It was investigated whether commonly used factor score estimates lead to the same reproduced covariance matrix of observed variables. This was achieved by means of Schonemann and Steiger's (1976) regression component analysis, since it is possible to compute the reproduced covariance matrices of the regression ...
ERIC Educational Resources Information Center
The solution of weighted least-square problems obtained with the Givens-Gentleman orthogonal transformation method is shown to be equivalent to that in the statistical orbit determination theory. By using this method, the covariance analysis quantities of statistical significance, such as the considered covariance matrix and ...
NASA Astrophysics Data System (ADS)
Multidimensional nuclear magnetic resonance (NMR) experiments measure spin-spin correlations, which provide important information about bond connectivities and molecular structure. However, direct observation of certain kinds of correlations can be very time-consuming due to limitations in sensitivity and resolution. Covariance NMR derives correlations between spins via the ...
PubMed Central
... Title : The Covariance Matrix of Normal Order Statistics. ... Corporate Author : STANFORD UNIV CA DEPT OF STATISTICS. ...
DTIC Science & Technology
... The derivatives of covariance matrix elements with respect to the transmitter polarization reveal interesting functional relations between ...
... parameters and launch parameter covariance matrix for the booster. ... Thus, there are cases when the launch parameter covariance matrix could ...
The polynomial growth curve model of Potthoff and Roy, defined by a matrix of observations from n individuals at q different times, is considered. A general model of covariance matrices was achieved for analysis of repeated measurements. Some special matr...
National Technical Information Service (NTIS)
SummaryIn recent years, many methods have been developed for regression in high-dimensional settings. We propose covariance-regularized regression, a family of methods that use a shrunken estimate of the inverse covariance matrix of the features in order to achieve superior prediction. An estimate of the inverse ...
The sandwich estimator, often known as the robust covariance matrix estimator or the empirical covariance matrix estimator, has achieved increasing use with the growing popularity of generalized estimating equations. Its virtue is that it provides consistent estimates of the ...
E-print Network
Covariance Tapering in Spatial Statistics Hao Zhang Department of Statistics Department of Forestry such that the resulting matrix is still positive definite. Covariance tapering assures that the tapered covariance matrix is positive definite while retaining most of the information. The tapered ...
... When Sigma is the population covariance matrix of a multivariate random distribution, partial covariances and correlations can be defined in terms ...
... Title : Geometric Characterization of Eigenvalues of Covariance Matrix for Two- Source Array Processing. Descriptive Note : Professional paper,. ...
... whose covariance matrix has the linear structure ... Descriptors : *MULTIVARIATE ANALYSIS, ALGEBRA, DISTRIBUTION FUNCTIONS, ASYMPTOTIC ...
A simplified method to estimate covariance matrix the model parameters is presented in this work and applied to calculate covariance matrix of calculated cross sections on (sup 19)F.
To improve the response to unexpected thermally induced disturbances, two new controllers are designed for the Hubble Space Telescope (HST) using covariance control techniques. The first controller minimizes the required control effort subject to inequality constraints on the output covariance matrix. The second controller is designed ...
Abstract: In many applications in statistics, finance, and insurance/reinsurance , one seeks a solution of finding a covariance matrix satisfying a large ...
... Accession Number : ADA236924. Title : Eigenvalues of Covariance Matrix for Two-Source Array Processing. Descriptive Note : Professional paper,. ...
Page 1. Covariance Recovery from a Square Root Information Matrix for Data Association Michael Kaess CSAIL, Massachusetts ...
... Page 3. Robust ABF with Moving Ships � ABF ... hydrophone covariance matrix � When ships move, the time average is not the appropriate ...
The estimated parameter covariance matrix is1. ( ). (. )(. ) ( ). 2. T. T ..... The estimated parameter covariance matrix is1,8. ( ) ( ). 1. 2. 1. T p. ?. ? ...
NASA Website
Health parameter covariance matrix. PLA. Power lever angle. R. Sensor measurement covariance matrix. V*. Health parameter vector to tuner vector linear ...
The multinomial probit model has emerged as a useful framework for modeling nominal categorical data, but extending such models to multivariate measures presents computational challenges. Following a Bayesian paradigm, we use a Markov chain Monte Carlo (MCMC) method to analyze multivariate nominal measures through multivariate multinomial probit models. As with a univariate version of the model, ...
PubMed
inertial navigation system for the Abort Guidance. System (AGS) ..... W matrix instead of error covariance matrix. W-matrix instead of error covariance matrix .... Earth gravity model: spherical and J2 (equatorial bulge) terms only ...
A straightforward derivation is presented for the covariance matrix of evaluated cross sections based on the covariance matrix of the experimental data and propagation through nuclear model parameters. 10 refs.
DOE Information Bridge
... of the geoid and deflections of the vertical at any ... GRAVITY, ANOMALIES), INTEGRALS, NUMERICAL INTEGRATION, DEFLECTION, NUMERICAL ...
University Engineering Department DARPA Visit #12;Khe Chai Sim & Mark Gales: Precision and Covariance Matrix Engineering Department DARPA Visit 1 #12;Khe Chai Sim & Mark Gales: Precision and Covariance Matrix Modelling University Engineering Department DARPA Visit 2 #12;Khe Chai Sim & Mark Gales: ...
We present the techniques used to identify electrons in the D0 detector. The D0 electron identification algorithm is based on the full covariance matrix of energy deposits in the calorimeter cells occupied by an electromagnetic shower and information from the central tracking system. The method exploits the fine longitudinal and transverse segmentation of ...
Energy Citations Database
This report investigates forms of the state noise covariance matrix in the Kalman Filter. This matrix, denoted Q sub d, incorporates the effects of random errors driving the system dynamics into the filter computations. The Q sub d matrix is derived by in...
The optimal (adaptive) linear combiner (beamformer) weights for a sensor array are expressed in terms of the inverse of the multi-channel (MC) covariance matrix. Rather than form an estimate of the covariance matrix directly from the available data and in...
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, ar...
... averaging over training range-gates to obtain covariance matrix estimates. ... independent covariance 1 R that is to be estimated. An initial estimate of ...
Kalman filter, the state-noise covariance extrap- olation step is given by where FF: is the statenoise covariance and 0 is the system transition matrix. ...
In adaptive ultrasound imaging, accurate estimation of the array covariance matrix is of great importance, and biases the performance of the adaptive beamformer. The more accurately the covariance matrix can be estimated, the better the resolution and contrast can be achieved in the ultrasound ...
An improved computational form of the discrete Kalman filter is derived using an upper triangular factorization of the error covariance matrix. The covariance P is factored such that P = UDUT where U is unit upper triangular and D is diagonal. Recursions ...
Covariance Tapering for Interpolation of Large Spatial Datasets Reinhard FURRER, Marc G. GENTON. In this article, we show that tapering the correct covariance matrix with an appropriate compactly supported to an asymptotically optimal mean squared error. The effect of tapering is to create a sparse approximate linear system
Covariance Tapering for Interpolation of Large Spatial Datasets Reinhard Furrer, Marc G. Genton of the observations. In this article, we show that tapering the correct covariance matrix with an appropriate leads to an asymptotically optimal mean squared error. The effect of tapering is to create a sparse
Abstract�Adaptive-array beamforming achieves high resolution and sidelobe suppression by producing sharp nulls in the adaptive beampattern. Large-aperture sonar arrays with many elements have small resolution cells; interferers may move through many resolution cells in the time required for accumulating a full-rank sample covariance ...
Covariance Tapering for Likelihood-Based Estimation in Large Spatial Data Sets Cari G. KAUFMAN. This article proposes the method of covariance tapering to approximate the likelihood in this setting. In this approach, covariance matrixes are "tapered," or multiplied element wise by a sparse correlation ...
The best fits to data which are affected by systematic uncertainties on the normalization factor have the tendency to produce curves lower than expected, if the covariance matrix of the data points is used in the definition of the (chi)(sup 2). This paper...
The construction of D-optimal designs for multiresponse experiments was previously considered. This algorithm required that the variance covariance matrix, Sigma, of the responses be known. This is rarely the case in practice. The primary objective of thi...
FACOV. R*8. I. 6X6. COVARIANCE MATRIX (ROLL AND. ALTITUDE DEVIATION). PYCOV. R*8 . I. 8X8. COVARIANCE MATRIX PITCH AND YAW) ...
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 ther...
... covariance matrix. Hence, the covariance matrix calculated without high measurement-error characters provided a better evaluation process for the shape axes (Fig. 4). As ... ...
NBII National Biological Information Infrastructure
The procedure to follow to obtain the variance-covariance matrix of an integral experiment is described. Results from integral measurements of fission cross sections performed at NBS and ORNL are used for the sample problem. It is indicated how to calcula...
0, and C, the covariance matrix of the miss components. IS gLVCLL by. C = E[(Ubx )(Uax) T] = UMUT ) wliere M is the covariance matrix of injection errors. ...
The covariance matrix of quantities derived from measured data via nonlinear relations are only approximate since they are functions of the measured data taken as estimates for the true values of the measured quantities. The evaluation of such derived qua...
This report describes the performance of the covariance matrix estimator in non-Gaussian spherically invariant random processes (SIRP). Analytic expressions are derived for the variance of the estimator. Specific consideration is given to the special case...
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 uncertain...
Covariance analysis quantities of statistical significance such as considered covariance matrix and sensitivity matrix are derived in the formalism of Givens - Gentleman orthogonal transformation. A program named CAPS is coded. The underlying principles, structure and usage are described in detail.
Different simulation runs using the same random numbers can be analyzed without the blocking concept. Instead the linear regression model with a non-diagonal covariance matrix applies. This covariance matrix does not need to have a specific pattern such a...
Adaptive arrays and tracking share many concepts mathematical tools, practical issues, and algorithms. For example, ill-conditioning of the sample covariance matrix for adaptive arrays and ill-conditioning of the covariance matrix in a Kalman filter are b...
A clustering algorithm, using a proximity measure as a weighted Euclidean distance in which the weight matrix is the inverse of the estimated covariance matrix for each cluster, is presented. Each sample is assigned to the cluster for which this weighted ...
D. Wechsler (2008b) reported confirmatory factor analyses (CFAs) with standardization data (ages 16-69 years) for 10 core and 5 supplemental subtests from the Wechsler Adult Intelligence Scale-Fourth Edition (WAIS-IV). Analyses of the 15 subtests supported 4 hypothesized oblique factors (Verbal Comprehension, Working Memory, Perceptual Reasoning, and Processing Speed) but also revealed unexplained ...
Covariance and Sensitivity Data Generation at ORNL L. C. Leal, H. Derrien, N. M. Larson, and A as variance and covariance data. In this paper we will address the generation of covariance data-matrix theory. Two approaches are used in SAMMY for the generation of resonance-parameter covariance ...
... power spectral density (IPSD) matrix, The IPSD is used in computing the linear dependency structure of nonstationary time series. An example of ...
The estimated parameter covariance matrix is computed from8. ) ( ) 1. 2. T. T. ( ) ( )(. ?. ?. ?. Cov. E. ??. . ?. = . ? ? ? ? ?. ?. = ?. ?. ? ...
... OF TECH CAMBRIDGE COMPUTER SCIENCE AND ARTIFICIAL ... Categories : NUMERICAL MATHEMATICS STATISTICS AND PROBABILITY ...
... the trace of the covariance matrix is the same as the norm of the ... of real symmetric n � n matrices A such that the upper triangular elements are ...
... Joint asymptotic normally of this spectral estimator is established; a precise asymptotic expression for the covariance matrix of the limiting ...
... Abstract : In the problem of estimating the covariance matrix of a multivariate normal population James and Stein obtained a minimax estimator by ...
... the surface, and then lifting it back to the ... final chaser and target condition perturbations are then ... C, designates a state or perturbation propagated to ...
quaternion produces dependence between the quaternion elements. In reality, though, this phenomenon does not occur. The covariance matrix is not singular, ...
Principal Component Analysis. V Series expansion of multivariate functions: where are the eigenfunctions of the covariance matrix ...
... between the Lie algebra, the covariant form of the S-matrix, and the uniqueness of K, the generator of pure Lorentz transformations, is thereby ...
Radar Scene Generation For Tactical Decision Aids. We have studied the Mueller matrix and polarization covariance matrix for polari- metric radar systems. ...
... effect sizes, X is a design matrix of independent variables containing grouping variables and continuous covariates, and β ... the species, X is a design matrix of independent variables, β is a vector of ...
The report is an analysis of the accuracy limitations of hyperbolic multilateration systems. A central result is a demonstration that the inverse of the covariance matrix for positional errors corresponds to the moment of inertia matrix of a simple mass c...
The Covariance NMR Toolbox is a new software suite that provides a streamlined implementation of covariance-based analysis of multi-dimensional NMR data. The Covariance NMR Toolbox uses the MATLAB or, alternatively, the freely available GNU OCTAVE computer language, providing a user-friendly environment in which to apply and explore ...
Acoustic Emission (AE) is the transient energy released through mechanical waves during incremental damage processes within materials. AE is used as a non-destructive testing (NDT) tool in inspecting structures, design analysis and material characterization among many other applications. AE observation as a non-invasive way to predict crack growth has one major problem. AE counts display sudden ...
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 ...
Computational power of Graphical Processing Units and multicore CPUs was harnessed by the nuclear data evaluation code SAMMY to speed up computations of large Resonance Parameter Covariance Matrices (RPCMs). This was accomplished by linking SAMMY to vendor-optimized implementations of the matrix-matrix multiplication subroutine of the Basic Linear Algebra ...
The design of triangular fiber Bragg grating (TFBG) filters using covariance matrix adapted evolution strategy (CMAES) algorithm is presented. The TFBG filter can be used as a readout device in FBG-based sensor applications. Two different filter design problems are investigated. The first problem is the design of TFBG filter for a specified bandwidth. The ...
This paper considers estimating a covariance matrix of p variables from n observations by either banding or tapering the sample covariance matrix, or estimating a banded version of the inverse of the covariance. We show that these estimates are consistent in the operator norm as long as (log ...
We present an extension to the theory of compliance matrices, which is valid for arbitrary nonstationary points on the potential energy hypersurface. It is shown that compliance matrices computed as the inverse of the covariant Hessian matrix obey the same invariance properties with respect to different internal coordinate systems as they do for stationary ...
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 ...
The Fisher information matrix for the estimated parameters in a multiple logistic regression can be approximated by the augmented Hessian matrix of the moment generating function for the covariates. The approximation is valid when the probability of response is small. With its use one can obtain a simple closed form estimate of the ...
The comparative approach is routinely used to test for possible correlations between phenotypic or life-history traits. To correct for phylogenetic inertia, the method of independent contrasts assumes that continuous characters evolve along the phylogeny according to a multivariate Brownian process. Brownian diffusion processes have also been used to describe time variations of the parameters of ...
The essence of this paper is to enlighten the consistency achieved nowadays in nuclear data and uncertainties assessments in terms of compound nucleus reaction theory from neutron separation energy to continuum. Making the continuity of theories used in resolved (R-matrix theory), unresolved resonance (average R-matrix theory) and ...
New evaluations for the tungsten isotopes {sup 180,182,183,184,186}W in the neutron energy range up to 60 MeV were produced. In the resonance range only minor adjustments to the resonance parameters were made due to a lack of adequate experimental data. Evaluations in the fast energy region were based on nuclear model calculations using the EMPIRE-2.19 code. Recently derived dispersive ...
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 ...
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 ...
The latest nuclear data are used to examine the sensitivity of the least squares adjustment of the {sup 235}U fission spectrum to the measured reaction rates, dosimetry cross sections, and prior spectrum covariance matrix. All of these parameters were found to be very important in the spectrum adjustment. The most significant deficiency in the nuclear data ...
We present a simple and intuitive derivation of the track parameter covariance matrix due to multiple Coulomb scattering for use in track fitting with the Kalman filter. We derive all the covariance matrix elements for two experimentally relevant track parameterizations (i.e. x and y slopes and intercepts, and ...
In this paper, the authors derived asmptotic distributions of changes in certain functions of the eigenvalues of the sample covariance matrix, MANOVA matrix and canonical correlation matrix when some variables are added to the original sets of variables. ...
Other than cleavage site mutations, there is little data on specific positions within Gag that impact on HIV protease inhibitor susceptibility. We have recently shown that non-cleavage site mutations in gag, particularly within matrix protein can restore replication capacity and further reduce protease inhibitor drug susceptibility when coexpressed with a drug-resistant ...
Experiment E05-115 at Thomas Jefferson National Accelerator Facility is a spectroscopic study that will measure the excitation spectra of hypernuclei in the (e,e'K^+) reaction on light-to-medium mass targets. A time-of-flight system consisting of three scintillator planes (ToF) is part of the particle identification. To achieve the required accuracy, raw time measurements, TDC ...
We present what we believe to be a new algorithm, FRactal Iterative Method (FRiM), aiming at the reconstruction of the optical wavefront from measurements provided by a wavefront sensor. As our application is adaptive optics on extremely large telescopes, our algorithm was designed with speed and best quality in mind. The latter is achieved thanks to a regularization that ...
This paper develops a possible method for computing the asymptotic sampling variance-covariance matrix of joint maximum likelihood estimates in item response theory when both item parameters and abilities are unknown. For a set of artificial data, results...
The range-dependent nature of the surface clutter power spectrum observed in monostatic or bistatic airborne radar systems results in a mismatch of the clutter covariance matrix (computed from a secondary set of range-cell data) relative to that of a poss...
Maximum-likelihood estimation of a constrained initial condition covariance matrix is treated. The associated cross-sectional observations are generated from non-identically distributed realizations of a linear dynamic system. The covariance matrix is constrained to both band-block structure and to general positive ...
Genetically correlated traits do not evolve independently, and the covariances between traits affect the rate at which a population adapts to a specified selection regime. To measure the impact of genetic covariances on the rate of adaptation, we compare the rate fitness increases given the observed G matrix to the expected rate if all ...
), Rajaratnam et al. (2008), Levina et al. (2008), and Peng et al. (2009). Bickel and Levina (2008a) and Rothman to be compared are the LW estimate in Ledoit and Wolf (2006), the banding estimate in Bickel and Levina (2008b - ). Based on the sample covariance matrix S = (sij), the banding estimate 11 #12;in Bickel and Levina (2008b
Using 1000 ray-tracing simulations for a ?-dominated cold dark model in Sato et al., we study the covariance matrix of cosmic shear correlation functions, which is the standard statistics used in previous measurements. The shear correlation function of a particular separation angle is affected by Fourier modes over a wide range of multipoles, even beyond a ...
A covariant theory of polarization analysis of spin 1 particles is developed. The 16-dimensional covariant scattering equation is reduced to the three-dimensional scattering equation. Lorentz transformations and space rotations giving rise to relativistic corrections (when the scattering theory is applied to multiple scattering experiments) are ...
In this paper, we address an adaptive detection of range-spread targets or targets embedded in Gaussian noise with unknown covariance matrix by the generalized detector (GD) based on the generalized approach to signal processing (GASP) in noise. We assume that cells or secondary data that are free of signal components are available. Those secondary data ...
The covariance matrix of quantities derived from measured data via nonlinear relations are only approximate since they are functions of the measured data taken as estimates for the true values of the measured quantities. The evaluation of such derived quantities entails new estimates for the true values of the measured quantities and consequently implies a ...
The purpose of this study was to examine the potential of using optical information processing technology for adaptive antenna beamforming and null steering. The adaptive beamforming/null steering problem consists of estimation of the covariance matrix of the noise field and inversion of the covariance matrix to ...
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 ...
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 ...
A new stochastic inverse algorithm for the inversion of three-dimensional (3-D) electrical resistivity tomography (ERT) data has been developed and tested using both synthetic and field data. My stochastic inverse algorithm produced satisfactory inverse solutions that were very similar to those of the commonly used Occam's inversion. The ill-posed 3-D stochastic inverse problems were stabilized by ...
The ENDF/B-V included cross section covariance data, but covariances could not be encoded for all the important data types. New ENDF-6 covariance formats are outlined including those for cross-file (MF) covariances, resonance parameters over the whole range, and secondary energy and angle distributions. One ...
... Final parameter covariance matrix npar x npar array, or 0 if not desired */ }; /* Convenience typedefs */ typedef struct mp_par_struct mp_par; ...
F1, : consider parameter covariance matrix. The considered parameters are assumed to be uncorre- lated, thus Fy is diagonal, with the associated consider ...
Description Finds the maximum likelihood estimate of the mean vector and variance-covariance matrix for multivariate normal data with missing values.
... The genetic covariance between characters maintained by pleiotropic mutations. Genetics 94:203�215. PubMedLande, R. 1984. The genetic ... ...
Mar 13, 2008 ... enforce uncertainty in parameters in a deterministic manner using convex ..... mean-covariance matrix is equivalent to characterizing the cone ...
... other processes that are part of the data ... contribute during the measurement process, are developed ... white Gaussian noise with covariance matrix R ...
Methods for testing assumptions as to the normality of the distribution of initial imperfections in shell profiles in Monte Carlo simulations are assessed. Confidence intervals for the mean values and for the elements of the variance-covariance matrix are...
... The sample complex vector signal processes, Z Sub K ... definite, maximum likelihood estimate (MLE) R of the signal process covariance matrix R ...
Jan 22, 2011 ... Title: Spectroscopic Observations of Low Lying Gas Clouds: Sensitivity of Detection by Method of Covariance Matrix. Authors: Margolis, J. ...
J.F. Bonnans and A. Shapiro. Perturbation Analysis of Optimization Problems. Springer Series in Operations Research and Financial Engineering. Springer, ...
Jun 16, 2011 ... It produces a conditionally unbiased covariance matrix for the vector estimate of population totals without approximations or ad hoc ...
Treesearch
... This term is the output of a high-frequency linear system driven by a product of the parameter covariance matrix and a Kalman-filter innovation ...
This final report reviews the following work: minimax estimation of the mean vectors of multivariate normal distributions and of the slopes of multivariate linear regression models when the error covariance matrix is unknown; minimax properties of general...
This annual report reviews work on the following topics: minimax estimation of the mean vectors of multivariate normal distributions with unknown covariance matrix; minimax and computational stability properties of generalized ridge regression estimators ...
7.2.2 GPS Analysis, IVP Determination, Alignment and SINEX. ... accurate connections and their associated variance covariance matrix, and provided an un- ...
... All simulations have suggested that using a sliding window to sample the covariance matrix to process each range bin does not statistically improve ...
... Corporate Author : STANFORD UNIV CALIF DEPT OF STATISTICS. Personal Author(s) : Shaman,Paul. Report Date : 05 AUG 1968. ...
directed principal components analysis (DPCA). The filters generated by DPCA can be identified off line by ... clustering, covariance matrixes in DPCA, and ...
... Mahalanobis distance classifiers based on the use of full covariance matrix are able to detect EEG activity related to imagination of movement with ...
May 7, 2010... Final parameter covariance matrix npar x npar array, or 0 if not desired */ char version[20]; /* MPFIT version string */ }; ...
... line arrays such as the MPL digital array ... array covariance matrix to be processed by the various ... produce a realistic wavefield to process with the pro ...
... A-1 Appendix B: Weibull Distribution Transformations..... BI Appendix C: Estimate For Parameter Covariance Matrix ..... C-1 V ...
The construction of designs in paired comparison experiments is considered. The procedures that are used in paired comparisons to estimate the parameters yield a covariance matrix that depends on the unknown parameters. The assumption of no treatment diff...
... the number of possible scenarios makes comparison of algorithms ... we note that their method can use ... The eigenvalue problem can be rewritten as ...
... temporal and/or spatial correlations. ... Descriptors : *EIGENVALUES, *DIRECTION FINDING ... WEIGHTING FUNCTIONS, CORRELATION, SIGNALS ...
... 1655-1663. 2004 doi: 10.1554/04-130 EPISTASIS AND THE TEMPORAL CHANGE IN THE ADDITIVE VARIANCE- ... covaria...
... P�x = unconditioned estimated parameter covariance matrix �x = estimated parameter vector conditioned on �xn = ��Nint ...
, Thamnophis elegans, was five times less likely to be detected at trout-containing vs. trout-free sites
... are the eigenvectors of the covariance matrix, and principal component scores represent the magnitude of the projection of the ... al., 2003), both taxa show similar spread in principal component scores (...
State estimation techniques effectively provide mean state estimates. However, the theoretical state
NASA Technical Reports Server (NTRS)
The estimated parameter covariance matrix can be expressed using Eq. (11) with substitutions from the definitions in. Eqs. (16) and (18), covlo)o ...
... transformation method, the BASS-ALE method and the narrowband MUSIC algorithnt. ... I 5.2 Covariance matrix computation for narrowband MUSIC ...
... A method for obtaining the exact solution for large- ... t) for the system equations is a sparse matrix in ... linear functions of the nominal states, or implicitly ...
The program PUFF-IV is used to process resonance parameter covariance information given in ENDF/B File 32 and point wise covariance matrices given in ENDF/B File 33 into group-averaged covariances matrices on a user-supplied group structure. For large resonance covariance matrices, found for example in 235U, the ...
The polarization analysis of light is typically carried out using modulation schemes. The light of an unknown polarization state is passed through a set of known modulation optics, and a detector is used to measure the total intensity passing the system. The modulation optics is modified several times, and, with the aid of several such measurements, the unknown polarization state of the light can ...
Aims:The maximum-likelihood method is the standard approach to obtain model fits to observational data and the corresponding confidence regions. We investigate possible sources of bias in the log-likelihood function and its subsequent analysis, focusing on estimators of the inverse covariance matrix. Furthermore, we study under which circumstances the ...
Beamformers are a commonly used method for doing source localization from magnetoencephalography (MEG) data. A key ingredient in a beamformer is the estimation of the data covariance matrix. When the noise levels are high, or when there is only a small amount of data available, the data covariance matrix is ...
This study develops a method for analytically propagating a covariance matrix to a maneuver condition to be used in linear covariance analysis for planning the rendezvous phase of a space mission. With the generalized formulation of a condition transition matrix, an analytic method of propagating an augmented ...
Since the advent of target tracking systems employing a diverse mixture of sensors, there has been increasing recognition by air defense system planners and other military system analysts of the need to integrate these tracks so that a clear air picture can be obtained in a command center. A popular methodology to achieve this goal is to perform track-to-track fusion, which ...
In this paper, we present an original method to evaluate the deformations in the third cerebral ventricle on a brain cine- MR imaging. First, a segmentation process, based on a fractional differentiation method, is directly applied on a 2D+t dataset to detect the contours of the region of interest (i.e. lamina terminalis). Then, the successive segmented contours are matched using a procedure of ...
Structural equation modeling provides the framework for investigating experimental effects on the basis of variances and covariances in repeated measurements. A special type of confirmatory factor analysis as part of this framework enables the appropriate representation of the experimental effect and the separation of experimental and nonexperimental parts of variance. The ...
In this paper, we present Confetti, a novel point-based rendering approach based on object-space point interpolation of densely sampled surfaces. We introduce the concept of a transformation-invariant covariance matrix of a set of points which can efficiently be used to determine splat sizes in a multiresolution point hierarchy. We also analyze continuous ...
In order to achieve a precise positioning solution from GPS, the carrier-phase measurements with correctly resolved integer ambiguities must be used. Based on the integration of GPS with pseudolites and Inertial Navigation Systems (INS), this paper proposes an effective procedure for single-frequency carrier-phase integer ambiguity resolution. With the inclusion of pseudolites ...
Aspects of antenna null rotation are considered along with electronic null steering, constrained power minimization, weak signal adaptation, the sidelobe canceller, the Davies beamformer, and multiple null formation with N complex coefficients. Vector and matrix techniques are discussed along with optimal antennas and adaptive solutions of optimal antennas, taking into account ...
It was recently shown that applying a Gaussianizing transform, such as a logarithm, to the nonlinear matter density field extends the range of scales, by a factor of a few smaller, where the power spectrum excels at describing the ?field. Such a transform dramatically reduces nonlinearities in both the covariance and the shape of the power spectrum. Here, analyzing Coyote ...
BackgroundThe aim was to predict breeding values of non-phenotyped individuals based on a dataset prepared for the 13th QTL-MAS Workshop in Wageningen.MethodsGenetic co-variance matrices between animals were estimated with three methods: one using pedigree information only and two based on SNP markers from the first chromosome. Quadratic regression of ...
The Fisher information matrix for the estimated parameters in a multiple logistic regression can be approximated by the augmented Hessian matrix of the moment generating function for the covariates. The approximation is valid when the probability of respo...
A computer program named HEATEST required excessive computer time to evaluate the matrix Riccati equation for temperature covariance. Alternative numerical methods were employed to compute the Riccati equation, and the HEATEST program execution time was r...
state transition matrix from time t t o k thrust noise. DEFINITION rtate control covariance ...... Ik+l,k represents the state transition matrix and ...
Application of the method of multiple linear regression as a data-analysis technique for gamma-ray scintillation spectrometer data requires knowledge of (1) the response matrix of the spectrometer and (2) the covariance matrix of the unknown spectrum (or ...
Description A robust constrained L1 minimization method for estimating a large sparse inverse covariance matrix (aka precision matrix), and recovering its support for building graphical models. The computation uses linear programming.
The formula for the expectation of the product of four scalar real Gaussian random variables is generalized to matrix-valued (real or complex) Gaussian random variables. As an application of the extended formula, a simple derivation of the covariance matr...
In this article exact tests for the equality of parameters from several correlated linear response models with an unknown variance-covariance matrix sigma are presented. The models are assumed to be of the same form and to contain the same set of input va...
A covariant formulation of the scattering matrix theory in the p- representation is given. The unitarity and causality conditions are included. (auth)
The covariant equation of motion previously obtained for the scattering matrix is investigated in the framework of perturbation theory. A specific diagram technique is developed. (auth)
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. ...
Multivariate curve resolution (MCR) has been applied to separate pure spectra and pure decay profiles of DOSY NMR data. Given good initial guesses of the pure decay profiles, and combined with the nonlinear least square regression (NLR), MCR can result in good separation of the pure components. Nevertheless, due to the presence of artefacts in experimental data, validation of a MCR model is still ...
Estimation of covariance matricesCovariance matrix Basic problem in multivariate statistics: by sampling from a high-dimensional distribution, determine its covariance structure. Principal Component Analysis (PCA): detect the principal axes along which most dependence occurs: PCA of a multivariate Gaussian ...
This paper considers regularizing a covariance matrix of p variables estimated from n observations, by hard thresholding. We show that the thresholded estimate is consistent in the operator norm as long as the true covariance matrix is sparse in a suitable sense, the variables are Gaussian or sub-Gaussian, and (log ...
The Fisher information matrix for the estimated parameters in a multiple logistic regression can be approximated by the augmented Hessian matrix of the moment generating function for the covariates. The approximation is valid when the probability of response is small. With its use one can obtain a simple closed-form estimate of the ...
We find a simple, accurate model for the covariance matrix of the real-space cosmological matter power spectrum on slightly nonlinear scales (k ~ 0.1-0.8 h Mpc�1 at z = 0), where off-diagonal matrix elements become substantial. The model includes a multiplicative, scale-independent modulation of the power spectrum. It has only one ...
This study is concerned with the development, examination and comparison of statistics for testing linear hypotheses about the means in the context of the multivariate normal linear model when the covariance matrix is known to conform to a linear pattern. Two likelihood ratio criteria appropriate for the problem are put forward initially, the standard ...
Interpolation of a spatially correlated random process is used in many areas. The best unbiased linear predictor, often called kriging predictor in geostatistical science, requires the solution of a large linear system based on the covariance matrix of the observations. In this article, we show that tapering the correct covariance ...
diverges to infinity at a rate slower than n. On the other hand, Bickel and Levina (2004) showed) and covari- ance matrix estimation (Bickel and Levina, 2008). There exist a few other sparse LDA methods, e as Bickel and Levina's (2008) thresholding estimator of the covariance matrix of x, but we allow the number
eigenvalues of the Kalman-filtered covariance matrix, Rm m. (Middle and Bottom) The solution (solid lines) u filtered covariance matrix, Rm m (Top). (Middle and Bottom) The solution (solid lines) u(x, t) of Eqs. 18); (Middle and Bottom) The solution (solid lines) u(x, t) of Eqs. 18�20 and the filtered solution ...
Let X be a p-variate (p>or=3) vector, normally distributed with unknown mean theta and unknown covariance matrix sigma. Let W:pXp be distributed independently of X, and let W have a Wishart distribution with n degrees of freedom and parameter sigma. It is...
A statistical model of voice recognition is studied. A covariance matrix of voice parameters was computed using data from 4 speakess, 20 samples and 28 parameters. Mahalanobis generalized distances were calculated using the covariance matrix. Each speaker...
Presents an algorithm for the production of a graphical diagram from a matrix formula in such a way that its components are logically and hierarchically arranged. The algorithm, which relies on the matrix equations of J. McArdle and R. McDonald (1984), calculates the individual path components of expected covariance between variables ...
Covariance matrices capture correlations that are invaluable in modeling real-life datasets. Using all d 2 elements of the covariance (in d dimensions) is costly and could result in over-fitting; and the simple diagonal approximation can be over-restrictive. We present an algorithm that improves upon the diagonal matrix by allowing a ...
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 ...
The error estimation for calculated quantities relies on nuclear data uncertainty information available in the basic nuclear data libraries such as the U.S. Evaluated Nuclear Data File (ENDF/B). The uncertainty files (covariance matrices) in the ENDF/B library are generally obtained from analysis of experimental data. In the resonance region, the computer code SAMMY is used ...
: Covariance matrix forecasts of financial asset returns are an important component of current practice in financial risk management. A wide variety of models are available for generating such forecasts. In this paper, we evaluate the relative performance of different covariance matrix forecasts using standard ...
We propose a probability model for random partitions in the presence of covariates. In other words, we develop a model-based clustering algorithm that exploits available covariates. The motivating application is predicting time to progression for patients in a breast cancer trial. We proceed by reporting a weighted average of the responses of clusters of ...
Covariance data are required to assess uncertainties in design parameters in several nuclear applications. The error estimation of calculated quantities relies on the nuclear data uncertainty information available in the basic nuclear data libraries, such as the US Evaluated Nuclear Data Library, ENDF/B. The uncertainty files in the ENDF/B library are obtained from the ...
Surface currents measured by high-frequency radars are objectively mapped using covariance matrices computed from hourly surface current vectors spanning two years. Since retrievals of surface radial velocities are inherently gappy in space and time, the irregular density of surface current data leads to negative eigenvalues in the sample covariance ...
A number of algorithms to solve large scale Kalman filtering problems has been introduced recently. The ensemble filter represents the probability density of the state estimate by a finite number of randomly generated system states. Other algorithms use a singular value decomposition to select the leading eigenvectors of the covariance matrix of the state ...
We present an algorithm for quickly generating multiple realizations of N-body simulations to be used, for example, for cosmological parameter estimation from surveys of large-scale structure. Our algorithm uses a new method to resample the large-scale (Gaussian-distributed) Fourier modes in a periodic N-body simulation box in a manner that properly accounts for the nonlinear mode coupling between ...
This article proposes a multilevel model for the assessment of school effectiveness where the intake achievement is a predictor and the response variable is the achievement in the subsequent periods. The achievement is a latent variable that can be estimated on the basis of an item response theory model and hence subject to measurement ...
In a realistic passive sonar environment, the received signal consists of multipath arrivals from closely separated moving targets. The signals are contaminated by spatially correlated noise. The differential MUSIC has been proposed to estimate the DOAs in such a scenario. This method estimates the 'noise subspace' in order to estimate the DOAs. However, the 'noise subspace' estimate has to be ...
Objective: The aim of this review was to conduct an individual patient data (IPD) meta-analysis of randomized controlled clinical trials (RCTs) to evaluate whether baseline recession-, patient- and procedure-related factors can influence the achievement of complete root coverage (CRC). Material and Methods: A literature search with no restrictions regarding status or the ...
Linearized methods are presented for appraising image resolution and parameter accuracy in images generated with two and three dimensional non-linear electromagnetic inversion schemes. When direct matrix inversion is employed, the model resolution and model covariance matrices can be directly calculated. The columns of the model resolution ...
The space-wise approach is a multi-step collocation procedure, developed in the framework of the GOCE HPF data processing for the estimation of the spherical harmonic coefficients of the Earth gravitational field and their error covariance matrix. The main idea of the space-wise approach is to perform this estimation by exploiting the spatial correlation ...
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 ...
Based on the theory of covariance analysis, the achievable precision of the solution of the sub-daily term (SDT) of Earth's rotation with SLR is assessed. An efficient covariance analysis algorithm of Givens-Gentleman orthogonal transformation is developed, and the data reduction technique of multi-arc method is utilized. As an example ...
We discuss a treacherous point in light-front dynamics (LFD) which should be taken into account to restore complete equivalence with the manifestly covariant formalism. We present examples that require an inclusion of the arc contribution in the light-front energy contour integration in order to achieve the equivalence between the LFD result and the ...
Virtually all ceramic matrix composites require and interface coating between the fibers and matrix to achieve the desired mechanical performance. To date, the most effective interface materials for non- oxide matrix composites have been carbon and boron ...
We show that it is possible to define a Lorentz-covariant reduced spin density matrix for massive particles. Such a matrix allows one to calculate the mean values of observables connected with spin measurements (average polarizations). Moreover, it contains not only information about polarization of the particle but also information ...
It is shown that the covariance matrix of object location errors is identical for time of arrival (TOA) and time difference of arrival (TDOA) systems if the inverse of the covariance matrix of TOA (TDOA) errors is used as a weighting matrix. Also, with this weighting the location errors ...
One of the challenges of satellite characterization is the ability to not only determine the spacecraft orbit but also the spacecraft orientation, size, and material properties. A substantial amount of research has been conducted in using photometry and spectroscopy to give insight into these spacecraft properties, but this work has been traditionally decoupled from the orbit determination ...
Summary We introduce novel regression extrapolation based methods to correct the often large bias in subsampling variance estimation as well as hypothesis testing for spatial point and marked point processes. For variance estimation, our proposed estimators are linear combinations of the usual subsampling variance estimator based on subblock sizes in a continuous interval. We show that they can ...
This paper addresses depth discrimination of a water column target from bottom clutter discretes in wideband active sonar. To facilitate classification, the waveguide invariant property is used to derive multiple snapshots by uniformly sub-sampling the short-time Fourier transform (STFT) coefficients of a single ping of wideband active sonar data. The sub-sampled target snapshots are used to ...
Resonance-parameter covariance matrix (RPCM) evaluations in the resolved resonance region were done for 232Th, 233U, 235U, 238U, and 239Pu using the computer code SAMMY. The retroactive approach of the code SAMMY was used to generate the RPCMs for 233U, 235U. RPCMs for 232Th, 238U and 239Pu were generated together with the resonance parameter evaluations. ...
Resonance-parameter covariance matrix (RPCM) evaluations in the resolved resonance region were done for 232Th, 233U, 235U, 238U, and 239Pu using the computer code SAMMY. The retroactive approach of the code SAMMY was used to generate the RPCMs for 233U and 235U. RPCMs for 232Th, 238U and 239Pu were generated together with the resonance parameter ...
In order to perform evaluations by the least-squares method, the complete covariance matrix for the gamma-ray energy standards is needed. Some errors due to excluding the covariances from the statistical analysis are pointed out. The gamma-ray energies of 192Ir and 198Au, measured with curved-crystal spectrometers, were reanalyzed and ...
We suggest a procedure for estimating uncertainties in neutron cross sections calculated with a nuclear model descriptive of a specific mass region. It applies standard error propagation techniques, using a model-parameter covariance matrix. Generally, available codes do not generate covariance information in conjunction with their ...
When constructed response test items are scored by more than one rater, the repeated ratings allow for the consideration of individual rater bias and variability in estimating student proficiency. Several hierarchical models based on item response theory have been introduced to model such effects. In this article, the authors demonstrate how these models may be extended to include ...
