)variance structure among populations is striking and primarily attributable to shifts in the variance of a single key foraging trait (gill raker length). We then relate this divergence to an ecological selection proxy, to population divergence in trait means, and to the magnitude of sexual dimorphism within populations. This allows us to infer that evolution in (co)variances is linked to variation among habitats in the strength of resource-mediated disruptive selection. We further find that adaptive diversification in trait means among populations has primarily involved shifts in gill raker length. The direction of evolutionary trajectories is unrelated to the major axes of ancestral trait (co)variance. Our study demonstrates that natural selection drives both means and (co)variances deterministically in stickleback, and strongly challenges the view that the (co)variance structure biases the direction of adaptive diversification predictably even over moderate time spans.
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
Meng, Yang; Gao, Shesheng; Zhong, Yongmin; Hu, Gaoge; Subic, Aleksandar
2016-03-01
The use of the direct filtering approach for INS/GNSS integrated navigation introduces nonlinearity into the system state equation. As the unscented Kalman filter (UKF) is a promising method for nonlinear problems, an obvious solution is to incorporate the UKF concept in the direct filtering approach to address the nonlinearity involved in INS/GNSS integrated navigation. However, the performance of the standard UKF is dependent on the accurate statistical characterizations of system noise. If the noise distributions of inertial instruments and GNSS receivers are not appropriately described, the standard UKF will produce deteriorated or even divergent navigation solutions. This paper presents an adaptive UKF with noise statistic estimator to overcome the limitation of the standard UKF. According to the covariance matching technique, the innovation and residual sequences are used to determine the covariance matrices of the process and measurement noises. The proposed algorithm can estimate and adjust the system noise statistics online, and thus enhance the adaptive capability of the standard UKF. Simulation and experimental results demonstrate that the performance of the proposed algorithm is significantly superior to that of the standard UKF and adaptive-robust UKF under the condition without accurate knowledge on system noise, leading to improved navigation precision.
Estimating Cosmological Parameter Covariance
Taylor, Andy
2014-01-01
We investigate the bias and error in estimates of the cosmological parameter covariance matrix, due to sampling or modelling the data covariance matrix, for likelihood width and peak scatter estimators. We show that these estimators do not coincide unless the data covariance is exactly known. For sampled data covariances, with Gaussian distributed data and parameters, the parameter covariance matrix estimated from the width of the likelihood has a Wishart distribution, from which we derive the mean and covariance. This mean is biased and we propose an unbiased estimator of the parameter covariance matrix. Comparing our analytic results to a numerical Wishart sampler of the data covariance matrix we find excellent agreement. An accurate ansatz for the mean parameter covariance for the peak scatter estimator is found, and we fit its covariance to our numerical analysis. The mean is again biased and we propose an unbiased estimator for the peak parameter covariance. For sampled data covariances the width estimat...
Directory of Open Access Journals (Sweden)
S. Vathsal
1994-01-01
Full Text Available This paper provides an error model of the strapped down inertial navigation system in the state space format. A method to estimate the circular error probability is presented using time propagation of error covariance matrix. Numerical results have been obtained for a typical flight trajectory. Sensitivity studies have also been conducted for variation of sensor noise covariances and initial state uncertainty. This methodology seems to work in all the practical cases considered so far. Software has been tested for both the local vertical frame and the inertial frame. The covariance propagation technique provides accurate estimation of dispersions of position at impact. This in turn enables to estimate the circular error probability (CEP very accurately.
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.
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.
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
Information content of weak lensing bispectrum: including the non-Gaussian error covariance matrix
Kayo, Issha; Jain, Bhuvnesh
2013-01-01
We address a long-standing problem, how can we extract information in the non-Gaussian regime of weak lensing surveys, by accurate modeling of all relevant covariances between the power spectra and bispectra. We use 1000 ray-tracing simulation realizations for a Lambda-CDM model and an analytical halo model. We develop a formalism to describe the covariance matrices of power spectra and bispectra of all triangle configurations, which extend to 6-point correlation functions. We include a new contribution arising from coupling of the lensing Fourier modes with large-scale mass fluctuations on scales comparable with the survey region via halo bias theory, which we call the halo sample variance (HSV) effect. We show that the model predictions are in excellent agreement with the simulation results for the power spectrum and bispectrum covariances. The HSV effect gives a dominant contribution to the covariances at multipoles l > 10^3, which arise from massive halos with masses of about 10^14 solar masses and at rel...
Few Group Collapsing of Covariance Matrix Data Based on a Conservation Principle
Energy Technology Data Exchange (ETDEWEB)
H. Hiruta; G. Palmiotti; M. Salvatores; R. Arcilla, Jr.; R. D. McKnight; G. Aliberti; P. Oblozinsky; W. S. Yang
2008-12-01
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 the uncertainty calculated in a fine group energy structure for a specific integral parameter, using as weights the associated sensitivity coefficients, to be preserved at a broad energy group structure.
CSIR Research Space (South Africa)
Salmon, BP
2012-07-01
Full Text Available In this paper, the internal operations of an Extended Kalman Filter is investigated to see if any useful information can be derived to detect land cover change in a MODIS time series. The Extended Kalman Filter expands its internal covariance if a...
Fu, Zening; Chan, Shing-Chow; Di, Xin; Biswal, Bharat; Zhang, Zhiguo
2014-04-01
Time-varying covariance is an important metric to measure the statistical dependence between non-stationary biological processes. Time-varying covariance is conventionally estimated from short-time data segments within a window having a certain bandwidth, but it is difficult to choose an appropriate bandwidth to estimate covariance with different degrees of non-stationarity. This paper introduces a local polynomial regression (LPR) method to estimate time-varying covariance and performs an asymptotic analysis of the LPR covariance estimator to show that both the estimation bias and variance are functions of the bandwidth and there exists an optimal bandwidth to minimize the mean square error (MSE) locally. A data-driven variable bandwidth selection method, namely the intersection of confidence intervals (ICI), is adopted in LPR for adaptively determining the local optimal bandwidth that minimizes the MSE. Experimental results on simulated signals show that the LPR-ICI method can achieve robust and reliable performance in estimating time-varying covariance with different degrees of variations and under different noise scenarios, making it a powerful tool to study the dynamic relationship between non-stationary biomedical signals. Further, we apply the LPR-ICI method to estimate time-varying covariance of functional magnetic resonance imaging (fMRI) signals in a visual task for the inference of dynamic functional brain connectivity. The results show that the LPR-ICI method can effectively capture the transient connectivity patterns from fMRI.
Epistasis and the temporal change in the additive variance-covariance matrix induced by drift.
López-Fanjul, Carlos; Fernández, Almudena; Toro, Miguel A
2004-08-01
The effect of population bottlenecks on the components of the genetic covariance generated by two neutral independent epistatic loci has been studied theoretically (additive, covA; dominance, covD; additive-by-additive, covAA; additive-by-dominance, covAD; and dominance-by-dominance, covDD). The additive-by-additive model and a more general model covering all possible types of marginal gene action at the single-locus level (additive/dominance epistatic model) were considered. The covariance components in an infinitely large panmictic population (ancestral components) were compared with their expected values at equilibrium over replicates randomly derived from the base population, after t consecutive bottlenecks of equal size N (derived components). Formulae were obtained in terms of the allele frequencies and effects at each locus, the corresponding epistatic effects and the inbreeding coefficient Ft. These expressions show that the contribution of nonadditive loci to the derived additive covariance (covAt) does not linearly decrease with inbreeding, as in the pure additive case, and may initially increase or even change sign in specific situations. Numerical examples were also analyzed, restricted for simplicity to the case of all covariance components being positive. For additive-by-additive epistasis, the condition covAt > covA only holds for high frequencies of the allele decreasing the metric traits at each locus (negative allele) if epistasis is weak, or for intermediate allele frequencies if it is strong. For the additive/dominance epistatic model, however, covAt > covA applies for low frequencies of the negative alleles at one or both loci and mild epistasis, but this result can be progressively extended to intermediate frequencies as epistasis becomes stronger. Without epistasis the same qualitative results were found, indicating that marginal dominance induced by epistasis can be considered as the primary cause of an increase of the additive covariance
Calculation of Covariance Matrix for Multi-mode Gaussian States in Decoherence Processes
Institute of Scientific and Technical Information of China (English)
XIANG Shao-Hua; SHAO Bin; SONG Ke-Hui
2009-01-01
We investigate the dynamics of n single-mode continuous variable systems in a generic Gaussian state under the influence of the independent and correlated noises making use of the characteristic function method.In two models the bath is assumed to be a squeezed thermal one.We derive an explicit input-output expression between the initial and final covariance matrices.As an example,we study the evolution of entanglement of three-mode Gaussian state embedded in two noisy models.
A Fast Algorithm for the Computation of HAC Covariance Matrix Estimators
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Jochen Heberle
2017-01-01
Full Text Available This paper considers the algorithmic implementation of the heteroskedasticity and autocorrelation consistent (HAC estimation problem for covariance matrices of parameter estimators. We introduce a new algorithm, mainly based on the fast Fourier transform, and show via computer simulation that our algorithm is up to 20 times faster than well-established alternative algorithms. The cumulative effect is substantial if the HAC estimation problem has to be solved repeatedly. Moreover, the bandwidth parameter has no impact on this performance. We provide a general description of the new algorithm as well as code for a reference implementation in R.
Badyaev, Alexander V
2010-04-12
The link between adaptation and evolutionary change remains the most central and least understood evolutionary problem. Rapid evolution and diversification of avian beaks is a textbook example of such a link, yet the mechanisms that enable beak's precise adaptation and extensive adaptability are poorly understood. Often observed rapid evolutionary change in beaks is particularly puzzling in light of the neo-Darwinian model that necessitates coordinated changes in developmentally distinct precursors and correspondence between functional and genetic modularity, which should preclude evolutionary diversification. I show that during first 19 generations after colonization of a novel environment, house finches (Carpodacus mexicanus) express an array of distinct, but adaptively equivalent beak morphologies-a result of compensatory developmental interactions between beak length and width in accommodating microevolutionary change in beak depth. Directional selection was largely confined to the elimination of extremes formed by these developmental interactions, while long-term stabilizing selection along a single axis-beak depth-was mirrored in the structure of beak's additive genetic covariance. These results emphasize three principal points. First, additive genetic covariance structure may represent a historical record of the most recurrent developmental and functional interactions. Second, adaptive equivalence of beak configurations shields genetic and developmental variation in individual components from depletion by natural selection. Third, compensatory developmental interactions among beak components can generate rapid reorganization of beak morphology under novel conditions and thus greatly facilitate both the evolution of precise adaptation and extensive diversification, thereby linking adaptation and adaptability in this classic example of Darwinian evolution.
Hurtado Rúa, Sandra M; Mazumdar, Madhu; Strawderman, Robert L
2015-12-30
Bayesian meta-analysis is an increasingly important component of clinical research, with multivariate meta-analysis a promising tool for studies with multiple endpoints. Model assumptions, including the choice of priors, are crucial aspects of multivariate Bayesian meta-analysis (MBMA) models. In a given model, two different prior distributions can lead to different inferences about a particular parameter. A simulation study was performed in which the impact of families of prior distributions for the covariance matrix of a multivariate normal random effects MBMA model was analyzed. Inferences about effect sizes were not particularly sensitive to prior choice, but the related covariance estimates were. A few families of prior distributions with small relative biases, tight mean squared errors, and close to nominal coverage for the effect size estimates were identified. Our results demonstrate the need for sensitivity analysis and suggest some guidelines for choosing prior distributions in this class of problems. The MBMA models proposed here are illustrated in a small meta-analysis example from the periodontal field and a medium meta-analysis from the study of stroke. Copyright © 2015 John Wiley & Sons, Ltd.
Al Saleh, Salwa
2016-10-01
This paper completes a previous published work that calculated analytically the relativistic wavefunctions for bound electron in a Compton diffusion process. This work calculates the relativistic propagator and the Wronskian of the two associated Feynman diagrams of Compton diffusion (emission first and absorption first). Then find an explicit expression for the covariant matrix elements separated into two parts: spin-angular part and radial part. Using these explicit expressions, the effective cross-section for Compton diffusion in the most general form is obtained in terms of basic dynamical and static quantities, like electron's and photon's 4-momenta and atomic number. The form of the cross-section is put ready for numerical calculations.
Adaptive system noise covariance for performance enhancement of Kalman filter-based algorithms
Lee, Vika; Chan, Keith C. C.; Leung, Henry
1996-06-01
Several designs of Kalman filters and the interacting multiple models algorithm were used in real tracking tasks involving high dynamic targets. The data were obtained through the joint effort of the defense departments of Canada and the US. Their performance, measured in terms of positional deviation and the number of track losses, are rather unsatisfactory even though they perform particularly well when using simulated data. To identify the reasons behind, we compared and analyzed the differences between the model assumptions behind the design of these Kalman filters and the model required for accurate tracking of these targets. In this paper, we discussed our findings. Moreover, based on the characteristics of real tracking data, we present an alternative methodology for measuring the effectiveness of various Kalman filter based trackers in stressful environmental. It can also be used to explain the well known characteristics of Kalman filter. A lower bound for the deviation, obtained from this equation, shows that deviation could be too large to manage if noise bandwidth is as high as the real data instead of a pre-assumed magnitude. Instead of having to redesign many existing Kalman filters to suit for stressful environment, we developed a design-independent module that can be added to different types of Kalman filters based trackers to enhance their performance in the tracking high dynamic targets. The module is called adaptive systems noise covariance estimation. It is not only safe (i.e. almost no negative effect) but it can sometimes even double the performance of trackers in stressful environment.
Simulations of Baryon Acoustic Oscillations II: Covariance matrix of the matter power spectrum
Takahashi, Ryuichi; Takada, Masahiro; Matsubara, Takahiko; Sugiyama, Naoshi; Kayo, Issha; Nishizawa, Atsushi J; Nishimichi, Takahiro; Saito, Shun; Taruya, Atsushi
2009-01-01
We use 5000 cosmological N-body simulations of 1(Gpc/h)^3 box for the concordance LCDM model in order to study the sampling variances of nonlinear matter power spectrum. We show that the non-Gaussian errors can be important even on large length scales relevant for baryon acoustic oscillations (BAO). Our findings are (1) the non-Gaussian errors degrade the cumulative signal-to-noise ratios (S/N) for the power spectrum amplitude by up to a factor of 2 and 4 for redshifts z=1 and 0, respectively. (2) There is little information on the power spectrum amplitudes in the quasi-nonlinear regime, confirming the previous results. (3) The distribution of power spectrum estimators at BAO scales, among the realizations, is well approximated by a Gaussian distribution with variance that is given by the diagonal covariance component. (4) For the redshift-space power spectrum, the degradation in S/N by non-Gaussian errors is mitigated due to nonlinear redshift distortions. (5) For an actual galaxy survey, the additional shot...
Evolution Strategies with Optimal Covariance Matrix Update Applied to Sustainable Wave Energy
DEFF Research Database (Denmark)
Rodríguez Arbonès, Dídac
modification slightly affects cumulative step-size adaptation, a mechanism often used for adapting the global step-size parameter in the CMA-ES. While this has a negligible effect on the performance, the time benefit from the Cholesky update and the memory savings from storing a Cholesky factor instead......Modern society depends heavily on fossil fuels. We rely on this source of energy for everything, from food and clothing production to daily transportation. Even the Internet is mostly powered by these sources of energy. This reliance has led us to a high-risk situation where all that we take...... for granted is in jeopardy. Some of the causes of modern armed conflicts can be traced back to the scarcity and struggle over these types of energy sources. Extracting energy from renewable sources can decrease our dependency on fossil fuels. There exist many types of renewable energy sources, and wave energy...
Dimauro, C; Cellesi, M; Pintus, M A; Macciotta, N P P
2011-12-01
In genomic selection (GS) programmes, direct genomic values (DGV) are evaluated using information provided by high-density SNP chip. Being DGV accuracy strictly dependent on SNP density, it is likely that an increase in the number of markers per chip will result in severe computational consequences. Aim of present work was to test the effectiveness of principal component analysis (PCA) carried out by chromosome in reducing the marker dimensionality for GS purposes. A simulated data set of 5700 individuals with an equal number of SNP distributed over six chromosomes was used. PCs were extracted both genome-wide (ALL) and separately by chromosome (CHR) and used to predict DGVs. In the ALL scenario, the SNP variance-covariance matrix (S) was singular, positive semi-definite and contained null information which introduces 'spuriousness' in the derived results. On the contrary, the S matrix for each chromosome (CHR scenario) had a full rank. Obtained DGV accuracies were always better for CHR than ALL. Moreover, in the latter scenario, DGV accuracies became soon unsettled as the number of animals decreases, whereas in CHR, they remain stable till 900-1000 individuals. In real applications where a 54k SNP chip is used, the largest number of markers per chromosome is approximately 2500. Thus, a number of around 3000 genotyped animals could lead to reliable results when the original SNP variables are replaced by a reduced number of PCs. © 2011 Blackwell Verlag GmbH.
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
Guo, Rongyan; Wang, Hongyan
2016-07-01
In this work, the issue of robust waveform optimization is addressed in the presence of clutter to improve the worst-case estimation accuracy for collocated multiple-input multiple-output (MIMO) radar. Robust design is necessary due to the fact that waveform design may be sensitive to uncertainties in the initial parameter estimates. Following the min-max approach, the robust waveform covariance matrix design is formulated here on the basis of Cramér-Rao Bound to ease this sensitivity systematically for improving the worst-case accuracy. To tackle the resultant complicated and nonlinear problem, a new diagonal loading (DL)-based iterative approach is developed, in which the inner optimization problem can first be decomposed to some independent subproblems by using the Hadamard's inequality, and then these subproblems can be reformulated into convex issues by using DL method, as well as the outer optimization problem can also be relaxed to a convex issue by translating the nonlinear function into a linear one, and, hence, both of them can be solved very effectively. An optimal solution to the original problem can be obtained via the least-squares fitting of the solution acquired by the iterative approach. Numerical simulations show the efficiency of the proposed method.
Pitchers, W R; Brooks, R; Jennions, M D; Tregenza, T; Dworkin, I; Hunt, J
2013-05-01
Phenotypic integration and plasticity are central to our understanding of how complex phenotypic traits evolve. Evolutionary change in complex quantitative traits can be predicted using the multivariate breeders' equation, but such predictions are only accurate if the matrices involved are stable over evolutionary time. Recent study, however, suggests that these matrices are temporally plastic, spatially variable and themselves evolvable. The data available on phenotypic variance-covariance matrix (P) stability are sparse, and largely focused on morphological traits. Here, we compared P for the structure of the complex sexual advertisement call of six divergent allopatric populations of the Australian black field cricket, Teleogryllus commodus. We measured a subset of calls from wild-caught crickets from each of the populations and then a second subset after rearing crickets under common-garden conditions for three generations. In a second experiment, crickets from each population were reared in the laboratory on high- and low-nutrient diets and their calls recorded. In both experiments, we estimated P for call traits and used multiple methods to compare them statistically (Flury hierarchy, geometric subspace comparisons and random skewers). Despite considerable variation in means and variances of individual call traits, the structure of P was largely conserved among populations, across generations and between our rearing diets. Our finding that P remains largely stable, among populations and between environmental conditions, suggests that selection has preserved the structure of call traits in order that they can function as an integrated unit.
Performance evaluation of sensor allocation algorithm based on covariance control
Institute of Scientific and Technical Information of China (English)
无
2005-01-01
The covariance control capability of sensor allocation algorithms based on covariance control strategy is an important index to evaluate the performance of these algorithms. Owing to lack of standard performance metric indices to evaluate covariance control capability, sensor allocation ratio, etc, there are no guides to follow in the design procedure of sensor allocation algorithm in practical applications. To meet these demands, three quantified performance metric indices are presented, which are average covariance misadjustment quantity (ACMQ), average sensor allocation ratio (ASAR) and matrix metric influence factor (MMIF), where ACMQ, ASAR and MMIF quantify the covariance control capability, the usage of sensor resources and the robustness of sensor allocation algorithm, respectively. Meanwhile, a covariance adaptive sensor allocation algorithm based on a new objective function is proposed to improve the covariance control capability of the algorithm based on information gain. The experiment results show that the proposed algorithm have the advantage over the preceding sensor allocation algorithm in covariance control capability and robustness.
Adaptive Covariance Estimation Method for LiDAR-Aided Multi-Sensor Integrated Navigation Systems
Directory of Open Access Journals (Sweden)
Shifei Liu
2015-01-01
Full Text Available The accurate estimation of measurements covariance is a fundamental problem in sensors fusion algorithms and is crucial for the proper operation of filtering algorithms. This paper provides an innovative solution for this problem and realizes the proposed solution on a 2D indoor navigation system for unmanned ground vehicles (UGVs that fuses measurements from a MEMS-grade gyroscope, speed measurements and a light detection and ranging (LiDAR sensor. A computationally efficient weighted line extraction method is introduced, where the LiDAR intensity measurements are used, such that the random range errors and systematic errors due to surface reflectivity in LiDAR measurements are considered. The vehicle pose change is obtained from LiDAR line feature matching, and the corresponding pose change covariance is also estimated by a weighted least squares-based technique. The estimated LiDAR-based pose changes are applied as periodic updates to the Inertial Navigation System (INS in an innovative extended Kalman filter (EKF design. Besides, the influences of the environment geometry layout and line estimation error are discussed. Real experiments in indoor environment are performed to evaluate the proposed algorithm. The results showed the great consistency between the LiDAR-estimated pose change covariance and the true accuracy. Therefore, this leads to a significant improvement in the vehicle’s integrated navigation accuracy.
1998-01-01
In the following short paper we list some useful results concerning determinants and inverses of matrices. First we show, how to calculate determinants of $d \\times d$ matrices, if their traces are known. As a next step $4 \\times 4$ matrices are expressed in terms of Dirac covariants. The third step is the calculation of the corresponding inverse matrices in terms of Dirac covariants.
Institute of Scientific and Technical Information of China (English)
林旭; 罗志才
2015-01-01
采用Kalman滤波方法进行钟差参数计算和预报时，需确定Kalman滤波噪声协方差矩阵。针对这一问题，提出了一种新的卫星钟差Kalman滤波噪声协方差估计方法，通过建立新息的相关函数序列与未知的噪声参数间的线性函数模型，采用最小二乘法进行噪声参数估计。采用精密钟差数据进行钟差参数估计和预报分析，结果表明，该方法具有较好的收敛性，并与顾及随机噪声模型的开窗分类因子自适应抗差估计方法进行对比分析，验证了新方法的正确性和有效性。%The satellite clock plays a key role in the global navigation satellite system (GNSS). The accuracy of GNSS and its applications depend on the quality of the satellite clock. Therefore, precisely estimating and predicting the satellite clock is an important issue in the fields of GNSS and its application. As an optimal estimation algorithm, Kalman filter has been used to estimate and predict the satellite clock. However, in a conventional Kalman filter algorithm, the noise covariance matrices of satellite clock need to be predetermined, which restricts its further applications since the noise covariance matrices, especially the process noise covariance matrix, are usually unknown in the real cases. With inappropriate noise covariance matrices, the state estimation of conventional Kalman filter is suboptimal. To cope with this problem, a new noise covariance matrix estimation method of Kalman filter is proposed, and then we apply it to the problem of satellite clock estimation and prediction. Considering the fact that the process noise covariance matrix depends on the unknown noise parameters, the problem of estimating process noise covariance matrix can be solved by estimating the unknown noise parameters. First, the correlation between the Kalman innovations is used to establish a linear relationship with the unknown noise parameters. Then the unknown parameters can be
Adaptive evolution of the matrix extracellular phosphoglycoprotein in mammals.
Machado, João Paulo; Johnson, Warren E; O'Brien, Stephen J; Vasconcelos, Vítor; Antunes, Agostinho
2011-11-21
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. 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. MEPE shows a high number of selection signatures, revealing the crucial role of positive selection in the evolution of this SIBLING member. The selection
Blot, L.; Corasaniti, P. S.; Alimi, J.-M.; Reverdy, V.; Rasera, Y.
2015-01-01
The upcoming generation of galaxy surveys will probe the distribution of matter in the Universe with unprecedented accuracy. Measurements of the matter power spectrum at different scales and red shifts will provide stringent constraints on the cosmological parameters. However, on non-linear scales this will require an accurate evaluation of the covariance matrix. Here, we compute the covariance matrix of the three-dimensional matter density power spectrum for the concordance ΛCDM cosmology from an ensemble of N-body simulations of the Dark Energy Universe Simulation - Parallel Universe Runs (DEUS-PUR). This consists of 12 288 realizations of a (656 h-1 Mpc)3 simulation box with 2563 particles. We combine this set with an auxiliary sample of 96 simulations of the same volume with 10243 particles. We find the N-body mass resolution effect to be an important source of systematic errors on the covariance at high redshift and small intermediate scales. We correct for this effect by introducing an empirical statistical method which provide an accurate determination of the covariance matrix over a wide range of scales including the baryon oscillations interval. Contrary to previous studies that used smaller N-body ensembles, we find the power spectrum distribution to significantly deviate from expectations of a Gaussian random density field at k ≳ 0.25 h Mpc-1 and z < 0.5. This suggests that for the finite-volume surveys, an unbiased estimate of the ensemble-averaged band power at these scales and red shifts may require a more careful assessment of non-Gaussian errors than previously considered.
COVARIATE-ADAPTIVE CLUSTERING OF EXPOSURES FOR AIR POLLUTION EPIDEMIOLOGY COHORTS.
Keller, Joshua P; Drton, Mathias; Larson, Timothy; Kaufman, Joel D; Sandler, Dale P; Szpiro, Adam A
2017-03-01
Cohort studies in air pollution epidemiology aim to establish associations between health outcomes and air pollution exposures. Statistical analysis of such associations is complicated by the multivariate nature of the pollutant exposure data as well as the spatial misalignment that arises from the fact that exposure data are collected at regulatory monitoring network locations distinct from cohort locations. We present a novel clustering approach for addressing this challenge. Specifically, we present a method that uses geographic covariate information to cluster multi-pollutant observations and predict cluster membership at cohort locations. Our predictive k-means procedure identifies centers using a mixture model and is followed by multi-class spatial prediction. In simulations, we demonstrate that predictive k-means can reduce misclassification error by over 50% compared to ordinary k-means, with minimal loss in cluster representativeness. The improved prediction accuracy results in large gains of 30% or more in power for detecting effect modification by cluster in a simulated health analysis. In an analysis of the NIEHS Sister Study cohort using predictive k-means, we find that the association between systolic blood pressure (SBP) and long-term fine particulate matter (PM2.5) exposure varies significantly between different clusters of PM2.5 component profiles. Our cluster-based analysis shows that for subjects assigned to a cluster located in the Midwestern U.S., a 10 μg/m(3) difference in exposure is associated with 4.37 mmHg (95% CI, 2.38, 6.35) higher SBP.
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
Condition Number Regularized Covariance Estimation.
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.
Condition Number Regularized Covariance Estimation*
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
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.
Mäkelä, Jarmo; Susiluoto, Jouni; Markkanen, Tiina; Aurela, Mika; Järvinen, Heikki; Mammarella, Ivan; Hagemann, Stefan; Aalto, Tuula
2016-12-01
We examined parameter optimisation in the JSBACH (Kaminski et al., 2013; Knorr and Kattge, 2005; Reick et al., 2013) ecosystem model, applied to two boreal forest sites (Hyytiälä and Sodankylä) in Finland. We identified and tested key parameters in soil hydrology and forest water and carbon-exchange-related formulations, and optimised them using the adaptive Metropolis (AM) algorithm for Hyytiälä with a 5-year calibration period (2000-2004) followed by a 4-year validation period (2005-2008). Sodankylä acted as an independent validation site, where optimisations were not made. The tuning provided estimates for full distribution of possible parameters, along with information about correlation, sensitivity and identifiability. Some parameters were correlated with each other due to a phenomenological connection between carbon uptake and water stress or other connections due to the set-up of the model formulations. The latter holds especially for vegetation phenology parameters. The least identifiable parameters include phenology parameters, parameters connecting relative humidity and soil dryness, and the field capacity of the skin reservoir. These soil parameters were masked by the large contribution from vegetation transpiration. In addition to leaf area index and the maximum carboxylation rate, the most effective parameters adjusting the gross primary production (GPP) and evapotranspiration (ET) fluxes in seasonal tuning were related to soil wilting point, drainage and moisture stress imposed on vegetation. For daily and half-hourly tunings the most important parameters were the ratio of leaf internal CO2 concentration to external CO2 and the parameter connecting relative humidity and soil dryness. Effectively the seasonal tuning transferred water from soil moisture into ET, and daily and half-hourly tunings reversed this process. The seasonal tuning improved the month-to-month development of GPP and ET, and produced the most stable estimates of water use
Blot, Linda; Alimi, Jean-Michel; Reverdy, Vincent; Rasera, Yann
2014-01-01
The upcoming generation of galaxy surveys will probe the distribution of matter in the universe with unprecedented accuracy. Measurements of the matter power spectrum at different scales and redshifts will provide stringent constraints on the cosmological parameters. However, on non-linear scales this will require an accurate evaluation of the covariance matrix. Here, we compute the covariance matrix of the matter power spectrum for the concordance $\\Lambda$CDM cosmology from an ensemble of N-body simulations of the Dark Energy Universe Simulation - Parallel Universe Runs (DEUS-PUR). This consists of 12288 realizations of a $(656\\,h^{-1}\\,\\textrm{Mpc})^3$ simulation box with $256^3$ particles. We combine this set with an auxiliary sample of 96 simulations of the same volume with $1024^3$ particles to assess the impact of non-Gaussian uncertainties due to mass resolution effects. We find this to be an important source of systematic errors at high redshift and small intermediate scales. We introduce an empirica...
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.
Directory of Open Access Journals (Sweden)
Ludwig Kohaupt
2015-12-01
Full Text Available For a linear stochastic vibration model in state-space form, $ \\dot{x}(t = A x(t+b(t, \\, x(0=x_0, $ with system matrix A and white noise excitation $ b(t $, under certain conditions, the solution $ x(t $ is a random vector that can be completely described by its mean vector, $ m_x(t:=m_{x(t} $, and its covariance matrix, $ P_x(t:=P_{x(t} $. If matrix $ A $ is asymptotically stable, then $ m_x(t \\rightarrow 0 \\, (t \\rightarrow \\infty $ and $ P_x(t \\rightarrow P \\, (t \\rightarrow \\infty $, where $ P $ is a positive (semi-definite matrix. As the main new points, in this paper, we derive two-sided bounds on $ \\Vert m_x(t\\Vert _2 $ and $ \\Vert P_x(t- P\\Vert _2 $ as well as formulas for the right norm derivatives $ D_+^k \\Vert P_x(t- P\\Vert _2, \\, k=0,1,2 $, and apply these results to the computation of the best constants in the two-sided bounds. The obtained results are of special interest to applied mathematicians and engineers.
Energy Technology Data Exchange (ETDEWEB)
Gullberg, Grant T.; Huesman, Ronald H.; Reutter, Bryan W.; Qi,Jinyi; Ghosh Roy, Dilip N.
2004-01-01
In dynamic cardiac SPECT estimates of kinetic parameters ofa one-compartment perfusion model are usually obtained in a two stepprocess: 1) first a MAP iterative algorithm, which properly models thePoisson statistics and the physics of the data acquisition, reconstructsa sequence of dynamic reconstructions, 2) then kinetic parameters areestimated from time activity curves generated from the dynamicreconstructions. This paper provides a method for calculating thecovariance matrix of the kinetic parameters, which are determined usingweighted least squares fitting that incorporates the estimated varianceand covariance of the dynamic reconstructions. For each transaxial slicesets of sequential tomographic projections are reconstructed into asequence of transaxial reconstructions usingfor each reconstruction inthe time sequence an iterative MAP reconstruction to calculate themaximum a priori reconstructed estimate. Time-activity curves for a sumof activity in a blood region inside the left ventricle and a sum in acardiac tissue region are generated. Also, curves for the variance of thetwo estimates of the sum and for the covariance between the two ROIestimates are generated as a function of time at convergence using anexpression obtained from the fixed-point solution of the statisticalerror of the reconstruction. A one-compartment model is fit to the tissueactivity curves assuming a noisy blood input function to give weightedleast squares estimates of blood volume fraction, wash-in and wash-outrate constants specifying the kinetics of 99mTc-teboroxime for theleftventricular myocardium. Numerical methods are used to calculate thesecond derivative of the chi-square criterion to obtain estimates of thecovariance matrix for the weighted least square parameter estimates. Eventhough the method requires one matrix inverse for each time interval oftomographic acquisition, efficient estimates of the tissue kineticparameters in a dynamic cardiac SPECT study can be obtained with
Robust DTC-SVM Method for Matrix Converter Drives with Model Reference Adaptive Control Scheme
DEFF Research Database (Denmark)
Lee, Kyo Beum; Huh, Sunghoi; Sim, Kyung-Hun
2007-01-01
strategy using space vector modulations and a deadbeat algorithm in the stator flux reference frame. The lumped disturbances such as parameter variation and load disturbance of the system are estimated by a neuro-sliding mode approach based on model reference adaptive control (MRAC). An adaptive observer......This paper presents a new robust DTC-SVM control system for high performance induction motor drives fed by a matrix converter with variable structure - model reference adaptive control scheme (VS-MRAC). It is possible to combine the advantages of matrix converters with the advantages of the DTC...
Adaptive control of linear multivariable systems with high frequency gain matrix hurwitz
Institute of Scientific and Technical Information of China (English)
Ying ZHOU; Yuqiang WU; Shumin FEI
2005-01-01
A new adaptive control scheme is proposed for multivariable model reference adaptive control(MRAC) systems based on the nonlinear backstepping approach with vector form.The assumption on a priori knowledge of the high frequency gain matrix in existing results is relaxed and the new required condition for the high frequency gain matrix can be easily checked for certain plants so that the proposed method is widely applicable.This control scheme guarantees the global stability of the closed-loop systems and the tracking error can be arbitrary small.The simulation result for an application example shows the validity of the proposed nonlinear adaptive scheme.
Multiple Kernel Learning for adaptive graph regularized nonnegative matrix factorization
Wang, Jim Jing-Yan
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.
Cholesky Factorization for Covariance Matrix Recovery%Cholesky分解在协方差矩阵恢复中的使用
Institute of Scientific and Technical Information of China (English)
杜航原; 郝燕玲; 赵玉新; 陈立娟
2012-01-01
For simultaneous localization and mapping based on sparse extended information filter, we compare the principles of nearest neighbor data association, maximum likelihood data association and joint compatibility test data association, and discuss the requirements of marginal covariance matrix recovering in data association. A computationally efficient approach based on Cholesky factorization is proposed to exactly recover the marginal covariance from information matrix. In the simulation, we compare the proposed algorithm with covariance bound approximation, and analyze three common data association approaches using the proposed algorithm in SLAM based on a sparse extended information filter. The results show that the proposed recovery algorithm is suitable for various data association approaches, leading to high localization accuracy and reduced computational complexity. Performance of different data association approaches in SEIF-SLAM are discussed.%针对基于稀疏扩展信息滤波的同步定位与地图创建(simultaneous localization and mapping,SLAM)问题,分析并比较了最近邻数据关联、极大似然数据关联以及联合相容性检验数据关联的原理,阐述了边缘协方差矩阵恢复的必要性.在此基础上提出一种利用Cholesky分解由信息矩阵准确恢复协方差任意元素的方法,该方法具有较高的计算效率.在仿真实验中将该方法与协方差边界估计法比较,并分别用于3种数据关联算法的比较分析,表明所提出的方法适用于多种数据关联方法,能在保证定位精度的同时有效控制算法复杂度.最后对各种数据关联算法在稀疏扩展信息滤波SLAM中的性能进行了讨论.
Allner, S.; Koehler, T.; Fehringer, A.; Birnbacher, L.; Willner, M.; Pfeiffer, F.; Noël, P. B.
2016-05-01
The purpose of this work is to develop an image-based de-noising algorithm that exploits complementary information and noise statistics from multi-modal images, as they emerge in x-ray tomography techniques, for instance grating-based phase-contrast CT and spectral CT. Among the noise reduction methods, image-based de-noising is one popular approach and the so-called bilateral filter is a well known algorithm for edge-preserving filtering. We developed a generalization of the bilateral filter for the case where the imaging system provides two or more perfectly aligned images. The proposed generalization is statistically motivated and takes the full second order noise statistics of these images into account. In particular, it includes a noise correlation between the images and spatial noise correlation within the same image. The novel generalized three-dimensional bilateral filter is applied to the attenuation and phase images created with filtered backprojection reconstructions from grating-based phase-contrast tomography. In comparison to established bilateral filters, we obtain improved noise reduction and at the same time a better preservation of edges in the images on the examples of a simulated soft-tissue phantom, a human cerebellum and a human artery sample. The applied full noise covariance is determined via cross-correlation of the image noise. The filter results yield an improved feature recovery based on enhanced noise suppression and edge preservation as shown here on the example of attenuation and phase images captured with grating-based phase-contrast computed tomography. This is supported by quantitative image analysis. Without being bound to phase-contrast imaging, this generalized filter is applicable to any kind of noise-afflicted image data with or without noise correlation. Therefore, it can be utilized in various imaging applications and fields.
Allner, S; Koehler, T; Fehringer, A; Birnbacher, L; Willner, M; Pfeiffer, F; Noël, P B
2016-05-21
The purpose of this work is to develop an image-based de-noising algorithm that exploits complementary information and noise statistics from multi-modal images, as they emerge in x-ray tomography techniques, for instance grating-based phase-contrast CT and spectral CT. Among the noise reduction methods, image-based de-noising is one popular approach and the so-called bilateral filter is a well known algorithm for edge-preserving filtering. We developed a generalization of the bilateral filter for the case where the imaging system provides two or more perfectly aligned images. The proposed generalization is statistically motivated and takes the full second order noise statistics of these images into account. In particular, it includes a noise correlation between the images and spatial noise correlation within the same image. The novel generalized three-dimensional bilateral filter is applied to the attenuation and phase images created with filtered backprojection reconstructions from grating-based phase-contrast tomography. In comparison to established bilateral filters, we obtain improved noise reduction and at the same time a better preservation of edges in the images on the examples of a simulated soft-tissue phantom, a human cerebellum and a human artery sample. The applied full noise covariance is determined via cross-correlation of the image noise. The filter results yield an improved feature recovery based on enhanced noise suppression and edge preservation as shown here on the example of attenuation and phase images captured with grating-based phase-contrast computed tomography. This is supported by quantitative image analysis. Without being bound to phase-contrast imaging, this generalized filter is applicable to any kind of noise-afflicted image data with or without noise correlation. Therefore, it can be utilized in various imaging applications and fields.
Improved Mainlobe Interference Suppression Based on Blocking Matrix Preprocess
National Research Council Canada - National Science Library
Yang, Jie; Liu, Congfeng
2015-01-01
... on the combination of diagonal loading and linear constraints. Therein, the reason for mainlobe direction shifting is analyzed and found to be that the covariance matrix mismatch exists in the realization of the adaptive beamforming...
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.
Kunieda, Satoshi
2017-09-01
We report the status of the R-matrix code AMUR toward consistent cross-section evaluation and covariance analysis for the light-mass nuclei. The applicable limit of the code is extended by including computational capability for the charged-particle elastic scattering cross-sections and the neutron capture cross-sections as example results are shown in the main texts. A simultaneous analysis is performed on the 17O compound system including the 16O(n,tot) and 13C(α,n)16O reactions together with the 16O(n,n) and 13C(α,α) scattering cross-sections. It is found that a large theoretical background is required for each reaction process to obtain a simultaneous fit with all the experimental cross-sections we analyzed. Also, the hard-sphere radii should be assumed to be different from the channel radii. Although these are technical approaches, we could learn roles and sources of the theoretical background in the standard R-matrix.
Extracellular matrix adaptation of tendon and skeletal muscle to exercise
DEFF Research Database (Denmark)
Kjaer, Michael; Magnusson, Peter; Krogsgaard, Michael
2006-01-01
The extracellular matrix (ECM) of connective tissues enables linking to other tissues, and plays a key role in force transmission and tissue structure maintenance in tendons, ligaments, bone and muscle. ECM turnover is influenced by physical activity, and both collagen synthesis and metalloprotease......-beta and IL-6 is enhanced following exercise. For tendons, metabolic activity (e.g. detected by positron emission tomography scanning), circulatory responses (e.g. as measured by near-infrared spectroscopy and dye dilution) and collagen turnover are markedly increased after exercise. Tendon blood flow...... is supported by findings of gender-related differences in the activation of collagen synthesis with exercise. These findings may provide the basis for understanding tissue overloading and injury in both tendons and skeletal muscle....
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.
Covariance Applications with Kiwi
Mattoon, C. M.; Brown, D.; Elliott, J. B.
2012-05-01
The Computational Nuclear Physics group at Lawrence Livermore National Laboratory (LLNL) is developing a new tool, named `Kiwi', that is intended as an interface between the covariance data increasingly available in major nuclear reaction libraries (including ENDF and ENDL) and large-scale Uncertainty Quantification (UQ) studies. Kiwi is designed to integrate smoothly into large UQ studies, using the covariance matrix to generate multiple variations of nuclear data. The code has been tested using critical assemblies as a test case, and is being integrated into LLNL's quality assurance and benchmarking for nuclear data.
Covariance Applications with Kiwi
Directory of Open Access Journals (Sweden)
Elliott J.B.
2012-05-01
Full Text Available The Computational Nuclear Physics group at Lawrence Livermore National Laboratory (LLNL is developing a new tool, named ‘Kiwi’, that is intended as an interface between the covariance data increasingly available in major nuclear reaction libraries (including ENDF and ENDL and large-scale Uncertainty Quantification (UQ studies. Kiwi is designed to integrate smoothly into large UQ studies, using the covariance matrix to generate multiple variations of nuclear data. The code has been tested using critical assemblies as a test case, and is being integrated into LLNL's quality assurance and benchmarking for nuclear data.
Directory of Open Access Journals (Sweden)
Guo Yong
2014-04-01
Full Text Available This paper investigates two finite-time controllers for attitude control of spacecraft based on rotation matrix by an adaptive backstepping method. Rotation matrix can overcome the drawbacks of unwinding which makes a spacecraft perform a large-angle maneuver when a small-angle maneuver in the opposite rotational direction is sufficient to achieve the objective. With the use of adaptive control, the first robust finite-time controller is continuous without a chattering phenomenon. The second robust finite-time controller can compensate external disturbances with unknown bounds. Theoretical analysis shows that both controllers can make a spacecraft following a time-varying reference attitude signal in finite time and guarantee the stability of the overall closed-loop system. Numerical simulations are presented to demonstrate the effectiveness of the proposed control schemes.
Adaptive quasi-likelihood estimate in generalized linear models
Institute of Scientific and Technical Information of China (English)
CHEN Xia; CHEN Xiru
2005-01-01
This paper gives a thorough theoretical treatment on the adaptive quasilikelihood estimate of the parameters in the generalized linear models. The unknown covariance matrix of the response variable is estimated by the sample. It is shown that the adaptive estimator defined in this paper is asymptotically most efficient in the sense that it is asymptotic normal, and the covariance matrix of the limit distribution coincides with the one for the quasi-likelihood estimator for the case that the covariance matrix of the response variable is completely known.
General covariance in computational electrodynamics
DEFF Research Database (Denmark)
Shyroki, Dzmitry; Lægsgaard, Jesper; Bang, Ole;
2007-01-01
We advocate the generally covariant formulation of Maxwell equations as underpinning some recent advances in computational electrodynamics—in the dimensionality reduction for separable structures; in mesh truncation for finite-difference computations; and in adaptive coordinate mapping as opposed...
A spin-adapted Density Matrix Renormalization Group algorithm for quantum chemistry
Sharma, Sandeep
2014-01-01
We extend the spin-adapted density matrix renormalization group (DMRG) algorithm of McCulloch and Gulacsi [Europhys. Lett.57, 852 (2002)] to quantum chemical Hamiltonians. This involves two key modifications to the non-spin-adapted DMRG algorithm: the use of a quasi-density matrix to ensure that the renormalised DMRG states are eigenvalues of $S^2$ , and the use of the Wigner-Eckart theorem to greatly reduce the overall storage and computational cost. We argue that the advantages of the spin-adapted DMRG algorithm are greatest for low spin states. Consequently, we also implement the singlet-embedding strategy of Nishino et al [Phys. Rev. E61, 3199 (2000)] which allows us to target high spin states as a component of a mixed system which is overall held in a singlet state. We evaluate our algorithm on benchmark calculations on the Fe$_2$S$_2$ and Cr$_2$ transition metal systems. By calculating the full spin ladder of Fe$_2$S$_2$ , we show that the spin-adapted DMRG algorithm can target very closely spaced spin ...
The EPS Matrix as an Adaptive Bastion for Biofilms: Introduction to Special Issue
Directory of Open Access Journals (Sweden)
Alan W. Decho
2013-11-01
Full Text Available The process of biofilm formation has knowingly, and even unsuspectingly, baffled scientists for almost as long as the field of microbiology itself has existed. This Special Issue of the International Journal of Molecular Sciences (IJMS specifically addresses an important component of the biofilm, the extracellular matrix. This matrix forms the protective secretions that surround biofilm cells and afford a “built environment” to contain biofilm processes. During the earlier days of microbiology, it was intriguing to Claude ZoBell that attached bacteria sometimes were able to proliferate when their planktonic counterparts were unable to grow [1]. During the 1970s, this attached state was beginning to be explored [2], and it was realized to be anchored in a matrix of slime-like molecules. The slime-like matrix together with cells was to be called the “biofilm”, a term developed by the late Bill Costerton, Bill Characklis and colleagues. The scientific revelation that attached bacteria were different from free (i.e., planktonic cells in their physiological behavior and adaptability, launched an era of focused exploration in this area of microbiology. It was initially surprising, though not unexpected in retrospect, that interest in biofilms has grown and now infiltrates virtually all aspects of our scientific study. Since that time there has been a near-exponential growth in the numbers of scientific publications addressing biofilms owing to their immediate relevance to ecology, biotechnology, health and industry.
High resolution MUSIC algorithm reconstructing covariance matrix in low SNR%低信噪比下重构协方差矩阵的高分辨MUSIC算法
Institute of Scientific and Technical Information of China (English)
杜梓冰; 杨坤德
2013-01-01
在满足对称分布的海洋噪声场中，为提高低信噪比条件下目标方位估计性能，提出一种重构信号协方差矩阵的MUSIC算法。利用数据协方差矩阵虚部与对称噪声无关的性质，根据协方差矩阵虚部和虚部MUSIC算法的预估角重构出信号协方差矩阵，在此基础上实现MUSIC算法。仿真结果表明，所提算法相比常规MUSIC算法能有效降低对称噪声的影响，提高方位估计性能，并避免双边谱的出现，有更高的分辨率和更低的分辨门限。还研究了协方差矩阵的Toeplitz修正处理对于MUSIC类算法的改善作用。仿真表明，Toeplitz修正处理能显著提高MUSIC类算法的分辨性能。%In the symmetrical distribution ocean noise field, in order to improve the direction-of-arrival (DOA) estima-tion performance in low signal-to-noise (SNR) conditions, a novel MUSIC algorithm that reconstructs the signal cova-riance matrix is presented. Based on the properties that the image part of data covariance matrix has no relation with the symmetric noise, the signal covariance matrix is reconstructed by using the image part of data covariance matrix and the DOA which is estimated by Image-MUSIC algorithm. Thus, the MUSIC algorithm is implemented. Simulation results show that the new algorithm effectively reduces the influence of symmetrical noise on DOA estimation and avoids bi-lateral spectrum arising, moreover the algorithm achieves higher resolution and lower resolution threshold in low SNR cases. Covariance matrix’s Toeplitz modification that aims to improve the DOA estimation performance of MUSIC algorithm has also been studied, and simulation results show that the resolution of MUSIC algorithm is improved sig-nificantly.
Adaptive rheology and ordering of cell cytoskeleton govern matrix rigidity sensing
Gupta, Mukund; Sarangi, Bibhu Ranjan; Deschamps, Joran; Nematbakhsh, Yasaman; Callan-Jones, Andrew; Margadant, Felix; Mège, René-Marc; Lim, Chwee Teck; Voituriez, Raphaël; Ladoux, Benoît
2015-01-01
Matrix rigidity sensing regulates a large variety of cellular processes and has important implications for tissue development and disease. However, how cells probe matrix rigidity, and hence respond to it, remains unclear. Here, we show that rigidity sensing and adaptation emerge naturally from actin cytoskeleton remodeling. Our in vitro experiments and theoretical modeling demonstrate a bi-phasic rheology of the actin cytoskeleton, which transitions from fluid on soft substrates to solid on stiffer ones. Furthermore, we find that increasing substrate stiffness correlates with the emergence of an orientational order in actin stress fibers, which exhibit an isotropic to nematic transition that we characterize quantitatively in the framework of active matter theory. These findings imply mechanisms mediated by a large-scale reinforcement of actin structures under stress, which could be the mechanical drivers of substrate stiffness dependent cell shape changes and cell polarity. PMID:26109233
Colorimetric Analysis Using Scene-Adaptive Color Conversion Matrix of Calibrated CIS
Directory of Open Access Journals (Sweden)
Sung-Hak Lee
2016-01-01
Full Text Available The RGB signals of different CISs (color image sensors do not register the same values for the same viewing scene owing to their different spectral sensitivity and white balance mechanisms. Thus, CISs must be characterized based on CIE standard observation for colorimetric purposes. One general method for characterizing CISs is least square polynomial modeling to derive the colorimetric transfer matrix between RGB outputs and CIE XYZ tristimulus inputs. However, the transfer matrix that is obtained under the standard CIE illumination is unable to estimate various conditions of a CIS that is operated under various illuminations with varying chromaticity and luminance. Therefore, repeated experiments are necessary to obtain accurate colorimetric analysis results. This paper presents a scene-adaptive colorimetric analysis method using images captured by a general consumer camera under various environments.
Numerical control matrix rotation for the LINC-NIRVANA multiconjugate adaptive optics system
Arcidiacono, Carmelo; Bertram, Thomas; Ragazzoni, Roberto; Farinato, Jacopo; Esposito, Simone; Riccardi, Armando; Pinna, Enrico; Puglisi, Alfio; Fini, Luca; Xompero, Marco; Busoni, Lorenzo; Quiros-Pacheco, Fernando; Briguglio, Runa
2010-07-01
LINC-NIRVANA will realize the interferometric imaging focal station of the Large Binocular Telescope. A double Layer Oriented multi-conjugate adaptive optics system assists the two arms of the interferometer, supplying high order wave-front correction. In order to counterbalance the field rotation, mechanical derotation for the two ground wave-front sensors, and optical derotators for the mid-high layers sensors fix the positions of the focal planes with respect to the pyramids aboard the wave-front sensors. The derotation introduces pupil images rotation on the wavefront sensors: the projection of the deformable mirrors on the sensor consequently change. The proper adjustment of the control matrix will be applied in real-time through numerical computation of the new matrix. In this paper we investigate the temporal and computational aspects related to the pupils rotation, explicitly computing the wave-front errors that may be generated.
Directory of Open Access Journals (Sweden)
R.R. Joshi
2007-06-01
Full Text Available A systematic controller design and implementation for a matrix-converter-based induction motor drive system is proposed. A nonlinear adaptive backstepping controller is proposed to improve the speed and position responses of the induction motor system. By using the proposed adaptive backstepping controller, the system can track a time-varying speed command and a time-varying position command well. Moreover, the system has a good load disturbance rejection capability. The realization of the controller is very simple. All of the control loops, including the current loop, speed loop and position loop, are implemented by a digital signal processor. Several experimental results are given to validate the theoretical analysis.
Universality of Covariance Matrices
Pillai, Natesh S
2011-01-01
We prove the universality of covariance matrices of the form $H_{N \\times N} = {1 \\over N} \\tp{X}X$ where $[X]_{M \\times N}$ is a rectangular matrix with independent real valued entries $[x_{ij}]$ satisfying $\\E \\,x_{ij} = 0$ and $\\E \\,x^2_{ij} = {1 \\over M}$, $N, M\\to \\infty$. Furthermore it is assumed that these entries have sub-exponential tails. We will study the asymptotics in the regime $N/M = d_N \\in (0,\\infty), \\lim_{N\\to \\infty}d_N \
Levy Matrices and Financial Covariances
Burda, Zdzislaw; Jurkiewicz, Jerzy; Nowak, Maciej A.; Papp, Gabor; Zahed, Ismail
2003-10-01
In a given market, financial covariances capture the intra-stock correlations and can be used to address statistically the bulk nature of the market as a complex system. We provide a statistical analysis of three SP500 covariances with evidence for raw tail distributions. We study the stability of these tails against reshuffling for the SP500 data and show that the covariance with the strongest tails is robust, with a spectral density in remarkable agreement with random Lévy matrix theory. We study the inverse participation ratio for the three covariances. The strong localization observed at both ends of the spectral density is analogous to the localization exhibited in the random Lévy matrix ensemble. We discuss two competitive mechanisms responsible for the occurrence of an extensive and delocalized eigenvalue at the edge of the spectrum: (a) the Lévy character of the entries of the correlation matrix and (b) a sort of off-diagonal order induced by underlying inter-stock correlations. (b) can be destroyed by reshuffling, while (a) cannot. We show that the stocks with the largest scattering are the least susceptible to correlations, and likely candidates for the localized states. We introduce a simple model for price fluctuations which captures behavior of the SP500 covariances. It may be of importance for assets diversification.
A Charrelation Matrix-Based Blind Adaptive Detector for DS-CDMA Systems.
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.
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.
STATISTICAL SPACE-TIME ADAPTIVE PROCESSING ALGORITHM
Institute of Scientific and Technical Information of China (English)
Yang Jie
2010-01-01
For the slowly changed environment-range-dependent non-homogeneity,a new statistical space-time adaptive processing algorithm is proposed,which uses the statistical methods,such as Bayes or likelihood criterion to estimate the approximative covariance matrix in the non-homogeneous condition. According to the statistical characteristics of the space-time snapshot data,via defining the aggregate snapshot data and corresponding events,the conditional probability of the space-time snapshot data which is the effective training data is given,then the weighting coefficients are obtained for the weighting method. The theory analysis indicates that the statistical methods of the Bayes and likelihood criterion for covariance matrix estimation are more reasonable than other methods that estimate the covariance matrix with the use of training data except the detected outliers. The last simulations attest that the proposed algorithms can estimate the covariance in the non-homogeneous condition exactly and have favorable characteristics.
Matrix metalloproteinases in a sea urchin ligament with adaptable mechanical properties.
Directory of Open Access Journals (Sweden)
Ana R Ribeiro
Full Text Available Mutable collagenous tissues (MCTs of echinoderms show reversible changes in tensile properties (mutability that are initiated and modulated by the nervous system via the activities of cells known as juxtaligamental cells. The molecular mechanism underpinning this mechanical adaptability has still to be elucidated. Adaptable connective tissues are also present in mammals, most notably in the uterine cervix, in which changes in stiffness result partly from changes in the balance between matrix metalloproteinases (MMPs and tissue inhibitors of metalloproteinases (TIMPs. There have been no attempts to assess the potential involvement of MMPs in the echinoderm mutability phenomenon, apart from studies dealing with a process whose relationship to the latter is uncertain. In this investigation we used the compass depressor ligaments (CDLs of the sea-urchin Paracentrotus lividus. The effect of a synthetic MMP inhibitor - galardin - on the biomechanical properties of CDLs in different mechanical states ("standard", "compliant" and "stiff" was evaluated by dynamic mechanical analysis, and the presence of MMPs in normal and galardin-treated CDLs was determined semi-quantitatively by gelatin zymography. Galardin reversibly increased the stiffness and storage modulus of CDLs in all three states, although its effect was significantly lower in stiff than in standard or compliant CDLs. Gelatin zymography revealed a progressive increase in total gelatinolytic activity between the compliant, standard and stiff states, which was possibly due primarily to higher molecular weight components resulting from the inhibition and degradation of MMPs. Galardin caused no change in the gelatinolytic activity of stiff CDLs, a pronounced and statistically significant reduction in that of standard CDLs, and a pronounced, but not statistically significant, reduction in that of compliant CDLs. Our results provide evidence that MMPs may contribute to the variable tensility of the
Directory of Open Access Journals (Sweden)
Hsuan-Yu Lin
2008-11-01
Full Text Available Adaptive reduced-rank (RR multistage matrix Wiener filtering (MMWF techniques, based on the minimum mean-square error (MMSE criterion, are proposed for direct-sequence (DS ultra-wideband (UWB communication systems. These RR-MMWF-based algorithms employ an adaptive fuzzy-inference determined filter stage. As a consequence, the proposed schemes achieve a substantial saving in complexity without compromising system performance and dynamic convergence/tracking capability. Additionally, the fuzzy-logic-controlled matrix conjugate gradient (MCG algorithm is developed for a robust and reduced-rank implementation of the full-rank MMWF. Simulations are conducted to illustrate the convergence/tracking superiority and to provide a comparative evaluation of the proposed algorithms with the MMWF-based schemes using other adaptive stage-selecting criteria.
Institute of Scientific and Technical Information of China (English)
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.
Kun, David William
Unmanned aircraft systems (UASs) are gaining popularity in civil and commercial applications as their lightweight on-board computers become more powerful and affordable, their power storage devices improve, and the Federal Aviation Administration addresses the legal and safety concerns of integrating UASs in the national airspace. Consequently, many researchers are pursuing novel methods to control UASs in order to improve their capabilities, dependability, and safety assurance. The nonlinear control approach is a common choice as it offers several benefits for these highly nonlinear aerospace systems (e.g., the quadrotor). First, the controller design is physically intuitive and is derived from well known dynamic equations. Second, the final control law is valid in a larger region of operation, including far from the equilibrium states. And third, the procedure is largely methodical, requiring less expertise with gain tuning, which can be arduous for a novice engineer. Considering these facts, this thesis proposes a nonlinear controller design method that combines the advantages of adaptive robust control (ARC) with the powerful design tools of linear matrix inequalities (LMI). The ARC-LMI controller is designed with a discontinuous projection-based adaptation law, and guarantees a prescribed transient and steady state tracking performance for uncertain systems in the presence of matched disturbances. The norm of the tracking error is bounded by a known function that depends on the controller design parameters in a known form. Furthermore, the LMI-based part of the controller ensures the stability of the system while overcoming polytopic uncertainties, and minimizes the control effort. This can reduce the number of parameters that require adaptation, and helps to avoid control input saturation. These desirable characteristics make the ARC-LMI control algorithm well suited for the quadrotor UAS, which may have unknown parameters and may encounter external
Directory of Open Access Journals (Sweden)
Borui Li
2014-04-01
Full Text Available Traditional object tracking technology usually regards the target as a point source object. However, this approximation is no longer appropriate for tracking extended objects such as large targets and closely spaced group objects. Bayesian extended object tracking (EOT using a random symmetrical positive definite (SPD matrix is a very effective method to jointly estimate the kinematic state and physical extension of the target. The key issue in the application of this random matrix-based EOT approach is to model the physical extension and measurement noise accurately. Model parameter adaptive approaches for both extension dynamic and measurement noise are proposed in this study based on the properties of the SPD matrix to improve the performance of extension estimation. An interacting multi-model algorithm based on model parameter adaptive filter using random matrix is also presented. Simulation results demonstrate the effectiveness of the proposed adaptive approaches and multi-model algorithm. The estimation performance of physical extension is better than the other algorithms, especially when the target maneuvers. The kinematic state estimation error is lower than the others as well.
Deriving covariant holographic entanglement
Dong, Xi; Lewkowycz, Aitor; Rangamani, Mukund
2016-11-01
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.
Deriving covariant holographic entanglement
Dong, Xi; Rangamani, Mukund
2016-01-01
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 Renyi 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.
Multivariate covariance generalized linear models
DEFF Research Database (Denmark)
Bonat, W. H.; Jørgensen, Bent
2016-01-01
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...... 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...
Model Order Selection Rules for Covariance Structure Classification in Radar
Carotenuto, Vincenzo; De Maio, Antonio; Orlando, Danilo; Stoica, Petre
2017-10-01
The adaptive classification of the interference covariance matrix structure for radar signal processing applications is addressed in this paper. This represents a key issue because many detection architectures are synthesized assuming a specific covariance structure which may not necessarily coincide with the actual one due to the joint action of the system and environment uncertainties. The considered classification problem is cast in terms of a multiple hypotheses test with some nested alternatives and the theory of Model Order Selection (MOS) is exploited to devise suitable decision rules. Several MOS techniques, such as the Akaike, Takeuchi, and Bayesian information criteria are adopted and the corresponding merits and drawbacks are discussed. At the analysis stage, illustrating examples for the probability of correct model selection are presented showing the effectiveness of the proposed rules.
Li, Meng; Huang, Zhonghua
2016-10-01
Signal processing for an ultra-wideband radio fuze receiver involves some challenges: it requires high real-time performance; the output signal is mixed with broadband noise; and the signal-to-noise ratio (SNR) decreases with increased detection range. The adaptive line enhancement method is used to filter the output signal of the ultra-wideband radio fuze receiver, and thus suppress the wideband noise from the output signal of the receiver and extract the target characteristic signal. The filter input correlation matrix estimation algorithm is based on the delay factor of an adaptive line enhancer. The proposed adaptive algorithm was used to filter and reduce noise in the output signal from the fuze receiver. Simulation results showed that the SNR of the output signal after adaptive noise reduction was improved by 20 dB, which was higher than the SNR of the output signal after finite impulse response (FIR) filtering of around 10 dB.
Adaptive detection based on or thogonal par tition of the primary and secondary data
Institute of Scientific and Technical Information of China (English)
Weijian Liu; Wenchong Xie; Yongliang Wang
2014-01-01
A novel adaptive detection scheme both for point-like and distributed targets in the presence of Gaussian disturbance in the partial y homogeneous environment (PHE) is proposed. The novel detection scheme is based on the orthogonal projection technique. Both the case of known covariance matrix structure and the case of unknown covariance matrix structure are con-sidered. For the former case, the closed-form statistical pro-perty of the novel detectors is derived. When the covariance matrix is unknown, the corresponding detectors have higher probabilities of detection (PDs) than their natural competitors. Moreover, they ensure constant false alarm rate (CFAR) property.
Lund, E; Hughes, E W; Lopez Mateos, D; Salzburger, A; Strandlie, A
2008-01-01
In this paper we study the energy loss, its fluctuations, and the multiple scattering of particles passing through matter, with an emphasis on muons. In addition to the well-known Bethe-Bloch and Bethe-Heitler equations describing the mean energy loss from ionization and bremsstrahlung respectively, new parameterizations of the mean energy loss of muons from the direct e+e- pair production and photonuclear interactions are presented along with new estimates of the most probable energy loss and its fluctuations in the ATLAS calorimeters. Moreover, a new adaptive Highland/Moliere approach to finding the multiple scattering angle is taken to accomodate a wide range of scatterer thicknesses. Furthermore, tests of the muon energy loss, its fluctuations, and multiple scattering are done in the ATLAS calorimeters. The material effects described in this paper are all part of the simultaneous track and error propagation (STEP) algorithm of the common ATLAS tracking software.
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
Covariant Formulations of Superstring Theories.
Mikovic, Aleksandar Radomir
1990-01-01
Chapter 1 contains a brief introduction to the subject of string theory, and tries to motivate the study of superstrings and covariant formulations. Chapter 2 describes the Green-Schwarz formulation of the superstrings. The Hamiltonian and BRST structure of the theory is analysed in the case of the superparticle. Implications for the superstring case are discussed. Chapter 3 describes the Siegel's formulation of the superstring, which contains only the first class constraints. It is shown that the physical spectrum coincides with that of the Green-Schwarz formulation. In chapter 4 we analyse the BRST structure of the Siegel's formulation. We show that the BRST charge has the wrong cohomology, and propose a modification, called first ilk, which gives the right cohomology. We also propose another superparticle model, called second ilk, which has infinitely many coordinates and constraints. We construct the complete BRST charge for it, and show that it gives the correct cohomology. In chapter 5 we analyse the properties of the covariant vertex operators and the corresponding S-matrix elements by using the Siegel's formulation. We conclude that the knowledge of the ghosts is necessary, even at the tree level, in order to obtain the correct S-matrix. In chapter 6 we attempt to calculate the superstring loops, in a covariant gauge. We calculate the vacuum-to -vacuum amplitude, which is also the cosmological constant. We show that it vanishes to all loop orders, under the assumption that the free covariant gauge-fixed action exists. In chapter 7 we present our conclusions, and briefly discuss the random lattice approach to the string theory, as a possible way of resolving the problem of the covariant quantization and the nonperturbative definition of the superstrings.
Covariance NMR spectroscopy by singular value decomposition.
Trbovic, Nikola; Smirnov, Serge; Zhang, Fengli; Brüschweiler, Rafael
2004-12-01
Covariance NMR is demonstrated for homonuclear 2D NMR data collected using the hypercomplex and TPPI methods. Absorption mode 2D spectra are obtained by application of the square-root operation to the covariance matrices. The resulting spectra closely resemble the 2D Fourier transformation spectra, except that they are fully symmetric with the spectral resolution along both dimensions determined by the favorable resolution achievable along omega2. An efficient method is introduced for the calculation of the square root of the covariance spectrum by applying a singular value decomposition (SVD) directly to the mixed time-frequency domain data matrix. Applications are shown for 2D NOESY and 2QF-COSY data sets and computational benchmarks are given for data matrix dimensions typically encountered in practice. The SVD implementation makes covariance NMR amenable to routine applications.
Manifestly covariant electromagnetism
Energy Technology Data Exchange (ETDEWEB)
Hillion, P. [Institut Henri Poincare' , Le Vesinet (France)
1999-03-01
The conventional relativistic formulation of electromagnetism is covariant under the full Lorentz group. But relativity requires covariance only under the proper Lorentz group and the authors present here the formalism covariant under the complex rotation group isomorphic to the proper Lorentz group. The authors discuss successively Maxwell's equations, constitutive relations and potential functions. A comparison is made with the usual formulation.
修正协方差阵的投资组合方案及其稳定性%Optimal portfolio project with modified covariance matrix and its stability
Institute of Scientific and Technical Information of China (English)
储晨; 方兆本
2011-01-01
The reasons why the risk of optimal portfolio is unstable was analyzed.Based on this analysis a new method was tried,which was proposed by the previous researchers,to dispose the sample covariance matrix based on random matrix theory (RMT),to get a better result.Generally,we first give more sufficient and practical evidence to support the method's reasonableness and power,then apply this idea to simulate the “real investment”,using real data of the Chinese stock market.It is found that the results have better mean return and stability than those of the traditional method,and can be explained by the analysis of the prediction to the risk proposed by the previous researchers.%分析了最优化投资组合不稳定的原因,在此基础上,推广了由前人提出的一个新方法,即根据随机矩阵定理(RMT)对样本协差阵进行变化以得到一个更好的投资方案.首先对该方法中未证明的部分给出了更充分的实证理由,并运用这种思想,结合中国证券市场的实际数据,来模拟“真实的投资”.而这种方法得到的投资方案,较之传统方法有较好的收益率和方差,且可被前人对风险预测的分析方法所解释.
Institute of Scientific and Technical Information of China (English)
姚海祥; 马庆华
2011-01-01
本文在一般均值-风险模型的框架下,在无套利假设下研究了奇异协方差矩阵和任意收益率分布情形下的投资组合问题,得到了模型有效边界的本质特征,并给出了极大线性无关组的确定方法及表示系数的求解方法,最后根据这些结论提出了有效的、操作性强的投资策略.%Under general mean-risk framework and arbitrage-free market hypothesis, this paper study the portfolio selection problem with any asset return distribution and singular covariance matrix. First we obtain the characteristic of the effcient frontier of the mean-risk model, further propose a method for obtaining the maximal linearly independent group. Finally, as an application of our results, we present some investment strategies.
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 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...... matrix dynamics. Our empirical results show that the new mixing approach provides superior forecasts compared to multivariate volatility specifications using single sources of information....
Representations of Inverse Covariances by Differential Operators
Institute of Scientific and Technical Information of China (English)
Qin XU
2005-01-01
In the cost function of three- or four-dimensional variational data assimilation, each term is weighted by the inverse of its associated error covariance matrix and the background error covariance matrix is usually much larger than the other covariance matrices. Although the background error covariances are traditionally normalized and parameterized by simple smooth homogeneous correlation functions, the covariance matrices constructed from these correlation functions are often too large to be inverted or even manipulated. It is thus desirable to find direct representations of the inverses of background errorcorrelations. This problem is studied in this paper. In particular, it is shown that the background term can be written into ∫ dx|Dv(x)|2, that is, a squared L2 norm of a vector differential operator D, called the D-operator, applied to the field of analysis increment v(x). For autoregressive correlation functions, the Doperators are of finite orders. For Gaussian correlation functions, the D-operators are of infinite order. For practical applications, the Gaussian D-operators must be truncated to finite orders. The truncation errors are found to be small even when the Gaussian D-operators are truncated to low orders. With a truncated D-operator, the background term can be easily constructed with neither inversion nor direct calculation of the covariance matrix. D-operators are also derived for non-Gaussian correlations and transformed into non-isotropic forms.
Souza, Luiz C. G.; Bigot, P.
2016-10-01
One of the most well-known techniques of optimal control is the theory of Linear Quadratic Regulator (LQR). This method was originally applied only to linear systems but has been generalized for non-linear systems: the State Dependent Riccati Equation (SDRE) technique. One of the advantages of SDRE is that the weight matrix selection is the same as in LQR. The difference is that weights are not necessarily constant: they can be state dependent. Then, it gives an additional flexibility to design the control law. Many are applications of SDRE for simulation or real time control but generally SDRE weights are chosen constant so no advantage of this flexibility is taken. This work serves to show through simulation that state dependent weights matrix can improve SDRE control performance. The system is a non-linear flexible rotatory beam. In a brief first part SDRE theory will be explained and the non-linear model detailed. Then, influence of SDRE weight matrix associated with the state Q will be analyzed to get some insight in order to assume a state dependent law. Finally, these laws are tested and compared to constant weight matrix Q. Based on simulation results; one concludes showing the benefits of using an adaptive weight Q rather than a constant one.
Kriging approach for the experimental cross-section covariances estimation
Directory of Open Access Journals (Sweden)
Garlaud A.
2013-03-01
Full Text Available In the classical use of a generalized χ2 to determine the evaluated cross section uncertainty, we need the covariance matrix of the experimental cross sections. The usual propagation error method to estimate the covariances is hardly usable and the lack of data prevents from using the direct empirical estimator. We propose in this paper to apply the kriging method which allows to estimate the covariances via the distances between the points and with some assumptions on the covariance matrix structure. All the results are illustrated with the 2555Mn nucleus measurements.
Estimation of Low-Rank Covariance Function
Koltchinskii, Vladimir; Lounici, Karim; Tsybakov, Alexander B.
2015-01-01
We consider the problem of estimating a low rank covariance function $K(t,u)$ of a Gaussian process $S(t), t\\in [0,1]$ based on $n$ i.i.d. copies of $S$ observed in a white noise. We suggest a new estimation procedure adapting simultaneously to the low rank structure and the smoothness of the covariance function. The new procedure is based on nuclear norm penalization and exhibits superior performances as compared to the sample covariance function by a polynomial factor in the sample size $n$...
Covariant Hamiltonian field theory
Giachetta, G; Sardanashvily, G
1999-01-01
We study the relationship between the equations of first order Lagrangian field theory on fiber bundles and the covariant Hamilton equations on the finite-dimensional polysymplectic phase space of covariant Hamiltonian field theory. The main peculiarity of these Hamilton equations lies in the fact that, for degenerate systems, they contain additional gauge fixing conditions. We develop the BRST extension of the covariant Hamiltonian formalism, characterized by a Lie superalgebra of BRST and anti-BRST symmetries.
Institute of Scientific and Technical Information of China (English)
樊华; 山秀明; 任勇; 袁坚
2011-01-01
neglects the random variations in the controlled TCP flows. This paper aims at revealing the un-negligible influence from such random variations to the system stability. Using TCP/RED（TCP flows with random early detection） as an example, based on the stochastic differential equations of the system, the system is converted into a multi-dimensional linear time-invariant system with mixed additive and multiplicative noises by linearization at the equilibrium point. Then, generalized TCP flow control equations for continuous-time and discrete-time cases respectively are given, which are the first-degree time-invariant stochastic differential or difference equations with multi-noise-inputs. After that, the covariance matrix equation for such generalized system is derived; and based on this matrix equation, the sufficient and necessary condition when the covariance matrix has an asymptotically stable limit is presented, together with the expression of this limit. In engineering design, this condition can be regarded as a substitute criterion for estimating the motion domain. Finally, this general condition is applied to a specific example for demonstrating the change of the stability range when the stability of covariance is considered. Moreover, the results in this paper can be extended to the nonlinear system or time-varying system when treated by similar methods used in deterministic cases.
Adapting collagen/CNT matrix in directing hESC differentiation
Sridharan, Indumathi; Kim, Taeyoung; Wang, Rong
2009-01-01
The lineage selection in human embryonic stem cell (hESC) differentiation relies on both the growth factors and small molecules in the media and the physical characteristics of the micro-environment. In this work, we utilized various materials, including the collagen-carbon nanotube (collagen/CNT) composite material, as cell culture matrices to examine the impact of matrix properties on hESC differentiation. Our AFM analysis indicated that the collagen/CNT formed rigid fibril bundles, which p...
Adapting collagen/CNT matrix in directing hESC differentiation.
Sridharan, Indumathi; Kim, Taeyoung; Wang, Rong
2009-04-17
The lineage selection in human embryonic stem cell (hESC) differentiation relies on both the growth factors and small molecules in the media and the physical characteristics of the micro-environment. In this work, we utilized various materials, including the collagen-carbon nanotube (collagen/CNT) composite material, as cell culture matrices to examine the impact of matrix properties on hESC differentiation. Our AFM analysis indicated that the collagen/CNT formed rigid fibril bundles, which polarized the growth and differentiation of hESCs, resulting in more than 90% of the cells to the ectodermal lineage in Day 3 in the media commonly used for spontaneous differentiation. We also observed the differentiated cells followed the coarse alignment of the collagen/CNT matrix. The research not only revealed the responsiveness of hESCs to matrix properties, but also provided a simple yet efficient way to direct the hESC differentiation, and imposed the potential of forming neural-cell based bio-devices for further applications.
Group Lasso estimation of high-dimensional covariance matrices
Bigot, Jérémie; Loubes, Jean-Michel; Alvarez, Lilian Muniz
2010-01-01
In this paper, we consider the Group Lasso estimator of the covariance matrix of a stochastic process corrupted by an additive noise. We propose to estimate the covariance matrix in a high-dimensional setting under the assumption that the process has a sparse representation in a large dictionary of basis functions. Using a matrix regression model, we propose a new methodology for high-dimensional covariance matrix estimation based on empirical contrast regularization by a group Lasso penalty. Using such a penalty, the method selects a sparse set of basis functions in the dictionary used to approximate the process, leading to an approximation of the covariance matrix into a low dimensional space. Consistency of the estimator is studied in Frobenius and operator norms and an application to sparse PCA is proposed.
Institute of Scientific and Technical Information of China (English)
崔先强; 杨元喜; 张晓东
2012-01-01
To use Kalman filtering for kinematic navigation and positioning, we have to deal with function model and stochastic model. The precision and reliability of kinematic Kalman filtering are affected remarkably from the function model and stochastic model errors. Adaptive fitting of both colored noise and covariance matrices by using moving windows are presented based on the assumption that the observation and dynamic model noises mainly include the colored noises with first order self-correlation character. The expressions to calculate the colored noise estimators and covariance matrices of the modified observations and predicted states are obtained. Feasibility and practicability of the model and algorithm are tested by an example. It is shown that the Kalman filtering, based on the adaptive fittings of the colored noises and covariance matrices, can be effectivein resisting the influence of the colored noises on the navigation results.%使用Kalman滤波进行动态导航定位解算需要涉及函数模型和随机模型，而在实际应用中，精确的函数模型和随机模型很难直接给出，因此，动态Kalman滤波的精度和可靠性将会受到函数模型误差和随机模型误差的影响．在假设观测噪声和动力学模型噪声主要是具有一阶自相关特性的有色噪声的基础上，提出了一种基于移动窗口的有色噪声函数模型和随机模型的自适应拟合法．给出了计算有色噪声估值和噪声协方差矩阵的表达式，并利用实测数据验证了模型及算法的可行性和实用性．计算结果表明，该算法能有效抵制有色噪声对导航滤波结果的影响．
Temporal extracellular matrix adaptations in ligament during wound healing and hindlimb unloading.
Martinez, D A; Vailas, A C; Vanderby, R; Grindeland, R E
2007-10-01
Previous data from spaceflight studies indicate that injured muscle and bone heal slowly and abnormally compared with ground controls, strongly suggesting that ligaments or tendons may not repair optimally as well. Thus the objective of this study was to investigate the biochemical and molecular gene expression of the collagen extracellular matrix in response to medial collateral ligament (MCL) injury repair in hindlimb unloaded (HLU) rodents. Male rats were assigned to 3- and 7-wk treatment groups with three subgroups each: sham control, ambulatory healing (Amb-healing), and HLU-healing groups. Amb- and HLU-healing animals underwent bilateral surgical transection of their MCLs, whereas control animals were subjected to sham surgeries. All surgeries were performed under isoflurane anesthesia. After 3 wk or 7 wk of HLU, rats were euthanized and MCLs were surgically isolated and prepared for molecular or biochemical analyses. Hydroxyproline concentration and hydroxylysylpyridinoline collagen cross-link contents were measured by HPLC and showed a substantial decrement in surgical groups. MCL tissue cellularity, quantified by DNA content, remained significantly elevated in all HLU-healing groups vs. Amb-healing groups. MCL gene expression of collagen type I, collagen type III, collagen type V, fibronectin, decorin, biglycan, lysyl oxidase, matrix metalloproteinase-2, and tissue inhibitor of matrix metalloproteinase-1, measured by real-time quantitative PCR, demonstrated differential expression in the HLU-healing groups compared with Amb-healing groups at both the 3- and 7-wk time points. Together, these data suggest that HLU affects dense fibrous connective tissue wound healing and confirms previous morphological and biomechanical data that HLU inhibits the ligament repair processes.
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.
A Decision Tree-Structured Algorithm of Speaker Adaptation Based on Gaussian Similarity Analysis
Institute of Scientific and Technical Information of China (English)
WU Ji; WANG Zuoying
2001-01-01
Gaussian Similarity Analysis (GSA)algorithm can be used to estimate the similarity between two Gaussian distributed variables with full covariance matrix. Based on this algorithm, we propose a method in speaker adaptation of covariance. It is different from the traditional algorithms, which mainly focus on the adaptation of mean vector of state observation probability density. A binary decision tree is constructed offline with the similarity measure and the adaptation procedure is data-driven. It can be shown from the experiments that we can get a significant further improvement over the mean vectors adaptation.
Directory of Open Access Journals (Sweden)
Antônio Fernando Branco Costa
2010-01-01
Full Text Available O gráfico T² de Hotelling e o gráfico |S| da variância generalizada são utilizados para monitorar o vetor de médias e a matriz de covariâncias de processos multivariados. Neste artigo, propõe-se o uso de um único gráfico de controle para o monitoramento de processos bivariados, isto é, o gráfico de controle MCMAX cujo valor da estatística de monitoramento corresponde ao maior valor em módulo de quatro medidas amostrais das duas características de qualidade sob monitoramento, isto é, as suas médias e variâncias padronizadas. O usuário de gráficos de controle já está bem familiarizado com médias e variâncias amostrais; o mesmo não pode ser dito a respeito da estatística de Hotelling ou da variância generalizada. Conseqüentemente, ele preferirá usar o gráfico de controle proposto ao invés dos gráficos conjuntos de T² e |S|. Além disso, o usuário, em geral, se sente mais seguro em intervir no processo somente após a ocorrência de um segundo ponto na região de ação do gráfico. Se o sinal for dado por dois pontos, não necessariamente vizinhos, porém próximos e na região de ação, o gráfico proposto terá um desempenho geral superior ao dos gráficos conjuntos de T² e |S| na detecção de desajustes do processo, exceto quando a correlação entre as duas características de qualidade for muito alta. Quando a correlação é muito alta e a causa especial desloca a média e/ou aumenta a variância de apenas uma das variáveis, X ou Y, os gráficos de T² e |S| são, em geral, mais ágeis do que gráfico MCMAX.The control chart and the generalized variance |S| chart are used for monitoring the mean vector and the covariance matrix of multivariate processes. In this article, we propose the use of a single chart for monitoring bivariate processes, that is, the MCMAX control chart based on a new statistic, which corresponds to the maximum among four sample values: the standardized sample means (in module and
GH/IGF-I axis and matrix adaptation of the musculotendinous tissue to exercise in humans
DEFF Research Database (Denmark)
Heinemeier, K M; Mackey, Abigail; Doessing, S
2012-01-01
Exercise is not only associated with adaptive responses within skeletal muscle fibers but also with induction of collagen synthesis both in muscle and adjacent connective tissue. Additionally, exercise and training leads to activation of the systemic growth hormone/insulin-like growth factor I axis...... (GH/IGF-I), as well as increased local IGF-I expression. Studies in humans with pathologically high levels of GH/IGF-I, and in healthy humans who receive either weeks of GH administration or acute injection of IGF-I into connective tissue, demonstrate increased expression and synthesis of collagen...
Hansen, John A
2016-10-13
Assessment of individuals on the autism spectrum often includes a measure of nonverbal IQ. One such measure is the Raven's Standard Progressive Matrices (RSPM). For large research studies with participants distributed nationally it is desirable for assessments to be available online. Because time is a premium, it is ideal that the measure produces accurate scores quickly. The Hansen Research Services Matrix Adaptive Test (HRS-MAT) addresses these needs and with similar psychometric properties of the RSPM. Scores based on the HRS-MAT correlated at r = .81 with those of the RSPM. In adult-child pairs, HRS-MAT scores correlated at approximately r = .50. Details from respondents in a national sample and psychometric properties including reliability and validity are discussed.
Adaptation response of Pseudomonas fragi on refrigerated solid matrix to a moderate electric field.
Chen, Wenbo; Hu, Honghai; Zhang, Chunjiang; Huang, Feng; Zhang, Dequan; Zhang, Hong
2017-02-10
Moderate electric field (MEF) technology is a promising food preservation strategy since it relies on physical properties-rather than chemical additives-to preserve solid cellular foods during storage. However, the effectiveness of long-term MEF exposure on the psychrotrophic microorganisms responsible for the food spoilage at cool temperatures remains unclear. The spoilage-associated psychrotroph Pseudomonas fragi MC16 was obtained from pork samples stored at 7 °C. Continuous MEF treatment attenuated growth and resulted in subsequent adaptation of M16 cultured on nutrient agar plates at 7 °C, compared to the control cultures, as determined by biomass analysis and plating procedures. Moreover, intracellular dehydrogenase activity and ATP levels also indicated an initial effect of MEF treatment followed by cellular recovery, and extracellular β-galactosidase activity assays indicated no obvious changes in cell membrane permeability. Furthermore, microscopic observations using scanning and transmission electron microscopy revealed that MEF induced sublethal cellular injury during early treatment stages, but no notable changes in morphology or cytology on subsequent days. Our study provides direct evidence that psychrotrophic P. fragi MC16 cultured on nutrient agar plates at 7 °C are capable of adapting to MEF treatment.
Progress on Nuclear Data Covariances: AFCI-1.2 Covariance Library
Energy Technology Data Exchange (ETDEWEB)
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
2009-09-28
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{sup -5} eV to 19.6 MeV. BNL contributed improved covariance data for the following materials: {sup 23}Na and {sup 55}Mn where more detailed evaluation was done; improvements in major structural materials {sup 52}Cr, {sup 56}Fe and {sup 58}Ni; improved estimates for remaining structural materials and fission products; improved covariances for 14 minor actinides, and estimates of mubar covariances for {sup 23}Na and {sup 56}Fe. LANL contributed improved covariance data for {sup 235}U and {sup 239}Pu including prompt neutron fission spectra and completely new evaluation for {sup 240}Pu. New R-matrix evaluation for {sup 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.
Accurate covariance estimation of galaxy-galaxy weak lensing: limitations of jackknife covariance
Shirasaki, Masato; Miyatake, Hironao; Takahashi, Ryuichi; Hamana, Takashi; Nishimichi, Takahiro; Murata, Ryoma
2016-01-01
We develop a method to simulate galaxy-galaxy weak lensing by utilizing all-sky, light-cone simulations. We populate a real catalog of source galaxies into a light-cone simulation realization, simulate the lensing effect on each galaxy, and then identify lensing halos that are considered to host galaxies or clusters of interest. We use the mock catalog to study the error covariance matrix of galaxy-galaxy weak lensing and find that the super-sample covariance (SSC), which arises from density fluctuations with length scales comparable with or greater than a size of survey area, gives a dominant source of the sample variance. We then compare the full covariance with the jackknife (JK) covariance, the method that estimates the covariance from the resamples of the data itself. We show that, although the JK method gives an unbiased estimator of the covariance in the shot noise or Gaussian regime, it always over-estimates the true covariance in the sample variance regime, because the JK covariance turns out to be a...
Optimal Design of Large Dimensional Adaptive Subspace Detectors
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.
Covariance fitting of highly correlated $B_K$ data
Yoon, Boram; Jung, Chulwoo; Lee, Weonjong
2011-01-01
We present the reason why we use the diagonal approximation (uncorrelated fitting) when we perform the data analysis of highly correlated $B_K$ data on the basis of the SU(2) staggered chiral perturbation theory. Basically, the essence of the problem is that we do not have enough statistics to determine the small eigenvalues of the covariance matrix with a high precision. As a result, we have the smallest eigenvalue, which is smaller than the statistical error of the covariance matrix, corresponding to an unphysical eigenmode. We have applied a number of prescriptions available in the market such as the cutoff method and modified covariance matrix method. It turns out that the cutoff method is not a good prescription and the modified covariance matrix method is an even worse one. The diagonal approximation turns out to be a good prescription if the data points are somehow correlated and the statistics are relatively poor.
Wishart distributions for decomposable covariance graph models
Khare, Kshitij; 10.1214/10-AOS841
2011-01-01
Gaussian covariance graph models encode marginal independence among the components of a multivariate random vector by means of a graph $G$. These models are distinctly different from the traditional concentration graph models (often also referred to as Gaussian graphical models or covariance selection models) since the zeros in the parameter are now reflected in the covariance matrix $\\Sigma$, as compared to the concentration matrix $\\Omega =\\Sigma^{-1}$. The parameter space of interest for covariance graph models is the cone $P_G$ of positive definite matrices with fixed zeros corresponding to the missing edges of $G$. As in Letac and Massam [Ann. Statist. 35 (2007) 1278--1323], we consider the case where $G$ is decomposable. In this paper, we construct on the cone $P_G$ a family of Wishart distributions which serve a similar purpose in the covariance graph setting as those constructed by Letac and Massam [Ann. Statist. 35 (2007) 1278--1323] and Dawid and Lauritzen [Ann. Statist. 21 (1993) 1272--1317] do in ...
Linear transformations of variance/covariance matrices.
Parois, Pascal; Lutz, Martin
2011-07-01
Many applications in crystallography require the use of linear transformations on parameters and their standard uncertainties. While the transformation of the parameters is textbook knowledge, the transformation of the standard uncertainties is more complicated and needs the full variance/covariance matrix. For the transformation of second-rank tensors it is suggested that the 3 × 3 matrix is re-written into a 9 × 1 vector. The transformation of the corresponding variance/covariance matrix is then straightforward and easily implemented into computer software. This method is applied in the transformation of anisotropic displacement parameters, the calculation of equivalent isotropic displacement parameters, the comparison of refinements in different space-group settings and the calculation of standard uncertainties of eigenvalues.
Adaptive Sampling for Learning Gaussian Processes Using Mobile Sensor Networks
Directory of Open Access Journals (Sweden)
Yunfei Xu
2011-03-01
Full Text Available This paper presents a novel class of self-organizing sensing agents that adaptively learn an anisotropic, spatio-temporal Gaussian process using noisy measurements and move in order to improve the quality of the estimated covariance function. This approach is based on a class of anisotropic covariance functions of Gaussian processes introduced to model a broad range of spatio-temporal physical phenomena. The covariance function is assumed to be unknown a priori. Hence, it is estimated by the maximum a posteriori probability (MAP estimator. The prediction of the field of interest is then obtained based on the MAP estimate of the covariance function. An optimal sampling strategy is proposed to minimize the information-theoretic cost function of the Fisher Information Matrix. Simulation results demonstrate the effectiveness and the adaptability of the proposed scheme.
Directory of Open Access Journals (Sweden)
Greenhaff Paul L
2005-09-01
Full Text Available Abstract Background Regular exercise reduces cardiovascular and metabolic disease partly through improved aerobic fitness. The determinants of exercise-induced gains in aerobic fitness in humans are not known. We have demonstrated that over 500 genes are activated in response to endurance-exercise training, including modulation of muscle extracellular matrix (ECM genes. Real-time quantitative PCR, which is essential for the characterization of lower abundance genes, was used to examine 15 ECM genes potentially relevant for endurance-exercise adaptation. Twenty-four sedentary male subjects undertook six weeks of high-intensity aerobic cycle training with muscle biopsies being obtained both before and 24 h after training. Subjects were ranked based on improvement in aerobic fitness, and two cohorts were formed (n = 8 per group: the high-responder group (HRG; peak rate of oxygen consumption increased by +0.71 ± 0.1 L min-1; p -1, ns. ECM genes profiled included the angiopoietin 1 and related genes (angiopoietin 2, tyrosine kinase with immunoglobulin-like and EGF-like domains 1 (TIE1 and 2 (TIE2, vascular endothelial growth factor (VEGF and related receptors (VEGF receptor 1, VEGF receptor 2 and neuropilin-1, thrombospondin-4, α2-macroglobulin and transforming growth factor β2. Results neuropilin-1 (800%; p VEGF receptor 2 (300%; p VEGF receptor 1 mRNA actually declined in the LRG (p TIE1 and TIE2 mRNA levels were unaltered in the LRG, whereas transcription levels of both genes were increased by 2.5-fold in the HRG (p thrombospondin-4 (900%; p α2-macroglobulin (300%, p transforming growth factor β2 transcript increased only in the HRG (330%; p Conclusion We demonstrate for the first time that aerobic training activates angiopoietin 1 and TIE2 genes in human muscle, but only when aerobic capacity adapts to exercise-training. The fourfold-greater increase in aerobic fitness and markedly differing gene expression profile in the HRG indicates that
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.
Frasinski, Leszek J.
2016-08-01
Recent technological advances in the generation of intense femtosecond pulses have made covariance mapping an attractive analytical technique. The laser pulses available are so intense that often thousands of ionisation and Coulomb explosion events will occur within each pulse. To understand the physics of these processes the photoelectrons and photoions need to be correlated, and covariance mapping is well suited for operating at the high counting rates of these laser sources. Partial covariance is particularly useful in experiments with x-ray free electron lasers, because it is capable of suppressing pulse fluctuation effects. A variety of covariance mapping methods is described: simple, partial (single- and multi-parameter), sliced, contingent and multi-dimensional. The relationship to coincidence techniques is discussed. Covariance mapping has been used in many areas of science and technology: inner-shell excitation and Auger decay, multiphoton and multielectron ionisation, time-of-flight and angle-resolved spectrometry, infrared spectroscopy, nuclear magnetic resonance imaging, stimulated Raman scattering, directional gamma ray sensing, welding diagnostics and brain connectivity studies (connectomics). This review gives practical advice for implementing the technique and interpreting the results, including its limitations and instrumental constraints. It also summarises recent theoretical studies, highlights unsolved problems and outlines a personal view on the most promising research directions.
Covariant Bardeen perturbation formalism
Vitenti, S. D. P.; Falciano, F. T.; Pinto-Neto, N.
2014-05-01
In a previous work we obtained a set of necessary conditions for the linear approximation in cosmology. Here we discuss the relations of this approach with the so-called covariant perturbations. It is often argued in the literature that one of the main advantages of the covariant approach to describe cosmological perturbations is that the Bardeen formalism is coordinate dependent. In this paper we will reformulate the Bardeen approach in a completely covariant manner. For that, we introduce the notion of pure and mixed tensors, which yields an adequate language to treat both perturbative approaches in a common framework. We then stress that in the referred covariant approach, one necessarily introduces an additional hypersurface choice to the problem. Using our mixed and pure tensors approach, we are able to construct a one-to-one map relating the usual gauge dependence of the Bardeen formalism with the hypersurface dependence inherent to the covariant approach. Finally, through the use of this map, we define full nonlinear tensors that at first order correspond to the three known gauge invariant variables Φ, Ψ and Ξ, which are simultaneously foliation and gauge invariant. We then stress that the use of the proposed mixed tensors allows one to construct simultaneously gauge and hypersurface invariant variables at any order.
The design of adaptive measurement matrix in compressed sensing%压缩感知自适应观测矩阵设计
Institute of Scientific and Technical Information of China (English)
赵玉娟; 郑宝玉; 陈守宁
2012-01-01
稀疏表示、不相关观测和重构是影响压缩感知性能的三大要素,本文设计的自适应观测矩阵以高斯随机观测阵为初始矩阵,利用信号稀疏域系数的部分先验信息进行自适应变换,形成新的观测阵,当压缩感知矩阵对信号的稀疏系数进行投影时,可使得稀疏系数中的小系数更接近于零；同时,通过减少观测阵行向量的方式来减少观测值,从而应用自适应观测阵后的数据传输量与用高斯随机矩阵的数据传输量相差不大.自适应观测矩阵对压缩感知的性能改进体现在重构精度上,用迭代硬阈值算法作为重构算法,我们从理论和实验仿真两方面验证了自适应观测阵的性能要优于高斯随机矩阵.%Sparse representation, incorrelate projection and reconstruction are the three elements of compressed sensing, This paper uses Gaussian random matrix as original matrix, and adaptively transforms using the partial positional information of sparse coefficients, then forms a new adaptive measurement matrix. When the compressed sensing matrix projects the sparse coefficients, the small coefficients are more close to zero; at the same time, we decrease the measured values by reducing the columns of measurement matrix, thus the difference between the amount of data transmission using adaptive measurement matrix and the amount of data transmission using Gaussian random measurement is little. The improved performance of compressed sensing employed adaptive measurement matrix embodies in the reconstruction accuracy. When we use iterative hard thresholding as reconstruction algorithm; both theory and experiment verify the performance of adaptive measurement matrix better than Gaussian random measurement matrix.
Robust adaptive beamforming for MIMO monopulse radar
Rowe, William; Ström, Marie; Li, Jian; Stoica, Petre
2013-05-01
Researchers have recently proposed a widely separated multiple-input multiple-output (MIMO) radar using monopulse angle estimation techniques for target tracking. The widely separated antennas provide improved tracking performance by mitigating complex target radar cross-section fades and angle scintillation. An adaptive array is necessary in this paradigm because the direct path from any transmitter could act as a jammer at a receiver. When the target-free covariance matrix is not available, it is critical to include robustness into the adaptive beamformer weights. This work explores methods of robust adaptive monopulse beamforming techniques for MIMO tracking radar.
On the Estimation of Integrated Covariance Matrices of High Dimensional Diffusion Processes
Zheng, Xinghua
2010-01-01
We consider the estimation of integrated covariance matrices of high dimensional diffusion processes by using high frequency data. We start by studying the most commonly used estimator, the realized covariance matrix (RCV). We show that in the high dimensional case when the dimension p and the observation frequency n grow in the same rate, the limiting empirical spectral distribution of RCV depends on the covolatility processes not only through the underlying integrated covariance matrix Sigma, but also on how the covolatility processes vary in time. In particular, for two high dimensional diffusion processes with the same integrated covariance matrix, the empirical spectral distributions of their RCVs can be very different. Hence in terms of making inference about the spectrum of the integrated covariance matrix, the RCV is in general \\emph{not} a good proxy to rely on in the high dimensional case. We then propose an alternative estimator, the time-variation adjusted realized covariance matrix (TVARCV), for ...
Covariant canonical quantization
Energy Technology Data Exchange (ETDEWEB)
Hippel, G.M. von [University of Regina, Department of Physics, Regina, Saskatchewan (Canada); Wohlfarth, M.N.R. [Universitaet Hamburg, Institut fuer Theoretische Physik, Hamburg (Germany)
2006-09-15
We present a manifestly covariant quantization procedure based on the de Donder-Weyl Hamiltonian formulation of classical field theory. This procedure agrees with conventional canonical quantization only if the parameter space is d=1 dimensional time. In d>1 quantization requires a fundamental length scale, and any bosonic field generates a spinorial wave function, leading to the purely quantum-theoretical emergence of spinors as a byproduct. We provide a probabilistic interpretation of the wave functions for the fields, and we apply the formalism to a number of simple examples. These show that covariant canonical quantization produces both the Klein-Gordon and the Dirac equation, while also predicting the existence of discrete towers of identically charged fermions with different masses. Covariant canonical quantization can thus be understood as a ''first'' or pre-quantization within the framework of conventional QFT. (orig.)
Covariant canonical quantization
Von Hippel, G M; Hippel, Georg M. von; Wohlfarth, Mattias N.R.
2006-01-01
We present a manifestly covariant quantization procedure based on the de Donder-Weyl Hamiltonian formulation of classical field theory. Covariant canonical quantization agrees with conventional canonical quantization only if the parameter space is d=1 dimensional time. In d>1 quantization requires a fundamental length scale, and any bosonic field generates a spinorial wave function, leading to the purely quantum-theoretical emergence of spinors as a byproduct. We provide a probabilistic interpretation of the wave functions for the fields, and apply the formalism to a number of simple examples. These show that covariant canonical quantization produces both the Klein-Gordon and the Dirac equation, while also predicting the existence of discrete towers of identically charged fermions with different masses.
Trouble shooting for covariance fitting in highly correlated data
Yoon, Boram; Lee, Weonjong; Jung, Chulwoo
2011-01-01
We report a possible solution to the trouble that the covariance fitting fails when the data is highly correlated and the covariance matrix has small eigenvalues. As an example, we choose the data analysis of highly correlated $B_K$ data on the basis of the SU(2) staggered chiral perturbation theory. Basically, the essence of the problem is that we do not have an accurate fitting function so that we cannot fit the highly correlated and precise data. When some eigenvalues of the covariance matrix are small, even a tiny error of fitting function can produce large chi-square and spoil the fitting procedure. We have applied a number of prescriptions available in the market such as diagonal approximation and cutoff method. In addition, we present a new method, the eigenmode shift method which fine-tunes the fitting function while keeping the covariance matrix untouched.
Anisotropic k-Nearest Neighbor Search Using Covariance Quadtree
Marinho, Eraldo Pereira
2011-01-01
We present a variant of the hyper-quadtree that divides a multidimensional space according to the hyperplanes associated to the principal components of the data in each hyperquadrant. Each of the $2^\\lambda$ hyper-quadrants is a data partition in a $\\lambda$-dimension subspace, whose intrinsic dimensionality $\\lambda\\leq d$ is reduced from the root dimensionality $d$ by the principal components analysis, which discards the irrelevant eigenvalues of the local covariance matrix. In the present method a component is irrelevant if its length is smaller than, or comparable to, the local inter-data spacing. Thus, the covariance hyper-quadtree is fully adaptive to the local dimensionality. The proposed data-structure is used to compute the anisotropic K nearest neighbors (kNN), supported by the Mahalanobis metric. As an application, we used the present k nearest neighbors method to perform density estimation over a noisy data distribution. Such estimation method can be further incorporated to the smoothed particle h...
On covariance structure in noisy, big data
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.
Cross-covariance functions for multivariate geostatistics
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.
Conditioning of the stationary kriging matrices for some well-known covariance models
Energy Technology Data Exchange (ETDEWEB)
Posa, D. (IRMA-CNR, Bari (Italy))
1989-10-01
In this paper, the condition number of the stationary kriging matrix is studied for some well-known covariance models. Indeed, the robustness of the kriging weights is strongly affected by this measure. Such an analysis can justify the choice of a covariance function among other admissible models which could fit a given experimental covariance equally well.
Klacka, J
2001-01-01
Relativistically covariant form of equation of motion for real particle (body) under the action of electromagnetic radiation is derived. Equation of motion in the proper frame of the particle uses the radiation pressure cross section 3 $\\times$ 3 matrix. Obtained covariant equation of motion is compared with another covariant equation of motion which was presented more than one year ago.
Generalized Linear Covariance Analysis
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.
Batch Covariance Relaxation (BCR) Adaptive Processing.
1981-08-01
IR&D Final Report 7263-001F, March 15, 1980. [2] Hestenes, M. R. and Steifel, E., "Methods of Conjugate Gradients for Solving Linear Systems," Journal ...Wilkinson, J. H., "Notes on the Solution of Algebraic Linear Simultaneous Equations," Quarterly Journal of Mechanics and Applied Mathematics, pp. 149-173...00 0 In F- P0 -a -- -~e -2 - - -4 - o 0 p.. to I ~ a I I PzN N oin g I ’ l g 1 C 0I ~ Nw c gL Il* . a* . * p. 4r I-in a a4i * Csea OCI I2e Co , lk. lX
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.
A cautionary note on generalized linear models for covariance of unbalanced longitudinal data
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.
Turkoglu, Ahu N; Yucesoy, Can A
2016-05-03
BTX effects on muscular mechanics are highly important, but their mechanism and variability in due treatment course is not well understood. Recent modeling shows that partial muscle paralysis per se causes restricted sarcomere shortening due to muscle fiber-extracellular matrix (ECM) mechanical interactions. This leads to two notable acute-BTX effects compared to pre-BTX treatment condition: (1) enhanced potential of active force production of the non-paralyzed muscle parts, and (2) decreased muscle length range of force exertion (ℓrange). Recent experiments also indicate increased ECM stiffness of BTX treated muscle. Hence, altered muscle fiber-ECM interactions and BTX effects are plausible in due treatment course. Using finite element modeling, the aim was to test the following hypotheses: acute-BTX treatment effects elevate with increased ECM stiffness in the long-term, and are also persistent post-BTX treatment. Model results confirm these hypotheses and show that restricted sarcomere shortening effect becomes more pronounced in the long-term and is persistent or reversed (for longer muscle lengths) post-BTX treatment. Consequently, force production capacity of activated sarcomeres gets further enhanced in the long-term. Remarkably, such enhanced capacity becomes permanent for the entire muscle post-treatment. Shift of muscle optimum length to a shorter length is more pronounced in the long-term, some of which remains permanent post-treatment. Compared to Pre-BTX treatment, a narrower ℓrange (20.3%, 27.1% and 3.4%, acute, long-term and post-BTX treatment, respectively) is a consistent finding. We conclude that ECM adaptations can affect muscular mechanics adversely both during spasticity management and post-BTX treatment. Therefore, this issue deserves major future attention. Copyright © 2016 Elsevier Ltd. All rights reserved.
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...
2015-03-01
ALGORITHM—EIGENVALUE ESTIMATION OF HYPERSPECTRAL WISHART COVARIANCE MATRICES FROM A LIMITED NUMBER OF SAMPLES ECBC-TN-067 Avishai Ben-David...Estimation of Hyperspectral Wishart Covariance Matrices from a Limited Number of Samples 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT...covariance matrices and to recompute a revised covariance matrix from the eigenvalues. The MATLAB function is an implementation of the procedure developed
Saltas, Ippocratis D
2016-01-01
We derive the 1-loop effective action of the cubic Galileon coupled to quantum-gravitational fluctuations in a background and gauge-independent manner, employing the covariant framework of DeWitt and Vilkovisky. Although the bare action respects shift symmetry, the coupling to gravity induces an effective mass to the scalar, of the order of the cosmological constant, as a direct result of the non-flat field-space metric, the latter ensuring the field-reparametrization invariance of the formalism. Within a gauge-invariant regularization scheme, we discover novel, gravitationally induced non-Galileon higher-derivative interactions in the effective action. These terms, previously unnoticed within standard, non-covariant frameworks, are not Planck suppressed. Unless tuned to be sub-dominant, their presence could have important implications for the classical and quantum phenomenology of the theory.
Covariant approximation averaging
Shintani, Eigo; Blum, Thomas; Izubuchi, Taku; Jung, Chulwoo; Lehner, Christoph
2014-01-01
We present a new class of statistical error reduction techniques for Monte-Carlo simulations. Using covariant symmetries, we show that correlation functions can be constructed from inexpensive approximations without introducing any systematic bias in the final result. We introduce a new class of covariant approximation averaging techniques, known as all-mode averaging (AMA), in which the approximation takes account of contributions of all eigenmodes through the inverse of the Dirac operator computed from the conjugate gradient method with a relaxed stopping condition. In this paper we compare the performance and computational cost of our new method with traditional methods using correlation functions and masses of the pion, nucleon, and vector meson in $N_f=2+1$ lattice QCD using domain-wall fermions. This comparison indicates that AMA significantly reduces statistical errors in Monte-Carlo calculations over conventional methods for the same cost.
Using Analysis of Covariance (ANCOVA) with Fallible Covariates
Culpepper, Steven Andrew; Aguinis, Herman
2011-01-01
Analysis of covariance (ANCOVA) is used widely in psychological research implementing nonexperimental designs. However, when covariates are fallible (i.e., measured with error), which is the norm, researchers must choose from among 3 inadequate courses of action: (a) know that the assumption that covariates are perfectly reliable is violated but…
Using Analysis of Covariance (ANCOVA) with Fallible Covariates
Culpepper, Steven Andrew; Aguinis, Herman
2011-01-01
Analysis of covariance (ANCOVA) is used widely in psychological research implementing nonexperimental designs. However, when covariates are fallible (i.e., measured with error), which is the norm, researchers must choose from among 3 inadequate courses of action: (a) know that the assumption that covariates are perfectly reliable is violated but…
Covariant Magnetic Connection Hypersurfaces
Pegoraro, F
2016-01-01
In the single fluid, nonrelativistic, ideal-Magnetohydrodynamic (MHD) plasma description magnetic field lines play a fundamental role by defining dynamically preserved "magnetic connections" between plasma elements. Here we show how the concept of magnetic connection needs to be generalized in the case of a relativistic MHD description where we require covariance under arbitrary Lorentz transformations. This is performed by defining 2-D {\\it magnetic connection hypersurfaces} in the 4-D Minkowski space. This generalization accounts for the loss of simultaneity between spatially separated events in different frames and is expected to provide a powerful insight into the 4-D geometry of electromagnetic fields when ${\\bf E} \\cdot {\\bf B} = 0$.
Covariant Projective Extensions
Institute of Scientific and Technical Information of China (English)
许天周; 梁洁
2003-01-01
@@ The theory of crossed products of C*-algebras by groups of automorphisms is a well-developed area of the theory of operator algebras. Given the importance and the success ofthat theory, it is natural to attempt to extend it to a more general situation by, for example,developing a theory of crossed products of C*-algebras by semigroups of automorphisms, or evenof endomorphisms. Indeed, in recent years a number of papers have appeared that are concernedwith such non-classicaltheories of covariance algebras, see, for instance [1-3].
Covariance fitting of highly-correlated data in lattice QCD
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.
Earth Observing System Covariance Realism
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.
Land, M C
2001-01-01
This paper examines the Stark effect, as a first order perturbation of manifestly covariant hydrogen-like bound states. These bound states are solutions to a relativistic Schr\\"odinger equation with invariant evolution parameter, and represent mass eigenstates whose eigenvalues correspond to the well-known energy spectrum of the non-relativistic theory. In analogy to the nonrelativistic case, the off-diagonal perturbation leads to a lifting of the degeneracy in the mass spectrum. In the covariant case, not only do the spectral lines split, but they acquire an imaginary part which is lnear in the applied electric field, thus revealing induced bound state decay in first order perturbation theory. This imaginary part results from the coupling of the external field to the non-compact boost generator. In order to recover the conventional first order Stark splitting, we must include a scalar potential term. This term may be understood as a fifth gauge potential, which compensates for dependence of gauge transformat...
Institute of Scientific and Technical Information of China (English)
王燕; 杭晓晨; 姜东; 韩晓林; 费庆国
2015-01-01
Stochastic Subspace Identification is a parameter identification method,which can effectively obtain modal parameters from the structural signal under ambient excitation.The choice of Toeplitz matrix row number directly influences the accuracy of identification.By constructing a correlation matrix,the influence of the dimension of Toeplitz matrix i on the denoising ability via SVD was derived.The concept of condition number was introduced in solving the system matrix.According to the relationship between i and condition number of Toeplitz matrix,it is proved once again that i has influence on identification accuracy.Then the selection method of Toeplitz matrix row number i was studied. Two examplic simulations in regard to a two-degree spring mass vibration system and a cropped delta wing model were presented to show the method in the selection of i.The results show that on the premise of determining a suitable system order,the smaller the Toeplitz matrix condition number is,the higher the identification accuracy is.%随机子空间识别算法是一种基于环境激励的模态参数识别方法，仅需要响应时程便可识别模态参数。其中，协方差驱动随机子空间方法中Toeplitz矩阵行数的选取直接影响识别精度。通过构造相关矩阵，研究了Toeplitz矩阵行数i对协方差驱动随机子空间方法中奇异值分解去噪能力的影响。引入Toeplitz矩阵条件数，根据i与Toeplitz矩阵条件数的关系再次证明了i对识别精度的影响。研究了Toeplitz矩阵行数i的选择方法。采用两自由度弹簧振子系统和切尖三角翼模型两个仿真算例研究了Toeplitz矩阵行数i的选择方法。结果表明：在确定合适的系统阶数的前提下，Toeplitz矩阵的条件数越小识别精度越高。
Covariant holography of a tachyonic accelerating universe
Rozas-Fernández, Alberto
2014-01-01
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 $w=p/\\rho$, 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 analysed whether the existence of these preferred screens allows a mathematically consistent formulation of fundamental theories based on the existence of a S matrix at infinite distances.
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.)
Optimal covariate designs theory and applications
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...
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....
Hubeny, Veronika E
2014-01-01
A recently explored interesting quantity in AdS/CFT, dubbed 'residual entropy', characterizes the amount of collective ignorance associated with either boundary observers restricted to finite time duration, or bulk observers who lack access to a certain spacetime region. However, the previously-proposed expression for this quantity involving variation of boundary entanglement entropy (subsequently renamed to 'differential entropy') works only in a severely restrictive context. We explain the key limitations, arguing that in general, differential entropy does not correspond to residual entropy. Given that the concept of residual entropy as collective ignorance transcends these limitations, we identify two correspondingly robust, covariantly-defined constructs: a 'strip wedge' associated with boundary observers and a 'rim wedge' associated with bulk observers. These causal sets are well-defined in arbitrary time-dependent asymptotically AdS spacetimes in any number of dimensions. We discuss their relation, spec...
Covariant Macroscopic Quantum Geometry
Hogan, Craig J
2012-01-01
A covariant noncommutative algebra of position operators is presented, and interpreted as the macroscopic limit of a geometry that describes a collective quantum behavior of the positions of massive bodies in a flat emergent space-time. The commutator defines a quantum-geometrical relationship between world lines that depends on their separation and relative velocity, but on no other property of the bodies, and leads to a transverse uncertainty of the geometrical wave function that increases with separation. The number of geometrical degrees of freedom in a space-time volume scales holographically, as the surface area in Planck units. Ongoing branching of the wave function causes fluctuations in transverse position, shared coherently among bodies with similar trajectories. The theory can be tested using appropriately configured Michelson interferometers.
Saltas, Ippocratis D.; Vitagliano, Vincenzo
2017-05-01
We derive the 1-loop effective action of the cubic Galileon coupled to quantum-gravitational fluctuations in a background and gauge-independent manner, employing the covariant framework of DeWitt and Vilkovisky. Although the bare action respects shift symmetry, the coupling to gravity induces an effective mass to the scalar, of the order of the cosmological constant, as a direct result of the nonflat field-space metric, the latter ensuring the field-reparametrization invariance of the formalism. Within a gauge-invariant regularization scheme, we discover novel, gravitationally induced non-Galileon higher-derivative interactions in the effective action. These terms, previously unnoticed within standard, noncovariant frameworks, are not Planck suppressed. Unless tuned to be subdominant, their presence could have important implications for the classical and quantum phenomenology of the theory.
Covariant holographic entanglement negativity
Chaturvedi, Pankaj; Sengupta, Gautam
2016-01-01
We conjecture a holographic prescription for the covariant entanglement negativity of $d$-dimensional conformal field theories dual to non static bulk $AdS_{d+1}$ gravitational configurations in the framework of the $AdS/CFT$ correspondence. Application of our conjecture to a $AdS_3/CFT_2$ scenario involving bulk rotating BTZ black holes exactly reproduces the entanglement negativity of the corresponding $(1+1)$ dimensional conformal field theories and precisely captures the distillable quantum entanglement. Interestingly our conjecture for the scenario involving dual bulk extremal rotating BTZ black holes also accurately leads to the entanglement negativity for the chiral half of the corresponding $(1+1)$ dimensional conformal field theory at zero temperature.
Improved Step Size Adaptation for the MO-CMA-ES
DEFF Research Database (Denmark)
Voß, T.; Hansen, N.; Igel, Christian
2010-01-01
covariance matrix adaptation evolution strategy (CMA-ES). Step sizes (i.e., mutation strengths) are adapted on individual-level using an improved implementation of the 1/5-th success rule. In the original MO-CMA-ES, a mutation is regarded as successful if the offspring ranks better than its parent....... The new step size adaptation improves the performance of the MO-CMA-ES as shown empirically using a large set of benchmark functions. The new update scheme in general leads to larger step sizes and thereby counteracts premature convergence. The experiments comprise the first evaluation of the MO...
Knowledge-based adaptive polarimetric detection in heterogeneous clutter
Institute of Scientific and Technical Information of China (English)
Yinan Zhao,Fengcong Li,; Xiaolin Qiao
2014-01-01
The detection performance and the constant false alarm rate behavior of the conventional adaptive detectors are severely degraded in heterogeneous clutter. This paper designs and analy-ses a knowledge-based (KB) adaptive polarimetric detector in het-erogeneous clutter. The proposed detection scheme is composed of a data selector using polarization knowledge and an adaptive polarization detector using training data. A polarization data se-lector based on the maximum likelihood estimation is proposed to remove outliers from the heterogeneous training data. This selector can remove outliers effectively, thus the training data is purified for estimating the clutter covariance matrix. Consequently, the performance of the adaptive detector is improved. We assess the performance of the KB adaptive polarimetric detector and the adaptive polarimetric detector without a data selector using sim-ulated data and IPIX radar data. The results show that the KB adaptive polarization detector outperforms its non-KB counter-parts.
A simple procedure for the comparison of covariance matrices.
Garcia, Carlos
2012-11-21
Comparing the covariation patterns of populations or species is a basic step in the evolutionary analysis of quantitative traits. Here I propose a new, simple method to make this comparison in two population samples that is based on comparing the variance explained in each sample by the eigenvectors of its own covariance matrix with that explained by the covariance matrix eigenvectors of the other sample. The rationale of this procedure is that the matrix eigenvectors of two similar samples would explain similar amounts of variance in the two samples. I use computer simulation and morphological covariance matrices from the two morphs in a marine snail hybrid zone to show how the proposed procedure can be used to measure the contribution of the matrices orientation and shape to the overall differentiation. I show how this procedure can detect even modest differences between matrices calculated with moderately sized samples, and how it can be used as the basis for more detailed analyses of the nature of these differences. The new procedure constitutes a useful resource for the comparison of covariance matrices. It could fill the gap between procedures resulting in a single, overall measure of differentiation, and analytical methods based on multiple model comparison not providing such a measure.
A simple procedure for the comparison of covariance matrices
2012-01-01
Background Comparing the covariation patterns of populations or species is a basic step in the evolutionary analysis of quantitative traits. Here I propose a new, simple method to make this comparison in two population samples that is based on comparing the variance explained in each sample by the eigenvectors of its own covariance matrix with that explained by the covariance matrix eigenvectors of the other sample. The rationale of this procedure is that the matrix eigenvectors of two similar samples would explain similar amounts of variance in the two samples. I use computer simulation and morphological covariance matrices from the two morphs in a marine snail hybrid zone to show how the proposed procedure can be used to measure the contribution of the matrices orientation and shape to the overall differentiation. Results I show how this procedure can detect even modest differences between matrices calculated with moderately sized samples, and how it can be used as the basis for more detailed analyses of the nature of these differences. Conclusions The new procedure constitutes a useful resource for the comparison of covariance matrices. It could fill the gap between procedures resulting in a single, overall measure of differentiation, and analytical methods based on multiple model comparison not providing such a measure. PMID:23171139
A simple procedure for the comparison of covariance matrices
Directory of Open Access Journals (Sweden)
Garcia Carlos
2012-11-01
Full Text Available Abstract Background Comparing the covariation patterns of populations or species is a basic step in the evolutionary analysis of quantitative traits. Here I propose a new, simple method to make this comparison in two population samples that is based on comparing the variance explained in each sample by the eigenvectors of its own covariance matrix with that explained by the covariance matrix eigenvectors of the other sample. The rationale of this procedure is that the matrix eigenvectors of two similar samples would explain similar amounts of variance in the two samples. I use computer simulation and morphological covariance matrices from the two morphs in a marine snail hybrid zone to show how the proposed procedure can be used to measure the contribution of the matrices orientation and shape to the overall differentiation. Results I show how this procedure can detect even modest differences between matrices calculated with moderately sized samples, and how it can be used as the basis for more detailed analyses of the nature of these differences. Conclusions The new procedure constitutes a useful resource for the comparison of covariance matrices. It could fill the gap between procedures resulting in a single, overall measure of differentiation, and analytical methods based on multiple model comparison not providing such a measure.
Institute of Scientific and Technical Information of China (English)
马玲玲; 洪留荣; 胡倩
2014-01-01
实验鼠行为分类在神经科学、生物科学、药物开发等领域的研究中十分重要.针对行为分析中实验鼠肢体短小，提取相关信息困难问题，应用实验鼠轮廓抽取特征，进行实验鼠的行为分类.首先从视频每一帧图像中分割出实验鼠轮廓，并提取轮廓中心从八个方向上到轮廓边缘的距离以及轮廓区域的两个主分量，组成一个10维向量作为特征向量，最后应用协方差距离进行分类.实验结果显示，分类正确率达87.6%.%The classification of mice behavior is very important in the study fields of neuroscience, biological science, drug development and so on. In behavioral analysis,according to the short limbs of mice, it is difficult to extract relevant information. Mouse behavior is classified using characteristics of mouse contour. Firstly, mouse contour is extracted from each frame behavior video. Then, the distance of eight directions are calculated which are from contour center to edge and calculate two main components of contour. Using these values to form ten dimensional vectors as feature vector. Finally, calculate the log-covariance distance and classify. Experimental results show that the correct rate of classification is 87.6%.
Colautti, Robert I; Barrett, Spencer C H
2011-09-01
Evolution during biological invasion may occur over contemporary timescales, but the rate of evolutionary change may be inhibited by a lack of standing genetic variation for ecologically relevant traits and by fitness trade-offs among them. The extent to which these genetic constraints limit the evolution of local adaptation during biological invasion has rarely been examined. To investigate genetic constraints on life-history traits, we measured standing genetic variance and covariance in 20 populations of the invasive plant purple loosestrife (Lythrum salicaria) sampled along a latitudinal climatic gradient in eastern North America and grown under uniform conditions in a glasshouse. Genetic variances within and among populations were significant for all traits; however, strong intercorrelations among measurements of seedling growth rate, time to reproductive maturity and adult size suggested that fitness trade-offs have constrained population divergence. Evidence to support this hypothesis was obtained from the genetic variance-covariance matrix (G) and the matrix of (co)variance among population means (D), which were 79.8% (95% C.I. 77.7-82.9%) similar. These results suggest that population divergence during invasive spread of L. salicaria in eastern North America has been constrained by strong genetic correlations among life-history traits, despite large amounts of standing genetic variation for individual traits. © 2011 The Author(s).
DEFF Research Database (Denmark)
Kim, Won-Sang; Lee, Kyo-Beum; Huh, Sunghoi;
2007-01-01
This paper presents a new robust sensorless control system for high performance induction motor drives fed by a matrix converter with variable structure. The lumped disturbances such as parameter variation and load disturbance of the system are estimated by a variable structure approach based...
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.
Progress of Covariance Evaluation at the China Nuclear Data Center
Energy Technology Data Exchange (ETDEWEB)
Xu, R., E-mail: xuruirui@ciae.ac.cn [China Nuclear Data Center, P.O. Box, 275(41), Beijing 102413 (China); Zhang, Q. [China Nuclear Data Center, P.O. Box, 275(41), Beijing 102413 (China); Shanxi Normal University, Linfen, Shanxi Province 041004 (China); Zhang, Y.; Liu, T.; Ge, Z.; Lu, H.; Sun, Z.; Yu, B. [China Nuclear Data Center, P.O. Box, 275(41), Beijing 102413 (China); Tang, G. [Peking University, Beijing 100871 (China)
2015-01-15
Covariance evaluations at the China Nuclear Data Center focus on the cross sections of structural materials and actinides in the fast neutron energy range. In addition to the well-known Least-squares approach, a method based on the analysis of the sources of experimental uncertainties is especially introduced to generate a covariance matrix for a particular reaction for which multiple measurements are available. The scheme of the covariance evaluation flow is presented, and an example of n+{sup 90}Zr is given to illuminate the whole procedure. It is proven that the accuracy of measurements can be properly incorporated into the covariance and the long-standing small uncertainty problem can be avoided.
How covariant is the galaxy luminosity function?
Smith, Robert E
2012-01-01
We investigate the error properties of certain galaxy luminosity function (GLF) estimators. Using a cluster expansion of the density field, we show how, for both volume and flux limited samples, the GLF estimates are covariant. The covariance matrix can be decomposed into three pieces: a diagonal term arising from Poisson noise; a sample variance term arising from large-scale structure in the survey volume; an occupancy covariance term arising due to galaxies of different luminosities inhabiting the same cluster. To evaluate the theory one needs: the mass function and bias of clusters, and the conditional luminosity function (CLF). We use a semi-analytic model (SAM) galaxy catalogue from the Millennium run N-body simulation and the CLF of Yang et al. (2003) to explore these effects. The GLF estimates from the SAM and the CLF qualitatively reproduce results from the 2dFGRS. We also measure the luminosity dependence of clustering in the SAM and find reasonable agreement with 2dFGRS results for bright galaxies. ...
Covariant electromagnetic field lines
Hadad, Y.; Cohen, E.; Kaminer, I.; Elitzur, A. C.
2017-08-01
Faraday introduced electric field lines as a powerful tool for understanding the electric force, and these field lines are still used today in classrooms and textbooks teaching the basics of electromagnetism within the electrostatic limit. However, despite attempts at generalizing this concept beyond the electrostatic limit, such a fully relativistic field line theory still appears to be missing. In this work, we propose such a theory and define covariant electromagnetic field lines that naturally extend electric field lines to relativistic systems and general electromagnetic fields. We derive a closed-form formula for the field lines curvature in the vicinity of a charge, and show that it is related to the world line of the charge. This demonstrates how the kinematics of a charge can be derived from the geometry of the electromagnetic field lines. Such a theory may also provide new tools in modeling and analyzing electromagnetic phenomena, and may entail new insights regarding long-standing problems such as radiation-reaction and self-force. In particular, the electromagnetic field lines curvature has the attractive property of being non-singular everywhere, thus eliminating all self-field singularities without using renormalization techniques.
Sparse reduced-rank regression with covariance estimation
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.
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...... of the problem at hand. We develop a theory for the point process of the normalized eigenvalues of the sample covariance matrix in the case where rows and columns of the data are linearly dependent. Based on the weak convergence of this point process we derive the limit laws of various functionals...... of the eigenvalues. In the second part, we show that the largest and smallest eigenvalues of a highdimensional sample correlation matrix possess almost sure non-random limits if the truncated variance of the entry distribution is “almost slowly varying”, a condition we describe via moment properties of self...
Adaptive Kalman Filter of Transfer Alignment with Un-modeled Wing Flexure of Aircraft
Institute of Scientific and Technical Information of China (English)
无
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.
Spectral Density of Sample Covariance Matrices of Colored Noise
Dolezal, Emil
2008-01-01
We study the dependence of the spectral density of the covariance matrix ensemble on the power spectrum of the underlying multivariate signal. The white noise signal leads to the celebrated Marchenko-Pastur formula. We demonstrate results for some colored noise signals.
Modeling the Conditional Covariance between Stock and Bond Returns
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 asymmet
Institute of Scientific and Technical Information of China (English)
2015-01-01
针对经典高分辨波达方位(DOA)估计方法在低信噪比下分辨性能较差的问题，该文提出一种适用于主动探测系统的基于互相关矩阵的改进多重信号分类(MUSIC)高分辨方位估计方法(I-MUSIC)。该方法首先利用主动声呐发射信号已知的特性，将发射信号与阵元接收信号进行互相关，利用互相关序列形成新的空域协方差矩阵，再进行特征分解。理论分析表明，互相关处理在抑制噪声的同时保留了阵元之间的相位信息，可以得到比MUSIC方法更准确的子空间划分，进而提高低信噪比方位估计性能。在此基础上，提出一种基于相关时间门限的改进MUSIC高分辨方位估计(T-MUSIC)方法，通过对互相关序列设置时间门限进一步提高方位估计信噪比。仿真结果表明，与MUSIC方法相比，I-MUSIC与T-MUSIC可以分别使低信噪比时的估计性能提高3 dB和6 dB，相应平均估计误差分别为原方法的77%和53%。在阵元间接收噪声存在相关性时，T-MUSIC与I-MUSIC方法相比可获得8 dB的估计增益，估计性能更优。I-MUSIC 与 T-MUSIC 应用于多目标主动探测，可大幅提高探测系统在低信噪比下的方位估计性能。%In view of the poor performance of traditional Direction of Arrival (DOA) methods at low signal-to-noise ratios, an improved MUltiple SIgnal Classification (MUSIC) algorithm for DOA estimation applied to active detection system based on covariance matrix decomposition of cross-correlation (I-MUSIC) is proposed. Exploiting the transmission feature of active sonar, cross-correlation sequence between the transmitted signal and the array output is formulated. The spatial covariance matrix is then constructed from the sequence. Then matrix decomposition is implemented over the new spatial covariance matrix to estimate the DOA. It is proved that cross-correlation can suppress noise while preserving the phase information between array
Daniel Bartz; Kerr Hatrick; Hesse, Christian W.; Klaus-Robert M\\"uller; Steven Lemm
2011-01-01
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, w...
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…
Evaluation of covariance and information performance measures for dynamic object tracking
Yang, Chun; Blasch, Erik; Douville, Phil; Kaplan, Lance; Qiu, Di
2010-04-01
In surveillance and reconnaissance applications, dynamic objects are dynamically followed by track filters with sequential measurements. There are two popular implementations of tracking filters: one is the covariance or Kalman filter and the other is the information filter. Evaluation of tracking filters is important in performance optimization not only for tracking filter design but also for resource management. Typically, the information matrix is the inverse of the covariance matrix. The covariance filter-based approaches attempt to minimize the covariance matrix-based scalar indexes whereas the information filter-based methods aim at maximizing the information matrix-based scalar indexes. Such scalar performance measures include the trace, determinant, norms (1-norm, 2-norm, infinite-norm, and Forbenius norm), and eigenstructure of the covariance matrix or the information matrix and their variants. One natural question to ask is if the scalar track filter performance measures applied to the covariance matrix are equivalent to those applied to the information matrix? In this paper we show most of the scalar performance indexes are equivalent yet some are not. As a result, the indexes if used improperly would provide an "optimized" solution but in the wrong sense relative to track accuracy. The simulation indicated that all the seven indexes were successful when applied to the covariance matrix. However, the failed indexes for the information filter include the trace and the four norms (as defined in MATLAB) of the information matrix. Nevertheless, the determinant and the properly selected eigenvalue of the information matrix were successful to select the optimal sensor update configuration. The evaluation analysis of track measures can serve as a guideline to determine the suitability of performance measures for tracking filter design and resource management.
Lorentz Covariant Canonical Symplectic Algorithms for Dynamics of Charged Particles
Wang, Yulei; Qin, Hong
2016-01-01
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 (LCCSA) 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 discrete symplectic structure and Lorentz symmetry of the system. For situations with time-dependent electromagnetic fields, which is 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 th...
Covariant representations of subproduct systems
Viselter, Ami
2010-01-01
A celebrated theorem of Pimsner states that a covariant representation $T$ of a $C^*$-correspondence $E$ extends to a $C^*$-representation of the Toeplitz algebra of $E$ if and only if $T$ is isometric. This paper is mainly concerned with finding conditions for a covariant representation of a \\emph{subproduct system} to extend to a $C^*$-representation of the Toeplitz algebra. This framework is much more general than the former. We are able to find sufficient conditions, and show that in important special cases, they are also necessary. Further results include the universality of the tensor algebra, dilations of completely contractive covariant representations, Wold decompositions and von Neumann inequalities.
Disintegrating the fly: A mutational perspective on phenotypic integration and covariation.
Haber, Annat; Dworkin, Ian
2017-01-01
The structure of environmentally induced phenotypic covariation can influence the effective strength and magnitude of natural selection. Yet our understanding of the factors that contribute to and influence the evolutionary lability of such covariation is poor. Most studies have either examined environmental variation without accounting for covariation, or examined phenotypic and genetic covariation without distinguishing the environmental component. In this study, we examined the effect of mutational perturbations on different properties of environmental covariation, as well as mean shape. We use strains of Drosophila melanogaster bearing well-characterized mutations known to influence wing shape, as well as naturally derived strains, all reared under carefully controlled conditions and with the same genetic background. We find that mean shape changes more freely than the covariance structure, and that different properties of the covariance matrix change independently from each other. The perturbations affect matrix orientation more than they affect matrix eccentricity or total variance. Yet, mutational effects on matrix orientation do not cluster according to the developmental pathway that they target. These results suggest that it might be useful to consider a more general concept of "decanalization," involving all aspects of variation and covariation.
Vast Volatility Matrix Estimation using High Frequency Data for Portfolio Selection
Fan, Jianqing; Yu, Ke
2010-01-01
Portfolio allocation with gross-exposure constraint is an effective method to increase the efficiency and stability of selected portfolios among a vast pool of assets, as demonstrated in Fan et al (2008). 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 proposed by Barndorff-Nielsen et al (2008) for estimation of vast covariance matrix and compare their merits in the portfolio selection. We also establish the concentration inequalities of the estimates, which guarantee desirable properties of the estimated volatility matrix in vast asset ...
High-dimensional Sparse Inverse Covariance Estimation using Greedy Methods
Johnson, Christopher C; Ravikumar, Pradeep
2011-01-01
In this paper we consider the task of estimating the non-zero pattern of the sparse inverse covariance matrix of a zero-mean Gaussian random vector from a set of iid samples. Note that this is also equivalent to recovering the underlying graph structure of a sparse Gaussian Markov Random Field (GMRF). We present two novel greedy approaches to solving this problem. The first estimates the non-zero covariates of the overall inverse covariance matrix using a series of global forward and backward greedy steps. The second estimates the neighborhood of each node in the graph separately, again using greedy forward and backward steps, and combines the intermediate neighborhoods to form an overall estimate. The principal contribution of this paper is a rigorous analysis of the sparsistency, or consistency in recovering the sparsity pattern of the inverse covariance matrix. Surprisingly, we show that both the local and global greedy methods learn the full structure of the model with high probability given just $O(d\\log...
The feasibility of adapting a population-based asthma-specific job exposure matrix (JEM) to NHANES.
McHugh, Michelle K; Symanski, Elaine; Pompeii, Lisa A; Delclos, George L
2010-12-01
To determine the feasibility of applying a job exposure matrix (JEM) for classifying exposures to 18 asthmagens in the National Health and Nutrition Examination Survey (NHANES), 1999-2004. We cross-referenced 490 National Center for Health Statistics job codes used to develop the 40 NHANES occupation groups with 506 JEM job titles and assessed homogeneity in asthmagen exposure across job codes within each occupation group. In total, 399 job codes corresponded to one JEM job title, 32 to more than one job title, and 59 were not in the JEM. Three occupation groups had the same asthmagen exposure across job codes, 11 had no asthmagen exposure, and 26 groups had heterogeneous exposures across jobs codes. The NHANES classification of occupations limits the use of the JEM to evaluate the association between workplace exposures and asthma and more refined occupational data are needed to enhance work-related injury/illness surveillance efforts.
Positive semidefinite integrated covariance estimation, factorizations and asynchronicity
DEFF Research Database (Denmark)
Sauri, Orimar; Lunde, Asger; Laurent, Sébastien;
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...... observations as possible. The estimator is positive semidefinite by construction. We derive asymptotic results and confirm their good finite sample properties by means of a Monte Carlo simulation. In the application we forecast portfolio Value-at-Risk and sector risk exposures for a portfolio of 52 stocks. We...
A Blind Detection Algorithm Utilizing Statistical Covariance in Cognitive Radio
Directory of Open Access Journals (Sweden)
Yingxue Li
2012-11-01
Full Text Available As the expression of performance parameters are obtained using asymptotic method in most blind covariance detection algorithm, the paper presented a new blind detection algorithm using cholesky factorization. Utilizing random matrix theory, we derived the performance parameters using non-asymptotic method. The proposed method overcomes the noise uncertainty problem and performs well without any information about the channel, primary user and noise. Numerical simulation results demonstrate that the performance parameters expressions are correct and the new detector outperforms the other blind covariance detectors.
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 part...... of the derivation, we introduce a blurring operator At that acts on jet space contrary to doing spatial filtering and a scaling operator Ss. The stochastic Brownian image model is an example of a class of functions which are scale invariant with respect to the operators At and Ss. This paper also includes empirical...
Algorithme d'adaptation du filtre de Kalman aux variations soudaines de bruit
Canciu, Vintila
This research targets the case of Kalman filtering as applied to linear time-invariant systems having unknown process noise covariance and measurement noise covariance matrices and addresses the problem represented by the incomplete a priori knowledge of these two filter initialization parameters. The goal of this research is to determine in realtime both the process covariance matrix and the noise covariance matrix in the context of adaptive Kalman filtering. The resultant filter, called evolutionary adaptive Kalman filter, is able to adapt to sudden noise variations and constitutes a hybrid solution for adaptive Kalman filtering based on metaheuristic algorithms. MATLAB/Simulink simulation using several processes and covariance matrices plus comparison with other filters was selected as validation method. The Cramer-Rae Lower Bound (CRLB) was used as performance criterion. The thesis begins with a description of the problem under consideration (the design of a Kalman filter that is able to adapt to sudden noise variations) followed by a typical application (INS-GPS integrated navigation system) and by a statistical analysis of publications related to adaptive Kalman filtering. Next, the thesis presents the current architectures of the adaptive Kalman filtering: the innovation adaptive estimator (IAE) and the multiple model adaptive estimator (MMAE). It briefly presents their formulation, their behavior, and the limit of their performances. The thesis continues with the architectural synthesis of the evolutionary adaptive Kalman filter. The steps involved in the solution of the problem under consideration is also presented: an analysis of Kalman filtering and sub-optimal filtering methods, a comparison of current adaptive Kalman and sub-optimal filtering methods, the emergence of evolutionary adaptive Kalman filter as an enrichment of sub-optimal filtering with the help of biological-inspired computational intelligence methods, and the step-by-step architectural
DEFF Research Database (Denmark)
Kinnebrock, Silja; Podolskij, Mark
This paper introduces a new estimator to measure the ex-post covariation between high-frequency financial time series under market microstructure noise. We provide an asymptotic limit theory (including feasible central limit theorems) for standard methods such as regression, correlation analysis...... and covariance, for which we obtain the optimal rate of convergence. We demonstrate some positive semidefinite estimators of the covariation and construct a positive semidefinite estimator of the conditional covariance matrix in the central limit theorem. Furthermore, we indicate how the assumptions on the noise...
DEFF Research Database (Denmark)
Kinnebrock, Silja; Podolskij, Mark
and covariance, for which we obtain the optimal rate of convergence. We demonstrate some positive semidefinite estimators of the covariation and construct a positive semidefinite estimator of the conditional covariance matrix in the central limit theorem. Furthermore, we indicate how the assumptions on the noise......This paper introduces a new estimator to measure the ex-post covariation between high-frequency financial time series under market microstructure noise. We provide an asymptotic limit theory (including feasible central limit theorems) for standard methods such as regression, correlation analysis...
Calcul Stochastique Covariant à Sauts & Calcul Stochastique à Sauts Covariants
Maillard-Teyssier, Laurence
2003-01-01
We propose a stochastic covariant calculus forcàdlàg semimartingales in the tangent bundle $TM$ over a manifold $M$. A connection on $M$ allows us to define an intrinsic derivative ofa $C^1$ curve $(Y_t)$ in $TM$, the covariantderivative. More precisely, it is the derivative of$(Y_t)$ seen in a frame moving parallelly along its projection curve$(x_t)$ on $M$. With the transfer principle, Norris defined thestochastic covariant integration along a continuous semimartingale in$TM$. We describe t...
Covariate-free and Covariate-dependent Reliability.
Bentler, Peter M
2016-12-01
Classical test theory reliability coefficients are said to be population specific. Reliability generalization, a meta-analysis method, is the main procedure for evaluating the stability of reliability coefficients across populations. A new approach is developed to evaluate the degree of invariance of reliability coefficients to population characteristics. Factor or common variance of a reliability measure is partitioned into parts that are, and are not, influenced by control variables, resulting in a partition of reliability into a covariate-dependent and a covariate-free part. The approach can be implemented in a single sample and can be applied to a variety of reliability coefficients.
A New Adaptive Square-Root Unscented Kalman Filter for Nonlinear Systems with Additive Noise
Directory of Open Access Journals (Sweden)
Yong Zhou
2015-01-01
Full Text Available The Kalman filter (KF, extended KF, and unscented KF all lack a self-adaptive capacity to deal with system noise. This paper describes a new adaptive filtering approach for nonlinear systems with additive noise. Based on the square-root unscented KF (SRUKF, traditional Maybeck’s estimator is modified and extended to nonlinear systems. The square root of the process noise covariance matrix Q or that of the measurement noise covariance matrix R is estimated straightforwardly. Because positive semidefiniteness of Q or R is guaranteed, several shortcomings of traditional Maybeck’s algorithm are overcome. Thus, the stability and accuracy of the filter are greatly improved. In addition, based on three different nonlinear systems, a new adaptive filtering technique is described in detail. Specifically, simulation results are presented, where the new filter was applied to a highly nonlinear model (i.e., the univariate nonstationary growth model (UNGM. The UNGM is compared with the standard SRUKF to demonstrate its superior filtering performance. The adaptive SRUKF (ASRUKF algorithm can complete direct recursion and calculate the square roots of the variance matrixes of the system state and noise, which ensures the symmetry and nonnegative definiteness of the matrixes and greatly improves the accuracy, stability, and self-adaptability of the filter.
Directory of Open Access Journals (Sweden)
I PUTU EKA IRAWAN
2014-01-01
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.
Matrix algebra for higher order moments
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 corre
Minaker, Leia M; Raine, Kim D; Wild, T Cameron; Nykiforuk, Candace I J; Thompson, Mary E; Frank, Lawrence D
2014-02-15
Few studies have assessed the construct validity of measures of neighborhood food environment, which remains a major challenge in accurately assessing food access. In this study, we adapted a psychometric tool to examine the construct validity of 4 such measures for 3 constructs. We used 4 food-environment measures to collect objective data from 422 Ontario, Canada, food stores in 2010. Residents' perceptions of their neighborhood food environment were collected from 2,397 households between 2009 and 2010. Objective and perceptual data were aggregated within buffer zones around respondents' homes (at 250 m, 500 m, 1,000 m, and 1,500 m). We constructed multitrait-multimethod matrices for each scale to examine construct validity for the constructs of food availability, food quality, and food affordability. Convergent validity between objective measures decreased with increasing geographic scale. Convergent validity between objective and subjective measures increased with increasing geographic scale. High discriminant validity coefficients existed between food availability and food quality, indicating that these two constructs may not be distinct in this setting. We conclude that the construct validity of food environment measures varies over geographic scales, which has implications for research, policy, and practice.
Galaxy-galaxy lensing estimators and their covariance properties
Singh, Sukhdeep; Seljak, Uroš; Slosar, Anže; Gonzalez, Jose Vazquez
2016-01-01
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 catalogs and lenses (specifically the BOSS LOWZ sample). Using mock catalogs 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 density field instead of the over-density field, and that this leads to a significant error increase 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 over-density, we find that the errors are dominated by the shape noise and lens clustering, that empirically estimated covarianc...
Szekeres models: a covariant approach
Apostolopoulos, Pantelis S
2016-01-01
We exploit the 1+1+2 formalism to covariantly describe the inhomogeneous and anisotropic Szekeres models. It is shown that an \\emph{average scale length} can be defined \\emph{covariantly} which satisfies a 2d equation of motion driven from the \\emph{effective gravitational mass} (EGM) contained in the dust cloud. The contributions to the EGM are encoded to the energy density of the dust fluid and the free gravitational field $E_{ab}$. In addition the notions of the Apparent and Absolute Apparent Horizons are briefly discussed and we give an alternative gauge-invariant form to define them in terms of the kinematical variables of the spacelike congruences. We argue that the proposed program can be used in order to express the Sachs optical equations in a covariant form and analyze the confrontation of a spatially inhomogeneous irrotational overdense fluid model with the observational data.
Covariance evaluation work at LANL
Energy Technology Data Exchange (ETDEWEB)
Kawano, Toshihiko [Los Alamos National Laboratory; Talou, Patrick [Los Alamos National Laboratory; Young, Phillip [Los Alamos National Laboratory; Hale, Gerald [Los Alamos National Laboratory; Chadwick, M B [Los Alamos National Laboratory; Little, R C [Los Alamos National Laboratory
2008-01-01
Los Alamos evaluates covariances for nuclear data library, mainly for actinides above the resonance regions and light elements in the enUre energy range. We also develop techniques to evaluate the covariance data, like Bayesian and least-squares fitting methods, which are important to explore the uncertainty information on different types of physical quantities such as elastic scattering angular distribution, or prompt neutron fission spectra. This paper summarizes our current activities of the covariance evaluation work at LANL, including the actinide and light element data mainly for the criticality safety study and transmutation technology. The Bayesian method based on the Kalman filter technique, which combines uncertainties in the theoretical model and experimental data, is discussed.
Cosmic Censorship Conjecture revisited: Covariantly
Hamid, Aymen I M; Maharaj, Sunil D
2014-01-01
In this paper we study the dynamics of the trapped region using a frame independent semi-tetrad covariant formalism for general Locally Rotationally Symmetric (LRS) class II spacetimes. We covariantly prove some important geometrical results for the apparent horizon, and state the necessary and sufficient conditions for a singularity to be locally naked. These conditions bring out, for the first time in a quantitative and transparent manner, the importance of the Weyl curvature in deforming and delaying the trapped region during continual gravitational collapse, making the central singularity locally visible.
MIMO-radar Waveform Covariance Matrices for High SINR and Low Side-lobe Levels
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.
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
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
Transformation rule for covariance matrices under Bell-like detections
Spedalieri, Gaetana; Pirandola, Stefano
2013-01-01
Starting from the transformation rule of a covariance matrix under homodyne detections, we can easily derive a formula for the transformation of a covariance matrix of (n+2) bosonic modes under Bell-like detections, where the last two modes are combined in an arbitrary beam splitter (i.e., with arbitrary transmissivity) and then homodyned. This formula can be specialized to describe the standard Bell detection and the heterodyne measurement, which are exploited in many contexts, including protocols of quantum teleportation, entanglement swapping and quantum cryptography. Our general formula can be adopted to study these protocols in the presence of experimental imperfections or asymmetric setups, e.g., deriving from the use of unbalanced beam splitters.
Sparse Inverse Covariance Selection via Alternating Linearization Methods
Scheinberg, Katya; Goldfarb, Donald
2010-01-01
Gaussian graphical models are of great interest in statistical learning. Because the conditional independencies between different nodes correspond to zero entries in the inverse covariance matrix of the Gaussian distribution, one can learn the structure of the graph by estimating a sparse inverse covariance matrix from sample data, by solving a convex maximum likelihood problem with an $\\ell_1$-regularization term. In this paper, we propose a first-order method based on an alternating linearization technique that exploits the problem's special structure; in particular, the subproblems solved in each iteration have closed-form solutions. Moreover, our algorithm obtains an $\\epsilon$-optimal solution in $O(1/\\epsilon)$ iterations. Numerical experiments on both synthetic and real data from gene association networks show that a practical version of this algorithm outperforms other competitive algorithms.
Fast Component Pursuit for Large-Scale Inverse Covariance Estimation.
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 ℓ1 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.
Covariant description of isothermic surfaces
Tafel, Jacek
2014-01-01
We present a covariant formulation of the Gauss-Weingarten equations and the Gauss-Mainardi-Codazzi equations for surfaces in 3-dimensional curved spaces. We derive a coordinate invariant condition on the first and second fundamental form which is necessary and sufficient for the surface to be isothermic.
Covariation Neglect among Novice Investors
Hedesstrom, Ted Martin; Svedsater, Henrik; Garling, Tommy
2006-01-01
In 4 experiments, undergraduates made hypothetical investment choices. In Experiment 1, participants paid more attention to the volatility of individual assets than to the volatility of aggregated portfolios. The results of Experiment 2 show that most participants diversified even when this increased risk because of covariation between the returns…
Do Time-Varying Covariances, Volatility Comovement and Spillover Matter?
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...
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.
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.
IMPROVED COVARIANCE DRIVEN BLIND SUBSPACE IDENTIFICATION METHOD
Institute of Scientific and Technical Information of China (English)
ZHANG Zhiyi; FAN Jiangling; HUA Hongxing
2006-01-01
An improved covariance driven subspace identification method is presented to identify the weakly excited modes. In this method, the traditional Hankel matrix is replaced by a reformed one to enhance the identifiability of weak characteristics. The robustness of eigenparameter estimation to noise contamination is reinforced by the improved Hankel matrix. In combination with component energy index (CEI) which indicates the vibration intensity of signal components, an alternative stabilization diagram is adopted to effectively separate spurious and physical modes. Simulation of a vibration system of multiple-degree-of-freedom and experiment of a frame structure subject to wind excitation are presented to demonstrate the improvement of the proposed blind method. The performance of this blind method is assessed in terms of its capability in extracting the weak modes as well as the accuracy of estimated parameters. The results have shown that the proposed blind method gives a better estimation of the weak modes from response signals of small signal to noise ratio (SNR)and gives a reliable separation of spurious and physical estimates.
Aoki, H; Kawai, H; Kitazawa, Y; Tada, T; Tsuchiya, A
1999-01-01
We review our proposal for a constructive definition of superstring, type IIB matrix model. The IIB matrix model is a manifestly covariant model for space-time and matter which possesses N=2 supersymmetry in ten dimensions. We refine our arguments to reproduce string perturbation theory based on the loop equations. We emphasize that the space-time is dynamically determined from the eigenvalue distributions of the matrices. We also explain how matter, gauge fields and gravitation appear as fluctuations around dynamically determined space-time.
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.
Mukherjee, Saikat; Bandyopadhyay, Sudip; Paul, Amit Kumar; Adhikari, Satrajit
2013-04-25
We present the molecular symmetry (MS) adapted treatment of nonadiabatic coupling terms (NACTs) for the excited electronic states (2(2)E' and 1(2)A1') of Na3 cluster, where the adiabatic potential energy surfaces (PESs) and the NACTs are calculated at the MRCI level by using an ab initio quantum chemistry package (MOLPRO). The signs of the NACTs at each point of the configuration space (CS) are determined by employing appropriate irreducible representations (IREPs) arising due to MS group, and such terms are incorporated into the adiabatic to diabatic transformation (ADT) equations to obtain the ADT angles. Since those sign corrected NACTs and the corresponding ADT angles demonstrate the validity of curl condition for the existence of three-state (2(2)E' and 1(2)A1') sub-Hilbert space, it becomes possible to construct the continuous, single-valued, symmetric, and smooth 3 × 3 diabatic Hamiltonian matrix. Finally, nuclear dynamics has been carried out on such diabatic surfaces to explore whether our MS-based treatment of diabatization can reproduce the pattern of the experimental spectrum for system B of Na3 cluster.
Background error covariance modelling for convective-scale variational data assimilation
Petrie, R. E.
An essential component in data assimilation is the background error covariance matrix (B). This matrix regularizes the ill-posed data assimilation problem, describes the confidence of the background state and spreads information. Since the B-matrix is too large to represent explicitly it must be modelled. In variational data assimilation it is essentially a climatological approximation of the true covariances. Such a conventional covariance model additionally relies on the imposition of balance conditions. A toy model which is derived from the Euler equations (by making appropriate simplifications and introducing tuneable parameters) is used as a convective-scale system to investigate these issues. Its behaviour is shown to exhibit large-scale geostrophic and hydrostatic balance while permitting small-scale imbalance. A control variable transform (CVT) approach to modelling the B-matrix where the control variables are taken to be the normal modes (NM) of the linearized model is investigated. This approach is attractive for convective-scale covariance modelling as it allows for unbalanced as well as appropriately balanced relationships. Although the NM-CVT is not applied to a data assimilation problem directly, it is shown to be a viable approach to convective-scale covariance modelling. A new mathematically rigorous method to incorporate flow-dependent error covariances with the otherwise static B-matrix estimate is also proposed. This is an extension to the reduced rank Kalman filter (RRKF) where its Hessian singular vector calculation is replaced by an ensemble estimate of the covariances, and is known as the ensemble RRKF (EnRRKF). Ultimately it is hoped that together the NM-CVT and the EnRRKF would improve the predictability of small-scale features in convective-scale weather forecasting through the relaxation of inappropriate balance and the inclusion of flow-dependent covariances.
Institute of Scientific and Technical Information of China (English)
崔先强
2002-01-01
介绍了经典Kalman滤波和目前广泛使用的Sage自适应滤波,分析了基于新息向量、残差向量和状态改正数向量的自适应协方差估计存在的问题,提出了一种改进Sage自适应滤波的新方法.计算结果表明,该方法能有效地控制观测异常和动态扰动异常对噪声协方差估计的影响,在高动态GPS数据处理中具有较强的数值稳定性和自适应性.
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.
Fuzzy Adaptive Cubature Kalman Filter for Integrated Navigation Systems.
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.
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.
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.
Discrete Symmetries in Covariant LQG
Rovelli, Carlo
2012-01-01
We study time-reversal and parity ---on the physical manifold and in internal space--- in covariant loop gravity. We consider a minor modification of the Holst action which makes it transform coherently under such transformations. The classical theory is not affected but the quantum theory is slightly different. In particular, the simplicity constraints are slightly modified and this restricts orientation flips in a spinfoam to occur only across degenerate regions, thus reducing the sources of potential divergences.
Phenotypic covariance at species’ borders
2013-01-01
Background Understanding the evolution of species limits is important in ecology, evolution, and conservation biology. Despite its likely importance in the evolution of these limits, little is known about phenotypic covariance in geographically marginal populations, and the degree to which it constrains, or facilitates, responses to selection. We investigated phenotypic covariance in morphological traits at species’ borders by comparing phenotypic covariance matrices (P), including the degree of shared structure, the distribution of strengths of pair-wise correlations between traits, the degree of morphological integration of traits, and the ranks of matricies, between central and marginal populations of three species-pairs of coral reef fishes. Results Greater structural differences in P were observed between populations close to range margins and conspecific populations toward range centres, than between pairs of conspecific populations that were both more centrally located within their ranges. Approximately 80% of all pair-wise trait correlations within populations were greater in the north, but these differences were unrelated to the position of the sampled population with respect to the geographic range of the species. Conclusions Neither the degree of morphological integration, nor ranks of P, indicated greater evolutionary constraint at range edges. Characteristics of P observed here provide no support for constraint contributing to the formation of these species’ borders, but may instead reflect structural change in P caused by selection or drift, and their potential to evolve in the future. PMID:23714580
COVARIANCE CORRECTION FOR ESTIMATING GROUNDWATER ...
African Journals Online (AJOL)
2015-01-15
Jan 15, 2015 ... and V, which is known as observation noise, is a Gaussian process vector with ... where Kalman gain matrix (K ) which is the weighting matrix for ... The most widely used correlation function is compactly supported 5th order ...
Investigation of Sound Speed Errors in Adaptive Beamforming
DEFF Research Database (Denmark)
Holfort, Iben Kraglund; Gran, Fredrik; Jensen, Jørgen Arendt
2008-01-01
Previous studies have shown that adaptive beam-formers provide a significant increase of resolution and contrast, when the propagation speed is known precisely. This paper demonstrates the influence of sound speed errors on two adaptive beamformers; the minimum variance (MV) beamformer and the am......Previous studies have shown that adaptive beam-formers provide a significant increase of resolution and contrast, when the propagation speed is known precisely. This paper demonstrates the influence of sound speed errors on two adaptive beamformers; the minimum variance (MV) beamformer...... drop is proposed; diagonal loading (DL) and forward-backward (FB) averaging of the covariance matrix. The investigations show that DL provides a slightly decreased resolution and amplitude compared to FB. It is noted that APES provides more robust estimates than MV at the mere expense of a slight...
Energy Technology Data Exchange (ETDEWEB)
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.
Robust adaptive subspace detection in impulsive noise
Atitallah, Ismail Ben
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.
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...
Variations of cosmic large-scale structure covariance matrices across parameter space
Reischke, Robert; Schäfer, Björn Malte
2016-01-01
The likelihood function for cosmological parameters, given by e.g. weak lensing shear measurements, depends on contributions to the covariance induced by the nonlinear evolution of the cosmic web. As nonlinear clustering to date has only been described by numerical $N$-body simulations in a reliable and sufficiently precise way, the necessary computational costs for estimating those covariances at different points in parameter space are tremendous. In this work we describe the change of the matter covariance and of the weak lensing covariance matrix as a function of cosmological parameters by constructing a suitable basis, where we model the contribution to the covariance from nonlinear structure formation using Eulerian perturbation theory at third order. We show that our formalism is capable of dealing with large matrices and reproduces expected degeneracies and scaling with cosmological parameters in a reliable way. Comparing our analytical results to numerical simulations we find that the method describes...
Covariance specification and estimation to improve top-down Green House Gas emission estimates
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
Covariant constraints for generic massive gravity and analysis of its characteristics
Deser, S; Waldron, A; Zahariade, G
2014-01-01
We perform a covariant constraint analysis of massive gravity valid for its entire parameter space, demonstrating that the model generically propagates five degrees of freedom; this is also verified by a new and streamlined Hamiltonian description. The constraint's covariant expression permits computation of the model's caustics. Although new features such as the dynamical Riemann tensor appear in the characteristic matrix, the model still exhibits the pathologies uncovered in earlier work: superluminality and likely acausalities.
Robust adaptive regulation without persistent excitation
Lozano-Leal, Rogelio
1989-01-01
A globally convergent adaptive regulator for minimum- or nonminimum-phase systems subject to bounded disturbances and unmodeled dynamics is presented. The control strategy is designed for a particular input-output representation obtained from the state space representation of the system. The leading coefficient of the representation is the product of the observability and controllability matrices of the system. The controller scheme uses a Least-Squares identification algorithm a with dead zone. The dead zone is chosen to obtain convergence properties on the estimates and on the covariance matrix as well. This allows the definition of modified estimates which secure well-conditioned matrices in the adaptive control law. Explicit bounds on the plant output are given.
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.
Useful and little-known applications of the Least Square Method and some consequences of covariances
Helene, Otaviano; Mariano, Leandro; Guimarães-Filho, Zwinglio
2016-10-01
Covariances are as important as variances when dealing with experimental data and they must be considered in fitting procedures and adjustments in order to preserve the statistical properties of the adjusted quantities. In this paper, we apply the Least Square Method in matrix form to several simple problems in order to evaluate the consequences of covariances in the fitting procedure. Among the examples, we demonstrate how a measurement of a physical quantity can change the adopted value of all other covariant quantities and how a new single point (x , y) improves the parameters of a previously adjusted straight-line.
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.
Classification in medical image analysis using adaptive metric k-NN
DEFF Research Database (Denmark)
Chen, Chen; Chernoff, Konstantin; Karemore, Gopal
2010-01-01
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...
Directory of Open Access Journals (Sweden)
Xusheng Lei
2012-09-01
Full Text Available This paper presents an adaptive information fusion method to improve the accuracy and reliability of the altitude measurement information for small unmanned aerial rotorcraft during the landing process. Focusing on the low measurement performance of sensors mounted on small unmanned aerial rotorcraft, a wavelet filter is applied as a pre-filter to attenuate the high frequency noises in the sensor output. Furthermore, to improve altitude information, an adaptive extended Kalman filter based on a maximum a posteriori criterion is proposed to estimate measurement noise covariance matrix in real time. Finally, the effectiveness of the proposed method is proved by static tests, hovering flight and autonomous landing flight tests.
Least-Squares Data Adjustment with Rank-Deficient Data Covariance Matrices
Energy Technology Data Exchange (ETDEWEB)
Williams, J.G. [The University of Arizona, Tucson, AZ 85721-0119 (United States)
2011-07-01
A derivation of the linear least-squares adjustment formulae is required that avoids the assumption that the covariance matrix of prior parameters can be inverted. Possible proofs are of several kinds, including: (i) extension of standard results for the linear regression formulae, and (ii) minimization by differentiation of a quadratic form of the deviations in parameters and responses. In this paper, the least-squares adjustment equations are derived in both these ways, while explicitly assuming that the covariance matrix of prior parameters is singular. It will be proved that the solutions are unique and that, contrary to statements that have appeared in the literature, the least-squares adjustment problem is not ill-posed. No modification is required to the adjustment formulae that have been used in the past in the case of a singular covariance matrix for the priors. In conclusion: The linear least-squares adjustment formula that has been used in the past is valid in the case of a singular covariance matrix for the covariance matrix of prior parameters. Furthermore, it provides a unique solution. Statements in the literature, to the effect that the problem is ill-posed are wrong. No regularization of the problem is required. This has been proved in the present paper by two methods, while explicitly assuming that the covariance matrix of prior parameters is singular: i) extension of standard results for the linear regression formulae, and (ii) minimization by differentiation of a quadratic form of the deviations in parameters and responses. No modification is needed to the adjustment formulae that have been used in the past. (author)
Multigroup covariance matrices for fast-reactor studies
Energy Technology Data Exchange (ETDEWEB)
Smith, J.D. III; Broadhead, B.L.
1981-04-01
This report presents the multigroup covariance matrices based on the ENDF/B-V nuclear data evaluations. The materials and reactions have been chosen according to the specifications of ORNL-5517. Several cross section covariances, other than those specified by that report, are included due to the derived nature of the uncertainty files in ENDF/B-V. The materials represented are Ni, Cr, /sup 16/O, /sup 12/C, Fe, Na, /sup 235/U, /sup 238/U, /sup 239/Pu, /sup 240/Pu, /sup 241/Pu, and /sup 10/B (present due to its correlation to /sup 238/U). The data have been originally processed into a 52-group energy structure by PUFF-II and subsequently collapsed to smaller subgroup strutures. The results are illustrated in 52-group correlation matrix plots and tabulated into thirteen groups for convenience.
General Covariance from the Quantum Renormalization Group
Shyam, Vasudev
2016-01-01
The Quantum renormalization group (QRG) is a realisation of holography through a coarse graining prescription that maps the beta functions of a quantum field theory thought to live on the `boundary' of some space to holographic actions in the `bulk' of this space. A consistency condition will be proposed that translates into general covariance of the gravitational theory in the $D + 1$ dimensional bulk. This emerges from the application of the QRG on a planar matrix field theory living on the $D$ dimensional boundary. This will be a particular form of the Wess--Zumino consistency condition that the generating functional of the boundary theory needs to satisfy. In the bulk, this condition forces the Poisson bracket algebra of the scalar and vector constraints of the dual gravitational theory to close in a very specific manner, namely, the manner in which the corresponding constraints of general relativity do. A number of features of the gravitational theory will be fixed as a consequence of this form of the Po...
Robust Adaptive Beamforming against Signal Steering Vector Mismatch and Jammer Motion
Directory of Open Access Journals (Sweden)
Xiaojun Mao
2015-01-01
Full Text Available Since adaptive beamformer suffers from output performance degradation in the presence of interference nonstationarity and signal steering vector mismatch, a novel robust null broadening adaptive beamforming is proposed. The proposed method is realized by the combination of projection transform and diagonal loading techniques. First, a new projection matrix with null broadening ability is constructed and then projects the array received data onto the projection matrix. With the diagonal loading technique, a new sample covariance matrix is obtained. The theoretical analysis shows that the projection transform operation can expand the incident direction of the interference and improve orthogonality between the signal-plus-interference and the noise subspaces; thus the proposed beamformer can effectively broaden the jammer null and enhance the null depth. The analytical expressions of the proposed algorithm are also provided, which are efficient and easily solved. Simulation results are presented and demonstrated that the proposed beamformer can provide strong robustness against signal steering vector mismatch and jammer motion.
Covariant formulation of the governing equations of continuum mechanics in an Eulerian description
Schöberl, Markus; Schlacher, Kurt
2007-05-01
We present the balance relations for a continuum in the Eulerian formulation in a pure covariant fashion. Based on the analysis of nonrelativistic particle mechanics, we adapt the covariant description to the case of a continuum. The use of the covariant Nijenhuis differential as well as the splitting of the vertical configuration bundle are the key objects that allow a coordinate-free representation. We state the balance equations such that they are valid, also when time variant transformations are applied, which leads to a nontrivial space-time connection and a metric which explicitly depends on the time.
Covariant diagrams for one-loop matching
Zhang, Zhengkang
2016-01-01
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.
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.
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.
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 are obtai......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...... then propose a new heteroskedasticity consistent standard error formula that is fully automatic and robust to both (conditional) heteroskedasticity of unknown form and the inclusion of possibly many covariates. We apply our findings to three settings: (i) parametric linear models with many covariates, (ii...
TRANSPOSABLE REGULARIZED COVARIANCE MODELS WITH AN APPLICATION TO MISSING DATA IMPUTATION.
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.
SST data assimilation experiments using an adaptive variational method
Institute of Scientific and Technical Information of China (English)
无
2002-01-01
An adaptive variational data assimilation method is proposed by Zhu and Kamachi[1]. This method can adaptively adjust the model state without knowing explicitly the model error covariance matrix. The method enables very flexible ways to form some reduced order problems. A proper reduced order problem not only reduces computational burden but also leads to corrections that are more consistent with the model dynamics that trends to produce better forecast. These features make the adaptive variational method a good candidate for SST data assimilation because the model error of an ocean model is usually difficult to estimate. We applied this method to an SST data assimilation problem using the LOTUS data sets and an ocean mixed layer model (Mellor-Yamada level 2.5). Results of assimilation experiments showed good skill of improvement subsurface temperatures by assimilating surface observation alone.
Eigenvalue variance bounds for covariance matrices
Dallaporta, Sandrine
2013-01-01
This work is concerned with finite range bounds on the variance of individual eigenvalues of random covariance matrices, both in the bulk and at the edge of the spectrum. In a preceding paper, the author established analogous results for Wigner matrices and stated the results for covariance matrices. They are proved in the present paper. Relying on the LUE example, which needs to be investigated first, the main bounds are extended to complex covariance matrices by means of the Tao, Vu and Wan...
Covariance of Light-Front Models Pair Current
Pacheco-Bicudo-Cabral de Melo, J; Naus, H W L; Sauer, P U
1999-01-01
We compute the "+" component of the electromagnetic current of a composite spin-one two-fermion system for vanishing momentum transfer component $q^+=q^0+q^3$. In particular, we extract the nonvanishing pair production amplitude on the light-front. It is a consequence of the longitudinal zero momentum mode, contributing to the light-front current in the Breit-frame. The covariance of the current is violated, if such pair terms are not included in its matrix elements. We illustrate our discussion with some numerical examples.
Gaussian Fluctuations for Sample Covariance Matrices with Dependent Data
Friesen, Olga; Stolz, Michael
2012-01-01
It is known (Hofmann-Credner and Stolz (2008)) that the convergence of the mean empirical spectral distribution of a sample covariance matrix W_n = 1/n Y_n Y_n^t to the Mar\\v{c}enko-Pastur law remains unaffected if the rows and columns of Y_n exhibit some dependence, where only the growth of the number of dependent entries, but not the joint distribution of dependent entries needs to be controlled. In this paper we show that the well-known CLT for traces of powers of W_n also extends to the dependent case.
Kermarrec, Gaël; Schön, Steffen
2016-12-01
In this contribution, using the example of the Mátern covariance matrices, we study systematically the effect of apriori fully populated variance covariance matrices (VCM) in the Gauss-Markov model, by varying both the smoothness and the correlation length of the covariance function. Based on simulations where we consider a GPS relative positioning scenario with double differences, the true VCM is exactly known. Thus, an accurate study of parameters deviations with respect to the correlation structure is possible. By means of the mean-square error difference of the estimates obtained with the correct and the assumed VCM, the loss of efficiency when the correlation structure is missspecified is considered. The bias of the variance of unit weight is moreover analysed. By acting independently on the correlation length, the smoothness, the batch length, the noise level, or the design matrix, simulations allow to draw conclusions on the influence of these different factors on the least-squares results. Thanks to an adapted version of the Kermarrec-Schön model, fully populated VCM for GPS phase observations are computed where different correlation factors are resumed in a global covariance model with an elevation dependent weighting. Based on the data of the EPN network, two studies for different baseline lengths validate the conclusions of the simulations on the influence of the Mátern covariance parameters. A precise insight into the impact of apriori correlation structures when the VCM is entirely unknown highlights that both the correlation length and the smoothness defined in the Mátern model are important to get a lower loss of efficiency as well as a better estimation of the variance of unit weight. Consecutively, correlations, if present, should not be neglected for accurate test statistics. Therefore, a proposal is made to determine a mean value of the correlation structure based on a rough estimation of the Mátern parameters via maximum likelihood
Kermarrec, Gaël; Schön, Steffen
2017-05-01
In this contribution, using the example of the Mátern covariance matrices, we study systematically the effect of apriori fully populated variance covariance matrices (VCM) in the Gauss-Markov model, by varying both the smoothness and the correlation length of the covariance function. Based on simulations where we consider a GPS relative positioning scenario with double differences, the true VCM is exactly known. Thus, an accurate study of parameters deviations with respect to the correlation structure is possible. By means of the mean-square error difference of the estimates obtained with the correct and the assumed VCM, the loss of efficiency when the correlation structure is missspecified is considered. The bias of the variance of unit weight is moreover analysed. By acting independently on the correlation length, the smoothness, the batch length, the noise level, or the design matrix, simulations allow to draw conclusions on the influence of these different factors on the least-squares results. Thanks to an adapted version of the Kermarrec-Schön model, fully populated VCM for GPS phase observations are computed where different correlation factors are resumed in a global covariance model with an elevation dependent weighting. Based on the data of the EPN network, two studies for different baseline lengths validate the conclusions of the simulations on the influence of the Mátern covariance parameters. A precise insight into the impact of apriori correlation structures when the VCM is entirely unknown highlights that both the correlation length and the smoothness defined in the Mátern model are important to get a lower loss of efficiency as well as a better estimation of the variance of unit weight. Consecutively, correlations, if present, should not be neglected for accurate test statistics. Therefore, a proposal is made to determine a mean value of the correlation structure based on a rough estimation of the Mátern parameters via maximum likelihood
A trade-off solution between model resolution and covariance in surface-wave inversion
Xia, J.; Xu, Y.; Miller, R.D.; Zeng, C.
2010-01-01
Regularization is necessary for inversion of ill-posed geophysical problems. Appraisal of inverse models is essential for meaningful interpretation of these models. Because uncertainties are associated with regularization parameters, extra conditions are usually required to determine proper parameters for assessing inverse models. Commonly used techniques for assessment of a geophysical inverse model derived (generally iteratively) from a linear system are based on calculating the model resolution and the model covariance matrices. Because the model resolution and the model covariance matrices of the regularized solutions are controlled by the regularization parameter, direct assessment of inverse models using only the covariance matrix may provide incorrect results. To assess an inverted model, we use the concept of a trade-off between model resolution and covariance to find a proper regularization parameter with singular values calculated in the last iteration. We plot the singular values from large to small to form a singular value plot. A proper regularization parameter is normally the first singular value that approaches zero in the plot. With this regularization parameter, we obtain a trade-off solution between model resolution and model covariance in the vicinity of a regularized solution. The unit covariance matrix can then be used to calculate error bars of the inverse model at a resolution level determined by the regularization parameter. We demonstrate this approach with both synthetic and real surface-wave data. ?? 2010 Birkh??user / Springer Basel AG.
Variations of cosmic large-scale structure covariance matrices across parameter space
Reischke, Robert; Kiessling, Alina; Schäfer, Björn Malte
2017-03-01
The likelihood function for cosmological parameters, given by e.g. weak lensing shear measurements, depends on contributions to the covariance induced by the non-linear evolution of the cosmic web. As highly non-linear clustering to date has only been described by numerical N-body simulations in a reliable and sufficiently precise way, the necessary computational costs for estimating those covariances at different points in parameter space are tremendous. In this work, we describe the change of the matter covariance and the weak lensing covariance matrix as a function of cosmological parameters by constructing a suitable basis, where we model the contribution to the covariance from non-linear structure formation using Eulerian perturbation theory at third order. We show that our formalism is capable of dealing with large matrices and reproduces expected degeneracies and scaling with cosmological parameters in a reliable way. Comparing our analytical results to numerical simulations, we find that the method describes the variation of the covariance matrix found in the SUNGLASS weak lensing simulation pipeline within the errors at one-loop and tree-level for the spectrum and the trispectrum, respectively, for multipoles up to ℓ ≤ 1300. We show that it is possible to optimize the sampling of parameter space where numerical simulations should be carried out by minimizing interpolation errors and propose a corresponding method to distribute points in parameter space in an economical way.
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.
Cox regression with missing covariate data using a modified partial likelihood method
DEFF Research Database (Denmark)
Martinussen, Torben; Holst, Klaus K.; Scheike, Thomas H.
2016-01-01
us to calculate estimators without having to assume anything about the distribution of the covariates. We show that the proposed estimator is consistent and asymptotically normal, and derive a consistent estimator of the variance-covariance matrix that does not involve any choice of a perturbation......Missing covariate values is a common problem in survival analysis. In this paper we propose a novel method for the Cox regression model that is close to maximum likelihood but avoids the use of the EM-algorithm. It exploits that the observed hazard function is multiplicative in the baseline hazard...... function with the idea being to profile out this function before carrying out the estimation of the parameter of interest. In this step one uses a Breslow type estimator to estimate the cumulative baseline hazard function. We focus on the situation where the observed covariates are categorical which allows...
Scaling dimensions of manifestly generally covariant operators in two-dimensional quantum gravity
Nishimura, J; Tsuchiya, A; Jun Nishimura; Shinya Tamura; Asato Tsuchiya
1994-01-01
Using (2+$\\epsilon$)-dimensional quantum gravity recently formulated by Kawai, Kitazawa and Ninomiya, we calculate the scaling dimensions of manifestly generally covariant operators in two-dimensional quantum gravity coupled to $(p,q)$ minimal conformal matter. In the spectrum appear all the scaling dimensions of the scaling operators in the matrix model except the boundary operators, while there are also many others which have no corresponding scaling dimensions in the matrix model.
Covariance structure models of expectancy.
Henderson, M J; Goldman, M S; Coovert, M D; Carnevalla, N
1994-05-01
Antecedent variables under the broad categories of genetic, environmental and cultural influences have been linked to the risk for alcohol abuse. Such risk factors have not been shown to result in high correlations with alcohol consumption and leave unclear an understanding of the mechanism by which these variables lead to increased risk. This study employed covariance structure modeling to examine the mediational influence of stored information in memory about alcohol, alcohol expectancies in relation to two biologically and environmentally driven antecedent variables, family history of alcohol abuse and a sensation-seeking temperament in a college population. We also examined the effect of criterion contamination on the relationship between sensation-seeking and alcohol consumption. Results indicated that alcohol expectancy acts as a significant, partial mediator of the relationship between sensation-seeking and consumption, that family history of alcohol abuse is not related to drinking outcome and that overlap in items on sensation-seeking and alcohol consumption measures may falsely inflate their relationship.
On the Origin of Gravitational Lorentz Covariance
Khoury, Justin; Tolley, Andrew J
2013-01-01
We provide evidence that general relativity is the unique spatially covariant effective field theory of the transverse, traceless graviton degrees of freedom. The Lorentz covariance of general relativity, having not been assumed in our analysis, is thus plausibly interpreted as an accidental or emergent symmetry of the gravitational sector.
COVARIATION BIAS AND THE RETURN OF FEAR
de Jong, Peter; VANDENHOUT, MA; MERCKELBACH, H
1995-01-01
Several studies have indicated that phobic fear is accompanied by a covariation bias, i.e. that phobic Ss tend to overassociate fear relevant stimuli and aversive outcomes. Such a covariation bias seems to be a fairly direct and powerful way to confirm danger expectations and enhance fear. Therefore
Covariant derivative of fermions and all that
Shapiro, Ilya L
2016-01-01
We present detailed pedagogical derivation of covariant derivative of fermions and some related expressions, including commutator of covariant derivatives and energy-momentum tensor of a free Dirac field. The text represents a part of the initial chapter of a one-semester course on semiclassical gravity.
Institute of Scientific and Technical Information of China (English)
殷炼乾
2016-01-01
The estimation and forecast of portfolio market risks is always a very important aspect of risk manage-ment. This paper employs the realized co-variance matrix model, DCC-MVGARCH model, RiskMetrics model and multi-variants Orthogonal GARCH model to compare their forecast failure ratios of the value at risk of the Shanghai and Shenzhen stock index portfolio and also compare these models with a dynamic quantile test for the forecasting robust-ness. The results show that the realized co-variance matrix model based on high-frequency prices data can significantly improve the forecast accuracy of the portfolio market risk,and its failure rates are also strictly consistent with the corre-sponding confidence levels. Hence this model has achieved a good balance between high utilization of money and also its risk exposures of portfolio management.%组合风险的估计和预测一直都是风险管理中非常重要的一个方面。本文使用了利用高频数据信息的实现协方差矩阵、DCC-MVGARCH多元波动率模型、RiskMetrics模型和多元正交GARCH模型对沪深两市的指数资产组合风险在险价值的预测失败率进行了对比，并利用动态分位数检验方法对各模型的组合风险测度稳健性进行了对比研究。研究结果证明，基于高频数据的实现协方差矩阵模型能够显著提高组合风险测度的预测精度，且严格符合VaR置信区间所要求的失败率，能够很好地在提高资金使用效率与管理资产组合风险敞口间取得平衡。
Guo, Xiaojiang; Gao, Yesheng; Wang, Kaizhi; Liu, Xingzhao
2016-07-01
A spectrum reconstruction algorithm based on space-time adaptive processing (STAP) can effectively suppress azimuth ambiguity for multichannel synthetic aperture radar (SAR) systems in azimuth. However, the traditional STAP-based reconstruction approach has to estimate the covariance matrix and calculate matrix inversion (MI) for each Doppler frequency bin, which will result in a very large computational load. In addition, the traditional STAP-based approach has to know the exact platform velocity, pulse repetition frequency, and array configuration. Errors involving these parameters will significantly degrade the performance of ambiguity suppression. A modified STAP-based approach to solve these problems is presented. The traditional array steering vectors and corresponding covariance matrices are Doppler-variant in the range-Doppler domain. After preprocessing by a proposed phase compensation method, they would be independent of Doppler bins. Therefore, the modified STAP-based approach needs to estimate the covariance matrix and calculate MI only once. The computation load could be greatly reduced. Moreover, by combining the reconstruction method and a proposed adaptive parameter estimation method, the modified method is able to successfully achieve multichannel SAR signal reconstruction and suppress azimuth ambiguity without knowing the above parameters. Theoretical analysis and experiments showed the simplicity and efficiency of the proposed methods.
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.
Adaptive robust Kalman filtering for precise point positioning
Guo, Fei; Zhang, Xiaohong
2014-10-01
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.
AMA- and RWE- Based Adaptive Kalman Filter for Denoising Fiber Optic Gyroscope Drift Signal.
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.
The covariate-adjusted frequency plot.
Holling, Heinz; Böhning, Walailuck; Böhning, Dankmar; Formann, Anton K
2016-04-01
Count data arise in numerous fields of interest. Analysis of these data frequently require distributional assumptions. Although the graphical display of a fitted model is straightforward in the univariate scenario, this becomes more complex if covariate information needs to be included into the model. Stratification is one way to proceed, but has its limitations if the covariate has many levels or the number of covariates is large. The article suggests a marginal method which works even in the case that all possible covariate combinations are different (i.e. no covariate combination occurs more than once). For each covariate combination the fitted model value is computed and then summed over the entire data set. The technique is quite general and works with all count distributional models as well as with all forms of covariate modelling. The article provides illustrations of the method for various situations and also shows that the proposed estimator as well as the empirical count frequency are consistent with respect to the same parameter.
Garant, Dany; Hadfield, Jarrod D; Kruuk, Loeske E B; Sheldon, Ben C
2008-01-01
Global warming has had numerous effects on populations of animals and plants, with many species in temperate regions experiencing environmental change at unprecedented rates. Populations with low potential for adaptive evolutionary change and plasticity will have little chance of persistence in the face of environmental change. Assessment of the potential for adaptive evolution requires the estimation of quantitative genetic parameters, but it is as yet unclear what impact, if any, global warming will have on the expression of genetic variances and covariances. Here we assess the impact of a changing climate on the genetic architecture underlying three reproductive traits in a wild bird population. We use a large, long-term, data set collected on great tits (Parus major) in Wytham Woods, Oxford, and an 'animal model' approach to quantify the heritability of, and genetic correlations among, laying date, clutch size and egg mass during two periods with contrasting temperature conditions over a 40-year period (1965-1988 [cooler] vs. 1989-2004 [warmer]). We found significant additive genetic variance and heritability for all traits under both temperature regimes. We also found significant negative genetic covariances and correlations between clutch size and egg weight during both periods, and among laying date and clutch size in the colder years only. The overall G matrix comparison among periods, however, showed only a minor difference among periods, thus suggesting that genotype by environment interactions are negligible in this context. Our results therefore suggest that despite substantial changes in temperature and in mean laying date phenotype over the last decades, and despite the large sample sizes available, we are unable to detect any significant change in the genetic architecture of the reproductive traits studied.
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.
Solving the differential biochemical Jacobian from metabolomics covariance data.
Nägele, Thomas; Mair, Andrea; Sun, Xiaoliang; Fragner, Lena; Teige, Markus; Weckwerth, Wolfram
2014-01-01
High-throughput molecular analysis has become an integral part in organismal systems biology. In contrast, due to a missing systematic linkage of the data with functional and predictive theoretical models of the underlying metabolic network the understanding of the resulting complex data sets is lacking far behind. Here, we present a biomathematical method addressing this problem by using metabolomics data for the inverse calculation of a biochemical Jacobian matrix, thereby linking computer-based genome-scale metabolic reconstruction and in vivo metabolic dynamics. The incongruity of metabolome coverage by typical metabolite profiling approaches and genome-scale metabolic reconstruction was solved by the design of superpathways to define a metabolic interaction matrix. A differential biochemical Jacobian was calculated using an approach which links this metabolic interaction matrix and the covariance of metabolomics data satisfying a Lyapunov equation. The predictions of the differential Jacobian from real metabolomic data were found to be correct by testing the corresponding enzymatic activities. Moreover it is demonstrated that the predictions of the biochemical Jacobian matrix allow for the design of parameter optimization strategies for ODE-based kinetic models of the system. The presented concept combines dynamic modelling strategies with large-scale steady state profiling approaches without the explicit knowledge of individual kinetic parameters. In summary, the presented strategy allows for the identification of regulatory key processes in the biochemical network directly from metabolomics data and is a fundamental achievement for the functional interpretation of metabolomics data.
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.
Franklin, Joel N
2003-01-01
Mathematically rigorous introduction covers vector and matrix norms, the condition-number of a matrix, positive and irreducible matrices, much more. Only elementary algebra and calculus required. Includes problem-solving exercises. 1968 edition.
Earth Observing System Covariance Realism Updates
Ojeda Romero, Juan A.; Miguel, Fred
2017-01-01
This presentation will be given at the International Earth Science Constellation Mission Operations Working Group meetings June 13-15, 2017 to discuss the Earth Observing System Covariance Realism updates.
Covariant Quantization with Extended BRST Symmetry
Geyer, B; Lavrov, P M
1999-01-01
A short rewiev of covariant quantization methods based on BRST-antiBRST symmetry is given. In particular problems of correct definition of Sp(2) symmetric quantization scheme known as triplectic quantization are considered.
Conformally covariant parametrizations for relativistic initial data
Delay, Erwann
2017-01-01
We revisit the Lichnerowicz-York method, and an alternative method of York, in order to obtain some conformally covariant systems. This type of parametrization is certainly more natural for non constant mean curvature initial data.
Covariant action for type IIB supergravity
Sen, Ashoke
2016-07-01
Taking clues from the recent construction of the covariant action for type II and heterotic string field theories, we construct a manifestly Lorentz covariant action for type IIB supergravity, and discuss its gauge fixing maintaining manifest Lorentz invariance. The action contains a (non-gravitating) free 4-form field besides the usual fields of type IIB supergravity. This free field, being completely decoupled from the interacting sector, has no physical consequence.
Functional CLT for sample covariance matrices
Bai, Zhidong; Zhou, Wang; 10.3150/10-BEJ250
2010-01-01
Using Bernstein polynomial approximations, we prove the central limit theorem for linear spectral statistics of sample covariance matrices, indexed by a set of functions with continuous fourth order derivatives on an open interval including $[(1-\\sqrt{y})^2,(1+\\sqrt{y})^2]$, the support of the Mar\\u{c}enko--Pastur law. We also derive the explicit expressions for asymptotic mean and covariance functions.
On the covariance of residual lives
Directory of Open Access Journals (Sweden)
N. Unnikrishnan Nair
2007-10-01
Full Text Available Various properties of residual life such as mean, median, percentiles, variance etc have been discussed in literature on reliability and survival analysis. However a detailed study on covariance between residual lives in a two component system does not seem to have been undertaken. The present paper discusses various properties of product moment and covariance of residual lives. Relationships the product moment has with mean residual life and failure rate are studied and some characterizations are established.
Covariant Hamilton equations for field theory
Energy Technology Data Exchange (ETDEWEB)
Giachetta, Giovanni [Department of Mathematics and Physics, University of Camerino, Camerino (Italy); Mangiarotti, Luigi [Department of Mathematics and Physics, University of Camerino, Camerino (Italy)]. E-mail: mangiaro@camserv.unicam.it; Sardanashvily, Gennadi [Department of Theoretical Physics, Physics Faculty, Moscow State University, Moscow (Russian Federation)]. E-mail: sard@grav.phys.msu.su
1999-09-24
We study the relations between the equations of first-order Lagrangian field theory on fibre bundles and the covariant Hamilton equations on the finite-dimensional polysymplectic phase space of covariant Hamiltonian field theory. If a Lagrangian is hyperregular, these equations are equivalent. A degenerate Lagrangian requires a set of associated Hamiltonian forms in order to exhaust all solutions of the Euler-Lagrange equations. The case of quadratic degenerate Lagrangians is studied in detail. (author)
Economical Phase-Covariant Cloning of Qudits
Buscemi, F; Macchiavello, C; Buscemi, Francesco; Ariano, Giacomo Mauro D'; Macchiavello, Chiara
2004-01-01
We derive the optimal $N\\to M$ phase-covariant quantum cloning for equatorial states in dimension $d$ with $M=kd+N$, $k$ integer. The cloning maps are optimal for both global and single-qudit fidelity. The map is achieved by an ``economical'' cloning machine, which works without ancilla. The connection between optimal phase-covariant cloning and optimal multi-phase estimation is finally established.
Multilevel covariance regression with correlated random effects in the mean and variance structure.
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.
Baryon oscillations in galaxy and matter power-spectrum covariance matrices
Neyrinck, Mark C
2007-01-01
We investigate large-amplitude baryon acoustic oscillations (BAO's) in off-diagonal entries of cosmological power-spectrum covariance matrices. These covariance-matrix BAO's describe the increased attenuation of power-spectrum BAO's caused by upward fluctuations in large-scale power. We derive an analytic approximation to covariance-matrix entries in the BAO regime, and check the analytical predictions using N-body simulations. These BAO's look much stronger than the BAO's in the power spectrum, but seem detectable only at about a one-sigma level in gigaparsec-scale galaxy surveys. In estimating cosmological parameters using matter or galaxy power spectra, including the covariance-matrix BAO's can have a several-percent effect on error-bar widths for some parameters directly related to the BAO's, such as the baryon fraction. Also, we find that including the numerous galaxies in small haloes in a survey can reduce error bars in these cosmological parameters more than the simple reduction in shot noise might su...
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
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.
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.
Robust Mean and Covariance Structure Analysis through Iteratively Reweighted Least Squares.
Yuan, Ke-Hai; Bentler, Peter M.
2000-01-01
Adapts robust schemes to mean and covariance structures, providing an iteratively reweighted least squares approach to robust structural equation modeling. Each case is weighted according to its distance, based on first and second order moments. Test statistics and standard error estimators are given. (SLD)
Cudeck, Robert; And Others
1993-01-01
An implementation of the Gauss-Newton algorithm for the analysis of covariance structure that is specifically adapted for high-level computer languages is reviewed. This simple method for estimating structural equation models is useful for a variety of standard models, as is illustrated. (SLD)
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)
Directory of Open Access Journals (Sweden)
Marcin Studnicki
2016-06-01
Full Text Available 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.
Cognitive Radio Spectrum Sensing Algorithms based on Eigenvalue and Covariance methods
Directory of Open Access Journals (Sweden)
K.SESHU KUMAR
2013-04-01
Full Text Available Spectrum sensing method is the fundamental factor when we are working with cognitive radio systems. Main aim and fundamental problem of cognitive radio is to identify weather primary users in authorized or licensed spectrum is presented or not. Paper deals with a new scheme of sensing based on the eigenvalues concept. It contain signals of covariance matrix received by the secondary users. In this method we are suggested two algorithms of sensing, one algorithm established by the maximum to minimum eigenvalue ratio. Other algorithm focused on average to minimum eigenvalue ratio. These two are done by using random matrix theories (RMT, and also these RMT are latest and also produce some accurate results. Now we calculate the ratios of distributions and probabilities of detection (Pd and derive the probabilities of false alarm (Pfa for the proposed algorithms, and also finding thresholds values for given Pfa. This method will improve the problem of noise uncertainty, and also performance isimproved compare to energy detection when highly correlated signal is available. Paper also deals with another method is and also covariance methods. First one is statistical covariance method, it has different noise and received signal, and it is used for finding the primary users presence where there is only noise. These algorithms implemented by use of small number of received signal samples and processed to calculate the sample covariance matrix. By use of sample covariance matrix we are extracted two test statistics. Finally we compare these results and concluded that signal presence. These are used in many signal detection applications, and also do not need signal information, also noise power and channel. We did the Simulations based on two ways. First one is randomly generated signals. Other one is done by captured DTV signals taken from ATSV committee, these are broadcasting signals. These methods confirm and verifies the efficiency of the proposed
Sparse Matrix Inversion with Scaled Lasso
Sun, Tingni
2012-01-01
We propose a new method of learning a sparse nonnegative-definite target matrix. Our primary example of the target matrix is the inverse of a population covariance matrix or correlation matrix. The algorithm first estimates each column of the matrix by scaled Lasso, a joint estimation of regression coefficients and noise level, and then adjusts the matrix estimator to be symmetric. The procedure is efficient in the sense that the penalty level of the scaled Lasso for each column is completely determined by the data via convex minimization, without using cross-validation. We prove that this method guarantees the fastest proven rate of convergence in the spectrum norm under conditions of weaker form than those in the existing analyses of other $\\ell_1$ algorithms, and has faster guaranteed rate of convergence when the ratio of the $\\ell_1$ and spectrum norms of the target inverse matrix diverges to infinity. A simulation study also demonstrates the competitive performance of the proposed estimator.
Yi, Huiyue
2014-01-01
Estimating the number of signals is a fundamental problem in many scientific and engineering fields. As a well-known estimator based on random matrix theory (RMT), the RMT estimator estimates the number of signals via sequentially testing the likelihood of an eigenvalue as arising from a signal or from noise. However, the RMT estimator tends to down-estimate the number of signals as some signals will be buried in the interaction term among eigenvalues. In order to overcome this problem, we fo...
A hierarchical nest survival model integrating incomplete temporally varying covariates
Converse, Sarah J.; Royle, J. Andrew; Adler, Peter H.; Urbanek, Richard P.; Barzan, Jeb A.
2013-01-01
biting-insect hypothesis and other hypotheses for nesting failure in this reintroduced population; resulting inferences will support ongoing efforts to manage this population via an adaptive management approach. Wider application of our approach offers promise for modeling the effects of other temporally varying, but imperfectly observed covariates on nest survival, including the possibility of modeling temporally varying covariates collected from incubating adults.
DEFF Research Database (Denmark)
Wang, Yunlong; Soltani, Mohsen; Hussain, Dil muhammed Akbar
2016-01-01
Satellite tracking is a challenging task for marine applications due to the disturbance from ocean waves. An Attitude Heading and Reference System (AHRS) for measuring ship attitude, based on Microelectromechanical Systems (MEMS) sensors, is a key part for satellite tracking. In this paper......, 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...
Conservative Sample Size Determination for Repeated Measures Analysis of Covariance.
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.
Lorentz covariance of loop quantum gravity
Rovelli, Carlo
2010-01-01
The kinematics of loop gravity can be given a manifestly Lorentz-covariant formulation: the conventional SU(2)-spin-network Hilbert space can be mapped to a space K of SL(2,C) functions, where Lorentz covariance is manifest. K can be described in terms of a certain subset of the "projected" spin networks studied by Livine, Alexandrov and Dupuis. It is formed by SL(2,C) functions completely determined by their restriction on SU(2). These are square-integrable in the SU(2) scalar product, but not in the SL(2,C) one. Thus, SU(2)-spin-network states can be represented by Lorentz-covariant SL(2,C) functions, as two-component photons can be described in the Lorentz-covariant Gupta-Bleuler formalism. As shown by Wolfgang Wieland in a related paper, this manifestly Lorentz-covariant formulation can also be directly obtained from canonical quantization. We show that the spinfoam dynamics of loop quantum gravity is locally SL(2,C)-invariant in the bulk, and yields states that are preciseley in K on the boundary. This c...
Neural Network Aided Adaptive UKF Algorithm for GPS/INS Integration Navigation
Directory of Open Access Journals (Sweden)
TAN Xinglong
2015-04-01
Full Text Available The predicted residual vectors should be zero-mean Gaussian white noise, which is the precondition for multiple fading factors adaptive filtering algorithm based on statistical information in GPS/INS integration system. However the abnormalities in observations will affect the distribution of the residual vectors. In this paper, a neural network aided adaptive unscented Kalman filter (UKF algorithm with multiple fading factors based on singular value decomposition(SVD is proposed. The algorithm uses the neural network algorithm to weaken the influence of the observed abnormalities on the residual vectors. Singular value decomposition instead of unscented transformation is adopted to suppress negative definite variation in priori covariance matrix of UKF. Since single fading factor in poor tracking of multiple variables has the limitation, multiple fading factors to adjust the predicted-state covariance matrix are constructed with better robustness so that each filter channel has different adjustability. Finally, vehicle measurement data are collected to validate the proposed algorithm. It shows that the neural network algorithm can prevent the observed abnormalities from affecting the distribution of the residual vectors, expanding the applied range of the adaptive algorithm. The neural network algorithm aided SVD-UKF algorithm with multiple fading factors is able to remove influences of state anomalies on condition of the observed abnormalities. The accuracy and reliability of the navigation solution can be improved by this algorithm.
Universal correlations and power-law tails in financial covariance matrices
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.
Spike Triggered Covariance in Strongly Correlated Gaussian Stimuli
Aljadeff, Johnatan; Segev, Ronen; Berry, Michael J.; Sharpee, Tatyana O.
2013-01-01
Many biological systems perform computations on inputs that have very large dimensionality. Determining the relevant input combinations for a particular computation is often key to understanding its function. A common way to find the relevant input dimensions is to examine the difference in variance between the input distribution and the distribution of inputs associated with certain outputs. In systems neuroscience, the corresponding method is known as spike-triggered covariance (STC). This method has been highly successful in characterizing relevant input dimensions for neurons in a variety of sensory systems. So far, most studies used the STC method with weakly correlated Gaussian inputs. However, it is also important to use this method with inputs that have long range correlations typical of the natural sensory environment. In such cases, the stimulus covariance matrix has one (or more) outstanding eigenvalues that cannot be easily equalized because of sampling variability. Such outstanding modes interfere with analyses of statistical significance of candidate input dimensions that modulate neuronal outputs. In many cases, these modes obscure the significant dimensions. We show that the sensitivity of the STC method in the regime of strongly correlated inputs can be improved by an order of magnitude or more. This can be done by evaluating the significance of dimensions in the subspace orthogonal to the outstanding mode(s). Analyzing the responses of retinal ganglion cells probed with Gaussian noise, we find that taking into account outstanding modes is crucial for recovering relevant input dimensions for these neurons. PMID:24039563
Spike triggered covariance in strongly correlated gaussian stimuli.
Directory of Open Access Journals (Sweden)
Johnatan Aljadeff
Full Text Available Many biological systems perform computations on inputs that have very large dimensionality. Determining the relevant input combinations for a particular computation is often key to understanding its function. A common way to find the relevant input dimensions is to examine the difference in variance between the input distribution and the distribution of inputs associated with certain outputs. In systems neuroscience, the corresponding method is known as spike-triggered covariance (STC. This method has been highly successful in characterizing relevant input dimensions for neurons in a variety of sensory systems. So far, most studies used the STC method with weakly correlated Gaussian inputs. However, it is also important to use this method with inputs that have long range correlations typical of the natural sensory environment. In such cases, the stimulus covariance matrix has one (or more outstanding eigenvalues that cannot be easily equalized because of sampling variability. Such outstanding modes interfere with analyses of statistical significance of candidate input dimensions that modulate neuronal outputs. In many cases, these modes obscure the significant dimensions. We show that the sensitivity of the STC method in the regime of strongly correlated inputs can be improved by an order of magnitude or more. This can be done by evaluating the significance of dimensions in the subspace orthogonal to the outstanding mode(s. Analyzing the responses of retinal ganglion cells probed with [Formula: see text] Gaussian noise, we find that taking into account outstanding modes is crucial for recovering relevant input dimensions for these neurons.
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...... of side information at the decoder. For this problem, we derive lower and upper bounds on the rate-distortion function (RDF) for the Gaussian case, which in general do not coincide. We then provide some cases, where the RDF can be derived exactly. We also show that previous results on specific instances...... of this problem can be generalized using our results. We finally show that if the distortion measure is the mean squared error, or if it is replaced by a certain mutual information constraint, the optimal rate can be derived from our main result....
Source Coding in Networks with Covariance Distortion Constraints
DEFF Research Database (Denmark)
Zahedi, Adel; Østergaard, Jan; Jensen, Søren Holdt
2016-01-01
-distortion function (RDF). We then study the special cases and applications of this result. We show that two well-studied source coding problems, i.e. remote vector Gaussian Wyner-Ziv problems with mean-squared error and mutual information constraints are in fact special cases of our results. Finally, we apply our......We consider a source coding problem with a network scenario in mind, and formulate it as a remote vector Gaussian Wyner-Ziv problem under covariance matrix distortions. We define a notion of minimum for two positive-definite matrices based on which we derive an explicit formula for the rate...... results to a joint source coding and denoising problem. We consider a network with a centralized topology and a given weighted sum-rate constraint, where the received signals at the center are to be fused to maximize the output SNR while enforcing no linear distortion. We show that one can design...
Chiral Dynamics of Baryons in a Lorentz Covariant Quark Model
Faessler, A; Lyubovitskij, V E; Pumsa-ard, K; Faessler, Amand; Gutsche, Th.
2006-01-01
We develop a manifestly Lorentz covariant chiral quark model for the study of baryons as bound states of constituent quarks dressed by a cloud of pseudoscalar mesons. The approach is based on a non-linear chirally symmetric Lagrangian, which involves effective degrees of freedom - constituent quarks and the chiral (pseudoscalar meson) fields. In a first step, this Lagrangian can be used to perform a dressing of the constituent quarks by a cloud of light pseudoscalar mesons and other heavy states using the calculational technique of infrared dimensional regularization of loop diagrams. We calculate the dressed transition operators with a proper chiral expansion which are relevant for the interaction of quarks with external fields in the presence of a virtual meson cloud. In a second step, these dressed operators are used to calculate baryon matrix elements. Applications are worked out for the masses of the baryon octet, the meson-nucleon sigma terms, the magnetic moments of the baryon octet, the nucleon charge...
Eigenvalue distribution of large sample covariance matrices of linear processes
Pfaffel, Oliver
2012-01-01
We derive the distribution of the eigenvalues of a large sample covariance matrix when the data is dependent in time. More precisely, the dependence for each variable $i=1,...,p$ is modelled as a linear process $(X_{i,t})_{t=1,...,n}=(\\sum_{j=0}^\\infty c_j Z_{i,t-j})_{t=1,...,n}$, where $\\{Z_{i,t}\\}$ are assumed to be independent random variables with finite fourth moments. If the sample size $n$ and the number of variables $p=p_n$ both converge to infinity such that $y=\\lim_{n\\to\\infty}{n/p_n}>0$, then the empirical spectral distribution of $p^{-1}\\X\\X^T$ converges to a non\\hyp{}random distribution which only depends on $y$ and the spectral density of $(X_{1,t})_{t\\in\\Z}$. In particular, our results apply to (fractionally integrated) ARMA processes, which we illustrate by some examples.
Weighted principal component analysis: a weighted covariance eigendecomposition approach
Delchambre, Ludovic
2014-01-01
We present a new straightforward principal component analysis (PCA) method based on the diagonalization of the weighted variance-covariance matrix through two spectral decomposition methods: power iteration and Rayleigh quotient iteration. This method allows one to retrieve a given number of orthogonal principal components amongst the most meaningful ones for the case of problems with weighted and/or missing data. Principal coefficients are then retrieved by fitting principal components to the data while providing the final decomposition. Tests performed on real and simulated cases show that our method is optimal in the identification of the most significant patterns within data sets. We illustrate the usefulness of this method by assessing its quality on the extrapolation of Sloan Digital Sky Survey quasar spectra from measured wavelengths to shorter and longer wavelengths. Our new algorithm also benefits from a fast and flexible implementation.
Vast Volatility Matrix Estimation using High Frequency Data for Portfolio Selection.
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.
Vast Volatility Matrix Estimation using High Frequency Data for Portfolio Selection*
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
Pion generalized parton distributions within a fully covariant constituent quark model
Energy Technology Data Exchange (ETDEWEB)
Fanelli, Cristiano [Massachusetts Institute of Technology, Cambridge, MA (United States). Lab. for Nuclear Science; Pace, Emanuele [' ' Tor Vergata' ' Univ., Rome (Italy). Physics Dept.; INFN Sezione di TorVergata, Rome (Italy); Romanelli, Giovanni [Rutherford-Appleton Laboratory, Didcot (United Kingdom). STFC; Salme, Giovanni [Istituto Nazionale di Fisica Nucleare, Rome (Italy); Salmistraro, Marco [Rome La Sapienza Univ. (Italy). Physics Dept.; I.I.S. G. De Sanctis, Rome (Italy)
2016-05-15
We extend the investigation of the generalized parton distribution for a charged pion within a fully covariant constituent quark model, in two respects: (1) calculating the tensor distribution and (2) adding the treatment of the evolution, needed for achieving a meaningful comparison with both the experimental parton distribution and the lattice evaluation of the so-called generalized form factors. Distinct features of our phenomenological covariant quark model are: (1) a 4D Ansatz for the pion Bethe-Salpeter amplitude, to be used in the Mandelstam formula for matrix elements of the relevant current operators, and (2) only two parameters, namely a quark mass assumed to be m{sub q} = 220 MeV and a free parameter fixed through the value of the pion decay constant. The possibility of increasing the dynamical content of our covariant constituent quark model is briefly discussed in the context of the Nakanishi integral representation of the Bethe-Salpeter amplitude. (orig.)
Non-linear shrinkage estimation of large-scale structure covariance
Joachimi, Benjamin
2017-03-01
In many astrophysical settings, covariance matrices of large data sets have to be determined empirically from a finite number of mock realizations. The resulting noise degrades inference and precludes it completely if there are fewer realizations than data points. This work applies a recently proposed non-linear shrinkage estimator of covariance to a realistic example from large-scale structure cosmology. After optimizing its performance for the usage in likelihood expressions, the shrinkage estimator yields subdominant bias and variance comparable to that of the standard estimator with a factor of ∼50 less realizations. This is achieved without any prior information on the properties of the data or the structure of the covariance matrix, at a negligible computational cost.
Covariant Lyapunov vectors for rigid disk systems.
Bosetti, Hadrien; Posch, Harald A
2010-10-05
We carry out extensive computer simulations to study the Lyapunov instability of a two-dimensional hard-disk system in a rectangular box with periodic boundary conditions. The system is large enough to allow the formation of Lyapunov modes parallel to the x-axis of the box. The Oseledec splitting into covariant subspaces of the tangent space is considered by computing the full set of covariant perturbation vectors co-moving with the flow in tangent space. These vectors are shown to be transversal, but generally not orthogonal to each other. Only the angle between covariant vectors associated with immediate adjacent Lyapunov exponents in the Lyapunov spectrum may become small, but the probability of this angle to vanish approaches zero. The stable and unstable manifolds are transverse to each other and the system is hyperbolic.
Manifest Covariant Hamiltonian Theory of General Relativity
Cremaschini, Claudio
2016-01-01
The problem of formulating a manifest covariant Hamiltonian theory of General Relativity in the presence of source fields is addressed, by extending the so-called "DeDonder-Weyl" formalism to the treatment of classical fields in curved space-time. The theory is based on a synchronous variational principle for the Einstein equation, formulated in terms of superabundant variables. The technique permits one to determine the continuum covariant Hamiltonian structure associated with the Einstein equation. The corresponding continuum Poisson bracket representation is also determined. The theory relies on first-principles, in the sense that the conclusions are reached in the framework of a non-perturbative covariant approach, which allows one to preserve both the 4-scalar nature of Lagrangian and Hamiltonian densities as well as the gauge invariance property of the theory.
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)
Notes on Cosmic Censorship Conjecture revisited: Covariantly
Hamid, Aymen I M; Maharaj, Sunil D
2016-01-01
In this paper we study the dynamics of the trapped region using a frame independent semi-tetrad covariant formalism for general Locally Rotationally Symmetric (LRS) class II spacetimes. We covariantly prove some important geometrical results for the apparent horizon, and state the necessary and sufficient conditions for a singularity to be locally naked. These conditions bring out, for the first time in a quantitative and transparent manner, the importance of the Weyl curvature in deforming and delaying the trapped region during continual gravitational collapse, making the central singularity locally visible.
A covariant formulation of classical spinning particle
Cho, J H; Kim, J K; Jin-Ho Cho; Seungjoon Hyun; Jae-Kwan Kim
1994-01-01
Covariantly we reformulate the description of a spinning particle in terms of the which entails all possible constraints explicitly; all constraints can be obtained just from the Lagrangian. Furthermore, in this covariant reformulation, the Lorentz element is to be considered to evolve the momentum or spin component from an arbitrary fixed frame and not just from the particle rest frame. In distinction with the usual formulation, our system is directly comparable with the pseudo-classical formulation. We get a peculiar symmetry which resembles the supersymmetry of the pseudo-classical formulation.
A violation of the covariant entropy bound?
Masoumi, Ali
2014-01-01
Several arguments suggest that the entropy density at high energy density $\\rho$ should be given by the expression $s=K\\sqrt{\\rho/G}$, where $K$ is a constant of order unity. On the other hand the covariant entropy bound requires that the entropy on a light sheet be bounded by $A/4G$, where $A$ is the area of the boundary of the sheet. We find that in a suitably chosen cosmological geometry, the above expression for $s$ violates the covariant entropy bound. We consider different possible explanations for this fact; in particular the possibility that entropy bounds should be defined in terms of volumes of regions rather than areas of surfaces.
Matrix Factorization and Matrix Concentration
Mackey, Lester
2012-01-01
Motivated by the constrained factorization problems of sparse principal components analysis (PCA) for gene expression modeling, low-rank matrix completion for recommender systems, and robust matrix factorization for video surveillance, this dissertation explores the modeling, methodology, and theory of matrix factorization.We begin by exposing the theoretical and empirical shortcomings of standard deflation techniques for sparse PCA and developing alternative methodology more suitable for def...
Robust Burg estimation of stationary autoregressive mixtures covariance
Decurninge, Alexis; Barbaresco, Frédéric
2015-01-01
Burg estimators are classically used for the estimation of the autocovariance of a stationary autoregressive process. We propose to consider scale mixtures of stationary autoregressive processes, a non-Gaussian extension of the latter. The traces of such processes are Spherically Invariant Random Vectors (SIRV) with a constraint on the scatter matrix due to the autoregressive model. We propose adaptations of the Burg estimators to the considered models and their associated robust versions based on geometrical considerations.
Galea, Gabriel L; Meakin, Lee B; Harris, Marie A; Delisser, Peter J; Lanyon, Lance E; Harris, Stephen E; Price, Joanna S
2017-01-30
In old animals, bone's ability to adapt its mass and architecture to functional load-bearing requirements is diminished, resulting in bone loss characteristic of osteoporosis. Here we investigate transcriptomic changes associated with this impaired adaptive response. Young adult (19-week-old) and aged (19-month-old) female mice were subjected to unilateral axial tibial loading and their cortical shells harvested for microarray analysis between 1h and 24h following loading (36 mice per age group, 6 mice per loading group at 6 time points). In non-loaded aged bones, down-regulated genes are enriched for MAPK, Wnt and cell cycle components, including E2F1. E2F1 is the transcription factor most closely associated with genes down-regulated by ageing and is down-regulated at the protein level in osteocytes. Genes up-regulated in aged bone are enriched for carbohydrate metabolism, TNFα and TGFβ superfamily components. Loading stimulates rapid and sustained transcriptional responses in both age groups. However, genes related to proliferation are predominantly up-regulated in the young and down-regulated in the aged following loading, whereas those implicated in bioenergetics are down-regulated in the young and up-regulated in the aged. Networks of inter-related transcription factors regulated by E2F1 are loading-responsive in both age groups. Loading regulates genes involved in similar signalling cascades in both age groups, but these responses are more sustained in the young than aged. From this we conclude that cells in aged bone retain the capability to sense and transduce loading-related stimuli, but their ability to translate acute responses into functionally relevant outcomes is diminished.
Covariances of the few-group homogenized cross-sections for diffusion calculation
Energy Technology Data Exchange (ETDEWEB)
Sánchez-Cervera, S.; Castro, S.; García-Herranz, N.
2015-07-01
In the context of the NEA/OECD benchmark for Uncertainty Analysis in Modelling (UAM), Exercise I-3 consists of neutronic calculations to propagate uncertainties to core parameters such as k-effective or power distribution. In core simulators, the input uncertainties arise, among others, from few-group lattice-averaged cross-section uncertainties. In this paper, an analysis of those uncertainties due to nuclear data is performed. The core analyzed in Exercise I-3 is the initial loading of the PWR TMI-1, composed by 11 different types of fuel assemblies. By statistically sampling the nuclear data input, the sequence SAMPLER from SCALE system (using its NEWT lattice code) allows to obtain the few-group homogenized cross-sections and with a statistical analysis generates the covariance matrices. The correlations among different reactions and energy groups of the covariance matrices are analyzed. The impact of burnable poisons, control rods or the environment of the assembly is also assessed. It is shown the importance of the correlation between different assembly types. The global covariance matrix will permit to compute the uncertainties in k-eff in a core simulator, once sensitivity coefficients are known. Only if the complete covariance matrix is considered, similar uncertainties to the ones provided by other methodologies are obtained. (Author)
Directory of Open Access Journals (Sweden)
Berge Léonie
2016-01-01
Full Text Available As the need for precise handling of nuclear data covariances grows ever stronger, no information about covariances of prompt fission neutron spectra (PFNS are available in the evaluated library JEFF-3.2, although present in ENDF/B-VII.1 and JENDL-4.0 libraries for the main fissile isotopes. The aim of this work is to provide an estimation of covariance matrices related to PFNS, in the frame of some commonly used models for the evaluated files, such as the Maxwellian spectrum, the Watt spectrum, or the Madland-Nix spectrum. The evaluation of PFNS through these models involves an adjustment of model parameters to available experimental data, and the calculation of the spectrum variance-covariance matrix arising from experimental uncertainties. We present the results for thermal neutron induced fission of 235U. The systematic experimental uncertainties are propagated via the marginalization technique available in the CONRAD code. They are of great influence on the final covariance matrix, and therefore, on the spectrum uncertainty band width. In addition to this covariance estimation work, we have also investigated the importance on a reactor calculation of the fission spectrum model choice. A study of the vessel fluence depending on the PFNS model is presented. This is done through the propagation of neutrons emitted from a fission source in a simplified PWR using the TRIPOLI-4® code. This last study includes thermal fission spectra from the FIFRELIN Monte-Carlo code dedicated to the simulation of prompt particles emission during fission.
Berge, Léonie; Litaize, Olivier; Serot, Olivier; Archier, Pascal; De Saint Jean, Cyrille; Pénéliau, Yannick; Regnier, David
2016-02-01
As the need for precise handling of nuclear data covariances grows ever stronger, no information about covariances of prompt fission neutron spectra (PFNS) are available in the evaluated library JEFF-3.2, although present in ENDF/B-VII.1 and JENDL-4.0 libraries for the main fissile isotopes. The aim of this work is to provide an estimation of covariance matrices related to PFNS, in the frame of some commonly used models for the evaluated files, such as the Maxwellian spectrum, the Watt spectrum, or the Madland-Nix spectrum. The evaluation of PFNS through these models involves an adjustment of model parameters to available experimental data, and the calculation of the spectrum variance-covariance matrix arising from experimental uncertainties. We present the results for thermal neutron induced fission of 235U. The systematic experimental uncertainties are propagated via the marginalization technique available in the CONRAD code. They are of great influence on the final covariance matrix, and therefore, on the spectrum uncertainty band width. In addition to this covariance estimation work, we have also investigated the importance on a reactor calculation of the fission spectrum model choice. A study of the vessel fluence depending on the PFNS model is presented. This is done through the propagation of neutrons emitted from a fission source in a simplified PWR using the TRIPOLI-4® code. This last study includes thermal fission spectra from the FIFRELIN Monte-Carlo code dedicated to the simulation of prompt particles emission during fission.
Institute of Scientific and Technical Information of China (English)
ZHANG Wei-min; CAO Xiao-qun; XIAO Qin-nong; SONG Jun-qiang; ZHU Xiao-qian; WANG Shu-chang
2010-01-01
Background error covariance plays an important role in any variational data assimilation system,because it determines how information from observations is spread in model space and between different model variables.In this paper,the use of orthogonal wavelets in representation of background error covariance over a limited area is studied.Based on the WRF model and its 3D-VAR system,an algorithm using orthogonal wavelets to model background error covariance is developed.Because each wavelet function contains information on both position and scale,using a diagonal correlation matrix in wavelet space gives the possibility to represent some anisotropic and inhomogeneous characteristics of background error covariance.The experiments show that local correlation functions are better modeled than spectral methods.The formulation of wavelet background error covariance is tested with the typhoon Kaemi (2006).The results of experiments indicate that the subsequent forecasts of typhoon Kaemi's track and intensity are significantly improved by the new method.
A full scale approximation of covariance functions for large spatial data sets
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.
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.
Diallo, Thierno M O; Morin, Alexandre J S; Lu, HuiZhong
2017-03-01
This article evaluates the impact of partial or total covariate inclusion or exclusion on the class enumeration performance of growth mixture models (GMMs). Study 1 examines the effect of including an inactive covariate when the population model is specified without covariates. Study 2 examines the case in which the population model is specified with 2 covariates influencing only the class membership. Study 3 examines a population model including 2 covariates influencing the class membership and the growth factors. In all studies, we contrast the accuracy of various indicators to correctly identify the number of latent classes as a function of different design conditions (sample size, mixing ratio, invariance or noninvariance of the variance-covariance matrix, class separation, and correlations between the covariates in Studies 2 and 3) and covariate specification (exclusion, partial or total inclusion as influencing class membership, partial or total inclusion as influencing class membership, and the growth factors in a class-invariant or class-varying manner). The accuracy of the indicators shows important variation across studies, indicators, design conditions, and specification of the covariates effects. However, the results suggest that the GMM class enumeration process should be conducted without covariates, and should rely mostly on the Bayesian information criterion (BIC) and consistent Akaike information criterion (CAIC) as the most reliable indicators under conditions of high class separation (as indicated by higher entropy), versus the sample size adjusted BIC or CAIC (SBIC, SCAIC) and bootstrapped likelihood ratio test (BLRT) under conditions of low class separation (indicated by lower entropy). (PsycINFO Database Record
The Massless Spectrum of Covariant Superstrings
Grassi, P A; van Nieuwenhuizen, P
2002-01-01
We obtain the correct cohomology at any ghost number for the open and closed covariant superstring, quantized by an approach which we recently developed. We define physical states by the usual condition of BRST invariance and a new condition involving a new current which is related to a grading of the underlying affine Lie algebra.
EQUIVALENT MODELS IN COVARIANCE STRUCTURE-ANALYSIS
LUIJBEN, TCW
1991-01-01
Defining equivalent models as those that reproduce the same set of covariance matrices, necessary and sufficient conditions are stated for the local equivalence of two expanded identified models M1 and M2 when fitting the more restricted model M0. Assuming several regularity conditions, the rank def
Covariant formulation of pion-nucleon scattering
Lahiff, A. D.; Afnan, I. R.
A covariant model of elastic pion-nucleon scattering based on the Bethe-Salpeter equation is presented. The kernel consists of s- and u-channel nucleon and delta poles, along with rho and sigma exchange in the t-channel. A good fit is obtained to the s- and p-wave phase shifts up to the two-pion production threshold.
Approximate methods for derivation of covariance data
Energy Technology Data Exchange (ETDEWEB)
Tagesen, S. [Vienna Univ. (Austria). Inst. fuer Radiumforschung und Kernphysik; Larson, D.C. [Oak Ridge National Lab., TN (United States)
1992-12-31
Several approaches for the derivation of covariance information for evaluated nuclear data files (EFF2 and ENDF/B-VI) have been developed and used at IRK and ORNL respectively. Considerations, governing the choice of a distinct method depending on the quantity and quality of available data are presented, advantages/disadvantages are discussed and examples of results are given.
Covariance of noncommutative Grassmann star product
Daoud, M.
2004-01-01
Using the Coherent states of many fermionic degrees of freedom labeled by Gra\\ss mann variables, we introduce the noncommutative (precisely non anticommutative) Gra\\ss mann star product. The covariance of star product under unitary transformations, particularly canonical ones, is studied. The super star product, based on supercoherent states of supersymmetric harmonic oscillator, is also considered.
Covariance of the selfdual vector model
2004-01-01
The Poisson algebra between the fields involved in the vectorial selfdual action is obtained by means of the reduced action. The conserved charges associated with the invariance under the inhomogeneous Lorentz group are obtained and its action on the fields. The covariance of the theory is proved using the Schwinger-Dirac algebra. The spin of the excitations is discussed.
Linear transformations of variance/covariance matrices
Parois, P.J.A.; Lutz, M.
2011-01-01
Many applications in crystallography require the use of linear transformations on parameters and their standard uncertainties. While the transformation of the parameters is textbook knowledge, the transformation of the standard uncertainties is more complicated and needs the full variance/covariance
Covariant Photon Quantization in the SME
Colladay, Don
2013-01-01
The Gupta Bleuler quantization procedure is applied to the SME photon sector. A direct application of the method to the massless case fails due to an unavoidable incompleteness in the polarization states. A mass term can be included into the photon lagrangian to rescue the quantization procedure and maintain covariance.
Covariant derivative expansion of the heat kernel
Energy Technology Data Exchange (ETDEWEB)
Salcedo, L.L. [Universidad de Granada, Departamento de Fisica Moderna, Granada (Spain)
2004-11-01
Using the technique of labeled operators, compact explicit expressions are given for all traced heat kernel coefficients containing zero, two, four and six covariant derivatives, and for diagonal coefficients with zero, two and four derivatives. The results apply to boundaryless flat space-times and arbitrary non-Abelian scalar and gauge background fields. (orig.)
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....
Bodewig, E
1959-01-01
Matrix Calculus, Second Revised and Enlarged Edition focuses on systematic calculation with the building blocks of a matrix and rows and columns, shunning the use of individual elements. The publication first offers information on vectors, matrices, further applications, measures of the magnitude of a matrix, and forms. The text then examines eigenvalues and exact solutions, including the characteristic equation, eigenrows, extremum properties of the eigenvalues, bounds for the eigenvalues, elementary divisors, and bounds for the determinant. The text ponders on approximate solutions, as well
Strong subadditivity for log-determinant of covariance matrices and its applications
Adesso, Gerardo
2016-01-01
We prove that the log-determinant of the covariance matrix obeys the strong subadditivity inequality for arbitrary tripartite states of multimode continuous variable quantum systems. This establishes general limitations on the distribution of information encoded in the second moments of canonically conjugate operators. We show that such an inequality implies a strict monogamy-type relation for joint Einstein-Podolsky-Rosen steerability of single modes by Gaussian measurements performed on multiple groups of modes.
Covariant Helicity Amplitude Analysis for J/ψ→γPP
Institute of Scientific and Technical Information of China (English)
WU Ning; RUAN Tu-Nan
2001-01-01
Covariant helicity amplitude analysis for the process of J/ψ →γPP is discussed.Starting from the S- matrix elements of decay process,we deduce the formulae of helicity coupling amplitudes for two-body decay process. These formulae are used to analyze intermediate resonance states in the process of J/ψ decay to γππ,γKK,γηη＇ etc.
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 maximu...... class of prior distributions generalizing the inverse Wishart prior, discuss its properties, and demonstrate the estimator on simulated and real data....
Volatility and covariation of financial assets: a high-frequency analysis
Cartea, Alvaro; Karyampas, Dimitrios
2009-01-01
Using high frequency data for the price dynamics of equities we measure the impact that market microstructure noise has on estimates of the: (i) volatility of returns; and (ii) variance-covariance matrix of n assets. We propose a Kalman-filter-based methodology that allows us to deconstruct price series into the true efficient price and the microstructure noise. This approach allows us to employ volatility estimators that achieve very low Root Mean Squared Errors (RMSEs) compared to other est...
A Path Following Algorithm for Sparse Pseudo-Likelihood Inverse Covariance Estimation (SPLICE)
2008-07-24
the performance of covariance matrix estimates used by classifiers based on linear discriminant analysis (Bickel and Levina , 2004) and in Kalman...selection method is presented in Bilmes (2000). More recently, Bickel and Levina (2008) have obtained conditions ensuring consistency in the operator...lattice systems. Journal of the Royal Statistical Society, Series B 36, 2, 192–236. Bickel, P. and Levina , E. 2004. Some theory for fisher’s linear
Unravelling Lorentz Covariance and the Spacetime Formalism
Directory of Open Access Journals (Sweden)
Cahill R. T.
2008-10-01
Full Text Available We report the discovery of an exact mapping from Galilean time and space coordinates to Minkowski spacetime coordinates, showing that Lorentz covariance and the space-time construct are consistent with the existence of a dynamical 3-space, and absolute motion. We illustrate this mapping first with the standard theory of sound, as vibrations of a medium, which itself may be undergoing fluid motion, and which is covariant under Galilean coordinate transformations. By introducing a different non-physical class of space and time coordinates it may be cast into a form that is covariant under Lorentz transformations wherein the speed of sound is now the invariant speed. If this latter formalism were taken as fundamental and complete we would be lead to the introduction of a pseudo-Riemannian spacetime description of sound, with a metric characterised by an invariant speed of sound. This analysis is an allegory for the development of 20th century physics, but where the Lorentz covariant Maxwell equations were constructed first, and the Galilean form was later constructed by Hertz, but ignored. It is shown that the Lorentz covariance of the Maxwell equations only occurs because of the use of non-physical space and time coordinates. The use of this class of coordinates has confounded 20th century physics, and resulted in the existence of a allowing dynamical 3-space being overlooked. The discovery of the dynamics of this 3-space has lead to the derivation of an extended gravity theory as a quantum effect, and confirmed by numerous experiments and observations
Unravelling Lorentz Covariance and the Spacetime Formalism
Directory of Open Access Journals (Sweden)
Cahill R. T.
2008-10-01
Full Text Available We report the discovery of an exact mapping from Galilean time and space coordinates to Minkowski spacetime coordinates, showing that Lorentz covariance and the space- time construct are consistent with the existence of a dynamical 3-space, and “absolute motion”. We illustrate this mapping first with the standard theory of sound, as vibra- tions of a medium, which itself may be undergoing fluid motion, and which is covari- ant under Galilean coordinate transformations. By introducing a different non-physical class of space and time coordinates it may be cast into a form that is covariant under “Lorentz transformations” wherein the speed of sound is now the “invariant speed”. If this latter formalism were taken as fundamental and complete we would be lead to the introduction of a pseudo-Riemannian “spacetime” description of sound, with a metric characterised by an “invariant speed of sound”. This analysis is an allegory for the development of 20th century physics, but where the Lorentz covariant Maxwell equa- tions were constructed first, and the Galilean form was later constructed by Hertz, but ignored. It is shown that the Lorentz covariance of the Maxwell equations only occurs because of the use of non-physical space and time coordinates. The use of this class of coordinates has confounded 20th century physics, and resulted in the existence of a “flowing” dynamical 3-space being overlooked. The discovery of the dynamics of this 3-space has lead to the derivation of an extended gravity theory as a quantum effect, and confirmed by numerous experiments and observations
A New Bias Corrected Version of Heteroscedasticity Consistent Covariance Estimator
Directory of Open Access Journals (Sweden)
Munir Ahmed
2016-06-01
Full Text Available In the presence of heteroscedasticity, different available flavours of the heteroscedasticity consistent covariance estimator (HCCME are used. However, the available literature shows that these estimators can be considerably biased in small samples. Cribari–Neto et al. (2000 introduce a bias adjustment mechanism and give the modified White estimator that becomes almost bias-free even in small samples. Extending these results, Cribari-Neto and Galvão (2003 present a similar bias adjustment mechanism that can be applied to a wide class of HCCMEs’. In the present article, we follow the same mechanism as proposed by Cribari-Neto and Galvão to give bias-correction version of HCCME but we use adaptive HCCME rather than the conventional HCCME. The Monte Carlo study is used to evaluate the performance of our proposed estimators.
Tucker, Bram
2007-06-01
This paper begins with the hypothesis that Mikea, participants in a mixed foraging-fishing-farming-herding economy of southwestern Madagascar, may attempt to reduce interannual variance in food supply caused by unpredictable rainfall by following a simple rule-of-thumb: Practice an even mix of activities that covary positively with rainfall and activities that covary negatively with rainfall. Results from a historical matrix participatory exercise confirm that Mikea perceive that foraging and farming outcomes covary positively or negatively with rainfall. This paper further considers whether Mikea learn about covariation through personal observation and memory recall (individual learning) or through socially transmitted ethnotheory (social learning). Dual inheritance theory models by Boyd and Richerson (1988) predict that individual learning is more effective in spatially and temporally variable environments such as the Mikea Forest. In contrast, the psychological literature suggests that individuals judge covariation poorly when memory of past events is required, unless they share a socially learned theory that a covariation should exist (Nisbett and Ross 1980). Results suggest that Mikea rely heavily on shared ethnotheory when judging covariation, but individuals continually strive to improve their judgment through individual observation.
High-dimensional statistical inference: From vector to matrix
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 nuclear norm minimization. Moreover, for any epsilon > 0, delta kA 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 estimator is easy to implement via convex programming and performs well numerically. The techniques and main results developed in the chapter also have implications to other related statistical problems. An application to estimation of spiked covariance matrices from one-dimensional random projections is considered. The results demonstrate that it is still possible to accurately estimate the covariance matrix of a high-dimensional distribution based only on one-dimensional projections. For the third part of the thesis, we consider another setting of low-rank matrix completion. Current literature
Classification in medical images using adaptive metric k-NN
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.
Covariance of dynamic strain responses for structural damage detection
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.
Rajendran, R; Prabhavathi, P; Karthiksundaram, S; Pattab, S; Kumar, S Dinesh; Santhanam, P
2015-01-01
The present experiments were studied on bioremediation of denim industry wastewater by using polyurethane foam (PU foam) immobilized bacterial cells. About 30 indigenous adapted bacterial strains were isolated from denim textile effluent out of which only four isolates were found to be efficient against crude indigo carmine degradation using broth decolorization method. The selected bacterial strains were identified as Actinomyces sp., (PK07), Pseudomonas sp., (PK18), Stenotrophomonas sp., (PK23) and Staphylococcus sp., (PK28) based on microscopic and biochemical characteristics. The bacterial immobilized cells have the highest number of viable cells (PK07, PK18, PK23 and PK28 appeared to be 1 x 10(8), 1 x 10(9), 1 x 10(6) and 1 x 10(7) CFU/ml respectively) and maximum attachment efficiency of 92% on PU foam. The complete degradation using a consortium of PU foam immobilized cells was achieved at pH 6, 27 degrees C, 100% of substrate concentration and allowed to develop biofilm for one day (1.5% W/V). In SEM analysis, it was found that immobilization of bacterial cells using PUF stably maintained the production of various extracellular enzymes at levels higher than achieved with suspended forms. Finally, isatin and anthranilic acid were found to be degradation products by NMR and TLC. The decolorized dye was not toxic to monkey kidney cell (HBL 100) at a concentration of 50 μl and 95% of cell viability was retained. A mathematical model that describes bacterial transport with biodegradation involves a set of coupled reaction equations with non-standard numerical approach based on the time step scheme.
Janes, Holly; Pepe, Margaret S
2009-06-01
Recent scientific and technological innovations have produced an abundance of potential markers that are being investigated for their use in disease screening and diagnosis. In evaluating these markers, it is often necessary to account for covariates associated with the marker of interest. Covariates may include subject characteristics, expertise of the test operator, test procedures or aspects of specimen handling. In this paper, we propose the covariate-adjusted receiver operating characteristic curve, a measure of covariate-adjusted classification accuracy. Nonparametric and semiparametric estimators are proposed, asymptotic distribution theory is provided and finite sample performance is investigated. For illustration we characterize the age-adjusted discriminatory accuracy of prostate-specific antigen as a biomarker for prostate cancer.
Chi, Zhiyi
2010-01-01
Two extensions of generalized linear models are considered. In the first one, response variables depend on multiple linear combinations of covariates. In the second one, only response variables are observed while the linear covariates are missing. We derive stochastic Lipschitz continuity results for the loss functions involved in the regression problems and apply them to get bounds on estimation error for Lasso. Multivariate comparison results on Rademacher complexity are obtained as tools to establish the stochastic Lipschitz continuity results.
Elze, Markus C; Gregson, John; Baber, Usman; Williamson, Elizabeth; Sartori, Samantha; Mehran, Roxana; Nichols, Melissa; Stone, Gregg W; Pocock, Stuart J
2017-01-24
Propensity scores (PS) are an increasingly popular method to adjust for confounding in observational studies. Propensity score methods have theoretical advantages over conventional covariate adjustment, but their relative performance in real-word scenarios is poorly characterized. We used datasets from 4 large-scale cardiovascular observational studies (PROMETHEUS, ADAPT-DES [the Assessment of Dual AntiPlatelet Therapy with Drug-Eluting Stents], THIN [The Health Improvement Network], and CHARM [Candesartan in Heart Failure-Assessment of Reduction in Mortality and Morbidity]) to compare the performance of conventional covariate adjustment with 4 common PS methods: matching, stratification, inverse probability weighting, and use of PS as a covariate. We found that stratification performed poorly with few outcome events, and inverse probability weighting gave imprecise estimates of treatment effect and undue influence to a small number of observations when substantial confounding was present. Covariate adjustment and matching performed well in all of our examples, although matching tended to give less precise estimates in some cases. PS methods are not necessarily superior to conventional covariate adjustment, and care should be taken to select the most suitable method. Copyright © 2017 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.
Survival prediction based on compound covariate under Cox proportional hazard models.
Directory of Open Access Journals (Sweden)
Takeshi Emura
Full Text Available Survival prediction from a large number of covariates is a current focus of statistical and medical research. In this paper, we study a methodology known as the compound covariate prediction performed under univariate Cox proportional hazard models. We demonstrate via simulations and real data analysis that the compound covariate method generally competes well with ridge regression and Lasso methods, both already well-studied methods for predicting survival outcomes with a large number of covariates. Furthermore, we develop a refinement of the compound covariate method by incorporating likelihood information from multivariate Cox models. The new proposal is an adaptive method that borrows information contained in both the univariate and multivariate Cox regression estimators. We show that the new proposal has a theoretical justification from a statistical large sample theory and is naturally interpreted as a shrinkage-type estimator, a popular class of estimators in statistical literature. Two datasets, the primary biliary cirrhosis of the liver data and the non-small-cell lung cancer data, are used for illustration. The proposed method is implemented in R package "compound.Cox" available in CRAN at http://cran.r-project.org/.
An Adjoint-Based Adaptive Ensemble Kalman Filter
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.
The effects of stress and sex on selection, genetic covariance, and the evolutionary response.
Holman, L; Jacomb, F
2017-08-01
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.
The Joint Adaptive Kalman Filter (JAKF) for Vehicle Motion State Estimation.
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.
Diagonally loaded SMI algorithm based on inverse matrix recursion
Institute of Scientific and Technical Information of China (English)
Cao Jianshu; Wang Xuegang
2007-01-01
The derivation of a diagonally loaded sample-matrix inversion (LSMI) algorithm on the busis of inverse matrix recursion (i.e. LSMI-IMR algorithm) is conducted by reconstructing the recursive formulation of covariance matrix. For the new algorithm, diagonal loading is by setting initial inverse matrix without any addition of computation. In addition, acorresponding improved recursive algorithm is presented, which is low computational complexity. This eliminates the complex multiplications of the scalar coefficient and updating matrix, resulting in significant computational savings.Simulations show that the LSMI-IMR algorithm is valid.
Covariant Quantization of CPT-violating Photons
Colladay, D; Noordmans, J P; Potting, R
2016-01-01
We perform the covariant canonical quantization of the CPT- and Lorentz-symmetry-violating photon sector of the minimal Standard-Model Extension, which contains a general (timelike, lightlike, or spacelike) fixed background tensor $k_{AF}^\\mu$. Well-known stability issues, arising from complex-valued energy states, are solved by introducing a small photon mass, orders of magnitude below current experimental bounds. We explicitly construct a covariant basis of polarization vectors, in which the photon field can be expanded. We proceed to derive the Feynman propagator and show that the theory is microcausal. Despite the occurrence of negative energies and vacuum-Cherenkov radiation, we do not find any runaway stability issues, because the energy remains bounded from below. An important observation is that the ordering of the roots of the dispersion relations is the same in any observer frame, which allows for a frame-independent condition that selects the correct branch of the dispersion relation. This turns ou...
Model selection for Poisson processes with covariates
Sart, Mathieu
2011-01-01
We observe $n$ inhomogeneous Poisson processes with covariates and aim at estimating their intensities. To handle this problem, we assume that the intensity of each Poisson process is of the form $s (\\cdot, x)$ where $x$ is the covariate and where $s$ is an unknown function. We propose a model selection approach where the models are used to approximate the multivariate function $s$. We show that our estimator satisfies an oracle-type inequality under very weak assumptions both on the intensities and the models. By using an Hellinger-type loss, we establish non-asymptotic risk bounds and specify them under various kind of assumptions on the target function $s$ such as being smooth or composite. Besides, we show that our estimation procedure is robust with respect to these assumptions.
Errors on errors - Estimating cosmological parameter covariance
Joachimi, Benjamin
2014-01-01
Current and forthcoming cosmological data analyses share the challenge of huge datasets alongside increasingly tight requirements on the precision and accuracy of extracted cosmological parameters. The community is becoming increasingly aware that these requirements not only apply to the central values of parameters but, equally important, also to the error bars. Due to non-linear effects in the astrophysics, the instrument, and the analysis pipeline, data covariance matrices are usually not well known a priori and need to be estimated from the data itself, or from suites of large simulations. In either case, the finite number of realisations available to determine data covariances introduces significant biases and additional variance in the errors on cosmological parameters in a standard likelihood analysis. Here, we review recent work on quantifying these biases and additional variances and discuss approaches to remedy these effects.
Chiral Four-Dimensional Heterotic Covariant Lattices
Beye, Florian
2014-01-01
In the covariant lattice formalism, chiral four-dimensional heterotic string vacua are obtained from certain even self-dual lattices which completely decompose into a left-mover and a right-mover lattice. The main purpose of this work is to classify all right-mover lattices that can appear in such a chiral model, and to study the corresponding left-mover lattices using the theory of lattice genera. In particular, the Smith-Minkowski-Siegel mass formula is employed to calculate a lower bound on the number of left-mover lattices. Also, the known relationship between asymmetric orbifolds and covariant lattices is considered in the context of our classification.
Twisted Covariant Noncommutative Self-dual Gravity
Estrada-Jimenez, S; Obregón, O; Ramírez, C
2008-01-01
A twisted covariant formulation of noncommutative self-dual gravity is presented. The recent formulation introduced by J. Wess and coworkers for constructing twisted Yang-Mills fields is used. It is shown that the noncommutative torsion is solved at any order of the $\\theta$-expansion in terms of the tetrad and the extra fields of the theory. In the process the first order expansion in $\\theta$ for the Pleba\\'nski action is explicitly obtained.
Covariant quantization of the CBS superparticle
Energy Technology Data Exchange (ETDEWEB)
Grassi, P.A. E-mail: pag5@nyu.edu; Policastro, G.; Porrati, M
2001-07-09
The quantization of the Casalbuoni-Brink-Schwarz superparticle is performed in an explicitly covariant way using the antibracket formalism. Since an infinite number of ghost fields are required, within a suitable off-shell twistor-like formalism, we are able to fix the gauge of each ghost sector without modifying the physical content of the theory. The computation reveals that the antibracket cohomology contains only the physical degrees of freedom.
Economical phase-covariant cloning with multiclones
Institute of Scientific and Technical Information of China (English)
Zhang Wen-Hai; Ye Liu
2009-01-01
This paper presents a very simple method to derive the explicit transformations of the optimal economical to M phase-covariant cloning. The fidelity of clones reaches the theoretic bound [D'Ariano G M and Macchiavello C 2003 Phys. Rcv. A 67 042306]. The derived transformations cover the previous contributions [Delgado Y,Lamata L et al,2007 Phys. Rev. Lett. 98 150502] in which M must be odd.
Unbiased risk estimation method for covariance estimation
Lescornel, Hélène; Chabriac, Claudie
2011-01-01
We consider a model selection estimator of the covariance of a random process. Using the Unbiased Risk Estimation (URE) method, we build an estimator of the risk which allows to select an estimator in a collection of model. Then, we present an oracle inequality which ensures that the risk of the selected estimator is close to the risk of the oracle. Simulations show the efficiency of this methodology.
Superfield quantization in Sp(2) covariant formalism
Lavrov, P M
2001-01-01
The rules of the superfield Sp(2) covariant quantization of the arbitrary gauge theories for the case of the introduction of the gauging with the derivative equations for the gauge functional are generalized. The possibilities of realization of the expanded anti-brackets are considered and it is shown, that only one of the realizations is compatible with the transformations of the expanded BRST-symmetry in the form of super translations along the Grassmann superspace coordinates
Covariant quantization of the CBS superparticle
Grassi, P. A.; Policastro, G.; Porrati, M.
2001-07-01
The quantization of the Casalbuoni-Brink-Schwarz superparticle is performed in an explicitly covariant way using the antibracket formalism. Since an infinite number of ghost fields are required, within a suitable off-shell twistor-like formalism, we are able to fix the gauge of each ghost sector without modifying the physical content of the theory. The computation reveals that the antibracket cohomology contains only the physical degrees of freedom.
Torsion and geometrostasis in covariant superstrings
Energy Technology Data Exchange (ETDEWEB)
Zachos, C.
1985-01-01
The covariant action for freely propagating heterotic superstrings consists of a metric and a torsion term with a special relative strength. It is shown that the strength for which torsion flattens the underlying 10-dimensional superspace geometry is precisely that which yields free oscillators on the light cone. This is in complete analogy with the geometrostasis of two-dimensional sigma-models with Wess-Zumino interactions. 13 refs.
Linear Covariance Analysis for a Lunar Lander
Jang, Jiann-Woei; Bhatt, Sagar; Fritz, Matthew; Woffinden, David; May, Darryl; Braden, Ellen; Hannan, Michael
2017-01-01
A next-generation lunar lander Guidance, Navigation, and Control (GNC) system, which includes a state-of-the-art optical sensor suite, is proposed in a concept design cycle. The design goal is to allow the lander to softly land within the prescribed landing precision. The achievement of this precision landing requirement depends on proper selection of the sensor suite. In this paper, a robust sensor selection procedure is demonstrated using a Linear Covariance (LinCov) analysis tool developed by Draper.
Covariant Calculus for Effective String Theories
Dass, N. D. Hari; Matlock, Peter
2007-01-01
A covariant calculus for the construction of effective string theories is developed. Effective string theory, describing quantum string-like excitations in arbitrary dimension, has in the past been constructed using the principles of conformal field theory, but not in a systematic way. Using the freedom of choice of field definition, a particular field definition is made in a systematic way to allow an explicit construction of effective string theories with manifest exact conformal symmetry. ...
Covariates of Craving in Actively Drinking Alcoholics
Chakravorty, Subhajit; Kuna, Samuel T.; Zaharakis, Nikola; O’Brien, Charles P.; Kampman, Kyle M.; Oslin, David
2010-01-01
The goal of this cross-sectional study was to assess the relationship of alcohol craving with biopsychosocial and addiction factors that are clinically pertinent to alcoholism treatment. Alcohol craving was assessed in 315 treatment-seeking, alcohol dependent subjects using the PACS questionnaire. Standard validated questionnaires were used to evaluate a variety of biological, addiction, psychological, psychiatric, and social factors. Individual covariates of craving included age, race, probl...
Covariance Between Genotypic Effects and its Use for Genomic Inference in Half-Sib Families
Directory of Open Access Journals (Sweden)
Dörte Wittenburg
2016-09-01
Full Text Available 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.
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
Covariance Between Genotypic Effects and its Use for Genomic Inference in Half-Sib Families.
Wittenburg, Dörte; Teuscher, Friedrich; Klosa, Jan; Reinsch, Norbert
2016-09-08
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.
Development of covariance capabilities in EMPIRE code
Energy Technology Data Exchange (ETDEWEB)
Herman,M.; Pigni, M.T.; Oblozinsky, P.; Mughabghab, S.F.; Mattoon, C.M.; Capote, R.; Cho, Young-Sik; Trkov, A.
2008-06-24
The nuclear reaction code EMPIRE has been extended to provide evaluation capabilities for neutron cross section covariances in the thermal, resolved resonance, unresolved resonance and fast neutron regions. The Atlas of Neutron Resonances by Mughabghab is used as a primary source of information on uncertainties at low energies. Care is taken to ensure consistency among the resonance parameter uncertainties and those for thermal cross sections. The resulting resonance parameter covariances are formatted in the ENDF-6 File 32. In the fast neutron range our methodology is based on model calculations with the code EMPIRE combined with experimental data through several available approaches. The model-based covariances can be obtained using deterministic (Kalman) or stochastic (Monte Carlo) propagation of model parameter uncertainties. We show that these two procedures yield comparable results. The Kalman filter and/or the generalized least square fitting procedures are employed to incorporate experimental information. We compare the two approaches analyzing results for the major reaction channels on {sup 89}Y. We also discuss a long-standing issue of unreasonably low uncertainties and link it to the rigidity of the model.
Adaptively robust filtering with classified adaptive factors
Institute of Scientific and Technical Information of China (English)
CUI Xianqiang; YANG Yuanxi
2006-01-01
The key problems in applying the adaptively robust filtering to navigation are to establish an equivalent weight matrix for the measurements and a suitable adaptive factor for balancing the contributions of the measurements and the predicted state information to the state parameter estimates. In this paper, an adaptively robust filtering with classified adaptive factors was proposed, based on the principles of the adaptively robust filtering and bi-factor robust estimation for correlated observations. According to the constant velocity model of Kalman filtering, the state parameter vector was divided into two groups, namely position and velocity. The estimator of the adaptively robust filtering with classified adaptive factors was derived, and the calculation expressions of the classified adaptive factors were presented. Test results show that the adaptively robust filtering with classified adaptive factors is not only robust in controlling the measurement outliers and the kinematic state disturbing but also reasonable in balancing the contributions of the predicted position and velocity, respectively, and its filtering accuracy is superior to the adaptively robust filter with single adaptive factor based on the discrepancy of the predicted position or the predicted velocity.
Covariant equations for the NN-πNN system
Phillips, D. R.; Afnan, I. R.
1995-05-01
We explain the deficiencies of the current NN-πNN equations, sketch the derivation of a set of covariant NN-πNN equations and describe the ways in which these equations differ from previous sets of covariant equations.
Symmetry and Covariance of Non-relativistic Quantum Mechanics
Omote, Minoru; kamefuchi, Susumu
2000-01-01
On the basis of a 5-dimensional form of space-time transformations non-relativistic quantum mechanics is reformulated in a manifestly covariant manner. The resulting covariance resembles that of the conventional relativistic quantum mechanics.
Craps, Ben; Nguyen, Kévin
2016-01-01
Matrix quantum mechanics offers an attractive environment for discussing gravitational holography, in which both sides of the holographic duality are well-defined. Similarly to higher-dimensional implementations of holography, collapsing shell solutions in the gravitational bulk correspond in this setting to thermalization processes in the dual quantum mechanical theory. We construct an explicit, fully nonlinear supergravity solution describing a generic collapsing dilaton shell, specify the holographic renormalization prescriptions necessary for computing the relevant boundary observables, and apply them to evaluating thermalizing two-point correlation functions in the dual matrix theory.
Inverse modeling of the terrestrial carbon flux in China with flux covariance among inverted regions
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.
Block-diagonal representations for covariance-based anomalous change detectors
Energy Technology Data Exchange (ETDEWEB)
Matsekh, Anna M [Los Alamos National Laboratory; Theiler, James P [Los Alamos National Laboratory
2010-01-01
We use singular vectors of the whitened cross-covariance matrix of two hyper-spectral images and the Golub-Kahan permutations in order to obtain equivalent tridiagonal representations of the coefficient matrices for a family of covariance-based quadratic Anomalous Change Detection (ACD) algorithms. Due to the nature of the problem these tridiagonal matrices have block-diagonal structure, which we exploit to derive analytical expressions for the eigenvalues of the coefficient matrices in terms of the singular values of the whitened cross-covariance matrix. The block-diagonal structure of the matrices of the RX, Chronochrome, symmetrized Chronochrome, Whitened Total Least Squares, Hyperbolic and Subpixel Hyperbolic Anomalous Change Detectors are revealed by the white singular value decomposition and Golub-Kahan transformations. Similarities and differences in the properties of these change detectors are illuminated by their eigenvalue spectra. We presented a methodology that provides the eigenvalue spectrum for a wide range of quadratic anomalous change detectors. Table I summarizes these results, and Fig. I illustrates them. Although their eigenvalues differ, we find that RX, HACD, Subpixel HACD, symmetrized Chronochrome, and WTLSQ share the same eigenvectors. The eigen vectors for the two variants of Chronochrome defined in (18) are different, and are different from each other, even though they share many (but not all, unless d{sub x} = d{sub y}) eigenvalues. We demonstrated that it is sufficient to compute SVD of the whitened cross covariance matrix of the data in order to almost immediately obtain highly structured sparse matrices (and their eigenvalue spectra) of the coefficient matrices of these ACD algorithms in the white SVD-transformed coordinates. Converting to the original non-white coordinates, these eigenvalues will be modified in magnitude but not in sign. That is, the number of positive, zero-valued, and negative eigenvalues will be conserved.