Structural Analysis of Covariance and Correlation Matrices.
Joreskog, Karl G.
1978-01-01
A general approach to analysis of covariance structures is considered, in which the variances and covariances or correlations of the observed variables are directly expressed in terms of the parameters of interest. The statistical problems of identification, estimation and testing of such covariance or correlation structures are discussed.…
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
Structural Equation Models in a Redundancy Analysis Framework With Covariates.
Lovaglio, Pietro Giorgio; Vittadini, Giorgio
2014-01-01
A recent method to specify and fit structural equation modeling in the Redundancy Analysis framework based on so-called Extended Redundancy Analysis (ERA) has been proposed in the literature. In this approach, the relationships between the observed exogenous variables and the observed endogenous variables are moderated by the presence of unobservable composites, estimated as linear combinations of exogenous variables. However, in the presence of direct effects linking exogenous and endogenous variables, or concomitant indicators, the composite scores are estimated by ignoring the presence of the specified direct effects. To fit structural equation models, we propose a new specification and estimation method, called Generalized Redundancy Analysis (GRA), allowing us to specify and fit a variety of relationships among composites, endogenous variables, and external covariates. The proposed methodology extends the ERA method, using a more suitable specification and estimation algorithm, by allowing for covariates that affect endogenous indicators indirectly through the composites and/or directly. To illustrate the advantages of GRA over ERA we propose a simulation study of small samples. Moreover, we propose an application aimed at estimating the impact of formal human capital on the initial earnings of graduates of an Italian university, utilizing a structural model consistent with well-established economic theory.
Dolan, C.V.; Molenaar, P.C.M.; Boomsma, D.I.
1991-01-01
D. Soerbom's (1974, 1976) simplex model approach to simultaneous analysis of means and covariance structure was applied to analysis of means observed in a single group. The present approach to the simultaneous biometric analysis of covariance and mean structure is based on the testable assumption
Robustness studies in covariance structure modeling - An overview and a meta-analysis
Hoogland, Jeffrey J.; Boomsma, A
In covariance structure modeling, several estimation methods are available. The robustness of an estimator against specific violations of assumptions can be determined empirically by means of a Monte Carlo study. Many such studies in covariance structure analysis have been published, but the
Fouladi, Rachel T.
2000-01-01
Provides an overview of standard and modified normal theory and asymptotically distribution-free covariance and correlation structure analysis techniques and details Monte Carlo simulation results on Type I and Type II error control. Demonstrates through the simulation that robustness and nonrobustness of structure analysis techniques vary as a…
Shaw, Simon C.; Goldstein, Michael
2017-01-01
We explore the effect of finite population sampling in design problems with many variables cross-classified in many ways. In particular, we investigate designs where we wish to sample individuals belonging to different groups for which the underlying covariance matrices are separable between groups and variables. We exploit the generalised conditional independence structure of the model to show how the analysis of the full model can be reduced to an interpretable series of lower dimensional p...
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.
A Phylogenetic Analysis of Shape Covariance Structure in the Anthropoid Skull
De Oliveira, Felipe; Marroig, Gabriel; Garcia, Guilherme
2016-01-01
Phenotypic traits evolve in a coordinated manner due to developmental and functional interactions, mediated by the dynamics of natural selection; the dependence between traits arising from these three factors is captured by genetic ( G ) and phenotypic ( P ) covariance matrices. Mammalian skull development produces an intricate pattern of tissue organization and mutual signaling that integrates this structure, although the set of functions it performs is quite disparate. Therefore, the interp...
Anderson, Ray; Skaggs, Todd; Alfieri, Joseph; Kustas, William; Wang, Dong; Ayars, James
2016-04-01
Partitioned land surfaces fluxes (e.g. evaporation, transpiration, photosynthesis, and ecosystem respiration) are needed as input, calibration, and validation data for numerous hydrological and land surface models. However, one of the most commonly used techniques for measuring land surface fluxes, Eddy Covariance (EC), can directly measure net, combined water and carbon fluxes (evapotranspiration and net ecosystem exchange/productivity). Analysis of the correlation structure of high frequency EC time series (hereafter flux partitioning or FP) has been proposed to directly partition net EC fluxes into their constituent components using leaf-level water use efficiency (WUE) data to separate stomatal and non-stomatal transport processes. FP has significant logistical and spatial representativeness advantages over other partitioning approaches (e.g. isotopic fluxes, sap flow, microlysimeters), but the performance of the FP algorithm is reliant on the accuracy of the intercellular CO2 (ci) concentration used to parameterize WUE for each flux averaging interval. In this study, we tested several parameterizations for ci as a function of atmospheric CO2 (ca), including (1) a constant ci/ca ratio for C3 and C4 photosynthetic pathway plants, (2) species-specific ci/ca-Vapor Pressure Deficit (VPD) relationships (quadratic and linear), and (3) generalized C3 and C4 photosynthetic pathway ci/ca-VPD relationships. We tested these ci parameterizations at three agricultural EC towers from 2011-present in C4 and C3 crops (sugarcane - Saccharum officinarum L. and peach - Prunus persica), and validated again sap-flow sensors installed at the peach site. The peach results show that the species-specific parameterizations driven FP algorithm came to convergence significantly more frequently (~20% more frequently) than the constant ci/ca ratio or generic C3-VPD relationship. The FP algorithm parameterizations with a generic VPD relationship also had slightly higher transpiration (5 Wm-2
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.
Ya-Ting Chang
2018-01-01
Full Text Available Purpose: In Parkinson’s disease with mild cognitive impairment (PD-MCI, we investigated the clinical significance of salience network (SN in depression and cognitive performance.Methods: Seventy seven PD-MCI patients that fulfilled multi-domain and non-amnestic subtype were included. Gray matter structural covariance networks were constructed by 3D T1-magnetic resonance imaging and seed based analysis. The patients were divided into two groups by psychiatric interviews and screening of Geriatric Depression Scale (GDS: PD-MCI with depression (PD-MCI-D or without depression (PD-MCI-ND. The seed or peak cluster volume, or the significant differences in the regression slopes in each seed-peak cluster correlation, were used to evaluate the significance with the neurobehavioral scores.Results: This study is the first to demonstrate that the PD-MCI-ND group presented a larger number of voxels of structural covariance in SN than the PD-MCI-D group. The right fronto-insular seed volumes and the peak cluster of left lingual gyrus showed significant inverse correlation with the Geriatric Depression Scale (GDS; r = -0.231, P = 0.046.Conclusions: This study is the first to validate the clinical significance of the SN in PD-MCI-D. The right insular seed value and the SN correlated with the severity of depression in PD-MCI.
Chang, Ya-Ting; Lu, Cheng-Hsien; Wu, Ming-Kung; Hsu, Shih-Wei; Huang, Chi-Wei; Chang, Wen-Neng; Lien, Chia-Yi; Lee, Jun-Jun; Chang, Chiung-Chih
2018-01-01
Purpose: In Parkinson’s disease with mild cognitive impairment (PD-MCI), we investigated the clinical significance of salience network (SN) in depression and cognitive performance. Methods: Seventy seven PD-MCI patients that fulfilled multi-domain and non-amnestic subtype were included. Gray matter structural covariance networks were constructed by 3D T1-magnetic resonance imaging and seed based analysis. The patients were divided into two groups by psychiatric interviews and screening of Geriatric Depression Scale (GDS): PD-MCI with depression (PD-MCI-D) or without depression (PD-MCI-ND). The seed or peak cluster volume, or the significant differences in the regression slopes in each seed-peak cluster correlation, were used to evaluate the significance with the neurobehavioral scores. Results: This study is the first to demonstrate that the PD-MCI-ND group presented a larger number of voxels of structural covariance in SN than the PD-MCI-D group. The right fronto-insular seed volumes and the peak cluster of left lingual gyrus showed significant inverse correlation with the Geriatric Depression Scale (GDS; r = -0.231, P = 0.046). Conclusions: This study is the first to validate the clinical significance of the SN in PD-MCI-D. The right insular seed value and the SN correlated with the severity of depression in PD-MCI. PMID:29375361
Chang, Ya-Ting; Lu, Cheng-Hsien; Wu, Ming-Kung; Hsu, Shih-Wei; Huang, Chi-Wei; Chang, Wen-Neng; Lien, Chia-Yi; Lee, Jun-Jun; Chang, Chiung-Chih
2017-01-01
Purpose: In Parkinson's disease with mild cognitive impairment (PD-MCI), we investigated the clinical significance of salience network (SN) in depression and cognitive performance. Methods: Seventy seven PD-MCI patients that fulfilled multi-domain and non-amnestic subtype were included. Gray matter structural covariance networks were constructed by 3D T1-magnetic resonance imaging and seed based analysis. The patients were divided into two groups by psychiatric interviews and screening of Geriatric Depression Scale (GDS): PD-MCI with depression (PD-MCI-D) or without depression (PD-MCI-ND). The seed or peak cluster volume, or the significant differences in the regression slopes in each seed-peak cluster correlation, were used to evaluate the significance with the neurobehavioral scores. Results: This study is the first to demonstrate that the PD-MCI-ND group presented a larger number of voxels of structural covariance in SN than the PD-MCI-D group. The right fronto-insular seed volumes and the peak cluster of left lingual gyrus showed significant inverse correlation with the Geriatric Depression Scale (GDS; r = -0.231, P = 0.046). Conclusions: This study is the first to validate the clinical significance of the SN in PD-MCI-D. The right insular seed value and the SN correlated with the severity of depression in PD-MCI.
Hoyle, R H
1991-02-01
Indirect measures of psychological constructs are vital to clinical research. On occasion, however, the meaning of indirect measures of psychological constructs is obfuscated by statistical procedures that do not account for the complex relations between items and latent variables and among latent variables. Covariance structure analysis (CSA) is a statistical procedure for testing hypotheses about the relations among items that indirectly measure a psychological construct and relations among psychological constructs. This article introduces clinical researchers to the strengths and limitations of CSA as a statistical procedure for conceiving and testing structural hypotheses that are not tested adequately with other statistical procedures. The article is organized around two empirical examples that illustrate the use of CSA for evaluating measurement models with correlated error terms, higher-order factors, and measured and latent variables.
Wu, Hao
2018-05-01
In structural equation modelling (SEM), a robust adjustment to the test statistic or to its reference distribution is needed when its null distribution deviates from a χ 2 distribution, which usually arises when data do not follow a multivariate normal distribution. Unfortunately, existing studies on this issue typically focus on only a few methods and neglect the majority of alternative methods in statistics. Existing simulation studies typically consider only non-normal distributions of data that either satisfy asymptotic robustness or lead to an asymptotic scaled χ 2 distribution. In this work we conduct a comprehensive study that involves both typical methods in SEM and less well-known methods from the statistics literature. We also propose the use of several novel non-normal data distributions that are qualitatively different from the non-normal distributions widely used in existing studies. We found that several under-studied methods give the best performance under specific conditions, but the Satorra-Bentler method remains the most viable method for most situations. © 2017 The British Psychological Society.
Gomez, Rapson
2009-01-01
Objective: This study used the mean and covariance structures analysis approach to examine the equality or invariance of ratings of the 18 ADHD symptoms. Method: 783 Australian and 928 Malaysian parents provided ratings for an ADHD rating scale. Invariance was tested across these groups (Comparison 1), and North European Australian (n = 623) and…
Altered structural covariance of the striatum in functional dyspepsia patients.
Liu, P; Zeng, F; Yang, F; Wang, J; Liu, X; Wang, Q; Zhou, G; Zhang, D; Zhu, M; Zhao, R; Wang, A; Gong, Q; Liang, F
2014-08-01
Functional dyspepsia (FD) is thought to be involved in dysregulation within the brain-gut axis. Recently, altered striatum activation has been reported in patients with FD. However, the gray matter (GM) volumes in the striatum and structural covariance patterns of this area are rarely explored. The purpose of this study was to examine the GM volumes and structural covariance patterns of the striatum between FD patients and healthy controls (HCs). T1-weighted magnetic resonance images were obtained from 44 FD patients and 39 HCs. Voxel-based morphometry (VBM) analysis was adopted to examine the GM volumes in the two groups. The caudate- or putamen-related regions identified from VBM analysis were then used as seeds to map the whole brain voxel-wise structural covariance patterns. Finally, a correlation analysis was used to investigate the effects of FD symptoms on the striatum. The results showed increased GM volumes in the bilateral putamen and right caudate. Compared with the structural covariance patterns of the HCs, the FD-related differences were mainly located in the amygdala, hippocampus/parahippocampus (HIPP/paraHIPP), thalamus, lingual gyrus, and cerebellum. And significant positive correlations were found between the volumes in the striatum and the FD duration in the patients. These findings provided preliminary evidence for GM changes in the striatum and different structural covariance patterns in patients with FD. The current results might expand our understanding of the pathophysiology of FD. © 2014 John Wiley & Sons Ltd.
Structural covariance networks in the mouse brain.
Pagani, Marco; Bifone, Angelo; Gozzi, Alessandro
2016-04-01
The presence of networks of correlation between regional gray matter volume as measured across subjects in a group of individuals has been consistently described in several human studies, an approach termed structural covariance MRI (scMRI). Complementary to prevalent brain mapping modalities like functional and diffusion-weighted imaging, the approach can provide precious insights into the mutual influence of trophic and plastic processes in health and pathological states. To investigate whether analogous scMRI networks are present in lower mammal species amenable to genetic and experimental manipulation such as the laboratory mouse, we employed high resolution morphoanatomical MRI in a large cohort of genetically-homogeneous wild-type mice (C57Bl6/J) and mapped scMRI networks using a seed-based approach. We show that the mouse brain exhibits robust homotopic scMRI networks in both primary and associative cortices, a finding corroborated by independent component analyses of cortical volumes. Subcortical structures also showed highly symmetric inter-hemispheric correlations, with evidence of distributed antero-posterior networks in diencephalic regions of the thalamus and hypothalamus. Hierarchical cluster analysis revealed six identifiable clusters of cortical and sub-cortical regions corresponding to previously described neuroanatomical systems. Our work documents the presence of homotopic cortical and subcortical scMRI networks in the mouse brain, thus supporting the use of this species to investigate the elusive biological and neuroanatomical underpinnings of scMRI network development and its derangement in neuropathological states. The identification of scMRI networks in genetically homogeneous inbred mice is consistent with the emerging view of a key role of environmental factors in shaping these correlational networks. Copyright © 2016 Elsevier Inc. All rights reserved.
A special covariance structure for random coefficient models with both between and within covariates
Riedel, K.S.
1990-07-01
We review random coefficient (RC) models in linear regression and propose a bias correction to the maximum likelihood (ML) estimator. Asymmptotic expansion of the ML equations are given when the between individual variance is much larger or smaller than the variance from within individual fluctuations. The standard model assumes all but one covariate varies within each individual, (we denote the within covariates by vector χ 1 ). We consider random coefficient models where some of the covariates do not vary in any single individual (we denote the between covariates by vector χ 0 ). The regression coefficients, vector β k , can only be estimated in the subspace X k of X. Thus the number of individuals necessary to estimate vector β and the covariance matrix Δ of vector β increases significantly in the presence of more than one between covariate. When the number of individuals is sufficient to estimate vector β but not the entire matrix Δ , additional assumptions must be imposed on the structure of Δ. A simple reduced model is that the between component of vector β is fixed and only the within component varies randomly. This model fails because it is not invariant under linear coordinate transformations and it can significantly overestimate the variance of new observations. We propose a covariance structure for Δ without these difficulties by first projecting the within covariates onto the space perpendicular to be between covariates. (orig.)
Fermionic covariant prolongation structure theory for supernonlinear evolution equation
Cheng Jipeng; Wang Shikun; Wu Ke; Zhao Weizhong
2010-01-01
We investigate the superprincipal bundle and its associated superbundle. The super(nonlinear)connection on the superfiber bundle is constructed. Then by means of the connection theory, we establish the fermionic covariant prolongation structure theory of the supernonlinear evolution equation. In this geometry theory, the fermionic covariant fundamental equations determining the prolongation structure are presented. As an example, the supernonlinear Schroedinger equation is analyzed in the framework of this fermionic covariant prolongation structure theory. We obtain its Lax pairs and Baecklund transformation.
Covariant Theory of Gravitation in the Spacetime with Finsler Structure
Huang, Xin-Bing
2007-01-01
The theory of gravitation in the spacetime with Finsler structure is constructed. It is shown that the theory keeps general covariance. Such theory reduces to Einstein's general relativity when the Finsler structure is Riemannian. Therefore, this covariant theory of gravitation is an elegant realization of Einstein's thoughts on gravitation in the spacetime with Finsler structure.
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.
A scale invariant covariance structure on jet space
Pedersen, Kim Steenstrup; Loog, Marco; Markussen, Bo
2005-01-01
This paper considers scale invariance of statistical image models. We study statistical scale invariance of the covariance structure of jet space under scale space blurring and derive the necessary structure and conditions of the jet covariance matrix in order for it to be scale invariant. As par...
Ole E. Barndorff-Nielsen; Neil Shephard
2002-01-01
This paper analyses multivariate high frequency financial data using realised covariation. We provide a new asymptotic distribution theory for standard methods such as regression, correlation analysis and covariance. It will be based on a fixed interval of time (e.g. a day or week), allowing the number of high frequency returns during this period to go to infinity. Our analysis allows us to study how high frequency correlations, regressions and covariances change through time. In particular w...
Cross-covariance based global dynamic sensitivity analysis
Shi, Yan; Lu, Zhenzhou; Li, Zhao; Wu, Mengmeng
2018-02-01
For identifying the cross-covariance source of dynamic output at each time instant for structural system involving both input random variables and stochastic processes, a global dynamic sensitivity (GDS) technique is proposed. The GDS considers the effect of time history inputs on the dynamic output. In the GDS, the cross-covariance decomposition is firstly developed to measure the contribution of the inputs to the output at different time instant, and an integration of the cross-covariance change over the specific time interval is employed to measure the whole contribution of the input to the cross-covariance of output. Then, the GDS main effect indices and the GDS total effect indices can be easily defined after the integration, and they are effective in identifying the important inputs and the non-influential inputs on the cross-covariance of output at each time instant, respectively. The established GDS analysis model has the same form with the classical ANOVA when it degenerates to the static case. After degeneration, the first order partial effect can reflect the individual effects of inputs to the output variance, and the second order partial effect can reflect the interaction effects to the output variance, which illustrates the consistency of the proposed GDS indices and the classical variance-based sensitivity indices. The MCS procedure and the Kriging surrogate method are developed to solve the proposed GDS indices. Several examples are introduced to illustrate the significance of the proposed GDS analysis technique and the effectiveness of the proposed solution.
Hierarchical multivariate covariance analysis of metabolic connectivity.
Carbonell, Felix; Charil, Arnaud; Zijdenbos, Alex P; Evans, Alan C; Bedell, Barry J
2014-12-01
Conventional brain connectivity analysis is typically based on the assessment of interregional correlations. Given that correlation coefficients are derived from both covariance and variance, group differences in covariance may be obscured by differences in the variance terms. To facilitate a comprehensive assessment of connectivity, we propose a unified statistical framework that interrogates the individual terms of the correlation coefficient. We have evaluated the utility of this method for metabolic connectivity analysis using [18F]2-fluoro-2-deoxyglucose (FDG) positron emission tomography (PET) data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. As an illustrative example of the utility of this approach, we examined metabolic connectivity in angular gyrus and precuneus seed regions of mild cognitive impairment (MCI) subjects with low and high β-amyloid burdens. This new multivariate method allowed us to identify alterations in the metabolic connectome, which would not have been detected using classic seed-based correlation analysis. Ultimately, this novel approach should be extensible to brain network analysis and broadly applicable to other imaging modalities, such as functional magnetic resonance imaging (MRI).
Linear Covariance Analysis and Epoch State Estimators
Markley, F. Landis; Carpenter, J. Russell
2014-01-01
This paper extends in two directions the results of prior work on generalized linear covariance analysis of both batch least-squares and sequential estimators. The first is an improved treatment of process noise in the batch, or epoch state, estimator with an epoch time that may be later than some or all of the measurements in the batch. The second is to account for process noise in specifying the gains in the epoch state estimator. We establish the conditions under which the latter estimator is equivalent to the Kalman filter.
Yoshida, Jun; Inoue, Yasunori; Morimoto, Satoshi; Tanaka, Toshikazu; Ogawa, Takayuki
2017-01-01
Background It is conceivable that contemporary valvular heart disease (VHD) is affected largely by an age-dependent atherosclerotic process, which is similar to that observed in coronary artery disease (CAD). However, a comorbid condition of VHD and CAD has not been precisely examined. The first objective of this study was to examine a possible comorbid condition. Provided that there is no comorbidity, the second objective was to search for the possible reasons by using conventional risk factors and plasma B-type natriuretic peptide (BNP) because BNP has a potentiality to suppress atherosclerotic development. Methods The study population consisted of 3,457 patients consecutively admitted to our institution. The possible comorbid condition of VHD and CAD and the factors that influence the comorbidity were examined by covariance structure analysis and multivariate analysis. Results The distribution of the patients with VHD and those with CAD in the histograms showed that the incidence of VHD and the severity of CAD rose with seniority in appearance. The real statistical analysis was planned by covariance structure analysis. The current path model revealed that aging was associated with VHD and CAD severity (P < 0.001 for each); however, as a notable result, there was an inverse association regarding the comorbid condition between VHD and CAD (Correlation coefficient [β]: -0.121, P < 0.001). As the second objective, to clarify the factors leading to this inverse association, the contribution of conventional risk factors, such as age, gender, hypertension, smoking, diabetes, obesity and dyslipidemia, to VHD and CAD were examined by multivariate analysis. However, these factors did not exert an opposing effect on VHD and CAD, and the inverse association defied explanation. Since different pathological mechanisms may contribute to the formation of VHD and CAD, a differentially proposed path model using plasma BNP revealed that an increase in plasma BNP being drawn by
Fukumoto, Risa; Kawai, Makoto; Minai, Kosuke; Ogawa, Kazuo; Yoshida, Jun; Inoue, Yasunori; Morimoto, Satoshi; Tanaka, Toshikazu; Nagoshi, Tomohisa; Ogawa, Takayuki; Yoshimura, Michihiro
2017-01-01
It is conceivable that contemporary valvular heart disease (VHD) is affected largely by an age-dependent atherosclerotic process, which is similar to that observed in coronary artery disease (CAD). However, a comorbid condition of VHD and CAD has not been precisely examined. The first objective of this study was to examine a possible comorbid condition. Provided that there is no comorbidity, the second objective was to search for the possible reasons by using conventional risk factors and plasma B-type natriuretic peptide (BNP) because BNP has a potentiality to suppress atherosclerotic development. The study population consisted of 3,457 patients consecutively admitted to our institution. The possible comorbid condition of VHD and CAD and the factors that influence the comorbidity were examined by covariance structure analysis and multivariate analysis. The distribution of the patients with VHD and those with CAD in the histograms showed that the incidence of VHD and the severity of CAD rose with seniority in appearance. The real statistical analysis was planned by covariance structure analysis. The current path model revealed that aging was associated with VHD and CAD severity (P < 0.001 for each); however, as a notable result, there was an inverse association regarding the comorbid condition between VHD and CAD (Correlation coefficient [β]: -0.121, P < 0.001). As the second objective, to clarify the factors leading to this inverse association, the contribution of conventional risk factors, such as age, gender, hypertension, smoking, diabetes, obesity and dyslipidemia, to VHD and CAD were examined by multivariate analysis. However, these factors did not exert an opposing effect on VHD and CAD, and the inverse association defied explanation. Since different pathological mechanisms may contribute to the formation of VHD and CAD, a differentially proposed path model using plasma BNP revealed that an increase in plasma BNP being drawn by VHD suppressed the
Abnormalities in structural covariance of cortical gyrification in schizophrenia
Palaniyappan, Lena; Park, Bert; Balain, Vijender; Dangi, Raj; Liddle, Peter
2014-01-01
The highly convoluted shape of the adult human brain results from several well-coordinated maturational events that start from embryonic development and extend through the adult life span. Disturbances in these maturational events can result in various neurological and psychiatric disorders, resulting in abnormal patterns of morphological relationship among cortical structures (structural covariance). Structural covariance can be studied using graph theory-based approaches that evaluate topol...
Ito, Satoshi; Nagoshi, Tomohisa; Minai, Kosuke; Kashiwagi, Yusuke; Sekiyama, Hiroshi; Yoshii, Akira; Kimura, Haruka; Inoue, Yasunori; Ogawa, Kazuo; Tanaka, Toshikazu D; Ogawa, Takayuki; Kawai, Makoto; Yoshimura, Michihiro
2017-01-01
Although glucose-insulin-potassium (GIK) therapy ought to be beneficial for ischemic heart disease in general, variable outcomes in many clinical trials of GIK in acute coronary syndrome (ACS) had a controversial impact. This study was designed to examine whether "insulin resistance" is involved in ACS and to clarify other potential intrinsic compensatory mechanisms for GIK tolerance through highly statistical procedure. We compared the degree of insulin resistance during ACS attack and remission phase after treatment in individual patients (n = 104). During ACS, homeostasis model assessment of insulin resistance (HOMA-IR) values were significantly increased (Pcovariance structure analysis with a strong impact (β: 0.398, P = 0.015). Intriguingly, a higher incidence of myocardial infarction relative to unstable angina pectoris, as well as a longer hospitalization period were observed in patients with larger ΔK, indicating that ΔK also reflects disease severity of ACS. Insulin resistance most likely increases during ACS; however, ΔK was positively correlated with plasma glucose level, which overwhelmed insulin resistance condition. The present study with covariance structure analysis suggests that there are potential endogenous glucose-coupled potassium lowering mechanisms, other than insulin, regulating glucose metabolism during ACS.
Siren, J; Ovaskainen, O; Merilä, J
2017-10-01
The genetic variance-covariance matrix (G) is a quantity of central importance in evolutionary biology due to its influence on the rate and direction of multivariate evolution. However, the predictive power of empirically estimated G-matrices is limited for two reasons. First, phenotypes are high-dimensional, whereas traditional statistical methods are tuned to estimate and analyse low-dimensional matrices. Second, the stability of G to environmental effects and over time remains poorly understood. Using Bayesian sparse factor analysis (BSFG) designed to estimate high-dimensional G-matrices, we analysed levels variation and covariation in 10,527 expressed genes in a large (n = 563) half-sib breeding design of three-spined sticklebacks subject to two temperature treatments. We found significant differences in the structure of G between the treatments: heritabilities and evolvabilities were higher in the warm than in the low-temperature treatment, suggesting more and faster opportunity to evolve in warm (stressful) conditions. Furthermore, comparison of G and its phenotypic equivalent P revealed the latter is a poor substitute of the former. Most strikingly, the results suggest that the expected impact of G on evolvability-as well as the similarity among G-matrices-may depend strongly on the number of traits included into analyses. In our results, the inclusion of only few traits in the analyses leads to underestimation in the differences between the G-matrices and their predicted impacts on evolution. While the results highlight the challenges involved in estimating G, they also illustrate that by enabling the estimation of large G-matrices, the BSFG method can improve predicted evolutionary responses to selection. © 2017 John Wiley & Sons Ltd.
Theory of Covariance Equivalent ARMAV Models of Civil Engineering Structures
Andersen, P.; Brincker, Rune; Kirkegaard, Poul Henning
1996-01-01
In this paper the theoretical background for using covariance equivalent ARMAV models in modal analysis is discussed. It is shown how to obtain a covariance equivalent ARMA model for a univariate linear second order continous-time system excited by Gaussian white noise. This result is generalized...
Theory of Covariance Equivalent ARMAV Models of Civil Engineering Structures
Andersen, P.; Brincker, Rune; Kirkegaard, Poul Henning
In this paper the theoretical background for using covariance equivalent ARMAV models in modal analysis is discussed. It is shown how to obtain a covariance equivalent ARMA model for a univariate linear second order continuous-time system excited by Gaussian white noise. This result is generalize...
Structural covariance networks across healthy young adults and their consistency.
Guo, Xiaojuan; Wang, Yan; Guo, Taomei; Chen, Kewei; Zhang, Jiacai; Li, Ke; Jin, Zhen; Yao, Li
2015-08-01
To investigate structural covariance networks (SCNs) as measured by regional gray matter volumes with structural magnetic resonance imaging (MRI) from healthy young adults, and to examine their consistency and stability. Two independent cohorts were included in this study: Group 1 (82 healthy subjects aged 18-28 years) and Group 2 (109 healthy subjects aged 20-28 years). Structural MRI data were acquired at 3.0T and 1.5T using a magnetization prepared rapid-acquisition gradient echo sequence for these two groups, respectively. We applied independent component analysis (ICA) to construct SCNs and further applied the spatial overlap ratio and correlation coefficient to evaluate the spatial consistency of the SCNs between these two datasets. Seven and six independent components were identified for Group 1 and Group 2, respectively. Moreover, six SCNs including the posterior default mode network, the visual and auditory networks consistently existed across the two datasets. The overlap ratios and correlation coefficients of the visual network reached the maximums of 72% and 0.71. This study demonstrates the existence of consistent SCNs corresponding to general functional networks. These structural covariance findings may provide insight into the underlying organizational principles of brain anatomy. © 2014 Wiley Periodicals, Inc.
Generalized Extreme Value model with Cyclic Covariate Structure ...
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enhances the estimation of the return period; however, its application is ...... Cohn T A and Lins H F 2005 Nature's style: Naturally trendy; GEOPHYSICAL ..... Final non-stationary GEV models with covariate structures shortlisted based on.
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....
Structural and Maturational Covariance in Early Childhood Brain Development.
Geng, Xiujuan; Li, Gang; Lu, Zhaohua; Gao, Wei; Wang, Li; Shen, Dinggang; Zhu, Hongtu; Gilmore, John H
2017-03-01
Brain structural covariance networks (SCNs) composed of regions with correlated variation are altered in neuropsychiatric disease and change with age. Little is known about the development of SCNs in early childhood, a period of rapid cortical growth. We investigated the development of structural and maturational covariance networks, including default, dorsal attention, primary visual and sensorimotor networks in a longitudinal population of 118 children after birth to 2 years old and compared them with intrinsic functional connectivity networks. We found that structural covariance of all networks exhibit strong correlations mostly limited to their seed regions. By Age 2, default and dorsal attention structural networks are much less distributed compared with their functional maps. The maturational covariance maps, however, revealed significant couplings in rates of change between distributed regions, which partially recapitulate their functional networks. The structural and maturational covariance of the primary visual and sensorimotor networks shows similar patterns to the corresponding functional networks. Results indicate that functional networks are in place prior to structural networks, that correlated structural patterns in adult may arise in part from coordinated cortical maturation, and that regional co-activation in functional networks may guide and refine the maturation of SCNs over childhood development. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.
Covariate analysis of bivariate survival data
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.
Abnormalities in structural covariance of cortical gyrification in schizophrenia.
Palaniyappan, Lena; Park, Bert; Balain, Vijender; Dangi, Raj; Liddle, Peter
2015-07-01
The highly convoluted shape of the adult human brain results from several well-coordinated maturational events that start from embryonic development and extend through the adult life span. Disturbances in these maturational events can result in various neurological and psychiatric disorders, resulting in abnormal patterns of morphological relationship among cortical structures (structural covariance). Structural covariance can be studied using graph theory-based approaches that evaluate topological properties of brain networks. Covariance-based graph metrics allow cross-sectional study of coordinated maturational relationship among brain regions. Disrupted gyrification of focal brain regions is a consistent feature of schizophrenia. However, it is unclear if these localized disturbances result from a failure of coordinated development of brain regions in schizophrenia. We studied the structural covariance of gyrification in a sample of 41 patients with schizophrenia and 40 healthy controls by constructing gyrification-based networks using a 3-dimensional index. We found that several key regions including anterior insula and dorsolateral prefrontal cortex show increased segregation in schizophrenia, alongside reduced segregation in somato-sensory and occipital regions. Patients also showed a lack of prominence of the distributed covariance (hubness) of cingulate cortex. The abnormal segregated folding pattern in the right peri-sylvian regions (insula and fronto-temporal cortex) was associated with greater severity of illness. The study of structural covariance in cortical folding supports the presence of subtle deviation in the coordinated development of cortical convolutions in schizophrenia. The heterogeneity in the severity of schizophrenia could be explained in part by aberrant trajectories of neurodevelopment.
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.
Modelling the Covariance Structure in Marginal Multivariate Count Models
Bonat, W. H.; Olivero, J.; Grande-Vega, M.
2017-01-01
The main goal of this article is to present a flexible statistical modelling framework to deal with multivariate count data along with longitudinal and repeated measures structures. The covariance structure for each response variable is defined in terms of a covariance link function combined...... be used to indicate whether there was statistical evidence of a decline in blue duikers and other species hunted during the study period. Determining whether observed drops in the number of animals hunted are indeed true is crucial to assess whether species depletion effects are taking place in exploited...... with a matrix linear predictor involving known matrices. In order to specify the joint covariance matrix for the multivariate response vector, the generalized Kronecker product is employed. We take into account the count nature of the data by means of the power dispersion function associated with the Poisson...
application of covariance analysis to feed/ ration experimental data
Prince Acheampong
ABSTRACT. The use Analysis of Covariance (ANOCOVA) to feed/ration experimental data for birds was examined. Correlation and Regression analyses were used to adjust for the covariate – initial weight of the experimental birds. The Fisher's F statistic for the straight forward Analysis of Variance (ANOVA) showed ...
Validity of covariance models for the analysis of geographical variation
Guillot, Gilles; Schilling, Rene L.; Porcu, Emilio
2014-01-01
1. Due to the availability of large molecular data-sets, covariance models are increasingly used to describe the structure of genetic variation as an alternative to more heavily parametrised biological models. 2. We focus here on a class of parametric covariance models that received sustained att...
Some remarks on estimating a covariance structure model from a sample correlation matrix
Maydeu Olivares, Alberto; Hernández Estrada, Adolfo
2000-01-01
A popular model in structural equation modeling involves a multivariate normal density with a structured covariance matrix that has been categorized according to a set of thresholds. In this setup one may estimate the covariance structure parameters from the sample tetrachoricl polychoric correlations but only if the covariance structure is scale invariant. Doing so when the covariance structure is not scale invariant results in estimating a more restricted covariance structure than the one i...
Comparing fixed effects and covariance structure estimators for panel data
Ejrnæs, Mette; Holm, Anders
2006-01-01
In this article, the authors compare the traditional econometric fixed effect estimator with the maximum likelihood estimator implied by covariance structure models for panel data. Their findings are that the maximum like lipoid estimator is remarkably robust to certain types of misspecifications...
The structural, connectomic and network covariance of the human brain.
Irimia, Andrei; Van Horn, John D
2013-02-01
Though it is widely appreciated that complex structural, functional and morphological relationships exist between distinct areas of the human cerebral cortex, the extent to which such relationships coincide remains insufficiently appreciated. Here we determine the extent to which correlations between brain regions are modulated by either structural, connectomic or network-theoretic properties using a structural neuroimaging data set of magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) volumes acquired from N=110 healthy human adults. To identify the linear relationships between all available pairs of regions, we use canonical correlation analysis to test whether a statistically significant correlation exists between each pair of cortical parcels as quantified via structural, connectomic or network-theoretic measures. In addition to this, we investigate (1) how each group of canonical variables (whether structural, connectomic or network-theoretic) contributes to the overall correlation and, additionally, (2) whether each individual variable makes a significant contribution to the test of the omnibus null hypothesis according to which no correlation between regions exists across subjects. We find that, although region-to-region correlations are extensively modulated by structural and connectomic measures, there are appreciable differences in how these two groups of measures drive inter-regional correlation patterns. Additionally, our results indicate that the network-theoretic properties of the cortex are strong modulators of region-to-region covariance. Our findings are useful for understanding the structural and connectomic relationship between various parts of the brain, and can inform theoretical and computational models of cortical information processing. Published by Elsevier Inc.
Structural Covariance Networks in Children with Autism or ADHD.
Bethlehem, R A I; Romero-Garcia, R; Mak, E; Bullmore, E T; Baron-Cohen, S
2017-08-01
While autism and attention-deficit/hyperactivity disorder (ADHD) are considered distinct conditions from a diagnostic perspective, clinically they share some phenotypic features and have high comorbidity. Regardless, most studies have focused on only one condition, with considerable heterogeneity in their results. Taking a dual-condition approach might help elucidate shared and distinct neural characteristics. Graph theory was used to analyse topological properties of structural covariance networks across both conditions and relative to a neurotypical (NT; n = 87) group using data from the ABIDE (autism; n = 62) and ADHD-200 datasets (ADHD; n = 69). Regional cortical thickness was used to construct the structural covariance networks. This was analysed in a theoretical framework examining potential differences in long and short-range connectivity, with a specific focus on relation between central graph measures and cortical thickness. We found convergence between autism and ADHD, where both conditions show an overall decrease in CT covariance with increased Euclidean distance between centroids compared with a NT population. The 2 conditions also show divergence. Namely, there is less modular overlap between the 2 conditions than there is between each condition and the NT group. The ADHD group also showed reduced cortical thickness and lower degree in hub regions than the autism group. Lastly, the ADHD group also showed reduced wiring costs compared with the autism groups. Our results indicate a need for taking an integrated approach when considering highly comorbid conditions such as autism and ADHD. Furthermore, autism and ADHD both showed alterations in the relation between inter-regional covariance and centroid distance, where both groups show a steeper decline in covariance as a function of distance. The 2 groups also diverge on modular organization, cortical thickness of hub regions and wiring cost of the covariance network. Thus, on some network features the
Paragrassmann analysis and covariant quantum algebras
Filippov, A.T.; Isaev, A.P.; Kurdikov, A.B.; Pyatov, P.N.
1993-01-01
This report is devoted to the consideration from the algebraic point of view the paragrassmann algebras with one and many paragrassmann generators Θ i , Θ p+1 i = 0. We construct the paragrassmann versions of the Heisenberg algebra. For the special case, this algebra is nothing but the algebra for coordinates and derivatives considered in the context of covariant differential calculus on quantum hyperplane. The parameter of deformation q in our case is (p+1)-root of unity. Our construction is nondegenerate only for even p. Taking bilinear combinations of paragrassmann derivatives and coordinates we realize generators for the covariant quantum algebras as tensor products of (p+1) x (p+1) matrices. (orig./HSI)
On the algebraic structure of covariant anomalies and covariant Schwinger terms
Kelnhofer, G.
1992-01-01
A cohomological characterization of covariant anomalies and covariant Schwinger terms in an anomalous Yang-Mills theory is formulated and w ill be geometrically interpreted. The BRS and anti-BRS transformations are defined as purely differential geometric objects. Finally the covariant descent equations are formulated within this context. (author)
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.
Hydrodynamic Covariant Symplectic Structure from Bilinear Hamiltonian Functions
Capozziello S.
2005-07-01
Full Text Available Starting from generic bilinear Hamiltonians, constructed by covariant vector, bivector or tensor fields, it is possible to derive a general symplectic structure which leads to holonomic and anholonomic formulations of Hamilton equations of motion directly related to a hydrodynamic picture. This feature is gauge free and it seems a deep link common to all interactions, electromagnetism and gravity included. This scheme could lead toward a full canonical quantization.
Buchy, Lisa; Barbato, Mariapaola; Makowski, Carolina; Bray, Signe; MacMaster, Frank P; Deighton, Stephanie; Addington, Jean
2017-11-01
People with psychosis show deficits recognizing facial emotions and disrupted activation in the underlying neural circuitry. We evaluated associations between facial emotion recognition and cortical thickness using a correlation-based approach to map structural covariance networks across the brain. Fifteen people with an early psychosis provided magnetic resonance scans and completed the Penn Emotion Recognition and Differentiation tasks. Fifteen historical controls provided magnetic resonance scans. Cortical thickness was computed using CIVET and analyzed with linear models. Seed-based structural covariance analysis was done using the mapping anatomical correlations across the cerebral cortex methodology. To map structural covariance networks involved in facial emotion recognition, the right somatosensory cortex and bilateral fusiform face areas were selected as seeds. Statistics were run in SurfStat. Findings showed increased cortical covariance between the right fusiform face region seed and right orbitofrontal cortex in controls than early psychosis subjects. Facial emotion recognition scores were not significantly associated with thickness in any region. A negative effect of Penn Differentiation scores on cortical covariance was seen between the left fusiform face area seed and right superior parietal lobule in early psychosis subjects. Results suggest that facial emotion recognition ability is related to covariance in a temporal-parietal network in early psychosis. Copyright © 2017 Elsevier B.V. All rights reserved.
Abnormalities in Structural Covariance of Cortical Gyrification in Parkinson's Disease.
Xu, Jinping; Zhang, Jiuquan; Zhang, Jinlei; Wang, Yue; Zhang, Yanling; Wang, Jian; Li, Guanglin; Hu, Qingmao; Zhang, Yuanchao
2017-01-01
Although abnormal cortical morphology and connectivity between brain regions (structural covariance) have been reported in Parkinson's disease (PD), the topological organizations of large-scale structural brain networks are still poorly understood. In this study, we investigated large-scale structural brain networks in a sample of 37 PD patients and 34 healthy controls (HC) by assessing the structural covariance of cortical gyrification with local gyrification index (lGI). We demonstrated prominent small-world properties of the structural brain networks for both groups. Compared with the HC group, PD patients showed significantly increased integrated characteristic path length and integrated clustering coefficient, as well as decreased integrated global efficiency in structural brain networks. Distinct distributions of hub regions were identified between the two groups, showing more hub regions in the frontal cortex in PD patients. Moreover, the modular analyses revealed significantly decreased integrated regional efficiency in lateral Fronto-Insula-Temporal module, and increased integrated regional efficiency in Parieto-Temporal module in the PD group as compared to the HC group. In summary, our study demonstrated altered topological properties of structural networks at a global, regional and modular level in PD patients. These findings suggests that the structural networks of PD patients have a suboptimal topological organization, resulting in less effective integration of information between brain regions.
Subirà, Marta; Cano, Marta; de Wit, Stella J; Alonso, Pino; Cardoner, Narcís; Hoexter, Marcelo Q; Kwon, Jun Soo; Nakamae, Takashi; Lochner, Christine; Sato, João R; Jung, Wi Hoon; Narumoto, Jin; Stein, Dan J; Pujol, Jesus; Mataix-Cols, David; Veltman, Dick J; Menchón, José M; van den Heuvel, Odile A; Soriano-Mas, Carles
2016-03-01
Frontostriatal and frontoamygdalar connectivity alterations in patients with obsessive-compulsive disorder (OCD) have been typically described in functional neuroimaging studies. However, structural covariance, or volumetric correlations across distant brain regions, also provides network-level information. Altered structural covariance has been described in patients with different psychiatric disorders, including OCD, but to our knowledge, alterations within frontostriatal and frontoamygdalar circuits have not been explored. We performed a mega-analysis pooling structural MRI scans from the Obsessive-compulsive Brain Imaging Consortium and assessed whole-brain voxel-wise structural covariance of 4 striatal regions (dorsal and ventral caudate nucleus, and dorsal-caudal and ventral-rostral putamen) and 2 amygdalar nuclei (basolateral and centromedial-superficial). Images were preprocessed with the standard pipeline of voxel-based morphometry studies using Statistical Parametric Mapping software. Our analyses involved 329 patients with OCD and 316 healthy controls. Patients showed increased structural covariance between the left ventral-rostral putamen and the left inferior frontal gyrus/frontal operculum region. This finding had a significant interaction with age; the association held only in the subgroup of older participants. Patients with OCD also showed increased structural covariance between the right centromedial-superficial amygdala and the ventromedial prefrontal cortex. This was a cross-sectional study. Because this is a multisite data set analysis, participant recruitment and image acquisition were performed in different centres. Most patients were taking medication, and treatment protocols differed across centres. Our results provide evidence for structural network-level alterations in patients with OCD involving 2 frontosubcortical circuits of relevance for the disorder and indicate that structural covariance contributes to fully characterizing brain
Gosho, Masahiko; Hirakawa, Akihiro; Noma, Hisashi; Maruo, Kazushi; Sato, Yasunori
2017-10-01
In longitudinal clinical trials, some subjects will drop out before completing the trial, so their measurements towards the end of the trial are not obtained. Mixed-effects models for repeated measures (MMRM) analysis with "unstructured" (UN) covariance structure are increasingly common as a primary analysis for group comparisons in these trials. Furthermore, model-based covariance estimators have been routinely used for testing the group difference and estimating confidence intervals of the difference in the MMRM analysis using the UN covariance. However, using the MMRM analysis with the UN covariance could lead to convergence problems for numerical optimization, especially in trials with a small-sample size. Although the so-called sandwich covariance estimator is robust to misspecification of the covariance structure, its performance deteriorates in settings with small-sample size. We investigated the performance of the sandwich covariance estimator and covariance estimators adjusted for small-sample bias proposed by Kauermann and Carroll ( J Am Stat Assoc 2001; 96: 1387-1396) and Mancl and DeRouen ( Biometrics 2001; 57: 126-134) fitting simpler covariance structures through a simulation study. In terms of the type 1 error rate and coverage probability of confidence intervals, Mancl and DeRouen's covariance estimator with compound symmetry, first-order autoregressive (AR(1)), heterogeneous AR(1), and antedependence structures performed better than the original sandwich estimator and Kauermann and Carroll's estimator with these structures in the scenarios where the variance increased across visits. The performance based on Mancl and DeRouen's estimator with these structures was nearly equivalent to that based on the Kenward-Roger method for adjusting the standard errors and degrees of freedom with the UN structure. The model-based covariance estimator with the UN structure under unadjustment of the degrees of freedom, which is frequently used in applications
Atrophy and structural covariance of the cholinergic basal forebrain in primary progressive aphasia.
Teipel, Stefan; Raiser, Theresa; Riedl, Lina; Riederer, Isabelle; Schroeter, Matthias L; Bisenius, Sandrine; Schneider, Anja; Kornhuber, Johannes; Fliessbach, Klaus; Spottke, Annika; Grothe, Michel J; Prudlo, Johannes; Kassubek, Jan; Ludolph, Albert; Landwehrmeyer, Bernhard; Straub, Sarah; Otto, Markus; Danek, Adrian
2016-10-01
Primary progressive aphasia (PPA) is characterized by profound destruction of cortical language areas. Anatomical studies suggest an involvement of cholinergic basal forebrain (BF) in PPA syndromes, particularly in the area of the nucleus subputaminalis (NSP). Here we aimed to determine the pattern of atrophy and structural covariance as a proxy of structural connectivity of BF nuclei in PPA variants. We studied 62 prospectively recruited cases with the clinical diagnosis of PPA and 31 healthy older control participants from the cohort study of the German consortium for frontotemporal lobar degeneration (FTLD). We determined cortical and BF atrophy based on high-resolution magnetic resonance imaging (MRI) scans. Patterns of structural covariance of BF with cortical regions were determined using voxel-based partial least square analysis. We found significant atrophy of total BF and BF subregions in PPA patients compared with controls [F(1, 82) = 20.2, p covariance analysis in healthy controls revealed associations of the BF nuclei, particularly the NSP, with left hemispheric predominant prefrontal, lateral temporal, and parietal cortical areas, including Broca's speech area (p covariance of the BF nuclei mostly with right but not with left hemispheric cortical areas (p covariance of the BF with left hemispheric cortical areas in healthy aging towards right hemispheric cortical areas in PPA, possibly reflecting a consequence of the profound and early destruction of cortical language areas in PPA. Copyright © 2016 The Author(s). Published by Elsevier Ltd.. All rights reserved.
Yee, Yohan; Fernandes, Darren J; French, Leon; Ellegood, Jacob; Cahill, Lindsay S; Vousden, Dulcie A; Spencer Noakes, Leigh; Scholz, Jan; van Eede, Matthijs C; Nieman, Brian J; Sled, John G; Lerch, Jason P
2018-05-18
An organizational pattern seen in the brain, termed structural covariance, is the statistical association of pairs of brain regions in their anatomical properties. These associations, measured across a population as covariances or correlations usually in cortical thickness or volume, are thought to reflect genetic and environmental underpinnings. Here, we examine the biological basis of structural volume covariance in the mouse brain. We first examined large scale associations between brain region volumes using an atlas-based approach that parcellated the entire mouse brain into 318 regions over which correlations in volume were assessed, for volumes obtained from 153 mouse brain images via high-resolution MRI. We then used a seed-based approach and determined, for 108 different seed regions across the brain and using mouse gene expression and connectivity data from the Allen Institute for Brain Science, the variation in structural covariance data that could be explained by distance to seed, transcriptomic similarity to seed, and connectivity to seed. We found that overall, correlations in structure volumes hierarchically clustered into distinct anatomical systems, similar to findings from other studies and similar to other types of networks in the brain, including structural connectivity and transcriptomic similarity networks. Across seeds, this structural covariance was significantly explained by distance (17% of the variation, up to a maximum of 49% for structural covariance to the visceral area of the cortex), transcriptomic similarity (13% of the variation, up to maximum of 28% for structural covariance to the primary visual area) and connectivity (15% of the variation, up to a maximum of 36% for structural covariance to the intermediate reticular nucleus in the medulla) of covarying structures. Together, distance, connectivity, and transcriptomic similarity explained 37% of structural covariance, up to a maximum of 63% for structural covariance to the
Contributions to Estimation and Testing Block Covariance Structures in Multivariate Normal Models
Liang, Yuli
2015-01-01
This thesis concerns inference problems in balanced random effects models with a so-called block circular Toeplitz covariance structure. This class of covariance structures describes the dependency of some specific multivariate two-level data when both compound symmetry and circular symmetry appear simultaneously. We derive two covariance structures under two different invariance restrictions. The obtained covariance structures reflect both circularity and exchangeability present in the data....
Language Ability Predicts Cortical Structure and Covariance in Boys with Autism Spectrum Disorder.
Sharda, Megha; Foster, Nicholas E V; Tryfon, Ana; Doyle-Thomas, Krissy A R; Ouimet, Tia; Anagnostou, Evdokia; Evans, Alan C; Zwaigenbaum, Lonnie; Lerch, Jason P; Lewis, John D; Hyde, Krista L
2017-03-01
There is significant clinical heterogeneity in language and communication abilities of individuals with Autism Spectrum Disorders (ASD). However, no consistent pathology regarding the relationship of these abilities to brain structure has emerged. Recent developments in anatomical correlation-based approaches to map structural covariance networks (SCNs), combined with detailed behavioral characterization, offer an alternative for studying these relationships. In this study, such an approach was used to study the integrity of SCNs of cortical thickness and surface area associated with language and communication, in 46 high-functioning, school-age children with ASD compared with 50 matched, typically developing controls (all males) with IQ > 75. Findings showed that there was alteration of cortical structure and disruption of fronto-temporal cortical covariance in ASD compared with controls. Furthermore, in an analysis of a subset of ASD participants, alterations in both cortical structure and covariance were modulated by structural language ability of the participants, but not communicative function. These findings indicate that structural language abilities are related to altered fronto-temporal cortical covariance in ASD, much more than symptom severity or cognitive ability. They also support the importance of better characterizing ASD samples while studying brain structure and for better understanding individual differences in language and communication abilities in ASD. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.
Structural Covariance of the Prefrontal-Amygdala Pathways Associated with Heart Rate Variability.
Wei, Luqing; Chen, Hong; Wu, Guo-Rong
2018-01-01
The neurovisceral integration model has shown a key role of the amygdala in neural circuits underlying heart rate variability (HRV) modulation, and suggested that reciprocal connections from amygdala to brain regions centered on the central autonomic network (CAN) are associated with HRV. To provide neuroanatomical evidence for these theoretical perspectives, the current study used covariance analysis of MRI-based gray matter volume (GMV) to map structural covariance network of the amygdala, and then determined whether the interregional structural correlations related to individual differences in HRV. The results showed that covariance patterns of the amygdala encompassed large portions of cortical (e.g., prefrontal, cingulate, and insula) and subcortical (e.g., striatum, hippocampus, and midbrain) regions, lending evidence from structural covariance analysis to the notion that the amygdala was a pivotal node in neural pathways for HRV modulation. Importantly, participants with higher resting HRV showed increased covariance of amygdala to dorsal medial prefrontal cortex and anterior cingulate cortex (dmPFC/dACC) extending into adjacent medial motor regions [i.e., pre-supplementary motor area (pre-SMA)/SMA], demonstrating structural covariance of the prefrontal-amygdala pathways implicated in HRV, and also implying that resting HRV may reflect the function of neural circuits underlying cognitive regulation of emotion as well as facilitation of adaptive behaviors to emotion. Our results, thus, provide anatomical substrates for the neurovisceral integration model that resting HRV may index an integrative neural network which effectively organizes emotional, cognitive, physiological and behavioral responses in the service of goal-directed behavior and adaptability.
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.
A pilot study of cognitive insight and structural covariance in first-episode psychosis.
Kuang, Corin; Buchy, Lisa; Barbato, Mariapaola; Makowski, Carolina; MacMaster, Frank P; Bray, Signe; Deighton, Stephanie; Addington, Jean
2017-01-01
Cognitive insight is described as a balance between one's self-reflectiveness (recognition and correction of dysfunctional reasoning), and self-certainty (overconfidence). Neuroimaging studies have linked the ventrolateral prefrontal cortex (VLPFC) to cognitive insight in people with psychosis. However, the relationship between cognitive insight and structural connectivity between the VLPFC and other brain areas is unknown. Here, we investigated the modulation of cognitive insight on structural covariance networks involving the VLPFC in a first-episode psychosis sample. Fifteen patients with a first-episode psychosis provided magnetic resonance (MR) scans and completed the Beck Cognitive Insight Scale (BCIS). MR scans were also available for 15 historical controls. Seed-based analysis of structural covariance was conducted using the Mapping Anatomical Correlations Across the Cerebral Cortex (MACACC) methodology, whereby Pearson correlation coefficients were extracted between seed regions in left and right VLPFC and cortical thickness across the brain. Structural covariance maps between groups were compared at each vertex. In first-episode subjects, we evaluated the modulation of BCIS scores on cortical covariance between VLPFC and every other vertex. Findings showed no significant group difference between first-episode psychosis subjects and controls in thickness covariance seeded from left or right VLPFC. However, in first-episode psychosis subjects, a positive association with self-certainty was found in networks seeded from both left and right VLPFC with thickness in medial frontal cortex and right pars triangularis. No significant associations were found for self-reflectiveness. These results suggest that self-certainty, but not self-reflectiveness, positively modulated cortical covariance in a frontal network in patients with a first-episode psychosis. Copyright © 2016 Elsevier B.V. All rights reserved.
Early grey matter changes in structural covariance networks in Huntington's disease.
Coppen, Emma M; van der Grond, Jeroen; Hafkemeijer, Anne; Rombouts, Serge A R B; Roos, Raymund A C
2016-01-01
Progressive subcortical changes are known to occur in Huntington's disease (HD), a hereditary neurodegenerative disorder. Less is known about the occurrence and cohesion of whole brain grey matter changes in HD. We aimed to detect network integrity changes in grey matter structural covariance networks and examined relationships with clinical assessments. Structural magnetic resonance imaging data of premanifest HD ( n = 30), HD patients (n = 30) and controls (n = 30) was used to identify ten structural covariance networks based on a novel technique using the co-variation of grey matter with independent component analysis in FSL. Group differences were studied controlling for age and gender. To explore whether our approach is effective in examining grey matter changes, regional voxel-based analysis was additionally performed. Premanifest HD and HD patients showed decreased network integrity in two networks compared to controls. One network included the caudate nucleus, precuneous and anterior cingulate cortex (in HD p covariance might be a sensitive approach to reveal early grey matter changes, especially for premanifest HD.
Paquola, Casey; Bennett, Maxwell; Lagopoulos, Jim
2018-05-15
Structural covariance networks (SCNs) may offer unique insights into the developmental impact of childhood maltreatment because they are thought to reflect coordinated maturation of distinct grey matter regions. T1-weighted magnetic resonance images were acquired from 121 young people with emerging mental illness. Diffusion weighted and resting state functional imaging was also acquired from a random subset of the participants (n=62). Ten study-specific SCNs were identified using a whole brain grey matter independent component analysis. The effects of childhood maltreatment and age on average grey matter density and the expression of each SCN were calculated. Childhood maltreatment was linked to age-related decreases in grey matter density across a SCN that overlapped with the default mode and fronto-parietal networks. Resting state functional connectivity and structural connectivity were calculated in the study-specific SCN and across the whole brain. Grey matter covariance was significantly correlated with rsFC across the SCN, and rsFC fully mediated the relationship between grey matter covariance and structural connectivity in the non-maltreated group. A unique association of grey matter covariance with structural connectivity was detected amongst individuals with a history of childhood maltreatment. Perturbation of grey matter development across the default mode and fronto-parietal networks following childhood maltreatment may have significant implications for mental well-being, given the networks' roles in self-referential activity. Cross-modal comparisons suggest reduced grey matter following childhood maltreatment could arise from deficient functional activity earlier in life.
Wu, Huawang; Sun, Hui; Wang, Chao; Yu, Lin; Li, Yilan; Peng, Hongjun; Lu, Xiaobing; Hu, Qingmao; Ning, Yuping; Jiang, Tianzi; Xu, Jinping; Wang, Jiaojian
2017-01-01
Major depressive disorder (MDD) is a common psychiatric disorder that is characterized by cognitive deficits and affective symptoms. To date, an increasing number of neuroimaging studies have focused on emotion regulation and have consistently shown that emotion dysregulation is one of the central features and underlying mechanisms of MDD. Although gray matter morphological abnormalities in regions within emotion regulation networks have been identified in MDD, the interactions and relationships between these gray matter structures remain largely unknown. Thus, in this study, we adopted a structural covariance method based on gray matter volume to investigate the brain morphological abnormalities within the emotion regulation networks in a large cohort of 65 MDD patients and 65 age- and gender-matched healthy controls. A permutation test with p covariance connectivity strengths between MDD patients and healthy controls. The structural covariance analysis revealed an increased correlation strength of gray matter volume between the left angular gyrus and the left amygdala and between the right angular gyrus and the right amygdala, as well as a decreased correlation strength of the gray matter volume between the right angular gyrus and the posterior cingulate cortex in MDD. Our findings support the notion that emotion dysregulation is an underlying mechanism of MDD by revealing disrupted structural covariance patterns in the emotion regulation network. Copyright Â© 2016 Elsevier Ltd. All rights reserved.
Selecting a separable parametric spatiotemporal covariance structure for longitudinal imaging data.
George, Brandon; Aban, Inmaculada
2015-01-15
Longitudinal imaging studies allow great insight into how the structure and function of a subject's internal anatomy changes over time. Unfortunately, the analysis of longitudinal imaging data is complicated by inherent spatial and temporal correlation: the temporal from the repeated measures and the spatial from the outcomes of interest being observed at multiple points in a patient's body. We propose the use of a linear model with a separable parametric spatiotemporal error structure for the analysis of repeated imaging data. The model makes use of spatial (exponential, spherical, and Matérn) and temporal (compound symmetric, autoregressive-1, Toeplitz, and unstructured) parametric correlation functions. A simulation study, inspired by a longitudinal cardiac imaging study on mitral regurgitation patients, compared different information criteria for selecting a particular separable parametric spatiotemporal correlation structure as well as the effects on types I and II error rates for inference on fixed effects when the specified model is incorrect. Information criteria were found to be highly accurate at choosing between separable parametric spatiotemporal correlation structures. Misspecification of the covariance structure was found to have the ability to inflate the type I error or have an overly conservative test size, which corresponded to decreased power. An example with clinical data is given illustrating how the covariance structure procedure can be performed in practice, as well as how covariance structure choice can change inferences about fixed effects. Copyright © 2014 John Wiley & Sons, Ltd.
Structure of Pioncare covariant tensor operators in quantum mechanical models
Polyzou, W.N.; Klink, W.H.
1988-01-01
The structure of operators that transform covariantly in Poincare invariant quantum mechanical models is analyzed. These operators are shown to have an interaction dependence that comes from the geometry of the Poincare group. The operators can be expressed in terms of matrix elements in a complete set of eigenstates of the mass and spin operators associated with the dynamical representation of the Poincare group. The matrix elements are factored into geometrical coefficients (Clebsch--Gordan coefficients for the Poincare group) and invariant matrix elements. The geometrical coefficients are fixed by the transformation properties of the operator and the eigenvalue spectrum of the mass and spin. The invariant matrix elements, which distinguish between different operators with the same transformation properties, are given in terms of a set of invariant form factors. copyright 1988 Academic Press, Inc
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)
An automated procedure for covariation-based detection of RNA structure
Winker, S.; Overbeek, R.; Woese, C.R.; Olsen, G.J.; Pfluger, N.
1989-12-01
This paper summarizes our investigations into the computational detection of secondary and tertiary structure of ribosomal RNA. We have developed a new automated procedure that not only identifies potential bondings of secondary and tertiary structure, but also provides the covariation evidence that supports the proposed bondings, and any counter-evidence that can be detected in the known sequences. A small number of previously unknown bondings have been detected in individual RNA molecules (16S rRNA and 7S RNA) through the use of our automated procedure. Currently, we are systematically studying mitochondrial rRNA. Our goal is to detect tertiary structure within 16S rRNA and quaternary structure between 16S and 23S rRNA. Our ultimate hope is that automated covariation analysis will contribute significantly to a refined picture of ribosome structure. Our colleagues in biology have begun experiments to test certain hypotheses suggested by an examination of our program's output. These experiments involve sequencing key portions of the 23S ribosomal RNA for species in which the known 16S ribosomal RNA exhibits variation (from the dominant pattern) at the site of a proposed bonding. The hope is that the 23S ribosomal RNA of these species will exhibit corresponding complementary variation or generalized covariation. 24 refs
An automated procedure for covariation-based detection of RNA structure
Winker, S.; Overbeek, R.; Woese, C.R.; Olsen, G.J.; Pfluger, N.
1989-12-01
This paper summarizes our investigations into the computational detection of secondary and tertiary structure of ribosomal RNA. We have developed a new automated procedure that not only identifies potential bondings of secondary and tertiary structure, but also provides the covariation evidence that supports the proposed bondings, and any counter-evidence that can be detected in the known sequences. A small number of previously unknown bondings have been detected in individual RNA molecules (16S rRNA and 7S RNA) through the use of our automated procedure. Currently, we are systematically studying mitochondrial rRNA. Our goal is to detect tertiary structure within 16S rRNA and quaternary structure between 16S and 23S rRNA. Our ultimate hope is that automated covariation analysis will contribute significantly to a refined picture of ribosome structure. Our colleagues in biology have begun experiments to test certain hypotheses suggested by an examination of our program's output. These experiments involve sequencing key portions of the 23S ribosomal RNA for species in which the known 16S ribosomal RNA exhibits variation (from the dominant pattern) at the site of a proposed bonding. The hope is that the 23S ribosomal RNA of these species will exhibit corresponding complementary variation or generalized covariation. 24 refs.
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.
A Systematic Approach for Identifying Level-1 Error Covariance Structures in Latent Growth Modeling
Ding, Cherng G.; Jane, Ten-Der; Wu, Chiu-Hui; Lin, Hang-Rung; Shen, Chih-Kang
2017-01-01
It has been pointed out in the literature that misspecification of the level-1 error covariance structure in latent growth modeling (LGM) has detrimental impacts on the inferences about growth parameters. Since correct covariance structure is difficult to specify by theory, the identification needs to rely on a specification search, which,…
Socio-Cognitive Phenotypes Differentially Modulate Large-Scale Structural Covariance Networks.
Valk, Sofie L; Bernhardt, Boris C; Böckler, Anne; Trautwein, Fynn-Mathis; Kanske, Philipp; Singer, Tania
2017-02-01
Functional neuroimaging studies have suggested the existence of 2 largely distinct social cognition networks, one for theory of mind (taking others' cognitive perspective) and another for empathy (sharing others' affective states). To address whether these networks can also be dissociated at the level of brain structure, we combined behavioral phenotyping across multiple socio-cognitive tasks with 3-Tesla MRI cortical thickness and structural covariance analysis in 270 healthy adults, recruited across 2 sites. Regional thickness mapping only provided partial support for divergent substrates, highlighting that individual differences in empathy relate to left insular-opercular thickness while no correlation between thickness and mentalizing scores was found. Conversely, structural covariance analysis showed clearly divergent network modulations by socio-cognitive and -affective phenotypes. Specifically, individual differences in theory of mind related to structural integration between temporo-parietal and dorsomedial prefrontal regions while empathy modulated the strength of dorsal anterior insula networks. Findings were robust across both recruitment sites, suggesting generalizability. At the level of structural network embedding, our study provides a double dissociation between empathy and mentalizing. Moreover, our findings suggest that structural substrates of higher-order social cognition are reflected rather in interregional networks than in the the local anatomical markup of specific regions per se. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.
Structural covariance networks across the life span, from 6 to 94 years of age.
DuPre, Elizabeth; Spreng, R Nathan
2017-10-01
Structural covariance examines covariation of gray matter morphology between brain regions and across individuals. Despite significant interest in the influence of age on structural covariance patterns, no study to date has provided a complete life span perspective-bridging childhood with early, middle, and late adulthood-on the development of structural covariance networks. Here, we investigate the life span trajectories of structural covariance in six canonical neurocognitive networks: default, dorsal attention, frontoparietal control, somatomotor, ventral attention, and visual. By combining data from five open-access data sources, we examine the structural covariance trajectories of these networks from 6 to 94 years of age in a sample of 1,580 participants. Using partial least squares, we show that structural covariance patterns across the life span exhibit two significant, age-dependent trends. The first trend is a stable pattern whose integrity declines over the life span. The second trend is an inverted-U that differentiates young adulthood from other age groups. Hub regions, including posterior cingulate cortex and anterior insula, appear particularly influential in the expression of this second age-dependent trend. Overall, our results suggest that structural covariance provides a reliable definition of neurocognitive networks across the life span and reveal both shared and network-specific trajectories.
Fitting direct covariance structures by the MSTRUCT modeling language of the CALIS procedure.
Yung, Yiu-Fai; Browne, Michael W; Zhang, Wei
2015-02-01
This paper demonstrates the usefulness and flexibility of the general structural equation modelling (SEM) approach to fitting direct covariance patterns or structures (as opposed to fitting implied covariance structures from functional relationships among variables). In particular, the MSTRUCT modelling language (or syntax) of the CALIS procedure (SAS/STAT version 9.22 or later: SAS Institute, 2010) is used to illustrate the SEM approach. The MSTRUCT modelling language supports a direct covariance pattern specification of each covariance element. It also supports the input of additional independent and dependent parameters. Model tests, fit statistics, estimates, and their standard errors are then produced under the general SEM framework. By using numerical and computational examples, the following tests of basic covariance patterns are illustrated: sphericity, compound symmetry, and multiple-group covariance patterns. Specification and testing of two complex correlation structures, the circumplex pattern and the composite direct product models with or without composite errors and scales, are also illustrated by the MSTRUCT syntax. It is concluded that the SEM approach offers a general and flexible modelling of direct covariance and correlation patterns. In conjunction with the use of SAS macros, the MSTRUCT syntax provides an easy-to-use interface for specifying and fitting complex covariance and correlation structures, even when the number of variables or parameters becomes large. © 2014 The British Psychological Society.
Ziyatdinov, Andrey; Vázquez-Santiago, Miquel; Brunel, Helena; Martinez-Perez, Angel; Aschard, Hugues; Soria, Jose Manuel
2018-02-27
Quantitative trait locus (QTL) mapping in genetic data often involves analysis of correlated observations, which need to be accounted for to avoid false association signals. This is commonly performed by modeling such correlations as random effects in linear mixed models (LMMs). The R package lme4 is a well-established tool that implements major LMM features using sparse matrix methods; however, it is not fully adapted for QTL mapping association and linkage studies. In particular, two LMM features are lacking in the base version of lme4: the definition of random effects by custom covariance matrices; and parameter constraints, which are essential in advanced QTL models. Apart from applications in linkage studies of related individuals, such functionalities are of high interest for association studies in situations where multiple covariance matrices need to be modeled, a scenario not covered by many genome-wide association study (GWAS) software. To address the aforementioned limitations, we developed a new R package lme4qtl as an extension of lme4. First, lme4qtl contributes new models for genetic studies within a single tool integrated with lme4 and its companion packages. Second, lme4qtl offers a flexible framework for scenarios with multiple levels of relatedness and becomes efficient when covariance matrices are sparse. We showed the value of our package using real family-based data in the Genetic Analysis of Idiopathic Thrombophilia 2 (GAIT2) project. Our software lme4qtl enables QTL mapping models with a versatile structure of random effects and efficient computation for sparse covariances. lme4qtl is available at https://github.com/variani/lme4qtl .
Maximum covariance analysis to identify intraseasonal oscillations over tropical Brazil
Barreto, Naurinete J. C.; Mesquita, Michel d. S.; Mendes, David; Spyrides, Maria H. C.; Pedra, George U.; Lucio, Paulo S.
2017-09-01
A reliable prognosis of extreme precipitation events in the tropics is arguably challenging to obtain due to the interaction of meteorological systems at various time scales. A pivotal component of the global climate variability is the so-called intraseasonal oscillations, phenomena that occur between 20 and 100 days. The Madden-Julian Oscillation (MJO), which is directly related to the modulation of convective precipitation in the equatorial belt, is considered the primary oscillation in the tropical region. The aim of this study is to diagnose the connection between the MJO signal and the regional intraseasonal rainfall variability over tropical Brazil. This is achieved through the development of an index called Multivariate Intraseasonal Index for Tropical Brazil (MITB). This index is based on Maximum Covariance Analysis (MCA) applied to the filtered daily anomalies of rainfall data over tropical Brazil against a group of covariates consisting of: outgoing longwave radiation and the zonal component u of the wind at 850 and 200 hPa. The first two MCA modes, which were used to create the { MITB}_1 and { MITB}_2 indices, represent 65 and 16 % of the explained variance, respectively. The combined multivariate index was able to satisfactorily represent the pattern of intraseasonal variability over tropical Brazil, showing that there are periods of activation and inhibition of precipitation connected with the pattern of MJO propagation. The MITB index could potentially be used as a diagnostic tool for intraseasonal forecasting.
Kim, Hee-Jong; Shin, Jeong-Hyeon; Han, Cheol E; Kim, Hee Jin; Na, Duk L; Seo, Sang Won; Seong, Joon-Kyung
2016-01-01
Cortical thinning patterns in Alzheimer's disease (AD) have been widely reported through conventional regional analysis. In addition, the coordinated variance of cortical thickness in different brain regions has been investigated both at the individual and group network levels. In this study, we aim to investigate network architectural characteristics of a structural covariance network (SCN) in AD, and further to show that the structural covariance connectivity becomes disorganized across the brain regions in AD, while the normal control (NC) subjects maintain more clustered and consistent coordination in cortical atrophy variations. We generated SCNs directly from T1-weighted MR images of individual patients using surface-based cortical thickness data, with structural connectivity defined as similarity in cortical thickness within different brain regions. Individual SCNs were constructed using morphometric data from the Samsung Medical Center (SMC) dataset. The structural covariance connectivity showed higher clustering than randomly generated networks, as well as similar minimum path lengths, indicating that the SCNs are "small world." There were significant difference between NC and AD group in characteristic path lengths (z = -2.97, p < 0.01) and small-worldness values (z = 4.05, p < 0.01). Clustering coefficients in AD was smaller than that of NC but there was no significant difference (z = 1.81, not significant). We further observed that the AD patients had significantly disrupted structural connectivity. We also show that the coordinated variance of cortical thickness is distributed more randomly from one region to other regions in AD patients when compared to NC subjects. Our proposed SCN may provide surface-based measures for understanding interaction between two brain regions with co-atrophy of the cerebral cortex due to normal aging or AD. We applied our method to the AD Neuroimaging Initiative (ADNI) data to show consistency in results with the SMC
Structural covariance networks across the life span, from 6 to 94 years of age
Elizabeth DuPre
2017-10-01
Full Text Available Structural covariance examines covariation of gray matter morphology between brain regions and across individuals. Despite significant interest in the influence of age on structural covariance patterns, no study to date has provided a complete life span perspective—bridging childhood with early, middle, and late adulthood—on the development of structural covariance networks. Here, we investigate the life span trajectories of structural covariance in six canonical neurocognitive networks: default, dorsal attention, frontoparietal control, somatomotor, ventral attention, and visual. By combining data from five open-access data sources, we examine the structural covariance trajectories of these networks from 6 to 94 years of age in a sample of 1,580 participants. Using partial least squares, we show that structural covariance patterns across the life span exhibit two significant, age-dependent trends. The first trend is a stable pattern whose integrity declines over the life span. The second trend is an inverted-U that differentiates young adulthood from other age groups. Hub regions, including posterior cingulate cortex and anterior insula, appear particularly influential in the expression of this second age-dependent trend. Overall, our results suggest that structural covariance provides a reliable definition of neurocognitive networks across the life span and reveal both shared and network-specific trajectories. The importance of life span perspectives is increasingly apparent in understanding normative interactions of large-scale neurocognitive networks. Although recent work has made significant strides in understanding the functional and structural connectivity of these networks, there has been comparatively little attention to life span trajectories of structural covariance networks. In this study we examine patterns of structural covariance across the life span for six neurocognitive networks. Our results suggest that networks exhibit
Altered Integration of Structural Covariance Networks in Young Children With Type 1 Diabetes.
Hosseini, S M Hadi; Mazaika, Paul; Mauras, Nelly; Buckingham, Bruce; Weinzimer, Stuart A; Tsalikian, Eva; White, Neil H; Reiss, Allan L
2016-11-01
Type 1 diabetes mellitus (T1D), one of the most frequent chronic diseases in children, is associated with glucose dysregulation that contributes to an increased risk for neurocognitive deficits. While there is a bulk of evidence regarding neurocognitive deficits in adults with T1D, little is known about how early-onset T1D affects neural networks in young children. Recent data demonstrated widespread alterations in regional gray matter and white matter associated with T1D in young children. These widespread neuroanatomical changes might impact the organization of large-scale brain networks. In the present study, we applied graph-theoretical analysis to test whether the organization of structural covariance networks in the brain for a cohort of young children with T1D (N = 141) is altered compared to healthy controls (HC; N = 69). While the networks in both groups followed a small world organization-an architecture that is simultaneously highly segregated and integrated-the T1D network showed significantly longer path length compared with HC, suggesting reduced global integration of brain networks in young children with T1D. In addition, network robustness analysis revealed that the T1D network model showed more vulnerability to neural insult compared with HC. These results suggest that early-onset T1D negatively impacts the global organization of structural covariance networks and influences the trajectory of brain development in childhood. This is the first study to examine structural covariance networks in young children with T1D. Improving glycemic control for young children with T1D might help prevent alterations in brain networks in this population. Hum Brain Mapp 37:4034-4046, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.
Molenaar, Dylan; Dolan, Conor V.; Wicherts, Jelle M.
2009-01-01
Research into sex differences in general intelligence, g, has resulted in two opposite views. In the first view, a g-difference is nonexistent, while in the second view, g is associated with a male advantage. Past research using Multi-Group Covariance and Mean Structure Analysis (MG-CMSA) found no sex difference in g. This failure raised the…
Loss of integrity and atrophy in cingulate structural covariance networks in Parkinson's disease.
de Schipper, Laura J; van der Grond, Jeroen; Marinus, Johan; Henselmans, Johanna M L; van Hilten, Jacobus J
2017-01-01
In Parkinson's disease (PD), the relation between cortical brain atrophy on MRI and clinical progression is not straightforward. Determination of changes in structural covariance networks - patterns of covariance in grey matter density - has shown to be a valuable technique to detect subtle grey matter variations. We evaluated how structural network integrity in PD is related to clinical data. 3 Tesla MRI was performed in 159 PD patients. We used nine standardized structural covariance networks identified in 370 healthy subjects as a template in the analysis of the PD data. Clinical assessment comprised motor features (Movement Disorder Society-Unified Parkinson's Disease Rating Scale; MDS-UPDRS motor scale) and predominantly non-dopaminergic features (SEverity of Non-dopaminergic Symptoms in Parkinson's Disease; SENS-PD scale: postural instability and gait difficulty, psychotic symptoms, excessive daytime sleepiness, autonomic dysfunction, cognitive impairment and depressive symptoms). Voxel-based analyses were performed within networks significantly associated with PD. The anterior and posterior cingulate network showed decreased integrity, associated with the SENS-PD score, p = 0.001 (β = - 0.265, η p 2 = 0.070) and p = 0.001 (β = - 0.264, η p 2 = 0.074), respectively. Of the components of the SENS-PD score, cognitive impairment and excessive daytime sleepiness were associated with atrophy within both networks. We identified loss of integrity and atrophy in the anterior and posterior cingulate networks in PD patients. Abnormalities of both networks were associated with predominantly non-dopaminergic features, specifically cognition and excessive daytime sleepiness. Our findings suggest that (components of) the cingulate networks display a specific vulnerability to the pathobiology of PD and may operate as interfaces between networks involved in cognition and alertness.
Nordin, Kristin; Persson, Jonas; Stening, Eva; Herlitz, Agneta; Larsson, Elna-Marie; Söderlund, Hedvig
2018-02-01
The hippocampus (HC) interacts with distributed brain regions to support memory and shows significant volume reductions in aging, but little is known about age effects on hippocampal whole-brain structural covariance. It is also unclear whether the anterior and posterior HC show similar or distinct patterns of whole-brain covariance and to what extent these are related to memory functions organized along the hippocampal longitudinal axis. Using the multivariate approach partial least squares, we assessed structural whole-brain covariance of the HC in addition to regional volume, in young, middle-aged and older adults (n = 221), and assessed associations with episodic and spatial memory. Based on findings of sex differences in both memory and brain aging, we further considered sex as a potential modulating factor of age effects. There were two main covariance patterns: one capturing common anterior and posterior covariance, and one differentiating the two regions by capturing anterior-specific covariance only. These patterns were differentially related to associative memory while unrelated to measures of single-item memory and spatial memory. Although patterns were qualitatively comparable across age groups, participants' expression of both patterns decreased with age, independently of sex. The results suggest that the organization of hippocampal structural whole-brain covariance remains stable across age, but that the integrity of these networks decreases as the brain undergoes age-related alterations. © 2017 Wiley Periodicals, Inc.
Ryu, Duchwan
2010-09-28
We consider nonparametric regression analysis in a generalized linear model (GLM) framework for data with covariates that are the subject-specific random effects of longitudinal measurements. The usual assumption that the effects of the longitudinal covariate processes are linear in the GLM may be unrealistic and if this happens it can cast doubt on the inference of observed covariate effects. Allowing the regression functions to be unknown, we propose to apply Bayesian nonparametric methods including cubic smoothing splines or P-splines for the possible nonlinearity and use an additive model in this complex setting. To improve computational efficiency, we propose the use of data-augmentation schemes. The approach allows flexible covariance structures for the random effects and within-subject measurement errors of the longitudinal processes. The posterior model space is explored through a Markov chain Monte Carlo (MCMC) sampler. The proposed methods are illustrated and compared to other approaches, the "naive" approach and the regression calibration, via simulations and by an application that investigates the relationship between obesity in adulthood and childhood growth curves. © 2010, The International Biometric Society.
Structural Covariance of Sensory Networks, the Cerebellum, and Amygdala in Autism Spectrum Disorder
Garrett J. Cardon
2017-11-01
Full Text Available Sensory dysfunction is a core symptom of autism spectrum disorder (ASD, and abnormalities with sensory responsivity and processing can be extremely debilitating to ASD patients and their families. However, relatively little is known about the underlying neuroanatomical and neurophysiological factors that lead to sensory abnormalities in ASD. Investigation into these aspects of ASD could lead to significant advancements in our general knowledge about ASD, as well as provide targets for treatment and inform diagnostic procedures. Thus, the current study aimed to measure the covariation of volumes of brain structures (i.e., structural magnetic resonance imaging that may be involved in abnormal sensory processing, in order to infer connectivity of these brain regions. Specifically, we quantified the structural covariation of sensory-related cerebral cortical structures, in addition to the cerebellum and amygdala by computing partial correlations between the structural volumes of these structures. These analyses were performed in participants with ASD (n = 36, as well as typically developing peers (n = 32. Results showed decreased structural covariation between sensory-related cortical structures, especially between the left and right cerebral hemispheres, in participants with ASD. In contrast, these same participants presented with increased structural covariation of structures in the right cerebral hemisphere. Additionally, sensory-related cerebral structures exhibited decreased structural covariation with functionally identified cerebellar networks. Also, the left amygdala showed significantly increased structural covariation with cerebral structures related to visual processing. Taken together, these results may suggest several patterns of altered connectivity both within and between cerebral cortices and other brain structures that may be related to sensory processing.
Tania Dehesh
2015-01-01
Full Text Available Background. Univariate meta-analysis (UM procedure, as a technique that provides a single overall result, has become increasingly popular. Neglecting the existence of other concomitant covariates in the models leads to loss of treatment efficiency. Our aim was proposing four new approximation approaches for the covariance matrix of the coefficients, which is not readily available for the multivariate generalized least square (MGLS method as a multivariate meta-analysis approach. Methods. We evaluated the efficiency of four new approaches including zero correlation (ZC, common correlation (CC, estimated correlation (EC, and multivariate multilevel correlation (MMC on the estimation bias, mean square error (MSE, and 95% probability coverage of the confidence interval (CI in the synthesis of Cox proportional hazard models coefficients in a simulation study. Result. Comparing the results of the simulation study on the MSE, bias, and CI of the estimated coefficients indicated that MMC approach was the most accurate procedure compared to EC, CC, and ZC procedures. The precision ranking of the four approaches according to all above settings was MMC ≥ EC ≥ CC ≥ ZC. Conclusion. This study highlights advantages of MGLS meta-analysis on UM approach. The results suggested the use of MMC procedure to overcome the lack of information for having a complete covariance matrix of the coefficients.
Dehesh, Tania; Zare, Najaf; Ayatollahi, Seyyed Mohammad Taghi
2015-01-01
Univariate meta-analysis (UM) procedure, as a technique that provides a single overall result, has become increasingly popular. Neglecting the existence of other concomitant covariates in the models leads to loss of treatment efficiency. Our aim was proposing four new approximation approaches for the covariance matrix of the coefficients, which is not readily available for the multivariate generalized least square (MGLS) method as a multivariate meta-analysis approach. We evaluated the efficiency of four new approaches including zero correlation (ZC), common correlation (CC), estimated correlation (EC), and multivariate multilevel correlation (MMC) on the estimation bias, mean square error (MSE), and 95% probability coverage of the confidence interval (CI) in the synthesis of Cox proportional hazard models coefficients in a simulation study. Comparing the results of the simulation study on the MSE, bias, and CI of the estimated coefficients indicated that MMC approach was the most accurate procedure compared to EC, CC, and ZC procedures. The precision ranking of the four approaches according to all above settings was MMC ≥ EC ≥ CC ≥ ZC. This study highlights advantages of MGLS meta-analysis on UM approach. The results suggested the use of MMC procedure to overcome the lack of information for having a complete covariance matrix of the coefficients.
Meyer Karin
2001-11-01
Full Text Available Abstract A random regression model for the analysis of "repeated" records in animal breeding is described which combines a random regression approach for additive genetic and other random effects with the assumption of a parametric correlation structure for within animal covariances. Both stationary and non-stationary correlation models involving a small number of parameters are considered. Heterogeneity in within animal variances is modelled through polynomial variance functions. Estimation of parameters describing the dispersion structure of such model by restricted maximum likelihood via an "average information" algorithm is outlined. An application to mature weight records of beef cow is given, and results are contrasted to those from analyses fitting sets of random regression coefficients for permanent environmental effects.
Nuamah, N.N.N.N.
1991-01-01
This paper postulates the assumptions underlying the Mean Approach model and recasts the re-expressions of the normal equations of this model in partitioned matrices of covariances. These covariance structures have been analysed. (author). 16 refs
The Performance Analysis Based on SAR Sample Covariance Matrix
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 NMR Processing and Analysis for Protein Assignment.
Harden, Bradley J; Frueh, Dominique P
2018-01-01
During NMR resonance assignment it is often necessary to relate nuclei to one another indirectly, through their common correlations to other nuclei. Covariance NMR has emerged as a powerful technique to correlate such nuclei without relying on error-prone peak peaking. However, false-positive artifacts in covariance spectra have impeded a general application to proteins. We recently introduced pre- and postprocessing steps to reduce the prevalence of artifacts in covariance spectra, allowing for the calculation of a variety of 4D covariance maps obtained from diverse combinations of pairs of 3D spectra, and we have employed them to assign backbone and sidechain resonances in two large and challenging proteins. In this chapter, we present a detailed protocol describing how to (1) properly prepare existing 3D spectra for covariance, (2) understand and apply our processing script, and (3) navigate and interpret the resulting 4D spectra. We also provide solutions to a number of errors that may occur when using our script, and we offer practical advice when assigning difficult signals. We believe such 4D spectra, and covariance NMR in general, can play an integral role in the assignment of NMR signals.
Covariance analysis of symmetry energy observables from heavy ion collision
Yingxun Zhang
2015-10-01
Full Text Available Using covariance analysis, we quantify the correlations between the interaction parameters in a transport model and the observables commonly used to extract information of the Equation of State of Asymmetric Nuclear Matter in experiments. By simulating 124Sn + 124Sn, 124Sn + 112Sn and 112Sn + 112Sn reactions at beam energies of 50 and 120 MeV per nucleon, we have identified that the nucleon effective mass splitting is most strongly correlated to the neutrons and protons yield ratios with high kinetic energy from central collisions especially at high incident energy. The best observable to determine the slope of the symmetry energy, L, at saturation density is the isospin diffusion observable even though the correlation is not very strong (∼0.7. Similar magnitude of correlation but opposite in sign exists for isospin diffusion and nucleon isoscalar effective mass. At 120 MeV/u, the effective mass splitting and the isoscalar effective mass also have opposite correlation for the double n/p and isoscaling p/p yield ratios. By combining data and simulations at different beam energies, it should be possible to place constraints on the slope of symmetry energy (L and effective mass splitting with reasonable uncertainties.
Xinwei Li
2018-05-01
Full Text Available The hippocampus plays an important role in memory function relying on information interaction between distributed brain areas. The hippocampus can be divided into the anterior and posterior sections with different structure and function along its long axis. The aim of this study is to investigate the effects of normal aging on the structural covariance of the anterior hippocampus (aHPC and the posterior hippocampus (pHPC. In this study, 240 healthy subjects aged 18–89 years were selected and subdivided into young (18–23 years, middle-aged (30–58 years, and older (61–89 years groups. The aHPC and pHPC was divided based on the location of uncal apex in the MNI space. Then, the structural covariance networks were constructed by examining their covariance in gray matter volumes with other brain regions. Finally, the influence of age on the structural covariance of these hippocampal sections was explored. We found that the aHPC and pHPC had different structural covariance patterns, but both of them were associated with the medial temporal lobe and insula. Moreover, both increased and decreased covariances were found with the aHPC but only increased covariance was found with the pHPC with age (p < 0.05, family-wise error corrected. These decreased connections occurred within the default mode network, while the increased connectivity mainly occurred in other memory systems that differ from the hippocampus. This study reveals different age-related influence on the structural networks of the aHPC and pHPC, providing an essential insight into the mechanisms of the hippocampus in normal aging.
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.
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.
PIGNI, M.T.; HERMAN, M.; OBLOZINSKY, P.
2008-01-01
We produced a large set of neutron cross section covariances in the energy range of 5 keV-20 MeV. The present set of data on 57 structural materials and 31 heavy nuclei follows our earlier work on 219 fission product materials and completes our extensive contribution to the low-fidelity covariance project (307 materials). This project aims to provide initial, low-fidelity yet consistent estimates of covariance data for nuclear criticality safety applications. The evaluation methodology combines the nuclear reaction model code EMPIRE which calculates sensitivity to nuclear reaction model parameters, and the Bayesian code KALMAN that propagates uncertainties of the model parameters to cross sections. Taking into account the large scale of the project, only marginal reference to experimental data was made. The covariances were derived from the perturbation of several key model parameters selected by the sensitivity analysis. These parameters refer to the optical model potential, the level densities and the strength of the pre-equilibrium emission. This work represents the first attempt ever to generate nuclear data covariances on such a large scale
Undesirable effects of covariance matrix techniques for error analysis
Seibert, D.
1994-01-01
Regression with χ 2 constructed from covariance matrices should not be used for some combinations of covariance matrices and fitting functions. Using the technique for unsuitable combinations can amplify systematic errors. This amplification is uncontrolled, and can produce arbitrarily inaccurate results that might not be ruled out by a χ 2 test. In addition, this technique can give incorrect (artificially small) errors for fit parameters. I give a test for this instability and a more robust (but computationally more intensive) method for fitting correlated data
Foroughi Pour, Ali; Dalton, Lori A
2018-03-21
Many bioinformatics studies aim to identify markers, or features, that can be used to discriminate between distinct groups. In problems where strong individual markers are not available, or where interactions between gene products are of primary interest, it may be necessary to consider combinations of features as a marker family. To this end, recent work proposes a hierarchical Bayesian framework for feature selection that places a prior on the set of features we wish to select and on the label-conditioned feature distribution. While an analytical posterior under Gaussian models with block covariance structures is available, the optimal feature selection algorithm for this model remains intractable since it requires evaluating the posterior over the space of all possible covariance block structures and feature-block assignments. To address this computational barrier, in prior work we proposed a simple suboptimal algorithm, 2MNC-Robust, with robust performance across the space of block structures. Here, we present three new heuristic feature selection algorithms. The proposed algorithms outperform 2MNC-Robust and many other popular feature selection algorithms on synthetic data. In addition, enrichment analysis on real breast cancer, colon cancer, and Leukemia data indicates they also output many of the genes and pathways linked to the cancers under study. Bayesian feature selection is a promising framework for small-sample high-dimensional data, in particular biomarker discovery applications. When applied to cancer data these algorithms outputted many genes already shown to be involved in cancer as well as potentially new biomarkers. Furthermore, one of the proposed algorithms, SPM, outputs blocks of heavily correlated genes, particularly useful for studying gene interactions and gene networks.
Disruption of structural covariance networks for language in autism is modulated by verbal ability.
Sharda, Megha; Khundrakpam, Budhachandra S; Evans, Alan C; Singh, Nandini C
2016-03-01
The presence of widespread speech and language deficits is a core feature of autism spectrum disorders (ASD). These impairments have often been attributed to altered connections between brain regions. Recent developments in anatomical correlation-based approaches to map structural covariance offer an effective way of studying such connections in vivo. In this study, we employed such a structural covariance network (SCN)-based approach to investigate the integrity of anatomical networks in fronto-temporal brain regions of twenty children with ASD compared to an age and gender-matched control group of twenty-two children. Our findings reflected large-scale disruption of inter and intrahemispheric covariance in left frontal SCNs in the ASD group compared to controls, but no differences in right fronto-temporal SCNs. Interhemispheric covariance in left-seeded networks was further found to be modulated by verbal ability of the participants irrespective of autism diagnosis, suggesting that language function might be related to the strength of interhemispheric structural covariance between frontal regions. Additionally, regional cortical thickening was observed in right frontal and left posterior regions, which was predicted by decreasing symptom severity and increasing verbal ability in ASD. These findings unify reports of regional differences in cortical morphology in ASD. They also suggest that reduced left hemisphere asymmetry and increased frontal growth may not only reflect neurodevelopmental aberrations but also compensatory mechanisms.
Covariate Imbalance and Adjustment for Logistic Regression Analysis of Clinical Trial Data
Ciolino, Jody D.; Martin, Reneé H.; Zhao, Wenle; Jauch, Edward C.; Hill, Michael D.; Palesch, Yuko Y.
2014-01-01
In logistic regression analysis for binary clinical trial data, adjusted treatment effect estimates are often not equivalent to unadjusted estimates in the presence of influential covariates. This paper uses simulation to quantify the benefit of covariate adjustment in logistic regression. However, International Conference on Harmonization guidelines suggest that covariate adjustment be pre-specified. Unplanned adjusted analyses should be considered secondary. Results suggest that that if adjustment is not possible or unplanned in a logistic setting, balance in continuous covariates can alleviate some (but never all) of the shortcomings of unadjusted analyses. The case of log binomial regression is also explored. PMID:24138438
Functional hypergraph uncovers novel covariant structures over neurodevelopment.
Gu, Shi; Yang, Muzhi; Medaglia, John D; Gur, Ruben C; Gur, Raquel E; Satterthwaite, Theodore D; Bassett, Danielle S
2017-08-01
Brain development during adolescence is marked by substantial changes in brain structure and function, leading to a stable network topology in adulthood. However, most prior work has examined the data through the lens of brain areas connected to one another in large-scale functional networks. Here, we apply a recently developed hypergraph approach that treats network connections (edges) rather than brain regions as the unit of interest, allowing us to describe functional network topology from a fundamentally different perspective. Capitalizing on a sample of 780 youth imaged as part of the Philadelphia Neurodevelopmental Cohort, this hypergraph representation of resting-state functional MRI data reveals three distinct classes of subnetworks (hyperedges): clusters, bridges, and stars, which respectively represent homogeneously connected, bipartite, and focal architectures. Cluster hyperedges show a strong resemblance to previously-described functional modules of the brain including somatomotor, visual, default mode, and salience systems. In contrast, star hyperedges represent highly localized subnetworks centered on a small set of regions, and are distributed across the entire cortex. Finally, bridge hyperedges link clusters and stars in a core-periphery organization. Notably, developmental changes within hyperedges are ordered in a similar core-periphery fashion, with the greatest developmental effects occurring in networked hyperedges within the functional core. Taken together, these results reveal a novel decomposition of the network organization of human brain, and further provide a new perspective on the role of local structures that emerge across neurodevelopment. Hum Brain Mapp 38:3823-3835, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.
Suh, Sooyeon; Kim, Hosung; Dang-Vu, Thien Thanh; Joo, Eunyeon; Shin, Chol
2016-01-01
Recent studies have suggested that structural abnormalities in insomnia may be linked with alterations in the default-mode network (DMN). This study compared cortical thickness and structural connectivity linked to the DMN in patients with persistent insomnia (PI) and good sleepers (GS). The current study used a clinical subsample from the longitudinal community-based Korean Genome and Epidemiology Study (KoGES). Cortical thickness and structural connectivity linked to the DMN in patients with persistent insomnia symptoms (PIS; n = 57) were compared to good sleepers (GS; n = 40). All participants underwent MRI acquisition. Based on literature review, we selected cortical regions corresponding to the DMN. A seed-based structural covariance analysis measured cortical thickness correlation between each seed region of the DMN and other cortical areas. Association of cortical thickness and covariance with sleep quality and neuropsychological assessments were further assessed. Compared to GS, cortical thinning was found in PIS in the anterior cingulate cortex, precentral cortex, and lateral prefrontal cortex. Decreased structural connectivity between anterior and posterior regions of the DMN was observed in the PIS group. Decreased structural covariance within the DMN was associated with higher PSQI scores. Cortical thinning in the lateral frontal lobe was related to poor performance in executive function in PIS. Disrupted structural covariance network in PIS might reflect malfunctioning of antero-posterior disconnection of the DMN during the wake to sleep transition that is commonly found during normal sleep. The observed structural network alteration may further implicate commonly observed sustained sleep difficulties and cognitive impairment in insomnia. © 2016 Associated Professional Sleep Societies, LLC.
Xu, Weijia; Ozer, Stuart; Gutell, Robin R
2009-01-01
With an increasingly large amount of sequences properly aligned, comparative sequence analysis can accurately identify not only common structures formed by standard base pairing but also new types of structural elements and constraints. However, traditional methods are too computationally expensive to perform well on large scale alignment and less effective with the sequences from diversified phylogenetic classifications. We propose a new approach that utilizes coevolutional rates among pairs of nucleotide positions using phylogenetic and evolutionary relationships of the organisms of aligned sequences. With a novel data schema to manage relevant information within a relational database, our method, implemented with a Microsoft SQL Server 2005, showed 90% sensitivity in identifying base pair interactions among 16S ribosomal RNA sequences from Bacteria, at a scale 40 times bigger and 50% better sensitivity than a previous study. The results also indicated covariation signals for a few sets of cross-strand base stacking pairs in secondary structure helices, and other subtle constraints in the RNA structure.
Covariant constraints for generic massive gravity and analysis of its characteristics
Deser, S.; Sandora, M.; Waldron, A.
2014-01-01
We perform a covariant constraint analysis of massive gravity valid for its entire parameter space, demonstrating that the model generically propagates 5 degrees of freedom; this is also verified by a new and streamlined Hamiltonian description. The constraint's covariant expression permits...
DANTE, Activation Analysis Neutron Spectra Unfolding by Covariance Matrix Method
Petilli, M.
1981-01-01
1 - Description of problem or function: The program evaluates activation measurements of reactor neutron spectra and unfolds the results for dosimetry purposes. Different evaluation options are foreseen: absolute or relative fluxes and different iteration algorithms. 2 - Method of solution: A least-square fit method is used. A correlation between available data and their uncertainties has been introduced by means of flux and activity variance-covariance matrices. Cross sections are assumed to be constant, i.e. with variance-covariance matrix equal to zero. The Lagrange multipliers method has been used for calculating the solution. 3 - Restrictions on the complexity of the problem: 9 activation experiments can be analyzed. 75 energy groups are accepted
Co-movements among financial stocks and covariance matrix analysis
Sharifi, Saba
2003-01-01
The major theories of finance leading into the main body of this research are discussed and our experiments on studying the risk and co-movements among stocks are presented. This study leads to the application of Random Matrix Theory (RMT) The idea of this theory refers to the importance of the empirically measured correlation (or covariance) matrix, C, in finance and particularly in the theory of optimal portfolios However, this matrix has recently come into question, as a large part of ...
Bias-Corrected Estimation of Noncentrality Parameters of Covariance Structure Models
Raykov, Tenko
2005-01-01
A bias-corrected estimator of noncentrality parameters of covariance structure models is discussed. The approach represents an application of the bootstrap methodology for purposes of bias correction, and utilizes the relation between average of resample conventional noncentrality parameter estimates and their sample counterpart. The…
Structural covariance and cortical reorganisation in schizophrenia: a MRI-based morphometric study.
Palaniyappan, Lena; Hodgson, Olha; Balain, Vijender; Iwabuchi, Sarina; Gowland, Penny; Liddle, Peter
2018-05-06
In patients with schizophrenia, distributed abnormalities are observed in grey matter volume. A recent hypothesis posits that these distributed changes are indicative of a plastic reorganisation process occurring in response to a functional defect in neuronal information transmission. We investigated the structural covariance across various brain regions in early-stage schizophrenia to determine if indeed the observed patterns of volumetric loss conform to a coordinated pattern of structural reorganisation. Structural magnetic resonance imaging scans were obtained from 40 healthy adults and 41 age, gender and parental socioeconomic status matched patients with schizophrenia. Volumes of grey matter tissue were estimated at the regional level across 90 atlas-based parcellations. Group-level structural covariance was studied using a graph theoretical framework. Patients had distributed reduction in grey matter volume, with high degree of localised covariance (clustering) compared with controls. Patients with schizophrenia had reduced centrality of anterior cingulate and insula but increased centrality of the fusiform cortex, compared with controls. Simulating targeted removal of highly central nodes resulted in significant loss of the overall covariance patterns in patients compared with controls. Regional volumetric deficits in schizophrenia are not a result of random, mutually independent processes. Our observations support the occurrence of a spatially interconnected reorganisation with the systematic de-escalation of conventional 'hub' regions. This raises the question of whether the morphological architecture in schizophrenia is primed for compensatory functions, albeit with a high risk of inefficiency.
Structural covariance in the hallucinating brain: a voxel-based morphometry study
Modinos, Gemma; Vercammen, Ans; Mechelli, Andrea; Knegtering, Henderikus; McGuire, Philip K.; Aleman, André
2009-01-01
Background Neuroimaging studies have indicated that a number of cortical regions express altered patterns of structural covariance in schizophrenia. The relation between these alterations and specific psychotic symptoms is yet to be investigated. We used voxel-based morphometry to examine regional grey matter volumes and structural covariance associated with severity of auditory verbal hallucinations. Methods We applied optimized voxel-based morphometry to volumetric magnetic resonance imaging data from 26 patients with medication-resistant auditory verbal hallucinations (AVHs); statistical inferences were made at p < 0.05 after correction for multiple comparisons. Results Grey matter volume in the left inferior frontal gyrus was positively correlated with severity of AVHs. Hallucination severity influenced the pattern of structural covariance between this region and the left superior/middle temporal gyri, the right inferior frontal gyrus and hippocampus, and the insula bilaterally. Limitations The results are based on self-reported severity of auditory hallucinations. Complementing with a clinician-based instrument could have made the findings more compelling. Future studies would benefit from including a measure to control for other symptoms that may covary with AVHs and for the effects of antipsychotic medication. Conclusion The results revealed that overall severity of AVHs modulated cortical intercorrelations between frontotemporal regions involved in language production and verbal monitoring, supporting the critical role of this network in the pathophysiology of hallucinations. PMID:19949723
Repairable system analysis in presence of covariates and random effects
Giorgio, M.; Guida, M.; Pulcini, G.
2014-01-01
This paper aims to model the failure pattern of repairable systems in presence of explained and unexplained heterogeneity. The failure pattern of each system is described by a Power Law Process. Part of the heterogeneity among the patterns is explained through the use of a covariate, and the residual unexplained heterogeneity (random effects) is modeled via a joint probability distribution on the PLP parameters. The proposed approach is applied to a real set of failure time data of powertrain systems mounted on 33 buses employed in urban and suburban routes. Moreover, the joint probability distribution on the PLP parameters estimated from the data is used as an informative prior to make Bayesian inference on the future failure process of a generic system belonging to the same population and employed in an urban or suburban route under randomly chosen working conditions. - Highlights: • We describe the failure process of buses powertrain system subject to heterogeneity. • Heterogeneity due to different service types is explained by a covariate. • Random effect is modeled through a joint pdf on failure process parameters. • The powertrain reliability under new future operating conditions is estimated
Covariant framework for a mass monopole as a field structure in general relativity
Schleifer, N.
1980-01-01
We present a covariant framework for what is usually referred to as a mass monopole, by utilizing certain scalar invariants that are functions of the eigenvalues of the Riemann tensor. We thus bridge one of the theoretical gaps in the Einstein-Infeld-Hoffmann (EIH) derivation of the equations of motion of particles from the field equations: the lack of a covariant characterization of those aspects of a particle's structure which influence its motion. We have succeeded in giving a covariant framework for a mass monopole, which is the particle type assumed by EIH in their derivation. This is accomplished by using only the field outside the mass (singularity) to describe its characteristics, thereby conforming to a pure field description of nature. The utility of the framework has been verified by applying it to two physically relevant situations. The first is that of a Kerr particle, and the second is that of one spherically symmetric mass orbiting another. Our framework does indeed correspond to the intuitively expected results. In addition, our novel use of eigenvalues of the Riemann tensor appears to be a possible avenue of approach to the covariant characterization of other particle structure
A comparison of methods to adjust for continuous covariates in the analysis of randomised trials
Brennan C. Kahan
2016-04-01
Full Text Available Abstract Background Although covariate adjustment in the analysis of randomised trials can be beneficial, adjustment for continuous covariates is complicated by the fact that the association between covariate and outcome must be specified. Misspecification of this association can lead to reduced power, and potentially incorrect conclusions regarding treatment efficacy. Methods We compared several methods of adjustment to determine which is best when the association between covariate and outcome is unknown. We assessed (a dichotomisation or categorisation; (b assuming a linear association with outcome; (c using fractional polynomials with one (FP1 or two (FP2 polynomial terms; and (d using restricted cubic splines with 3 or 5 knots. We evaluated each method using simulation and through a re-analysis of trial datasets. Results Methods which kept covariates as continuous typically had higher power than methods which used categorisation. Dichotomisation, categorisation, and assuming a linear association all led to large reductions in power when the true association was non-linear. FP2 models and restricted cubic splines with 3 or 5 knots performed best overall. Conclusions For the analysis of randomised trials we recommend (1 adjusting for continuous covariates even if their association with outcome is unknown; (2 keeping covariates as continuous; and (3 using fractional polynomials with two polynomial terms or restricted cubic splines with 3 to 5 knots when a linear association is in doubt.
Adlaf, E M; Kohn, P M
1989-07-01
Re-analysis employing covariance-structural models was conducted on Strickland's (1983) survey data on 772 drinking students from Grades 7, 9 and 11. These data bear on the relations among alcohol consumption, alcohol abuse, association with drinking peers and exposure to televised alcohol advertising. Whereas Strickland used a just-identified model which, therefore, could not be tested for goodness of fit, our re-analysis tested several alternative models, which could be contradicted by the data. One model did fit his data particularly well. Its major implications are as follows: (1) Symptomatic consumption, negative consequences and self-rated severity of alcohol-related problems apparently reflect a common underlying factor, namely alcohol abuse. (2) Use of alcohol to relieve distress and frequency of intoxication, however, appear not to reflect abuse, although frequent intoxication contributes substantially to it. (3). Alcohol advertising affects consumption directly and abuse indirectly, although peer association has far greater impact on both consumption and abuse. These findings are interpreted as lending little support to further restrictions on advertising.
Sun, Delin; Haswell, Courtney C; Morey, Rajendra A; De Bellis, Michael D
2018-04-10
Child maltreatment is a major cause of pediatric posttraumatic stress disorder (PTSD). Previous studies have not investigated potential differences in network architecture in maltreated youth with PTSD and those resilient to PTSD. High-resolution magnetic resonance imaging brain scans at 3 T were completed in maltreated youth with PTSD (n = 31), without PTSD (n = 32), and nonmaltreated controls (n = 57). Structural covariance network architecture was derived from between-subject intraregional correlations in measures of cortical thickness in 148 cortical regions (nodes). Interregional positive partial correlations controlling for demographic variables were assessed, and those correlations that exceeded specified thresholds constituted connections in cortical brain networks. Four measures of network centrality characterized topology, and the importance of cortical regions (nodes) within the network architecture were calculated for each group. Permutation testing and principle component analysis method were employed to calculate between-group differences. Principle component analysis is a methodological improvement to methods used in previous brain structural covariance network studies. Differences in centrality were observed between groups. Larger centrality was found in maltreated youth with PTSD in the right posterior cingulate cortex; smaller centrality was detected in the right inferior frontal cortex compared to youth resilient to PTSD and controls, demonstrating network characteristics unique to pediatric maltreatment-related PTSD. Larger centrality was detected in right frontal pole in maltreated youth resilient to PTSD compared to youth with PTSD and controls, demonstrating structural covariance network differences in youth resilience to PTSD following maltreatment. Smaller centrality was found in the left posterior cingulate cortex and in the right inferior frontal cortex in maltreated youth compared to controls, demonstrating attributes of structural
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...... process can be relaxed and how our method can be applied to non-synchronous observations. We also present an empirical study of how high-frequency correlations, regressions and covariances change through time....
The application of sparse estimation of covariance matrix to quadratic discriminant analysis
Sun, Jiehuan; Zhao, Hongyu
2015-01-01
Background Although Linear Discriminant Analysis (LDA) is commonly used for classification, it may not be directly applied in genomics studies due to the large p, small n problem in these studies. Different versions of sparse LDA have been proposed to address this significant challenge. One implicit assumption of various LDA-based methods is that the covariance matrices are the same across different classes. However, rewiring of genetic networks (therefore different covariance matrices) acros...
Accounting for covariate measurement error in a Cox model analysis of recurrence of depression.
Liu, K; Mazumdar, S; Stone, R A; Dew, M A; Houck, P R; Reynolds, C F
2001-01-01
When a covariate measured with error is used as a predictor in a survival analysis using the Cox model, the parameter estimate is usually biased. In clinical research, covariates measured without error such as treatment procedure or sex are often used in conjunction with a covariate measured with error. In a randomized clinical trial of two types of treatments, we account for the measurement error in the covariate, log-transformed total rapid eye movement (REM) activity counts, in a Cox model analysis of the time to recurrence of major depression in an elderly population. Regression calibration and two variants of a likelihood-based approach are used to account for measurement error. The likelihood-based approach is extended to account for the correlation between replicate measures of the covariate. Using the replicate data decreases the standard error of the parameter estimate for log(total REM) counts while maintaining the bias reduction of the estimate. We conclude that covariate measurement error and the correlation between replicates can affect results in a Cox model analysis and should be accounted for. In the depression data, these methods render comparable results that have less bias than the results when measurement error is ignored.
von Cramon-Taubadel, Noreen; Schroeder, Lauren
2016-10-01
Estimation of the variance-covariance (V/CV) structure of fragmentary bioarchaeological populations requires the use of proxy extant V/CV parameters. However, it is currently unclear whether extant human populations exhibit equivalent V/CV structures. Random skewers (RS) and hierarchical analyses of common principal components (CPC) were applied to a modern human cranial dataset. Cranial V/CV similarity was assessed globally for samples of individual populations (jackknifed method) and for pairwise population sample contrasts. The results were examined in light of potential explanatory factors for covariance difference, such as geographic region, among-group distance, and sample size. RS analyses showed that population samples exhibited highly correlated multivariate responses to selection, and that differences in RS results were primarily a consequence of differences in sample size. The CPC method yielded mixed results, depending upon the statistical criterion used to evaluate the hierarchy. The hypothesis-testing (step-up) approach was deemed problematic due to sensitivity to low statistical power and elevated Type I errors. In contrast, the model-fitting (lowest AIC) approach suggested that V/CV matrices were proportional and/or shared a large number of CPCs. Pairwise population sample CPC results were correlated with cranial distance, suggesting that population history explains some of the variability in V/CV structure among groups. The results indicate that patterns of covariance in human craniometric samples are broadly similar but not identical. These findings have important implications for choosing extant covariance matrices to use as proxy V/CV parameters in evolutionary analyses of past populations. © 2016 Wiley Periodicals, Inc.
Chang, Chiung-Chih; Chang, Ya-Ting; Huang, Chi-Wei; Tsai, Shih-Jen; Hsu, Shih-Wei; Huang, Shu-Hua; Lee, Chen-Chang; Chang, Wen-Neng; Lui, Chun-Chung; Lien, Chia-Yi
2018-02-08
Alzheimer's disease (AD) is a complex neurodegenerative disease, and genetic differences may mediate neuronal degeneration. In humans, a single-nucleotide polymorphism in the B-cell chronic lymphocytic leukemia/lymphoma-2 (Bcl-2) gene, rs956572, has been found to significantly modulate Bcl-2 protein expression in the brain. The Bcl-2 AA genotype has been associated with reduced Bcl-2 levels and lower gray matter volume in healthy populations. We hypothesized that different Bcl-2 genotype groups may modulate large-scale brain networks that determine neurobehavioral test scores. Gray matter structural covariance networks (SCNs) were constructed in 104 patients with AD using T1-weighted magnetic resonance imaging with seed-based correlation analysis. The patients were stratified into two genotype groups on the basis of Bcl-2 expression (G carriers, n = 76; A homozygotes, n = 28). Four SCNs characteristic of AD were constructed from seeds in the default mode network, salience network, and executive control network, and cognitive test scores served as the major outcome factor. For the G carriers, influences of the SCNs were observed mostly in the default mode network, of which the peak clusters anchored by the posterior cingulate cortex seed determined the cognitive test scores. In contrast, genetic influences in the A homozygotes were found mainly in the executive control network, and both the dorsolateral prefrontal cortex seed and the interconnected peak clusters were correlated with the clinical scores. Despite a small number of cases, the A homozygotes showed greater covariance strength than the G carriers among all four SCNs. Our results suggest that the Bcl-2 rs956572 polymorphism is associated with different strengths of structural covariance in AD that determine clinical outcomes. The greater covariance strength in the four SCNs shown in the A homozygotes suggests that different Bcl-2 polymorphisms play different modulatory roles.
Xu, Nan; Veesler, David; Doerschuk, Peter C; Johnson, John E
2018-05-01
The information content of cryo EM data sets exceeds that of the electron scattering potential (cryo EM) density initially derived for structure determination. Previously we demonstrated the power of data variance analysis for characterizing regions of cryo EM density that displayed functionally important variance anomalies associated with maturation cleavage events in Nudaurelia Omega Capensis Virus and the presence or absence of a maturation protease in bacteriophage HK97 procapsids. Here we extend the analysis in two ways. First, instead of imposing icosahedral symmetry on every particle in the data set during the variance analysis, we only assume that the data set as a whole has icosahedral symmetry. This change removes artifacts of high variance along icosahedral symmetry axes, but retains all of the features previously reported in the HK97 data set. Second we present a covariance analysis that reveals correlations in structural dynamics (variance) between the interior of the HK97 procapsid with the protease and regions of the exterior (not seen in the absence of the protease). The latter analysis corresponds well with hydrogen deuterium exchange studies previously published that reveal the same correlation. Copyright © 2018 Elsevier Inc. All rights reserved.
Multiple Imputation of a Randomly Censored Covariate Improves Logistic Regression Analysis.
Atem, Folefac D; Qian, Jing; Maye, Jacqueline E; Johnson, Keith A; Betensky, Rebecca A
2016-01-01
Randomly censored covariates arise frequently in epidemiologic studies. The most commonly used methods, including complete case and single imputation or substitution, suffer from inefficiency and bias. They make strong parametric assumptions or they consider limit of detection censoring only. We employ multiple imputation, in conjunction with semi-parametric modeling of the censored covariate, to overcome these shortcomings and to facilitate robust estimation. We develop a multiple imputation approach for randomly censored covariates within the framework of a logistic regression model. We use the non-parametric estimate of the covariate distribution or the semiparametric Cox model estimate in the presence of additional covariates in the model. We evaluate this procedure in simulations, and compare its operating characteristics to those from the complete case analysis and a survival regression approach. We apply the procedures to an Alzheimer's study of the association between amyloid positivity and maternal age of onset of dementia. Multiple imputation achieves lower standard errors and higher power than the complete case approach under heavy and moderate censoring and is comparable under light censoring. The survival regression approach achieves the highest power among all procedures, but does not produce interpretable estimates of association. Multiple imputation offers a favorable alternative to complete case analysis and ad hoc substitution methods in the presence of randomly censored covariates within the framework of logistic regression.
Yap, John Stephen; Fan, Jianqing; Wu, Rongling
2009-12-01
Estimation of the covariance structure of longitudinal processes is a fundamental prerequisite for the practical deployment of functional mapping designed to study the genetic regulation and network of quantitative variation in dynamic complex traits. We present a nonparametric approach for estimating the covariance structure of a quantitative trait measured repeatedly at a series of time points. Specifically, we adopt Huang et al.'s (2006, Biometrika 93, 85-98) approach of invoking the modified Cholesky decomposition and converting the problem into modeling a sequence of regressions of responses. A regularized covariance estimator is obtained using a normal penalized likelihood with an L(2) penalty. This approach, embedded within a mixture likelihood framework, leads to enhanced accuracy, precision, and flexibility of functional mapping while preserving its biological relevance. Simulation studies are performed to reveal the statistical properties and advantages of the proposed method. A real example from a mouse genome project is analyzed to illustrate the utilization of the methodology. The new method will provide a useful tool for genome-wide scanning for the existence and distribution of quantitative trait loci underlying a dynamic trait important to agriculture, biology, and health sciences.
A Matérn model of the spatial covariance structure of point rain rates
Sun, Ying
2014-07-15
It is challenging to model a precipitation field due to its intermittent and highly scale-dependent nature. Many models of point rain rates or areal rainfall observations have been proposed and studied for different time scales. Among them, the spectral model based on a stochastic dynamical equation for the instantaneous point rain rate field is attractive, since it naturally leads to a consistent space–time model. In this paper, we note that the spatial covariance structure of the spectral model is equivalent to the well-known Matérn covariance model. Using high-quality rain gauge data, we estimate the parameters of the Matérn model for different time scales and demonstrate that the Matérn model is superior to an exponential model, particularly at short time scales.
A Matérn model of the spatial covariance structure of point rain rates
Sun, Ying; Bowman, Kenneth P.; Genton, Marc G.; Tokay, Ali
2014-01-01
It is challenging to model a precipitation field due to its intermittent and highly scale-dependent nature. Many models of point rain rates or areal rainfall observations have been proposed and studied for different time scales. Among them, the spectral model based on a stochastic dynamical equation for the instantaneous point rain rate field is attractive, since it naturally leads to a consistent space–time model. In this paper, we note that the spatial covariance structure of the spectral model is equivalent to the well-known Matérn covariance model. Using high-quality rain gauge data, we estimate the parameters of the Matérn model for different time scales and demonstrate that the Matérn model is superior to an exponential model, particularly at short time scales.
Williams, A.G.
1998-01-01
There is a need for covariant solutions of bound state equations in order to construct realistic QCD based models of mesons and baryons. Furthermore, we ideally need to know the structure of these bound states in all kinematical regimes, which makes a direct solution in Minkowski space (without any 3-dimensional reductions) desirable. The Bethe-Salpeter equation (BSE) for bound states in scalar theories is reformulated and solved for arbitrary scattering kernels in terms of a generalized spectral representation directly in Minkowski space. This differs from the conventional Euclidean approach, where the BSE can only be solved in ladder approximation after a Wick rotation. An application of covariant Bethe-Salpeter solutions to a quark-diquark model of the nucleon is also briefly discussed. (orig.)
Covariance structure in the skull of Catarrhini: a case of pattern stasis and magnitude evolution.
de Oliveira, Felipe Bandoni; Porto, Arthur; Marroig, Gabriel
2009-04-01
The study of the genetic variance/covariance matrix (G-matrix) is a recent and fruitful approach in evolutionary biology, providing a window of investigating for the evolution of complex characters. Although G-matrix studies were originally conducted for microevolutionary timescales, they could be extrapolated to macroevolution as long as the G-matrix remains relatively constant, or proportional, along the period of interest. A promising approach to investigating the constancy of G-matrices is to compare their phenotypic counterparts (P-matrices) in a large group of related species; if significant similarity is found among several taxa, it is very likely that the underlying G-matrices are also equivalent. Here we study the similarity of covariance and correlation structure in a broad sample of Old World monkeys and apes (Catarrhini). We made phylogenetically structured comparisons of correlation and covariance matrices derived from 39 skull traits, ranging from between species to the superfamily level. We also compared the overall magnitude of integration between skull traits (r2) for all Catarrhini genera. Our results show that P-matrices were not strictly constant among catarrhines, but the amount of divergence observed among taxa was generally low. There was significant and positive correlation between the amount of divergence in correlation and covariance patterns among the 30 genera and their phylogenetic distances derived from a recently proposed phylogenetic hypothesis. Our data demonstrate that the P-matrices remained relatively similar along the evolutionary history of catarrhines, and comparisons with the G-matrix available for a New World monkey genus (Saguinus) suggests that the same holds for all anthropoids. The magnitude of integration, in contrast, varied considerably among genera, indicating that evolution of the magnitude, rather than the pattern of inter-trait correlations, might have played an important role in the diversification of the
Richard Ménard
2018-02-01
Full Text Available We present a general theory of estimation of analysis error covariances based on cross-validation as well as a geometric interpretation of the method. In particular, we use the variance of passive observation-minus-analysis residuals and show that the true analysis error variance can be estimated, without relying on the optimality assumption. This approach is used to obtain near optimal analyses that are then used to evaluate the air quality analysis error using several different methods at active and passive observation sites. We compare the estimates according to the method of Hollingsworth-Lönnberg, Desroziers et al., a new diagnostic we developed, and the perceived analysis error computed from the analysis scheme, to conclude that, as long as the analysis is near optimal, all estimates agree within a certain error margin.
Lefort-Besnard, Jérémy; Bassett, Danielle S; Smallwood, Jonathan; Margulies, Daniel S; Derntl, Birgit; Gruber, Oliver; Aleman, Andre; Jardri, Renaud; Varoquaux, Gaël; Thirion, Bertrand; Eickhoff, Simon B; Bzdok, Danilo
2018-02-01
Schizophrenia is a devastating mental disease with an apparent disruption in the highly associative default mode network (DMN). Interplay between this canonical network and others probably contributes to goal-directed behavior so its disturbance is a candidate neural fingerprint underlying schizophrenia psychopathology. Previous research has reported both hyperconnectivity and hypoconnectivity within the DMN, and both increased and decreased DMN coupling with the multimodal saliency network (SN) and dorsal attention network (DAN). This study systematically revisited network disruption in patients with schizophrenia using data-derived network atlases and multivariate pattern-learning algorithms in a multisite dataset (n = 325). Resting-state fluctuations in unconstrained brain states were used to estimate functional connectivity, and local volume differences between individuals were used to estimate structural co-occurrence within and between the DMN, SN, and DAN. In brain structure and function, sparse inverse covariance estimates of network coupling were used to characterize healthy participants and patients with schizophrenia, and to identify statistically significant group differences. Evidence did not confirm that the backbone of the DMN was the primary driver of brain dysfunction in schizophrenia. Instead, functional and structural aberrations were frequently located outside of the DMN core, such as in the anterior temporoparietal junction and precuneus. Additionally, functional covariation analyses highlighted dysfunctional DMN-DAN coupling, while structural covariation results highlighted aberrant DMN-SN coupling. Our findings reframe the role of the DMN core and its relation to canonical networks in schizophrenia. We thus underline the importance of large-scale neural interactions as effective biomarkers and indicators of how to tailor psychiatric care to single patients. © 2017 Wiley Periodicals, Inc.
Covariance Based Pre-Filters and Screening Criteria for Conjunction Analysis
George, E., Chan, K.
2012-09-01
Several relationships are developed relating object size, initial covariance and range at closest approach to probability of collision. These relationships address the following questions: - Given the objects' initial covariance and combined hard body size, what is the maximum possible value of the probability of collision (Pc)? - Given the objects' initial covariance, what is the maximum combined hard body radius for which the probability of collision does not exceed the tolerance limit? - Given the objects' initial covariance and the combined hard body radius, what is the minimum miss distance for which the probability of collision does not exceed the tolerance limit? - Given the objects' initial covariance and the miss distance, what is the maximum combined hard body radius for which the probability of collision does not exceed the tolerance limit? The first relationship above allows the elimination of object pairs from conjunction analysis (CA) on the basis of the initial covariance and hard-body sizes of the objects. The application of this pre-filter to present day catalogs with estimated covariance results in the elimination of approximately 35% of object pairs as unable to ever conjunct with a probability of collision exceeding 1x10-6. Because Pc is directly proportional to object size and inversely proportional to covariance size, this pre-filter will have a significantly larger impact on future catalogs, which are expected to contain a much larger fraction of small debris tracked only by a limited subset of available sensors. This relationship also provides a mathematically rigorous basis for eliminating objects from analysis entirely based on element set age or quality - a practice commonly done by rough rules of thumb today. Further, these relations can be used to determine the required geometric screening radius for all objects. This analysis reveals the screening volumes for small objects are much larger than needed, while the screening volumes for
Survival analysis with functional covariates for partial follow-up studies.
Fang, Hong-Bin; Wu, Tong Tong; Rapoport, Aaron P; Tan, Ming
2016-12-01
Predictive or prognostic analysis plays an increasingly important role in the era of personalized medicine to identify subsets of patients whom the treatment may benefit the most. Although various time-dependent covariate models are available, such models require that covariates be followed in the whole follow-up period. This article studies a new class of functional survival models where the covariates are only monitored in a time interval that is shorter than the whole follow-up period. This paper is motivated by the analysis of a longitudinal study on advanced myeloma patients who received stem cell transplants and T cell infusions after the transplants. The absolute lymphocyte cell counts were collected serially during hospitalization. Those patients are still followed up if they are alive after hospitalization, while their absolute lymphocyte cell counts cannot be measured after that. Another complication is that absolute lymphocyte cell counts are sparsely and irregularly measured. The conventional method using Cox model with time-varying covariates is not applicable because of the different lengths of observation periods. Analysis based on each single observation obviously underutilizes available information and, more seriously, may yield misleading results. This so-called partial follow-up study design represents increasingly common predictive modeling problem where we have serial multiple biomarkers up to a certain time point, which is shorter than the total length of follow-up. We therefore propose a solution to the partial follow-up design. The new method combines functional principal components analysis and survival analysis with selection of those functional covariates. It also has the advantage of handling sparse and irregularly measured longitudinal observations of covariates and measurement errors. Our analysis based on functional principal components reveals that it is the patterns of the trajectories of absolute lymphocyte cell counts, instead of
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.
Chan, David
2000-01-01
Demonstrates how the mean and covariance structure analysis model of D. Sorbom (1974) can be used to detect uniform and nonuniform differential item functioning (DIF) on polytomous ordered response items assumed to approximate a continuous scale. Uses results from 773 civil service employees administered the Kirton Adaption-Innovation Inventory…
Nguyen, Tuong-Vi; Wu, Mia; Lew, Jimin; Albaugh, Matthew D; Botteron, Kelly N; Hudziak, James J; Fonov, Vladimir S; Collins, D Louis; Campbell, Benjamin C; Booij, Linda; Herba, Catherine; Monnier, Patricia; Ducharme, Simon; McCracken, James T
2017-12-01
Existing studies suggest that dehydroepiandrosterone (DHEA) may be important for human brain development and cognition. For example, molecular studies have hinted at the critical role of DHEA in enhancing brain plasticity. Studies of human brain development also support the notion that DHEA is involved in preserving cortical plasticity. Further, some, though not all, studies show that DHEA administration may lead to improvements in working memory in adults. Yet these findings remain limited by an incomplete understanding of the specific neuroanatomical mechanisms through which DHEA may impact the CNS during development. Here we examined associations between DHEA, cortico-hippocampal structural covariance, and working memory (216 participants [female=123], age range 6-22 years old, mean age: 13.6 +/-3.6 years, each followed for a maximum of 3 visits over the course of 4 years). In addition to administering performance-based, spatial working memory tests to these children, we also collected ecological, parent ratings of working memory in everyday situations. We found that increasingly higher DHEA levels were associated with a shift toward positive insular-hippocampal and occipito-hippocampal structural covariance. In turn, DHEA-related insular-hippocampal covariance was associated with lower spatial working memory but higher overall working memory as measured by the ecological parent ratings. Taken together with previous research, these results support the hypothesis that DHEA may optimize cortical functions related to general attentional and working memory processes, but impair the development of bottom-up, hippocampal-to-cortical connections, resulting in impaired encoding of spatial cues. Copyright © 2017 Elsevier Ltd. All rights reserved.
Lin Jiang
2018-02-01
Full Text Available Benign childhood epilepsy with centrotemporal spikes (BECTS is associated with cognitive and language problems. According to recent studies, disruptions in brain structure and function in children with BECTS are beyond a Rolandic focus, suggesting atypical cortical development. However, previous studies utilizing surface-based metrics (e.g., cortical gyrification and their structural covariance networks at high resolution in children with BECTS are limited. Twenty-six children with BECTS (15 males/11 females; 10.35 ± 2.91 years and 26 demographically matched controls (15 males/11 females; 11.35 ± 2.51 years were included in this study and subjected to high-resolution structural brain MRI scans. The gyrification index was calculated, and structural brain networks were reconstructed based on the covariance of the cortical folding. In the BECTS group, significantly increased gyrification was observed in the bilateral Sylvain fissures and the left pars triangularis, temporal, rostral middle frontal, lateral orbitofrontal, and supramarginal areas (cluster-corrected p < 0.05. Global brain network measures were not significantly different between the groups; however, the nodal alterations were most pronounced in the insular, frontal, temporal, and occipital lobes (FDR corrected, p < 0.05. In children with BECTS, brain hubs increased in number and tended to shift to sensorimotor and temporal areas. Furthermore, we observed significantly positive relationships between the gyrification index and age (vertex p < 0.001, cluster-level correction as well as duration of epilepsy (vertex p < 0.001, cluster-level correction. Our results suggest that BECTS may be a condition that features abnormal over-folding of the Sylvian fissures and uncoordinated development of structural wiring, disrupted nodal profiles of centrality, and shifted hub distribution, which potentially represents a neuroanatomical hallmark of BECTS in the
Vogel, Curtis R; Tyler, Glenn A; Wittich, Donald J
2014-07-01
We introduce a framework for modeling, analysis, and simulation of aero-optics wavefront aberrations that is based on spatial-temporal covariance matrices extracted from wavefront sensor measurements. Within this framework, we present a quasi-homogeneous structure function to analyze nonhomogeneous, mildly anisotropic spatial random processes, and we use this structure function to show that phase aberrations arising in aero-optics are, for an important range of operating parameters, locally Kolmogorov. This strongly suggests that the d5/3 power law for adaptive optics (AO) deformable mirror fitting error, where d denotes actuator separation, holds for certain important aero-optics scenarios. This framework also allows us to compute bounds on AO servo lag error and predictive control error. In addition, it provides us with the means to accurately simulate AO systems for the mitigation of aero-effects, and it may provide insight into underlying physical processes associated with turbulent flow. The techniques introduced here are demonstrated using data obtained from the Airborne Aero-Optics Laboratory.
Covariant boost and structure functions of baryons in Gross-Neveu models
Brendel, Wieland; Thies, Michael
2010-01-01
Baryons in the large N limit of two-dimensional Gross-Neveu models are reconsidered. The time-dependent Dirac-Hartree-Fock approach is used to boost a baryon to any inertial frame and shown to yield the covariant energy-momentum relation. Momentum distributions are computed exactly in arbitrary frames and used to interpolate between the rest frame and the infinite momentum frame, where they are related to structure functions. Effects from the Dirac sea depend sensitively on the occupation fraction of the valence level and the bare fermion mass and do not vanish at infinite momentum. In the case of the kink baryon, they even lead to divergent quark and antiquark structure functions at x=0.
Holst, René; Jørgensen, Bent
2015-01-01
The paper proposes a versatile class of multiplicative generalized linear longitudinal mixed models (GLLMM) with additive dispersion components, based on explicit modelling of the covariance structure. The class incorporates a longitudinal structure into the random effects models and retains...... a marginal as well as a conditional interpretation. The estimation procedure is based on a computationally efficient quasi-score method for the regression parameters combined with a REML-like bias-corrected Pearson estimating function for the dispersion and correlation parameters. This avoids...... the multidimensional integral of the conventional GLMM likelihood and allows an extension of the robust empirical sandwich estimator for use with both association and regression parameters. The method is applied to a set of otholit data, used for age determination of fish....
Hislop, P.D.
1988-01-01
The Tomita modular operators and the duality property for the local von Neumann algebras in quantum field models describing free massless particles with arbitrary helicity are studied. It is proved that the representation of the Poincare group in each model extends to a unitary representation of SU(2, 2), a covering group of the conformal group. An irreducible set of ''standard'' linear fields is shown to be covariant with respect to this representation. The von Neumann algebras associated with wedge, double-cone, and lightcone regions generated by these fields are proved to be unitarily equivalent. The modular operators for these algebras are obtained in explicit form using the conformal covariance and the results of Bisognano and Wichmann on the modular structure of the wedge algebras. The modular automorphism groups are implemented by one-parameter groups of conformal transformations. The modular conjugation operators are used to prove the duality property for the double-cone algebras and the timelike duality property for the lightcone algebras. copyright 1988 Academic Press, Inc
Enhanced brainstem and cortical evoked response amplitudes: single-trial covariance analysis.
Galbraith, G C
2001-06-01
The purpose of the present study was to develop analytic procedures that improve the definition of sensory evoked response components. Such procedures could benefit all recordings but would especially benefit difficult recordings where many trials are contaminated by muscle and movement artifacts. First, cross-correlation and latency adjustment analyses were applied to the human brainstem frequency-following response and cortical auditory evoked response recorded on the same trials. Lagged cross-correlation functions were computed, for each of 17 subjects, between single-trial data and templates consisting of the sinusoid stimulus waveform for the brainstem response and the subject's own smoothed averaged evoked response P2 component for the cortical response. Trials were considered in the analysis only if the maximum correlation-squared (r2) exceeded .5 (negatively correlated trials were thus included). Identical correlation coefficients may be based on signals with quite different amplitudes, but it is possible to assess amplitude by the nonnormalized covariance function. Next, an algorithm is applied in which each trial with negative covariance is matched to a trial with similar, but positive, covariance and these matched-trial pairs are deleted. When an evoked response signal is present in the data, the majority of trials positively correlate with the template. Thus, a residual of positively correlated trials remains after matched covariance trials are deleted. When these residual trials are averaged, the resulting brainstem and cortical responses show greatly enhanced amplitudes. This result supports the utility of this analysis technique in clarifying and assessing evoked response signals.
Bucco, D.; Weiss, M.
2007-01-01
The COVariance and ADjoint Analysis Tool (COVAD) is a specially designed software tool, written for the Matlab/Simulink environment, which allows the user the capability to carry out system analysis and simulation using the adjoint, covariance or Monte Carlo methods. This paper describes phase one
Stening, Eva; Persson, Jonas; Eriksson, Elias; Wahlund, Lars-Olof; Zetterberg, Henrik; Söderlund, Hedvig
2017-05-30
Apolipoprotein E (APOE) ε4 has been associated with smaller hippocampal volumes in healthy aging, while findings in young adults are inconclusive. Previous studies have mostly used univariate methods, and without considering potential anterior/posterior differences. Here, we used a multivariate method, partial least squares, and assessed whole-brain structural covariance of the anterior (aHC) and posterior (pHC) hippocampus in young adults (n=97) as a function of APOE ε4 status and sex. Two significant patterns emerged: (1) specific structural covariance of the aHC with frontal regions, temporal and occipital areas in APOE ε4 women, whereas the volume of both the aHC and pHC in all other groups co-varied with frontal, parietal and cerebellar areas; and (2) opposite structural covariance of the pHC in ε4 carriers compared to the aHC in non-carriers, with the pHC of ε4 carriers covarying with parietal and frontal areas, and the aHC of ε4 non-carriers covarying with motor areas and the middle frontal gyrus. APOE ε4 has in young adults been associated with better episodic and spatial memory, functions involving the aHC and pHC, respectively. We found no associations between structural covariance and performance, suggesting that other factors underlie the performance differences seen between carriers and non-carriers. Our findings indicate that APOE ε4 carriers and non-carriers differ in hippocampal organization and that there are differences as a function of sex and hippocampal segment. They stress the need to consider the hippocampus as a heterogeneous structure, and highlight the benefits of multivariate methods in assessing group differences in the brain. Copyright © 2017 Elsevier B.V. All rights reserved.
Asanov, G.S.
1979-01-01
It is shown the description of gravitational field in the riemannian space-time by means of the absolute parallelism structure makes it possible to formulate an integrable covariant law of energy-momentum conservation for gravitational field, by imposing on the energy-momentum tensor the condition of vanishing of the covariant divergence (in the sense of the absolute parallelism). As a result of taking into account covariant constraints for the tetrads of the absolute parallelism, the Lagrangian density turns out to be not geometrised anymore and leads to the unambiguous conservation law of the type mentioned in the N-body problem. Covariant field equations imply the existence of the special euclidean coordinates outside of static neighbourhoods of gravitationing bodies. In these coordinates determined by the tetrads of the absolute parallelism, the linear approximation is not connected with any noncovariant assumptions
Runcie, Daniel E; Mukherjee, Sayan
2013-07-01
Quantitative genetic studies that model complex, multivariate phenotypes are important for both evolutionary prediction and artificial selection. For example, changes in gene expression can provide insight into developmental and physiological mechanisms that link genotype and phenotype. However, classical analytical techniques are poorly suited to quantitative genetic studies of gene expression where the number of traits assayed per individual can reach many thousand. Here, we derive a Bayesian genetic sparse factor model for estimating the genetic covariance matrix (G-matrix) of high-dimensional traits, such as gene expression, in a mixed-effects model. The key idea of our model is that we need consider only G-matrices that are biologically plausible. An organism's entire phenotype is the result of processes that are modular and have limited complexity. This implies that the G-matrix will be highly structured. In particular, we assume that a limited number of intermediate traits (or factors, e.g., variations in development or physiology) control the variation in the high-dimensional phenotype, and that each of these intermediate traits is sparse - affecting only a few observed traits. The advantages of this approach are twofold. First, sparse factors are interpretable and provide biological insight into mechanisms underlying the genetic architecture. Second, enforcing sparsity helps prevent sampling errors from swamping out the true signal in high-dimensional data. We demonstrate the advantages of our model on simulated data and in an analysis of a published Drosophila melanogaster gene expression data set.
Shen, Chung-Wei; Chen, Yi-Hau
2015-10-01
Missing observations and covariate measurement error commonly arise in longitudinal data. However, existing methods for model selection in marginal regression analysis of longitudinal data fail to address the potential bias resulting from these issues. To tackle this problem, we propose a new model selection criterion, the Generalized Longitudinal Information Criterion, which is based on an approximately unbiased estimator for the expected quadratic error of a considered marginal model accounting for both data missingness and covariate measurement error. The simulation results reveal that the proposed method performs quite well in the presence of missing data and covariate measurement error. On the contrary, the naive procedures without taking care of such complexity in data may perform quite poorly. The proposed method is applied to data from the Taiwan Longitudinal Study on Aging to assess the relationship of depression with health and social status in the elderly, accommodating measurement error in the covariate as well as missing observations. © The Author 2015. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
Funatogawa, Takashi; Funatogawa, Ikuko; Shyr, Yu
2011-05-01
When primary endpoints of randomized trials are continuous variables, the analysis of covariance (ANCOVA) with pre-treatment measurements as a covariate is often used to compare two treatment groups. In the ANCOVA, equal slopes (coefficients of pre-treatment measurements) and equal residual variances are commonly assumed. However, random allocation guarantees only equal variances of pre-treatment measurements. Unequal covariances and variances of post-treatment measurements indicate unequal slopes and, usually, unequal residual variances. For non-normal data with unequal covariances and variances of post-treatment measurements, it is known that the ANCOVA with equal slopes and equal variances using an ordinary least-squares method provides an asymptotically normal estimator for the treatment effect. However, the asymptotic variance of the estimator differs from the variance estimated from a standard formula, and its property is unclear. Furthermore, the asymptotic properties of the ANCOVA with equal slopes and unequal variances using a generalized least-squares method are unclear. In this paper, we consider non-normal data with unequal covariances and variances of post-treatment measurements, and examine the asymptotic properties of the ANCOVA with equal slopes using the variance estimated from a standard formula. Analytically, we show that the actual type I error rate, thus the coverage, of the ANCOVA with equal variances is asymptotically at a nominal level under equal sample sizes. That of the ANCOVA with unequal variances using a generalized least-squares method is asymptotically at a nominal level, even under unequal sample sizes. In conclusion, the ANCOVA with equal slopes can be asymptotically justified under random allocation. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Negash, A. W.; Mwambi, H.; Zewotir, T.; Eweke, G.
2014-06-01
The most common procedure for analyzing multi-environmental trials is based on the assumption that the residual error variance is homogenous across all locations considered. However, this may often be unrealistic, and therefore limit the accuracy of variety evaluation or the reliability of variety recommendations. The objectives of this study were to show the advantages of mixed models with spatial variance-covariance structures, and direct implications of model choice on the inference of varietal performance, ranking and testing based on two multi-environmental data sets from realistic national trials. A model comparison with a {chi}{sup 2}-test for the trials in the two data sets (wheat data set BW00RVTI and barley data set BW01RVII) suggested that selected spatial variance-covariance structures fitted the data significantly better than the ANOVA model. The forms of optimally-fitted spatial variance-covariance, ranking and consistency ratio test were not the same from one trial (location) to the other. Linear mixed models with single stage analysis including spatial variance-covariance structure with a group factor of location on the random model also improved the real estimation of genotype effect and their ranking. The model also improved varietal performance estimation because of its capacity to handle additional sources of variation, location and genotype by location (environment) interaction variation and accommodating of local stationary trend. (Author)
Chang, Yu-Tzu; Hsu, Shih-Wei; Tsai, Shih-Jen; Chang, Ya-Ting; Huang, Chi-Wei; Liu, Mu-En; Chen, Nai-Ching; Chang, Wen-Neng; Hsu, Jung-Lung; Lee, Chen-Chang; Chang, Chiung-Chih
2017-06-01
The 677 C to T transition in the MTHFR gene is a genetic determinant for hyperhomocysteinemia. We investigated whether this polymorphism modulates gray matter (GM) structural covariance networks independently of white-matter integrity in patients with Alzheimer's disease (AD). GM structural covariance networks were constructed by 3D T1-magnetic resonance imaging and seed-based analysis. The patients were divided into two genotype groups: C homozygotes (n = 73) and T carriers (n = 62). Using diffusion tensor imaging and white-matter parcellation, 11 fiber bundle integrities were compared between the two genotype groups. Cognitive test scores were the major outcome factors. The T carriers had higher homocysteine levels, lower posterior cingulate cortex GM volume, and more clusters in the dorsal medial lobe subsystem showing stronger covariance strength. Both posterior cingulate cortex seed and interconnected peak cluster volumes predicted cognitive test scores, especially in the T carriers. There were no between-group differences in fiber tract diffusion parameters. The MTHFR 677T polymorphism modulates posterior cingulate cortex-anchored structural covariance strength independently of white matter integrities. Hum Brain Mapp 38:3039-3051, 2017. © 2017 The Authors Human Brain Mapping Published Wiley by Periodicals, Inc. © 2017 The Authors Human Brain Mapping Published Wiley by Periodicals, Inc.
The application of sparse estimation of covariance matrix to quadratic discriminant analysis.
Sun, Jiehuan; Zhao, Hongyu
2015-02-18
Although Linear Discriminant Analysis (LDA) is commonly used for classification, it may not be directly applied in genomics studies due to the large p, small n problem in these studies. Different versions of sparse LDA have been proposed to address this significant challenge. One implicit assumption of various LDA-based methods is that the covariance matrices are the same across different classes. However, rewiring of genetic networks (therefore different covariance matrices) across different diseases has been observed in many genomics studies, which suggests that LDA and its variations may be suboptimal for disease classifications. However, it is not clear whether considering differing genetic networks across diseases can improve classification in genomics studies. We propose a sparse version of Quadratic Discriminant Analysis (SQDA) to explicitly consider the differences of the genetic networks across diseases. Both simulation and real data analysis are performed to compare the performance of SQDA with six commonly used classification methods. SQDA provides more accurate classification results than other methods for both simulated and real data. Our method should prove useful for classification in genomics studies and other research settings, where covariances differ among classes.
Chang, Jinyuan; Zhou, Wen; Zhou, Wen-Xin; Wang, Lan
2017-03-01
Comparing large covariance matrices has important applications in modern genomics, where scientists are often interested in understanding whether relationships (e.g., dependencies or co-regulations) among a large number of genes vary between different biological states. We propose a computationally fast procedure for testing the equality of two large covariance matrices when the dimensions of the covariance matrices are much larger than the sample sizes. A distinguishing feature of the new procedure is that it imposes no structural assumptions on the unknown covariance matrices. Hence, the test is robust with respect to various complex dependence structures that frequently arise in genomics. We prove that the proposed procedure is asymptotically valid under weak moment conditions. As an interesting application, we derive a new gene clustering algorithm which shares the same nice property of avoiding restrictive structural assumptions for high-dimensional genomics data. Using an asthma gene expression dataset, we illustrate how the new test helps compare the covariance matrices of the genes across different gene sets/pathways between the disease group and the control group, and how the gene clustering algorithm provides new insights on the way gene clustering patterns differ between the two groups. The proposed methods have been implemented in an R-package HDtest and are available on CRAN. © 2016, The International Biometric Society.
Xu, Xu Steven; Yuan, Min; Yang, Haitao; Feng, Yan; Xu, Jinfeng; Pinheiro, Jose
2017-01-01
Covariate analysis based on population pharmacokinetics (PPK) is used to identify clinically relevant factors. The likelihood ratio test (LRT) based on nonlinear mixed effect model fits is currently recommended for covariate identification, whereas individual empirical Bayesian estimates (EBEs) are considered unreliable due to the presence of shrinkage. The objectives of this research were to investigate the type I error for LRT and EBE approaches, to confirm the similarity of power between the LRT and EBE approaches from a previous report and to explore the influence of shrinkage on LRT and EBE inferences. Using an oral one-compartment PK model with a single covariate impacting on clearance, we conducted a wide range of simulations according to a two-way factorial design. The results revealed that the EBE-based regression not only provided almost identical power for detecting a covariate effect, but also controlled the false positive rate better than the LRT approach. Shrinkage of EBEs is likely not the root cause for decrease in power or inflated false positive rate although the size of the covariate effect tends to be underestimated at high shrinkage. In summary, contrary to the current recommendations, EBEs may be a better choice for statistical tests in PPK covariate analysis compared to LRT. We proposed a three-step covariate modeling approach for population PK analysis to utilize the advantages of EBEs while overcoming their shortcomings, which allows not only markedly reducing the run time for population PK analysis, but also providing more accurate covariate tests.
Ros, B.P.; Bijma, F.; de Munck, J.C.; de Gunst, M.C.M.
2016-01-01
This paper deals with multivariate Gaussian models for which the covariance matrix is a Kronecker product of two matrices. We consider maximum likelihood estimation of the model parameters, in particular of the covariance matrix. There is no explicit expression for the maximum likelihood estimator
Sarah Donegan
Full Text Available Treatment by covariate interactions can be explored in reviews using interaction analyses (e.g., subgroup analysis. Such analyses can provide information on how the covariate modifies the treatment effect and is an important methodological approach for personalising medicine. Guidance exists regarding how to apply such analyses but little is known about whether authors follow the guidance.Using published recommendations, we developed criteria to assess how well interaction analyses were designed, applied, interpreted, and reported. The Cochrane Database of Systematic Reviews was searched (8th August 2013. We applied the criteria to the most recently published review, with an accessible protocol, for each Cochrane Review Group. We excluded review updates, diagnostic test accuracy reviews, withdrawn reviews, and overviews of reviews. Data were summarised regarding reviews, covariates, and analyses.Each of the 52 included reviews planned or did interaction analyses; 51 reviews (98% planned analyses and 33 reviews (63% applied analyses. The type of analysis planned and the type subsequently applied (e.g., sensitivity or subgroup analysis was discrepant in 24 reviews (46%. No review reported how or why each covariate had been chosen; 22 reviews (42% did state each covariate a priori in the protocol but no review identified each post-hoc covariate as such. Eleven reviews (21% mentioned five covariates or less. One review reported planning to use a method to detect interactions (i.e., interaction test for each covariate; another review reported applying the method for each covariate. Regarding interpretation, only one review reported whether an interaction was detected for each covariate and no review discussed the importance, or plausibility, of the results, or the possibility of confounding for each covariate.Interaction analyses in Cochrane Reviews can be substantially improved. The proposed criteria can be used to help guide the reporting and
Huang, Chi-Wei; Hsu, Shih-Wei; Tsai, Shih-Jen; Chen, Nai-Ching; Liu, Mu-En; Lee, Chen-Chang; Huang, Shu-Hua; Chang, Weng-Neng; Chang, Ya-Ting; Tsai, Wan-Chen; Chang, Chiung-Chih
2017-01-18
Inflammatory processes play a pivotal role in the degenerative process of Alzheimer's disease. In humans, a biallelic (C/T) polymorphism in the promoter region (position-511) (rs16944) of the interleukin-1 beta gene has been significantly associated with differences in the secretory capacity of interleukin-1 beta. In this study, we investigated whether this functional polymorphism mediates the brain networks in patients with Alzheimer's disease. We enrolled a total of 135 patients with Alzheimer's disease (65 males, 70 females), and investigated their gray matter structural covariance networks using 3D T1 magnetic resonance imaging and their white matter macro-structural integrities using fractional anisotropy. The patients were classified into two genotype groups: C-carriers (n = 108) and TT-carriers (n = 27), and the structural covariance networks were constructed using seed-based analysis focusing on the default mode network medial temporal or dorsal medial subsystem, salience network and executive control network. Neurobehavioral scores were used as the major outcome factors for clinical correlations. There were no differences between the two genotype groups in the cognitive test scores, seed, or peak cluster volumes and white matter fractional anisotropy. The covariance strength showing C-carriers > TT-carriers was the entorhinal-cingulum axis. There were two peak clusters (Brodmann 6 and 10) in the salience network and four peak clusters (superior prefrontal, precentral, fusiform, and temporal) in the executive control network that showed C-carriers covariance strength. The salience network and executive control network peak clusters in the TT group and the default mode network peak clusters in the C-carriers strongly predicted the cognitive test scores. Interleukin-1 beta C-511 T polymorphism modulates the structural covariance strength on the anterior brain network and entorhinal-interconnected network which were independent of the white
Boulton, Stephen; Selvaratnam, Rajeevan; Ahmed, Rashik; Melacini, Giuseppe
2018-01-01
Mapping allosteric sites is emerging as one of the central challenges in physiology, pathology, and pharmacology. Nuclear Magnetic Resonance (NMR) spectroscopy is ideally suited to map allosteric sites, given its ability to sense at atomic resolution the dynamics underlying allostery. Here, we focus specifically on the NMR CHEmical Shift Covariance Analysis (CHESCA), in which allosteric systems are interrogated through a targeted library of perturbations (e.g., mutations and/or analogs of the allosteric effector ligand). The atomic resolution readout for the response to such perturbation library is provided by NMR chemical shifts. These are then subject to statistical correlation and covariance analyses resulting in clusters of allosterically coupled residues that exhibit concerted responses to the common set of perturbations. This chapter provides a description of how each step in the CHESCA is implemented, starting from the selection of the perturbation library and ending with an overview of different clustering options.
Madrigal, Pedro
2017-03-01
Computational evaluation of variability across DNA or RNA sequencing datasets is a crucial step in genomic science, as it allows both to evaluate reproducibility of biological or technical replicates, and to compare different datasets to identify their potential correlations. Here we present fCCAC, an application of functional canonical correlation analysis to assess covariance of nucleic acid sequencing datasets such as chromatin immunoprecipitation followed by deep sequencing (ChIP-seq). We show how this method differs from other measures of correlation, and exemplify how it can reveal shared covariance between histone modifications and DNA binding proteins, such as the relationship between the H3K4me3 chromatin mark and its epigenetic writers and readers. An R/Bioconductor package is available at http://bioconductor.org/packages/fCCAC/ . pmb59@cam.ac.uk. Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.
Analysis of stock investment selection based on CAPM using covariance and genetic algorithm approach
Sukono; Susanti, D.; Najmia, M.; Lesmana, E.; Napitupulu, H.; Supian, S.; Putra, A. S.
2018-03-01
Investment is one of the economic growth factors of countries, especially in Indonesia. Stocks is a form of investment, which is liquid. In determining the stock investment decisions which need to be considered by investors is to choose stocks that can generate maximum returns with a minimum risk level. Therefore, we need to know how to allocate the capital which may give the optimal benefit. This study discusses the issue of stock investment based on CAPM which is estimated using covariance and Genetic Algorithm approach. It is assumed that the stocks analyzed follow the CAPM model. To do the estimation of beta parameter on CAPM equation is done by two approach, first is to be represented by covariance approach, and second with genetic algorithm optimization. As a numerical illustration, in this paper analyzed ten stocks traded on the capital market in Indonesia. The results of the analysis show that estimation of beta parameters using covariance and genetic algorithm approach, give the same decision, that is, six underpriced stocks with buying decision, and four overpriced stocks with a sales decision. Based on the analysis, it can be concluded that the results can be used as a consideration for investors buying six under-priced stocks, and selling four overpriced stocks.
Structural covariance network centrality in maltreated youth with posttraumatic stress disorder.
Sun, Delin; Peverill, Matthew R; Swanson, Chelsea S; McLaughlin, Katie A; Morey, Rajendra A
2018-03-01
Childhood maltreatment is associated with posttraumatic stress disorder (PTSD) and elevated rates of adolescent and adult psychopathology including major depression, bipolar disorder, substance use disorders, and other medical comorbidities. Gray matter volume changes have been found in maltreated youth with (versus without) PTSD. However, little is known about the alterations of brain structural covariance network topology derived from cortical thickness in maltreated youth with PTSD. High-resolution T1-weighted magnetic resonance imaging scans were from demographically matched maltreated youth with PTSD (N = 24), without PTSD (N = 64), and non-maltreated healthy controls (n = 67). Cortical thickness data from 148 cortical regions was entered into interregional partial correlation analyses across participants. The supra-threshold correlations constituted connections in a structural brain network derived from four types of centrality measures (degree, betweenness, closeness, and eigenvector) estimated network topology and the importance of nodes. Between-group differences were determined by permutation testing. Maltreated youth with PTSD exhibited larger centrality in left anterior cingulate cortex than the other two groups, suggesting cortical network topology specific to maltreated youth with PTSD. Moreover, maltreated youth with versus without PTSD showed smaller centrality in right orbitofrontal cortex, suggesting that this may represent a vulnerability factor to PTSD following maltreatment. Longitudinal follow-up of the present results will help characterize the role that altered centrality plays in vulnerability and resilience to PTSD following childhood maltreatment. Copyright © 2017. Published by Elsevier Ltd.
Molenaar, P.C.M.; Nesselroade, J.R.
1998-01-01
The study of intraindividual variability pervades empirical inquiry in virtually all subdisciplines of psychology. The statistical analysis of multivariate time-series data - a central product of intraindividual investigations - requires special modeling techniques. The dynamic factor model (DFM),
Bun, M.; de Haan, M.
2010-01-01
We analyze the usefulness of the first stage F-statistic for detecting weak instruments in the IV model with a nonscalar error covariance structure. More in particular, we question the validity of the rule of thumb of a first stage F-statistic of 10 or higher for models with correlated errors
Small vessel disease is linked to disrupted structural network covariance in Alzheimer's disease.
Nestor, Sean M; Mišić, Bratislav; Ramirez, Joel; Zhao, Jiali; Graham, Simon J; Verhoeff, Nicolaas P L G; Stuss, Donald T; Masellis, Mario; Black, Sandra E
2017-07-01
Cerebral small vessel disease (SVD) is thought to contribute to Alzheimer's disease (AD) through abnormalities in white matter networks. Gray matter (GM) hub covariance networks share only partial overlap with white matter connectivity, and their relationship with SVD has not been examined in AD. We developed a multivariate analytical pipeline to elucidate the cortical GM thickness systems that covary with major network hubs and assessed whether SVD and neurodegenerative pathologic markers were associated with attenuated covariance network integrity in mild AD and normal elderly control subjects. SVD burden was associated with reduced posterior cingulate corticocortical GM network integrity and subneocorticocortical hub network integrity in AD. These findings provide evidence that SVD is linked to the selective disruption of cortical hub GM networks in AD brains and point to the need to consider GM hub covariance networks when assessing network disruption in mixed disease. Copyright © 2017 the Alzheimer's Association. Published by Elsevier Inc. All rights reserved.
Tsutsumi, Joshi; Minai, Kosuke; Kawai, Makoto; Ogawa, Kazuo; Inoue, Yasunori; Morimoto, Satoshi; Tanaka, Toshikazu; Nagoshi, Tomohisa; Ogawa, Takayuki; Yoshimura, Michihiro
2017-01-01
Obesity is believed to be one of the major risk factors for cardiovascular disease in Western countries. However, the effects of obesity should be continuously examined in the Japanese population because the average bodily habitus differs among countries. In this study, we collectively examined the significance of obesity and obesity-triggered risk factors including the low reactivity of B-type natriuretic peptide (BNP), for ischemic heart disease (IHD) in Japanese patients. The study patients consisted of 1252 subjects (IHD: n = 970; non-IHD: n = 282). Multiple logistic regression analysis revealed that dyslipidemia, hypertension, diabetes, and the low reactivity of BNP were significant risk factors for IHD, but body mass index (BMI) was not. A theoretical path model was proposed by positioning BMI at the top of the hierarchical model. Exploratory factor analysis revealed that BMI did not play a causative role in IHD (P = NS). BMI was causatively linked to other risk factors (Pobesity per se is not a strong risk factor for IHD in Japanese patients. However, several important risk factors triggered by obesity exhibited a causative role for IHD. The low reactivity of BNP is a substantial risk factor for IHD.
Automated vessel segmentation using cross-correlation and pooled covariance matrix analysis.
Du, Jiang; Karimi, Afshin; Wu, Yijing; Korosec, Frank R; Grist, Thomas M; Mistretta, Charles A
2011-04-01
Time-resolved contrast-enhanced magnetic resonance angiography (CE-MRA) provides contrast dynamics in the vasculature and allows vessel segmentation based on temporal correlation analysis. Here we present an automated vessel segmentation algorithm including automated generation of regions of interest (ROIs), cross-correlation and pooled sample covariance matrix analysis. The dynamic images are divided into multiple equal-sized regions. In each region, ROIs for artery, vein and background are generated using an iterative thresholding algorithm based on the contrast arrival time map and contrast enhancement map. Region-specific multi-feature cross-correlation analysis and pooled covariance matrix analysis are performed to calculate the Mahalanobis distances (MDs), which are used to automatically separate arteries from veins. This segmentation algorithm is applied to a dual-phase dynamic imaging acquisition scheme where low-resolution time-resolved images are acquired during the dynamic phase followed by high-frequency data acquisition at the steady-state phase. The segmented low-resolution arterial and venous images are then combined with the high-frequency data in k-space and inverse Fourier transformed to form the final segmented arterial and venous images. Results from volunteer and patient studies demonstrate the advantages of this automated vessel segmentation and dual phase data acquisition technique. Copyright © 2011 Elsevier Inc. All rights reserved.
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.
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.
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
Rodrigues, W. A.; Scanavini, M. E. F.; de Alcantara, L. P.
1990-02-01
In this paper a given spacetime theory T is characterized as the theory of a certain species of structure in the sense of Bourbaki [1]. It is then possible to clarify in a rigorous way the concepts of passive and active covariance of T under the action of the manifold mapping group G M . For each T, we define also an invariance group G I T and, in general, G I T ≠ G M . This group is defined once we realize that, for each τ ∈ ModT, each explicit geometrical object defining the structure can be classified as absolute or dynamical [2]. All spacetime theories possess also implicit geometrical objects that do not appear explicitly in the structure. These implicit objects are not absolute nor dynamical. Among them there are the reference frame fields, i.e., “timelike” vector fields X ∈ TU,U subseteq M M, where M is a manifold which is part of ST, a substructure for each τ ∈ ModT, called spacetime. We give a physically motivated definition of equivalent reference frames and introduce the concept of the equivalence group of a class of reference frames of kind X according to T, G X T. We define that T admits a weak principle of relativity (WPR) only if G X T ≠ identity for some X. If G X T = G I T for some X, we say that T admits a strong principle of relativity (PR). The results of this paper generalize and clarify several results obtained by Anderson [2], Scheibe [3], Hiskes [4], Recami and Rodrigues [5], Friedman [6], Fock [7], and Scanavini [8]. Among the novelties here, there is the realization that the definitions of G I T and G X T can be given only when certain boundary conditions for the equations of motion of T can be physically realizable in the domain U U subseteq M M, where a given reference frame is defined. The existence of physically realizable boundary conditions for each τ ∈ ModT (in ∂ U), in contrast with the mathematically possible boundary condition, is then seen to be essential for the validity of a principle of relativity for T
Nonlinear consider covariance analysis using a sigma-point filter formulation
Lisano, Michael E.
2006-01-01
The research reported here extends the mathematical formulation of nonlinear, sigma-point estimators to enable consider covariance analysis for dynamical systems. This paper presents a novel sigma-point consider filter algorithm, for consider-parameterized nonlinear estimation, following the unscented Kalman filter (UKF) variation on the sigma-point filter formulation, which requires no partial derivatives of dynamics models or measurement models with respect to the parameter list. It is shown that, consistent with the attributes of sigma-point estimators, a consider-parameterized sigma-point estimator can be developed entirely without requiring the derivation of any partial-derivative matrices related to the dynamical system, the measurements, or the considered parameters, which appears to be an advantage over the formulation of a linear-theory sequential consider estimator. It is also demonstrated that a consider covariance analysis performed with this 'partial-derivative-free' formulation yields equivalent results to the linear-theory consider filter, for purely linear problems.
Statistical Analysis of Deflation in Covariance and Resultant Pc Values for AQUA, AURA and TERRA
Hasan, Syed O.
2016-01-01
This presentation will display statistical analysis performed for raw conjunction CDMs received for the EOS Aqua, Aura and Terra satellites within the period of February 2015 through July 2016. The analysis performed indicates a discernable deflation in covariance calculated at the JSpOC after the utilization of the dynamic drag consider parameter was implemented operationally in May 2015. As a result, the overall diminution in the conjunction plane intersection of the primary and secondary objects appears to be leading to reduced probability of collision (Pc) values for these conjunction events. This presentation also displays evidence for this theory with analysis of Pc trending plots using data calculated by the SpaceNav CRMS system.
Covariance analysis of n + 7Li data for ENDF/B-VI
Young, P.G.
1988-01-01
A new covariance analysis of n/plus/ 7 Li experimental data has been completed for Version VI of ENDFB. The analysis basically updates our 1981 work for ENDFB-V.2 to include new data that has become available since that time and to incorporate cross correlations between different experiments. The bulk of the new measured data consists of some 10 new (or newly revised) tritium-production measurements involving about 70 new data points. The new analysis results in only small changes in the previous evaluation of the tritium-production cross section but significantly reduces the magnitudes of uncertainties due to the more extensive and accurate data base that was used
Okaro, Ikenna Anthony; Tao, Longbin
2016-01-01
This paper proposes an enhanced Weibull-Corrosion Covariate model for reliability assessment of a system facing operational stresses. The newly developed model is applied to a Subsea Gas Compression System planned for offshore West Africa to predict its reliability index. System technical failure was modelled by developing a Weibull failure model incorporating a physically tested corrosion profile as stress in order to quantify the survival rate of the system under additional operational covariates including marine pH, temperature and pressure. Using Reliability Block Diagrams and enhanced Fusell-Vesely formulations, the whole system was systematically decomposed to sub-systems to analyse the criticality of each component and optimise them. Human reliability was addressed using an enhanced barrier weighting method. A rapid degradation curve is obtained on a subsea system relative to the base case subjected to a time-dependent corrosion stress factor. It reveals that subsea system components failed faster than their Mean time to failure specifications from Offshore Reliability Database as a result of cumulative marine stresses exertion. The case study demonstrated that the reliability of a subsea system can be systematically optimised by modelling the system under higher technical and organisational stresses, prioritising the critical sub-systems and making befitting provisions for redundancy and tolerances. - Highlights: • Novel Weibull Corrosion-Covariate model for reliability analysis of subsea assets. • Predict the accelerated degradation profile of a subsea gas compression. • An enhanced optimisation method based on Fusell-Vesely decomposition process. • New optimisation approach for smoothening of over- and under-designed components. • Demonstrated a significant improvement in producing more realistic failure rate.
Geerligs, Linda; Cam-Can; Henson, Richard N
2016-07-15
Studies of brain-wide functional connectivity or structural covariance typically use measures like the Pearson correlation coefficient, applied to data that have been averaged across voxels within regions of interest (ROIs). However, averaging across voxels may result in biased connectivity estimates when there is inhomogeneity within those ROIs, e.g., sub-regions that exhibit different patterns of functional connectivity or structural covariance. Here, we propose a new measure based on "distance correlation"; a test of multivariate dependence of high dimensional vectors, which allows for both linear and non-linear dependencies. We used simulations to show how distance correlation out-performs Pearson correlation in the face of inhomogeneous ROIs. To evaluate this new measure on real data, we use resting-state fMRI scans and T1 structural scans from 2 sessions on each of 214 participants from the Cambridge Centre for Ageing & Neuroscience (Cam-CAN) project. Pearson correlation and distance correlation showed similar average connectivity patterns, for both functional connectivity and structural covariance. Nevertheless, distance correlation was shown to be 1) more reliable across sessions, 2) more similar across participants, and 3) more robust to different sets of ROIs. Moreover, we found that the similarity between functional connectivity and structural covariance estimates was higher for distance correlation compared to Pearson correlation. We also explored the relative effects of different preprocessing options and motion artefacts on functional connectivity. Because distance correlation is easy to implement and fast to compute, it is a promising alternative to Pearson correlations for investigating ROI-based brain-wide connectivity patterns, for functional as well as structural data. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.
Bachoc, Francois
2014-01-01
Covariance parameter estimation of Gaussian processes is analyzed in an asymptotic framework. The spatial sampling is a randomly perturbed regular grid and its deviation from the perfect regular grid is controlled by a single scalar regularity parameter. Consistency and asymptotic normality are proved for the Maximum Likelihood and Cross Validation estimators of the covariance parameters. The asymptotic covariance matrices of the covariance parameter estimators are deterministic functions of the regularity parameter. By means of an exhaustive study of the asymptotic covariance matrices, it is shown that the estimation is improved when the regular grid is strongly perturbed. Hence, an asymptotic confirmation is given to the commonly admitted fact that using groups of observation points with small spacing is beneficial to covariance function estimation. Finally, the prediction error, using a consistent estimator of the covariance parameters, is analyzed in detail. (authors)
Kisil, Vladimir V.
2010-01-01
The paper develops theory of covariant transform, which is inspired by the wavelet construction. It was observed that many interesting types of wavelets (or coherent states) arise from group representations which are not square integrable or vacuum vectors which are not admissible. Covariant transform extends an applicability of the popular wavelets construction to classic examples like the Hardy space H_2, Banach spaces, covariant functional calculus and many others. Keywords: Wavelets, cohe...
ERC analysis: web-based inference of gene function via evolutionary rate covariation.
Wolfe, Nicholas W; Clark, Nathan L
2015-12-01
The recent explosion of comparative genomics data presents an unprecedented opportunity to construct gene networks via the evolutionary rate covariation (ERC) signature. ERC is used to identify genes that experienced similar evolutionary histories, and thereby draws functional associations between them. The ERC Analysis website allows researchers to exploit genome-wide datasets to infer novel genes in any biological function and to explore deep evolutionary connections between distinct pathways and complexes. The website provides five analytical methods, graphical output, statistical support and access to an increasing number of taxonomic groups. Analyses and data at http://csb.pitt.edu/erc_analysis/ nclark@pitt.edu. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.
Covariance Analysis Tool (G-CAT) for Computing Ascent, Descent, and Landing Errors
Boussalis, Dhemetrios; Bayard, David S.
2013-01-01
G-CAT is a covariance analysis tool that enables fast and accurate computation of error ellipses for descent, landing, ascent, and rendezvous scenarios, and quantifies knowledge error contributions needed for error budgeting purposes. Because GCAT supports hardware/system trade studies in spacecraft and mission design, it is useful in both early and late mission/ proposal phases where Monte Carlo simulation capability is not mature, Monte Carlo simulation takes too long to run, and/or there is a need to perform multiple parametric system design trades that would require an unwieldy number of Monte Carlo runs. G-CAT is formulated as a variable-order square-root linearized Kalman filter (LKF), typically using over 120 filter states. An important property of G-CAT is that it is based on a 6-DOF (degrees of freedom) formulation that completely captures the combined effects of both attitude and translation errors on the propagated trajectories. This ensures its accuracy for guidance, navigation, and control (GN&C) analysis. G-CAT provides the desired fast turnaround analysis needed for error budgeting in support of mission concept formulations, design trade studies, and proposal development efforts. The main usefulness of a covariance analysis tool such as G-CAT is its ability to calculate the performance envelope directly from a single run. This is in sharp contrast to running thousands of simulations to obtain similar information using Monte Carlo methods. It does this by propagating the "statistics" of the overall design, rather than simulating individual trajectories. G-CAT supports applications to lunar, planetary, and small body missions. It characterizes onboard knowledge propagation errors associated with inertial measurement unit (IMU) errors (gyro and accelerometer), gravity errors/dispersions (spherical harmonics, masscons), and radar errors (multiple altimeter beams, multiple Doppler velocimeter beams). G-CAT is a standalone MATLAB- based tool intended to
Multivariate covariance generalized linear models
Bonat, W. H.; Jørgensen, Bent
2016-01-01
are fitted by using an efficient Newton scoring algorithm based on quasi-likelihood and Pearson estimating functions, using only second-moment assumptions. This provides a unified approach to a wide variety of types of response variables and covariance structures, including multivariate extensions......We propose a general framework for non-normal multivariate data analysis called multivariate covariance generalized linear models, designed to handle multivariate response variables, along with a wide range of temporal and spatial correlation structures defined in terms of a covariance link...... function combined with a matrix linear predictor involving known matrices. The method is motivated by three data examples that are not easily handled by existing methods. The first example concerns multivariate count data, the second involves response variables of mixed types, combined with repeated...
Janiga-Ćmiel Anna
2016-12-01
Full Text Available The paper looks at the issues related to the research on and assessment of the contagion effect. Based on several examinations of two selected EU countries, Poland paired with one of the EU member states; it presents the interaction between their economic development. A DCC-GARCH model constructed for the purpose of the study was used to generate a covariance matrix Ht, which enabled the calculation of correlation matrices Rt. The resulting variance vectors were used to present a linear correlation model on which a further analysis of the contagion effect was based. The aim of the study was to test a contagion effect among selected EU countries in the years 2000–2014. The transmission channel under study was the GDP of a selected country. The empirical studies confirmed the existence of the contagion effect between the economic development of the Polish and selected EU economies.
Islamiyati, A.; Fatmawati; Chamidah, N.
2018-03-01
The correlation assumption of the longitudinal data with bi-response occurs on the measurement between the subjects of observation and the response. It causes the auto-correlation of error, and this can be overcome by using a covariance matrix. In this article, we estimate the covariance matrix based on the penalized spline regression model. Penalized spline involves knot points and smoothing parameters simultaneously in controlling the smoothness of the curve. Based on our simulation study, the estimated regression model of the weighted penalized spline with covariance matrix gives a smaller error value compared to the error of the model without covariance matrix.
Familial aggregation and linkage analysis with covariates for metabolic syndrome risk factors.
Naseri, Parisa; Khodakarim, Soheila; Guity, Kamran; Daneshpour, Maryam S
2018-06-15
Mechanisms of metabolic syndrome (MetS) causation are complex, genetic and environmental factors are important factors for the pathogenesis of MetS In this study, we aimed to evaluate familial and genetic influences on metabolic syndrome risk factor and also assess association between FTO (rs1558902 and rs7202116) and CETP(rs1864163) genes' single nucleotide polymorphisms (SNP) with low HDL_C in the Tehran Lipid and Glucose Study (TLGS). The design was a cross-sectional study of 1776 members of 227 randomly-ascertained families. Selected families contained at least one affected metabolic syndrome and at least two members of the family had suffered a loss of HDL_C according to ATP III criteria. In this study, after confirming the familial aggregation with intra-trait correlation coefficients (ICC) of Metabolic syndrome (MetS) and the quantitative lipid traits, the genetic linkage analysis of HDL_C was performed using conditional logistic method with adjusted sex and age. The results of the aggregation analysis revealed a higher correlation between siblings than between parent-offspring pairs representing the role of genetic factors in MetS. In addition, the conditional logistic model with covariates showed that the linkage results between HDL_C and three marker, rs1558902, rs7202116 and rs1864163 were significant. In summary, a high risk of MetS was found in siblings confirming the genetic influences of metabolic syndrome risk factor. Moreover, the power to detect linkage increases in the one parameter conditional logistic model regarding the use of age and sex as covariates. Copyright © 2018. Published by Elsevier B.V.
Huitema, Bradley
2011-01-01
A complete guide to cutting-edge techniques and best practices for applying covariance analysis methods The Second Edition of Analysis of Covariance and Alternatives sheds new light on its topic, offering in-depth discussions of underlying assumptions, comprehensive interpretations of results, and comparisons of distinct approaches. The book has been extensively revised and updated to feature an in-depth review of prerequisites and the latest developments in the field. The author begins with a discussion of essential topics relating to experimental design and analysis
Zhaunerchyk, V.; Frasinski, L. J.; Eland, J. H. D.; Feifel, R.
2014-05-01
Multidimensional covariance analysis and its validity for correlation of processes leading to multiple products are investigated from a theoretical point of view. The need to correct for false correlations induced by experimental parameters which fluctuate from shot to shot, such as the intensity of self-amplified spontaneous emission x-ray free-electron laser pulses, is emphasized. Threefold covariance analysis based on simple extension of the two-variable formulation is shown to be valid for variables exhibiting Poisson statistics. In this case, false correlations arising from fluctuations in an unstable experimental parameter that scale linearly with signals can be eliminated by threefold partial covariance analysis, as defined here. Fourfold covariance based on the same simple extension is found to be invalid in general. Where fluctuations in an unstable parameter induce nonlinear signal variations, a technique of contingent covariance analysis is proposed here to suppress false correlations. In this paper we also show a method to eliminate false correlations associated with fluctuations of several unstable experimental parameters.
Scheinost, Dustin; Holmes, Sophie E; DellaGioia, Nicole; Schleifer, Charlie; Matuskey, David; Abdallah, Chadi G; Hampson, Michelle; Krystal, John H; Anticevic, Alan; Esterlis, Irina
2018-04-01
Converging evidence suggests that major depressive disorder (MDD) affects multiple large-scale brain networks. Analyses of the correlation or covariance of regional brain structure and function applied to structural and functional MRI data may provide insights into systems-level organization and structure-to-function correlations in the brain in MDD. This study applied tensor-based morphometry and intrinsic connectivity distribution to identify regions of altered volume and intrinsic functional connectivity in data from unmedicated individuals with MDD (n=17) and healthy comparison participants (HC, n=20). These regions were then used as seeds for exploratory anatomical covariance and connectivity analyses. Reduction in volume in the anterior cingulate cortex (ACC) and lower structural covariance between the ACC and the cerebellum were observed in the MDD group. Additionally, individuals with MDD had significantly lower whole-brain intrinsic functional connectivity in the medial prefrontal cortex (mPFC). This mPFC region showed altered connectivity to the ventral lateral PFC (vlPFC) and local circuitry in MDD. Global connectivity in the ACC was negatively correlated with reported depressive symptomatology. The mPFC-vlPFC connectivity was positively correlated with depressive symptoms. Finally, we observed increased structure-to-function correlation in the PFC/ACC in the MDD group. Although across all analysis methods and modalities alterations in the PFC/ACC were a common finding, each modality and method detected alterations in subregions belonging to distinct large-scale brain networks. These exploratory results support the hypothesis that MDD is a systems level disorder affecting multiple brain networks located in the PFC and provide new insights into the pathophysiology of this disorder.
Scheinost, Dustin; Holmes, Sophie E; DellaGioia, Nicole; Schleifer, Charlie; Matuskey, David; Abdallah, Chadi G; Hampson, Michelle; Krystal, John H; Anticevic, Alan; Esterlis, Irina
2018-01-01
Converging evidence suggests that major depressive disorder (MDD) affects multiple large-scale brain networks. Analyses of the correlation or covariance of regional brain structure and function applied to structural and functional MRI data may provide insights into systems-level organization and structure-to-function correlations in the brain in MDD. This study applied tensor-based morphometry and intrinsic connectivity distribution to identify regions of altered volume and intrinsic functional connectivity in data from unmedicated individuals with MDD (n=17) and healthy comparison participants (HC, n=20). These regions were then used as seeds for exploratory anatomical covariance and connectivity analyses. Reduction in volume in the anterior cingulate cortex (ACC) and lower structural covariance between the ACC and the cerebellum were observed in the MDD group. Additionally, individuals with MDD had significantly lower whole-brain intrinsic functional connectivity in the medial prefrontal cortex (mPFC). This mPFC region showed altered connectivity to the ventral lateral PFC (vlPFC) and local circuitry in MDD. Global connectivity in the ACC was negatively correlated with reported depressive symptomatology. The mPFC–vlPFC connectivity was positively correlated with depressive symptoms. Finally, we observed increased structure-to-function correlation in the PFC/ACC in the MDD group. Although across all analysis methods and modalities alterations in the PFC/ACC were a common finding, each modality and method detected alterations in subregions belonging to distinct large-scale brain networks. These exploratory results support the hypothesis that MDD is a systems level disorder affecting multiple brain networks located in the PFC and provide new insights into the pathophysiology of this disorder. PMID:28944772
Eugenio eAbela
2015-10-01
Full Text Available Aim: To describe structural covariance networks of grey matter volume (GMV change in 28 patients with first-ever stroke to the primary sensorimotor cortices, and to investigate their relationship to hand function recovery and local GMV change.Methods: Tensor based morphometry maps derived from high resolution structural images were subject to principal component analyses to identify the networks. We calculated correlations between network expression and local GMV change, sensorimotor hand function and lesion volume. To verify which of the structural covariance networks of GMV change have a significant relationship to hand function we performed an additional multivariate regression approach.Results: Expression of the second network, explaining 9.1% of variance, correlated with GMV increase in the medio-dorsal (md thalamus and hand motor skill. Patients with positive expression coefficients were distinguished by significantly higher GMV-increase of this structure during stroke recovery. Significant nodes of this network were located in md thalamus, dorsolateral prefrontal cortex and higher order sensorimotor cortices. Parameter of hand function had a unique relationship to the network and depended on an interaction between network expression and lesion volume. Inversely network expression is limited in patients with large lesion volumes.Conclusions: Chronic phase of sensorimotor cortical stroke has been characterized by a large scale covarying structural network in the ipsilesional hemisphere associated specifically with sensorimotor hand skill. Its expression is related to GMV-increase of md thalamus, one constituent of the network, and correlated with the cortico-striato-thalamic loop involved in control of motor execution and higher order sensorimotor cortices. A close relation between expression of this network with degree of recovery might indicate reduced compensatory resources in the impaired subgroup.
Mikosch, Jochen; Patchkovskii, Serguei
2013-10-01
We use an analytical theory of noisy Poisson processes, developed in the preceding companion publication, to compare coincidence and covariance measurement approaches in photoelectron and -ion spectroscopy. For non-unit detection efficiencies, coincidence data acquisition (DAQ) suffers from false coincidences. The rate of false coincidences grows quadratically with the rate of elementary ionization events. To minimize false coincidences for rare event outcomes, very low event rates may hence be required. Coincidence measurements exhibit high tolerance to noise introduced by unstable experimental conditions. Covariance DAQ on the other hand is free of systematic errors as long as stable experimental conditions are maintained. In the presence of noise, all channels in a covariance measurement become correlated. Under favourable conditions, covariance DAQ may allow orders of magnitude reduction in measurement times. Finally, we use experimental data for strong-field ionization of 1,3-butadiene to illustrate how fluctuations in experimental conditions can contaminate a covariance measurement, and how such contamination can be detected.
On generally covariant quantum field theory and generalized causal and dynamical structures
Bannier, U.
1988-01-01
We give an example of a generally covariant quasilocal algebra associated with the massive free field. Maximal, two-sided ideals of this algebra are algebraic representatives of external metric fields. In some sense, this algebra may be regarded as a concrete realization of Ekstein's ideas of presymmetry in quantum field theory. Using ideas from our example and from usual algebraic quantum field theory, we discuss a generalized scheme, in which maximal ideals are viewed as algebraic representatives of dynamical equations or Lagrangians. The considered frame is no quantum gravity, but may lead to further insight into the relation between quantum theory and space-time geometry. (orig.)
Kim, S
2008-03-01
Full Text Available used in Table 4 are as follow — βk: direct effect; βTk : total effect; and βsbk : superbeta. There are some interesting findings from the results presented in Table 4. For out- come variable Customer satisfaction, the superbeta measure was strongest... corresponding 95% HPD interval contains 0. This suggests that ignoring the heterogeneity and/or covariates gives different conclusions based on the total-effect measure. Also from Table 4, we see that for outcome variable Customer satisfaction, all the 3...
Linear Covariance Analysis For Proximity Operations Around Asteroid 2008 EV5
Wright, Cinnamon A.; Bhatt, Sagar; Woffinden, David; Strube, Matthew; D'Souza, Christopher; DeWeese, Keith
2015-01-01
The NASA initiative to collect an asteroid the Asteroid Robotic Redirect Mission (ARRM) is currently investigating the option of retrieving a boulder off an asteroid, demonstrating planetary defense with an enhanced gravity tractor technique and returning it to a lunar orbit. Techniques for accomplishing this are being investigated by the Satellite Servicing Capabilities Office (SSOO) and NASA GSFC in colloboration with JPL, NASA, JSC, LaRC, and Draper Laboratories Inc. Two critical phases of the mission are the descent to the boulder and the Enhanced Gravity Tractor-enhanced gravity tractor demonstration. A linear covariance analysis was done for these phases to assess the feasibility of these concepts with the proposed design of the sensor and actuaor suite of the Asteroid Redirect Vehicle (ARV). The sensor suite for this analysis will include a wide field of view camera, Lidar, and a MMU. The proposed asteroid of interest is currently the C-type asteroid 2008 EV5, a carbonaceous chondrite that is of high interest to the scientific community. This paper will present an overview of the analysis discuss sensor and actuator models and address the feasibility of descending to the boulder within the requirements as the feasibility of maintaining the halo orbit in order to demonstrate the Enhanced Gravity Tractor-enhanced gravity tractory technique.
NParCov3: A SAS/IML Macro for Nonparametric Randomization-Based Analysis of Covariance
Richard C. Zink
2012-07-01
Full Text Available Analysis of covariance serves two important purposes in a randomized clinical trial. First, there is a reduction of variance for the treatment effect which provides more powerful statistical tests and more precise confidence intervals. Second, it provides estimates of the treatment effect which are adjusted for random imbalances of covariates between the treatment groups. The nonparametric analysis of covariance method of Koch, Tangen, Jung, and Amara (1998 defines a very general methodology using weighted least-squares to generate covariate-adjusted treatment effects with minimal assumptions. This methodology is general in its applicability to a variety of outcomes, whether continuous, binary, ordinal, incidence density or time-to-event. Further, its use has been illustrated in many clinical trial settings, such as multi-center, dose-response and non-inferiority trials.NParCov3 is a SAS/IML macro written to conduct the nonparametric randomization-based covariance analyses of Koch et al. (1998. The software can analyze a variety of outcomes and can account for stratification. Data from multiple clinical trials will be used for illustration.
On the bilinear covariants associated to mass dimension one spinors
Silva, J.M.H. da; Villalobos, C.H.C.; Rogerio, R.J.B. [DFQ, UNESP, Guaratingueta, SP (Brazil); Scatena, E. [Universidade Federal de Santa Catarina-CEE, Blumenau, SC (Brazil)
2016-10-15
In this paper we approach the issue of Clifford algebra basis deformation, allowing for bilinear covariants associated to Elko spinors which satisfy the Fierz-Pauli-Kofink identities. We present a complete analysis of covariance, taking into account the involved dual structure associated to Elko spinors. Moreover, the possible generalizations to the recently presented new dual structure are performed. (orig.)
A class of covariate-dependent spatiotemporal covariance functions
Reich, Brian J; Eidsvik, Jo; Guindani, Michele; Nail, Amy J; Schmidt, Alexandra M.
2014-01-01
In geostatistics, it is common to model spatially distributed phenomena through an underlying stationary and isotropic spatial process. However, these assumptions are often untenable in practice because of the influence of local effects in the correlation structure. Therefore, it has been of prolonged interest in the literature to provide flexible and effective ways to model non-stationarity in the spatial effects. Arguably, due to the local nature of the problem, we might envision that the correlation structure would be highly dependent on local characteristics of the domain of study, namely the latitude, longitude and altitude of the observation sites, as well as other locally defined covariate information. In this work, we provide a flexible and computationally feasible way for allowing the correlation structure of the underlying processes to depend on local covariate information. We discuss the properties of the induced covariance functions and discuss methods to assess its dependence on local covariate information by means of a simulation study and the analysis of data observed at ozone-monitoring stations in the Southeast United States. PMID:24772199
Clarence C. Y. Kwan
2010-07-01
Full Text Available This study considers, from a pedagogic perspective, a crucial requirement for the covariance matrix of security returns in mean-variance portfolio analysis. Although the requirement that the covariance matrix be positive definite is fundamental in modern finance, it has not received any attention in standard investment textbooks. Being unaware of the requirement could cause confusion for students over some strange portfolio results that are based on seemingly reasonable input parameters. This study considers the requirement both informally and analytically. Electronic spreadsheet tools for constrained optimization and basic matrix operations are utilized to illustrate the various concepts involved.
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; Genton, Marc G.; Sun, Ying; Tempone, Raul
2015-01-01
We approximate large non-structured covariance matrices in the H-matrix format with a log-linear computational cost and storage O(n log n). We compute inverse, Cholesky decomposition and determinant in H-format. As an example we consider the class of Matern covariance functions, which are very popular in spatial statistics, geostatistics, machine learning and image analysis. Applications are: kriging and optimal design
Hierarchical matrix approximation of large covariance matrices
Litvinenko, Alexander; Genton, Marc G.; Sun, Ying; Tempone, Raul
2015-01-01
We approximate large non-structured covariance matrices in the H-matrix format with a log-linear computational cost and storage O(nlogn). We compute inverse, Cholesky decomposition and determinant in H-format. As an example we consider the class of Matern covariance functions, which are very popular in spatial statistics, geostatistics, machine learning and image analysis. Applications are: kriging and op- timal design.
Vansteelandt, S.; Martinussen, Torben; Tchetgen, E. J Tchetgen
2014-01-01
We consider additive hazard models (Aalen, 1989) for the effect of a randomized treatment on a survival outcome, adjusting for auxiliary baseline covariates. We demonstrate that the Aalen least-squares estimator of the treatment effect parameter is asymptotically unbiased, even when the hazard...... that, in view of its robustness against model misspecification, Aalen least-squares estimation is attractive for evaluating treatment effects on a survival outcome in randomized experiments, and the primary reasons to consider baseline covariate adjustment in such settings could be interest in subgroup......'s dependence on time or on the auxiliary covariates is misspecified, and even away from the null hypothesis of no treatment effect. We furthermore show that adjustment for auxiliary baseline covariates does not change the asymptotic variance of the estimator of the effect of a randomized treatment. We conclude...
Study of covariances involved in the k0 method of neutron activation analysis
Cardoso, Vanderlei
2011-01-01
This work aimed the development of a methodology for the treatment of uncertainty in the k 0 Method for Neutron Activation Analysis (NAA), comprehensively and accurately, by applying the covariance analysis methodology. All parameters involved in determining the concentration of a given element were analyzed with criteria in order to establish the correlations among them. Also established were the possible correlations between the concentrations of different elements for the same sample and for different samples. This procedure generated a large number of correlations that have been rigorously addressed. Data for analysis were obtained experimentally by means of irradiations performed at 24A irradiation position, near the core of the IEA-R1 research reactor, located at IPEN-CNEN/SP. The parameters α and f, characterizing the neutron field were determined by applying several methods from the literature. A detailed statistical treatment was applied to each measurement, verifying the various uncertainties and partial correlations. In order to deepen the study, targets of 64 Zn and 68 Zn were chosen, for which the nuclear parameters k 0 and Q 0 showed discrepancies in the literature in order to determine them experimentally. For 64 Zn, the values for these parameters resulted 5.63(8) x 10 -3 and 1.69(6), respectively. For 68 Zn they resulted 4.00(6) x 10 -4 and 2.34(4), respectively. These values were compared with data from the literature. The Monte Carlo method was applied at various stages of study, to allow accurate determination of some parameters needed for the complete data analysis. (author)
Geng, L. S.; Camalich, J. Martin; Vacas, M. J. Vicente
2009-01-01
We present a calculation of the leading SU(3)-breaking O(p 3 ) corrections to the electromagnetic moments and charge radius of the lowest-lying decuplet resonances in covariant chiral perturbation theory. In particular, the magnetic dipole moment of the members of the decuplet is predicted fixing the only low-energy constant (LEC) present up to this order with the well-measured magnetic dipole moment of the Ω - . We predict μ Δ ++ =6.04(13) and μ Δ + =2.84(2), which agree well with the current experimental information. For the electric quadrupole moment and the charge radius, we use state-of-the-art lattice QCD results to determine the corresponding LECs, whereas for the magnetic octupole moment there is no unknown LEC up to the order considered here, and we obtain a pure prediction. We compare our results with those reported in large N c , lattice QCD, heavy-baryon chiral perturbation theory, and other models.
Moeyaert, Mariola; Ugille, Maaike; Ferron, John M.; Beretvas, S. Natasha; Van den Noortgate, Wim
2016-01-01
The impact of misspecifying covariance matrices at the second and third levels of the three-level model is evaluated. Results indicate that ignoring existing covariance has no effect on the treatment effect estimate. In addition, the between-case variance estimates are unbiased when covariance is either modeled or ignored. If the research interest…
Estimating a structured covariance matrix from multi-lab measurements in high-throughput biology.
Franks, Alexander M; Csárdi, Gábor; Drummond, D Allan; Airoldi, Edoardo M
2015-03-01
We consider the problem of quantifying the degree of coordination between transcription and translation, in yeast. Several studies have reported a surprising lack of coordination over the years, in organisms as different as yeast and human, using diverse technologies. However, a close look at this literature suggests that the lack of reported correlation may not reflect the biology of regulation. These reports do not control for between-study biases and structure in the measurement errors, ignore key aspects of how the data connect to the estimand, and systematically underestimate the correlation as a consequence. Here, we design a careful meta-analysis of 27 yeast data sets, supported by a multilevel model, full uncertainty quantification, a suite of sensitivity analyses and novel theory, to produce a more accurate estimate of the correlation between mRNA and protein levels-a proxy for coordination. From a statistical perspective, this problem motivates new theory on the impact of noise, model mis-specifications and non-ignorable missing data on estimates of the correlation between high dimensional responses. We find that the correlation between mRNA and protein levels is quite high under the studied conditions, in yeast, suggesting that post-transcriptional regulation plays a less prominent role than previously thought.
Maintainability analysis considering time-dependent and time-independent covariates
Barabadi, Abbas; Barabady, Javad; Markeset, Tore
2011-01-01
Traditional parametric methods for assessing maintainability most often only consider time to repair (TTR) as a single explanatory variable. However, to predict availability more precisely for high availability systems, a better model is needed to quantify the effect of operational environment on maintainability. The proportional repair model (PRM), which is developed based on proportional hazard model (PHM), may be used to analyze maintainability in the present of covariates. In the PRM, the effect of covariates is considered to be time independent. However this assumption may not be valid for some situations. The aim of this paper is to develop the Cox regression model and its extension in the presence of time-dependent covariates for determining maintainability. A simple case study is used to demonstrate how the model can be applied in a real case.
ORACLE: an adjusted cross-section and covariance library for fast-reactor analysis
Yeivin, Y.; Marable, J.H.; Weisbin, C.R.; Wagschal, J.J.
1980-01-01
Benchmark integral-experiment values from six fast critical-reactor assemblies and two standard neutron fields are combined with corresponding calculations using group cross sections based on ENDF/B-V in a least-squares data adjustment using evaluated covariances from ENDF/B-V and supporting covariance evaluations. Purpose is to produce an adjusted cross-section and covariance library which is based on well-documented data and methods and which is suitable for fast-reactor design. By use of such a library, data- and methods-related biases of calculated performance parameters should be reduced and uncertainties of the calculated values minimized. Consistency of the extensive data base is analyzed using the chi-square test. This adjusted library ORACLE will be available shortly
Nikanjam, Mina; Stewart, Clinton F; Takimoto, Chris H; Synold, Timothy W; Beaty, Orren; Fouladi, Maryam; Capparelli, Edmund V
2015-03-01
To characterize the determinants of variability for oxaliplatin pharmacokinetics including age, renal function, and hepatic function in children and adults. Oxaliplatin pharmacokinetic data were combined from phase I and II clinical trials: three pediatric trials (Peds1-3) and two adult NCI organ dysfunction studies (Hepatic and Renal). A population pharmacokinetic model was developed utilizing platinum ultrafiltrate concentrations to characterize changes in oxaliplatin disposition with age and organ dysfunction along with other potential sources of oxaliplatin pharmacokinetic variability. A total of 1,508 concentrations from 186 children and adults were used in the study. The data were well described by a three-compartment model. Serum creatinine (SCR) was an independent predictor of clearance (CL) while age was an independent predictor of volume of distribution. Although age was a significant covariate on CL in the univariate analysis, age effects on CL were entirely accounted for by SCR. Gender, hepatic function, and race had no effect on CL or volume of distribution. Median CL values were 0.58 (Hepatic), 0.34 (Renal), 0.78 (Peds1), 0.74 (Peds2), and 0.81 (Peds3) (L/h/kg(0.75)). Monte Carlo simulations of the final model with 130 mg/m(2) yielded median AUC values of: 14.2 (2-6 years), 16.8 (6-12 years), 16.5 (12-18 years), and 17.3 (>18 years) (µg h/mL). Renal function had the greatest effect on CL with a small age effect seen on the distribution of oxaliplatin. Young pediatric patients had higher CL values than adults as a result of better renal function.
Central Russia agroecosystem monitoring with CO2 fluxes analysis by eddy covariance method
Joulia Meshalkina
2015-07-01
Full Text Available The eddy covariance (EC technique as a powerful statistics-based method of measurement and calculation the vertical turbulent fluxes of greenhouses gases within atmospheric boundary layers provides the continuous, long-term flux information integrated at the ecosystem scale. An attractive way to compare the agricultural practices influences on GHG fluxes is to divide a crop area into subplots managed in different ways. The research has been carried out in the Precision Farming Experimental Field of the Russian Timiryazev State Agricultural University (RTSAU, Moscow in 2013 under the support of RF Government grant # 11.G34.31.0079, EU grant # 603542 LUС4С (7FP and RF Ministry of education and science grant # 14-120-14-4266-ScSh. Arable Umbric Albeluvisols have around 1% of SOC, 5.4 pH (KCl and NPK medium-enhanced contents in sandy loam topsoil. The CO2 flux seasonal monitoring has been done by two eddy covariance stations located at the distance of 108 m. The LI-COR instrumental equipment was the same for the both stations. The stations differ only by current crop version: barley or vetch and oats. At both sites, diurnal patterns of NEE among different months were very similar in shape but varied slightly in amplitude. NEE values were about zero during spring time. CO2 fluxes have been intensified after crop emerging from values of 3 to 7 µmol/s∙m2 for emission, and from 5 to 20 µmol/s∙m2 for sink. Stabilization of the fluxes has come at achieving plants height of 10-12 cm. Average NEE was negative only in June and July. Maximum uptake was observed in June with average values about 8 µmol CO2 m−2 s−1. Although different kind of crops were planted on the fields A and B, GPP dynamics was quite similar for both sites: after reaching the peak values at the mid of June, GPP decreased from 4 to 0.5 g C CO2 m-2 d-1 at the end of July. The difference in crops harvesting time that was equal two weeks did not significantly influence the daily
Patterns of covariance between forest stand and canopy structure in the Pacific Northwest.
Michael A. Lefsky; Andrew T. Hudak; Warren B. Cohen; S.A. Acker
2005-01-01
In the past decade, LIDAR (light detection and ranging) has emerged as a powerful tool for remotely sensing forest canopy and stand structure, including the estimation of aboveground biomass and carbon storage. Numerous papers have documented the use of LIDAR measurements to predict important aspects of forest stand structure, including aboveground biomass. Other...
Hattori, Masasi; Oaksford, Mike
2007-01-01
In this article, 41 models of covariation detection from 2 x 2 contingency tables were evaluated against past data in the literature and against data from new experiments. A new model was also included based on a limiting case of the normative phi-coefficient under an extreme rarity assumption, which has been shown to be an important factor in…
Rebmann, C.; Göckede, M.; Foken, T.; Aubinet, M.; Aurela, M.; Berbigier, P.; Bernhofer, C.; Buchmann, N.; Carrara, A.; Cescatti, A.; Ceulemans, R.; Clement, R.; Elbers, J. A.; Granier, A.; Grünwald, T.; Guyon, D.; Havránková, Kateřina; Heinesch, B.; Knohl, A.; Laurila, T.; Longdoz, B.; Marcolla, B.; Markkanen, T.; Miglietta, F.; Moncrieff, J.; Montagnani, L.; Moors, E.; Nardino, M.; Ourcival, J.-M.; Rambal, S.; Rannik, Ü.; Rotenberg, E.; Sedlák, Pavel; Unterhuber, G.; Vesala, T.; Yakir, D.
2005-01-01
Roč. 80, - (2005), s. 121-141 ISSN 0177-798X Grant - others:Carboeuroflux(XE) EVK-2-CT-1999-00032 Institutional research plan: CEZ:AV0Z30420517; CEZ:AV0Z6087904 Keywords : Eddy covariance * Quality assurance * Quality control * Footprint modelling * Heterogeneity Subject RIV: DG - Athmosphere Sciences, Meteorology Impact factor: 1.295, year: 2005
Neaux, Dimitri; Guy, Franck; Gilissen, Emmanuel; Coudyzer, Walter; Vignaud, Patrick; Ducrocq, Stéphane
2013-01-01
The organization of the bony face is complex, its morphology being influenced in part by the rest of the cranium. Characterizing the facial morphological variation and craniofacial covariation patterns in extant hominids is fundamental to the understanding of their evolutionary history. Numerous studies on hominid facial shape have proposed hypotheses concerning the relationship between the anterior facial shape, facial block orientation and basicranial flexion. In this study we test these hypotheses in a sample of adult specimens belonging to three extant hominid genera (Homo, Pan and Gorilla). Intraspecific variation and covariation patterns are analyzed using geometric morphometric methods and multivariate statistics, such as partial least squared on three-dimensional landmarks coordinates. Our results indicate significant intraspecific covariation between facial shape, facial block orientation and basicranial flexion. Hominids share similar characteristics in the relationship between anterior facial shape and facial block orientation. Modern humans exhibit a specific pattern in the covariation between anterior facial shape and basicranial flexion. This peculiar feature underscores the role of modern humans' highly-flexed basicranium in the overall integration of the cranium. Furthermore, our results are consistent with the hypothesis of a relationship between the reduction of the value of the cranial base angle and a downward rotation of the facial block in modern humans, and to a lesser extent in chimpanzees.
A cautionary note on the use of information fit indexes in covariance structure modeling with means
Wicherts, J.M.; Dolan, C.V.
2004-01-01
Information fit indexes such as Akaike Information Criterion, Consistent Akaike Information Criterion, Bayesian Information Criterion, and the expected cross validation index can be valuable in assessing the relative fit of structural equation models that differ regarding restrictiveness. In cases
Stamovlasis, Dimitrios; Papageorgiou, George; Tsitsipis, Georgios; Tsikalas, Themistoklis; Vaiopoulou, Julie
2018-01-01
This paper illustrates two psychometric methods, latent class analysis (LCA) and taxometric analysis (TA) using empirical data from research probing children's mental representation in science learning. LCA is used to obtain a typology based on observed variables and to further investigate how the encountered classes might be related to external variables, where the effectiveness of classification process and the unbiased estimations of parameters become the main concern. In the step-wise LCA, the class membership is assigned and subsequently its relationship with covariates is established. This leading-edge modeling approach suffers from severe downward-biased estimations. The illustration of LCA is focused on alternative bias correction approaches and demonstrates the effect of modal and proportional class-membership assignment along with BCH and ML correction procedures. The illustration of LCA is presented with three covariates, which are psychometric variables operationalizing formal reasoning, divergent thinking and field dependence-independence, respectively. Moreover, taxometric analysis, a method designed to detect the type of the latent structural model, categorical or dimensional, is introduced, along with the relevant basic concepts and tools. TA was applied complementarily in the same data sets to answer the fundamental hypothesis about children's naïve knowledge on the matters under study and it comprises an additional asset in building theory which is fundamental for educational practices. Taxometric analysis provided results that were ambiguous as far as the type of the latent structure. This finding initiates further discussion and sets a problematization within this framework rethinking fundamental assumptions and epistemological issues.
Covariance-based Spatial Channel Structure Emulation for MIMO OTA Testing
Tsakalaki, Elpiniki; Alrabadi, Osama; Fan, Wei
2014-01-01
The paper presents a general framework for recreating the spatial channel structure in a MIMO over-the-air (OTA) multiprobe anechoic chamber testing setup. The idea is to find the power weights of the spatial taps (antenna probes) that minimize a certain distance between the spatial channel covar...
Cheung, Mike W. L.; Chan, Wai
2009-01-01
Structural equation modeling (SEM) is widely used as a statistical framework to test complex models in behavioral and social sciences. When the number of publications increases, there is a need to systematically synthesize them. Methodology of synthesizing findings in the context of SEM is known as meta-analytic SEM (MASEM). Although correlation…
Leal, Luiz C.; Arbanas, Goran; Derrien, Herve; Wiarda, Dorothea
2008-01-01
Resonance-parameter covariance matrix (RPCM) evaluations in the resolved resonance region were done for 232Th, 233U, 235U, 238U, and 239Pu using the computer code SAMMY. The retroactive approach of the code SAMMY was used to generate the RPCMs for 233U, 235U. RPCMs for 232Th, 238U and 239Pu were generated together with the resonance parameter evaluations. The RPCMs were then converted in the ENDF format using the FILE32 representation. Alternatively, for computer storage reasons, the FILE32 was converted in the FILE33 cross section covariance matrix (CSCM). Both representations were processed using the computer code PUFF-IV. This paper describes the procedures used to generate the RPCM with SAMMY.
Spearing, Debra; Woehlke, Paula
To assess the effect on discriminant analysis in terms of correct classification into two groups, the following parameters were systematically altered using Monte Carlo techniques: sample sizes; proportions of one group to the other; number of independent variables; and covariance matrices. The pairing of the off diagonals (or covariances) with…
Nsubuga-Nyombi, Tamara; Sensalire, Simon; Karamagi, Esther; Aloyo, Judith; Byabagambi, John; Rahimzai, Mirwais; Nabitaka, Linda Kisaakye; Calnan, Jacqueline
2018-03-31
As part of efforts to improve the prevention of mother-to-child transmission in Northern Uganda, we explored reasons for poor viral suppression among 122 pregnant and lactating women who were in care, received viral load tests, but had not achieved viral suppression and had more than 1000 copies/mL. Understanding the patient factors associated with low viral suppression was of interest to the Ministry of Health to guide the development of tools and interventions to achieve viral suppression for pregnant and lactating women newly initiating on ART as well as those on ART with unsuppressed viral load. A facility-based cross-sectional and mixed methods study design was used, with retrospective medical record review. We assessed 122 HIV-positive mothers with known low viral suppression across 31 health facilities in Northern Uganda. Adjusted odds ratios were used to determine the covariates of adherence among HIV positive mothers using logistic regression. A study among health care providers shed further light on predictors of low viral suppression and a history of low early retention. This study was part of a larger national evaluation of the performance of integrated care services for mothers. Adherence defined as taking antiretroviral medications correctly everyday was low at 67.2%. The covariates of low adherence are: taking other medications in addition to ART, missed appointments in the past 6 months, experienced violence in the past 6 months, and faces obstacles to treatment. Mothers who were experiencing each of these covariates were less likely to adhere to treatment. These covariates were triangulated with perspectives of health providers as covariates of low adherence and included: long distances to health facility, missed appointments, running out of pills, sharing antiretroviral drugs, violence, and social lifestyles such as multiple sexual partners coupled with non-disclosure to partners. Inadequate counseling, stigma, and lack of client identity are
Sekulic Damir
2017-10-01
Full Text Available The official reports on doping behavior in kickboxing are alarming, but there have been no empirical studies that examined this problem directly. The aim of this study was to investigate the prevalence, gender differences and covariates of potential-doping-behavior, in kickboxing athletes. A total of 130 high-level kickboxing athletes (92 males, 21.37 ± 4.83 years of age, 8.39 ± 5.73 years of training experience; 38 women, 20.31 ± 2.94 years of age; 9.84 ± 4.74 years of training experience completed questionnaires to study covariates and potential-doping behavior. The covariates were: sport factors (i.e. experience, success, doping-related factors (i.e. opinion about penalties for doping users, number of doping testing, potential-doping-behavior, etc., sociodemographic variables, task- and ego-motivation, knowledge on sports nutrition, and knowledge on doping. Gender-based differences were established by independent t-tests, and the Mann-Whitney test. Multinomial logistic regression analyses were performed to define the relationships between covariates and a tendency toward potential-doping behavior (positive tendency – neutral – negative tendency. The potential-doping behavior was higher in those athletes who perceived kickboxing as doping contaminated sport. The more experienced kickboxers were associated with positive intention toward potential-doping behavior. Positive intention toward potential-doping behavior was lower in those who had better knowledge on sports nutrition. The task- and ego-motivation were not associated to potential-doping behavior. Because of the high potential-doping-behavior (less than 50% of athletes showed a negative tendency toward doping, and similar prevalence of potential-doping behavior between genders, this study highlights the necessity of a systematic anti-doping campaign in kickboxing. Future studies should investigate motivational variables as being potentially related to doping behavior in younger
Sekulic, Damir; Zenic, Natasa; Versic, Sime; Maric, Dora; Gabrilo, Goran; Jelicic, Mario
2017-01-01
Abstract The official reports on doping behavior in kickboxing are alarming, but there have been no empirical studies that examined this problem directly. The aim of this study was to investigate the prevalence, gender differences and covariates of potential-doping-behavior, in kickboxing athletes. A total of 130 high-level kickboxing athletes (92 males, 21.37 ± 4.83 years of age, 8.39 ± 5.73 years of training experience; 38 women, 20.31 ± 2.94 years of age; 9.84 ± 4.74 years of training experience) completed questionnaires to study covariates and potential-doping behavior. The covariates were: sport factors (i.e. experience, success), doping-related factors (i.e. opinion about penalties for doping users, number of doping testing, potential-doping-behavior, etc.), sociodemographic variables, task- and ego-motivation, knowledge on sports nutrition, and knowledge on doping. Gender-based differences were established by independent t-tests, and the Mann-Whitney test. Multinomial logistic regression analyses were performed to define the relationships between covariates and a tendency toward potential-doping behavior (positive tendency – neutral – negative tendency). The potential-doping behavior was higher in those athletes who perceived kickboxing as doping contaminated sport. The more experienced kickboxers were associated with positive intention toward potential-doping behavior. Positive intention toward potential-doping behavior was lower in those who had better knowledge on sports nutrition. The task- and ego-motivation were not associated to potential-doping behavior. Because of the high potential-doping-behavior (less than 50% of athletes showed a negative tendency toward doping), and similar prevalence of potential-doping behavior between genders, this study highlights the necessity of a systematic anti-doping campaign in kickboxing. Future studies should investigate motivational variables as being potentially related to doping behavior in younger
High-Dimensional Multivariate Repeated Measures Analysis with Unequal Covariance Matrices
Harrar, Solomon W.; Kong, Xiaoli
2015-01-01
In this paper, test statistics for repeated measures design are introduced when the dimension is large. By large dimension is meant the number of repeated measures and the total sample size grow together but either one could be larger than the other. Asymptotic distribution of the statistics are derived for the equal as well as unequal covariance cases in the balanced as well as unbalanced cases. The asymptotic framework considered requires proportional growth of the sample sizes and the dimension of the repeated measures in the unequal covariance case. In the equal covariance case, one can grow at much faster rate than the other. The derivations of the asymptotic distributions mimic that of Central Limit Theorem with some important peculiarities addressed with sufficient rigor. Consistent and unbiased estimators of the asymptotic variances, which make efficient use of all the observations, are also derived. Simulation study provides favorable evidence for the accuracy of the asymptotic approximation under the null hypothesis. Power simulations have shown that the new methods have comparable power with a popular method known to work well in low-dimensional situation but the new methods have shown enormous advantage when the dimension is large. Data from Electroencephalograph (EEG) experiment is analyzed to illustrate the application of the results. PMID:26778861
Contributions to Large Covariance and Inverse Covariance Matrices Estimation
Kang, Xiaoning
2016-01-01
Estimation of covariance matrix and its inverse is of great importance in multivariate statistics with broad applications such as dimension reduction, portfolio optimization, linear discriminant analysis and gene expression analysis. However, accurate estimation of covariance or inverse covariance matrices is challenging due to the positive definiteness constraint and large number of parameters, especially in the high-dimensional cases. In this thesis, I develop several approaches for estimat...
Li, Siying; Koch, Gary G; Preisser, John S; Lam, Diana; Sanchez-Kam, Matilde
2017-01-01
Dichotomous endpoints in clinical trials have only two possible outcomes, either directly or via categorization of an ordinal or continuous observation. It is common to have missing data for one or more visits during a multi-visit study. This paper presents a closed form method for sensitivity analysis of a randomized multi-visit clinical trial that possibly has missing not at random (MNAR) dichotomous data. Counts of missing data are redistributed to the favorable and unfavorable outcomes mathematically to address possibly informative missing data. Adjusted proportion estimates and their closed form covariance matrix estimates are provided. Treatment comparisons over time are addressed with Mantel-Haenszel adjustment for a stratification factor and/or randomization-based adjustment for baseline covariables. The application of such sensitivity analyses is illustrated with an example. An appendix outlines an extension of the methodology to ordinal endpoints.
Boyarinov, V. F.; Grol, A. V.; Fomichenko, P. A.; Ternovykh, M. Yu
2017-01-01
This work is aimed at improvement of HTGR neutron physics design calculations by application of uncertainty analysis with the use of cross-section covariance information. Methodology and codes for preparation of multigroup libraries of covariance information for individual isotopes from the basic 44-group library of SCALE-6 code system were developed. A 69-group library of covariance information in a special format for main isotopes and elements typical for high temperature gas cooled reactors (HTGR) was generated. This library can be used for estimation of uncertainties, associated with nuclear data, in analysis of HTGR neutron physics with design codes. As an example, calculations of one-group cross-section uncertainties for fission and capture reactions for main isotopes of the MHTGR-350 benchmark, as well as uncertainties of the multiplication factor (k∞) for the MHTGR-350 fuel compact cell model and fuel block model were performed. These uncertainties were estimated by the developed technology with the use of WIMS-D code and modules of SCALE-6 code system, namely, by TSUNAMI, KENO-VI and SAMS. Eight most important reactions on isotopes for MHTGR-350 benchmark were identified, namely: 10B(capt), 238U(n,γ), ν5, 235U(n,γ), 238U(el), natC(el), 235U(fiss)-235U(n,γ), 235U(fiss).
Kodjo Glato
Full Text Available Sub-Saharan agriculture has been identified as vulnerable to ongoing climate change. Adaptation of agriculture has been suggested as a way to maintain productivity. Better knowledge of intra-specific diversity of varieties is prerequisites for the successful management of such adaptation. Among crops, root and tubers play important roles in food security and economic growth for the most vulnerable populations in Africa. Here, we focus on the sweet potato. The Sweet potato (Ipomoea batatas was domesticated in Central and South America and was later introduced into Africa and is now cultivated throughout tropical Africa. We evaluated its diversity in West Africa by sampling a region extending from the coastal area of Togo to the northern Sahelian region of Senegal that represents a range of climatic conditions. Using 12 microsatellite markers, we evaluated 132 varieties along this gradient. Phenotypic data from field trials conducted in three seasons was also obtained. Genetic diversity in West Africa was found to be 18% lower than in America. Genetic diversity in West Africa is structured into five groups, with some groups found in very specific climatic areas, e.g. under a tropical humid climate, or under a Sahelian climate. We also observed genetic groups that occur in a wider range of climates. The genetic groups were also associated with morphological differentiation, mainly the shape of the leaves and the color of the stem or root. This particular structure of diversity along a climatic gradient with association to phenotypic variability can be used for conservation strategies. If such structure is proved to be associated with specific climatic adaptation, it will also allow developing strategies to adapt agriculture to ongoing climate variation in West Africa.
Glato, Kodjo; Aidam, Atsou; Kane, Ndjido Ardo; Bassirou, Diallo; Couderc, Marie; Zekraoui, Leila; Scarcelli, Nora; Barnaud, Adeline; Vigouroux, Yves
2017-01-01
Sub-Saharan agriculture has been identified as vulnerable to ongoing climate change. Adaptation of agriculture has been suggested as a way to maintain productivity. Better knowledge of intra-specific diversity of varieties is prerequisites for the successful management of such adaptation. Among crops, root and tubers play important roles in food security and economic growth for the most vulnerable populations in Africa. Here, we focus on the sweet potato. The Sweet potato (Ipomoea batatas) was domesticated in Central and South America and was later introduced into Africa and is now cultivated throughout tropical Africa. We evaluated its diversity in West Africa by sampling a region extending from the coastal area of Togo to the northern Sahelian region of Senegal that represents a range of climatic conditions. Using 12 microsatellite markers, we evaluated 132 varieties along this gradient. Phenotypic data from field trials conducted in three seasons was also obtained. Genetic diversity in West Africa was found to be 18% lower than in America. Genetic diversity in West Africa is structured into five groups, with some groups found in very specific climatic areas, e.g. under a tropical humid climate, or under a Sahelian climate. We also observed genetic groups that occur in a wider range of climates. The genetic groups were also associated with morphological differentiation, mainly the shape of the leaves and the color of the stem or root. This particular structure of diversity along a climatic gradient with association to phenotypic variability can be used for conservation strategies. If such structure is proved to be associated with specific climatic adaptation, it will also allow developing strategies to adapt agriculture to ongoing climate variation in West Africa.
Anisotropic KPZ growth in 2+1 dimensions: fluctuations and covariance structure
Borodin, Alexei; Ferrari, Patrik L
2009-01-01
In Borodin and Ferrari (2008 arXiv:0804.3035) we studied an interacting particle system which can be also interpreted as a stochastic growth model. This model belongs to the anisotropic KPZ class in 2+1 dimensions. In this paper we present the results that are relevant from the perspective of stochastic growth models, in particular: (a) the surface fluctuations are asymptotically Gaussian on a √ln t scale and (b) the correlation structure of the surface is asymptotically given by the massless field
Brier, Matthew R; Mitra, Anish; McCarthy, John E; Ances, Beau M; Snyder, Abraham Z
2015-11-01
Functional connectivity refers to shared signals among brain regions and is typically assessed in a task free state. Functional connectivity commonly is quantified between signal pairs using Pearson correlation. However, resting-state fMRI is a multivariate process exhibiting a complicated covariance structure. Partial covariance assesses the unique variance shared between two brain regions excluding any widely shared variance, hence is appropriate for the analysis of multivariate fMRI datasets. However, calculation of partial covariance requires inversion of the covariance matrix, which, in most functional connectivity studies, is not invertible owing to rank deficiency. Here we apply Ledoit-Wolf shrinkage (L2 regularization) to invert the high dimensional BOLD covariance matrix. We investigate the network organization and brain-state dependence of partial covariance-based functional connectivity. Although RSNs are conventionally defined in terms of shared variance, removal of widely shared variance, surprisingly, improved the separation of RSNs in a spring embedded graphical model. This result suggests that pair-wise unique shared variance plays a heretofore unrecognized role in RSN covariance organization. In addition, application of partial correlation to fMRI data acquired in the eyes open vs. eyes closed states revealed focal changes in uniquely shared variance between the thalamus and visual cortices. This result suggests that partial correlation of resting state BOLD time series reflect functional processes in addition to structural connectivity. Copyright © 2015 Elsevier Inc. All rights reserved.
Funatogawa, Ikuko; Funatogawa, Takashi
2011-09-01
In randomized trials, an analysis of covariance (ANCOVA) is often used to analyze post-treatment measurements with pre-treatment measurements as a covariate to compare two treatment groups. Random allocation guarantees only equal variances of pre-treatment measurements. We hence consider data with unequal covariances and variances of post-treatment measurements without assuming normality. Recently, we showed that the actual type I error rate of the usual ANCOVA assuming equal slopes and equal residual variances is asymptotically at a nominal level under equal sample sizes, and that of the ANCOVA with unequal variances is asymptotically at a nominal level, even under unequal sample sizes. In this paper, we investigated the asymptotic properties of the ANCOVA with unequal slopes for such data. The estimators of the treatment effect at the observed mean are identical between equal and unequal variance assumptions, and these are asymptotically normal estimators for the treatment effect at the true mean. However, the variances of these estimators based on standard formulas are biased, and the actual type I error rates are not at a nominal level, irrespective of variance assumptions. In equal sample sizes, the efficiency of the usual ANCOVA assuming equal slopes and equal variances is asymptotically the same as those of the ANCOVA with unequal slopes and higher than that of the ANCOVA with equal slopes and unequal variances. Therefore, the use of the usual ANCOVA is appropriate in equal sample sizes. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Dappiaggi, Claudio [Erwin Schroedinger Institut fuer Mathematische Physik, Wien (Austria); Pinamonti, Nicola [Hamburg Univ. (Germany). 2. Inst. fuer Theoretische Physik; Porrmann, Martin [KwaZulu-Natal Univ. (South Africa). Quantum Research Group, School of Physics; National Institute for Theoretical Physics, Durban (South Africa)
2010-01-15
In the framework of the algebraic formulation, we discuss and analyse some new features of the local structure of a real scalar quantum field theory in a strongly causal spacetime. In particular we use the properties of the exponential map to set up a local version of a bulk-to-boundary correspondence. The bulk is a suitable subset of a geodesic neighbourhood of any but fixed point p of the underlying background, while the boundary is a part of the future light cone having p as its own tip. In this regime, we provide a novel notion for the extended *-algebra of Wick polynomials on the said cone and, on the one hand, we prove that it contains the information of the bulk counterpart via an injective *-homomorphism while, on the other hand, we associate to it a distinguished state whose pull-back in the bulk is of Hadamard form. The main advantage of this point of view arises if one uses the universal properties of the exponential map and of the light cone in order to show that, for any two given backgrounds M and M{sup '} and for any two subsets of geodesic neighbourhoods of two arbitrary points, it is possible to engineer the above procedure such that the boundary extended algebras are related via a restriction homomorphism. This allows for the pull-back of boundary states in both spacetimes and, thus, to set up a machinery which permits the comparison of expectation values of local field observables in M and M{sup '}. (orig.)
Dappiaggi, Claudio; Pinamonti, Nicola
2010-01-01
In the framework of the algebraic formulation, we discuss and analyse some new features of the local structure of a real scalar quantum field theory in a strongly causal spacetime. In particular we use the properties of the exponential map to set up a local version of a bulk-to-boundary correspondence. The bulk is a suitable subset of a geodesic neighbourhood of any but fixed point p of the underlying background, while the boundary is a part of the future light cone having p as its own tip. In this regime, we provide a novel notion for the extended *-algebra of Wick polynomials on the said cone and, on the one hand, we prove that it contains the information of the bulk counterpart via an injective *-homomorphism while, on the other hand, we associate to it a distinguished state whose pull-back in the bulk is of Hadamard form. The main advantage of this point of view arises if one uses the universal properties of the exponential map and of the light cone in order to show that, for any two given backgrounds M and M ' and for any two subsets of geodesic neighbourhoods of two arbitrary points, it is possible to engineer the above procedure such that the boundary extended algebras are related via a restriction homomorphism. This allows for the pull-back of boundary states in both spacetimes and, thus, to set up a machinery which permits the comparison of expectation values of local field observables in M and M ' . (orig.)
Lisano, Michael E.
2007-01-01
Recent literature in applied estimation theory reflects growing interest in the sigma-point (also called unscented ) formulation for optimal sequential state estimation, often describing performance comparisons with extended Kalman filters as applied to specific dynamical problems [c.f. 1, 2, 3]. Favorable attributes of sigma-point filters are described as including a lower expected error for nonlinear even non-differentiable dynamical systems, and a straightforward formulation not requiring derivation or implementation of any partial derivative Jacobian matrices. These attributes are particularly attractive, e.g. in terms of enabling simplified code architecture and streamlined testing, in the formulation of estimators for nonlinear spaceflight mechanics systems, such as filter software onboard deep-space robotic spacecraft. As presented in [4], the Sigma-Point Consider Filter (SPCF) algorithm extends the sigma-point filter algorithm to the problem of consider covariance analysis. Considering parameters in a dynamical system, while estimating its state, provides an upper bound on the estimated state covariance, which is viewed as a conservative approach to designing estimators for problems of general guidance, navigation and control. This is because, whether a parameter in the system model is observable or not, error in the knowledge of the value of a non-estimated parameter will increase the actual uncertainty of the estimated state of the system beyond the level formally indicated by the covariance of an estimator that neglects errors or uncertainty in that parameter. The equations for SPCF covariance evolution are obtained in a fashion similar to the derivation approach taken with standard (i.e. linearized or extended) consider parameterized Kalman filters (c.f. [5]). While in [4] the SPCF and linear-theory consider filter (LTCF) were applied to an illustrative linear dynamics/linear measurement problem, in the present work examines the SPCF as applied to
Hierarchical matrix approximation of large covariance matrices
Litvinenko, Alexander; Genton, Marc G.; Sun, Ying
2015-01-01
We approximate large non-structured Matérn covariance matrices of size n×n in the H-matrix format with a log-linear computational cost and storage O(kn log n), where rank k ≪ n is a small integer. Applications are: spatial statistics, machine learning and image analysis, kriging and optimal design.
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.
Mahdi Shariati, Mohammad; Su, Guosheng; Madsen, Per
2007-01-01
The reaction norm model is becoming a popular approach to study genotype x environment interaction (GxE), especially when there is a continuum of environmental effects. These effects are typically unknown, and an approximation that is used in the literature is to replace them by the phenotypic...... means of each environment. It has been shown that this method results in poor inferences and that a more satisfactory alternative is to infer environmental effects jointly with the other parameters of the model. Such a reaction norm model with unknown covariates and heterogeneous residual variances...... across herds was fitted to milk, protein, and fat yield of first-lactation Danish Holstein cows to investigate the presence of GxE. Data included 188,502 first test-day records from 299 herds and 3,775 herd-years in a time period ranging from 1991 to 2003. Variance components and breeding values were...
Converse, Sarah J.; Royle, J. Andrew; Urbanek, Richard P.
2012-01-01
Inbreeding depression is frequently a concern of managers interested in restoring endangered species. Decisions to reduce the potential for inbreeding depression by balancing genotypic contributions to reintroduced populations may exact a cost on long-term demographic performance of the population if those decisions result in reduced numbers of animals released and/or restriction of particularly successful genotypes (i.e., heritable traits of particular family lines). As part of an effort to restore a migratory flock of Whooping Cranes (Grus americana) to eastern North America using the offspring of captive breeders, we obtained a unique dataset which includes post-release mark-recapture data, as well as the pedigree of each released individual. We developed a Bayesian formulation of a multi-state model to analyze radio-telemetry, band-resight, and dead recovery data on reintroduced individuals, in order to track survival and breeding state transitions. We used studbook-based individual covariates to examine the comparative evidence for and degree of effects of inbreeding, genotype, and genotype quality on post-release survival of reintroduced individuals. We demonstrate implementation of the Bayesian multi-state model, which allows for the integration of imperfect detection, multiple data types, random effects, and individual- and time-dependent covariates. Our results provide only weak evidence for an effect of the quality of an individual's genotype in captivity on post-release survival as well as for an effect of inbreeding on post-release survival. We plan to integrate our results into a decision-analytic modeling framework that can explicitly examine tradeoffs between the effects of inbreeding and the effects of genotype and demographic stochasticity on population establishment.
The covariance matrix of the Potts model: A random cluster analysis
Borgs, C.; Chayes, J.T.
1996-01-01
We consider the covariance matrix, G mn = q 2 x ,m); δ(σ y ,n)>, of the d-dimensional q-states Potts model, rewriting it in the random cluster representation of Fortuin and Kasteleyn. In many of the q ordered phases, we identify the eigenvalues of this matrix both in terms of representations of the unbroken symmetry group of the model and in terms of random cluster connectivities and covariances, thereby attributing algebraic significance to these stochastic geometric quantities. We also show that the correlation length and the correlation length corresponding to the decay rate of one on the eigenvalues in the same as the inverse decay rate of the diameter of finite clusters. For dimension of d=2, we show that this correlation length and the correlation length of two-point function with free boundary conditions at the corresponding dual temperature are equal up to a factor of two. For systems with first-order transitions, this relation helps to resolve certain inconsistencies between recent exact and numerical work on correlation lengths at the self-dual point β o . For systems with second order transitions, this relation implies the equality of the correlation length exponents from above below threshold, as well as an amplitude ratio of two. In the course of proving the above results, we establish several properties of independent interest, including left continuity of the inverse correlation length with free boundary conditions and upper semicontinuity of the decay rate for finite clusters in all dimensions, and left continuity of the two-dimensional free boundary condition percolation probability at β o . We also introduce DLR equations for the random cluster model and use them to establish ergodicity of the free measure. In order to prove these results, we introduce a new class of events which we call decoupling events and two inequalities for these events
Cross-population myelination covariance of human cerebral cortex.
Ma, Zhiwei; Zhang, Nanyin
2017-09-01
Cross-population covariance of brain morphometric quantities provides a measure of interareal connectivity, as it is believed to be determined by the coordinated neurodevelopment of connected brain regions. Although useful, structural covariance analysis predominantly employed bulky morphological measures with mixed compartments, whereas studies of the structural covariance of any specific subdivisions such as myelin are rare. Characterizing myelination covariance is of interest, as it will reveal connectivity patterns determined by coordinated development of myeloarchitecture between brain regions. Using myelin content MRI maps from the Human Connectome Project, here we showed that the cortical myelination covariance was highly reproducible, and exhibited a brain organization similar to that previously revealed by other connectivity measures. Additionally, the myelination covariance network shared common topological features of human brain networks such as small-worldness. Furthermore, we found that the correlation between myelination covariance and resting-state functional connectivity (RSFC) was uniform within each resting-state network (RSN), but could considerably vary across RSNs. Interestingly, this myelination covariance-RSFC correlation was appreciably stronger in sensory and motor networks than cognitive and polymodal association networks, possibly due to their different circuitry structures. This study has established a new brain connectivity measure specifically related to axons, and this measure can be valuable to investigating coordinated myeloarchitecture development. Hum Brain Mapp 38:4730-4743, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.
Dimitrios Stamovlasis
2018-04-01
Full Text Available This paper illustrates two psychometric methods, latent class analysis (LCA and taxometric analysis (TA using empirical data from research probing children's mental representation in science learning. LCA is used to obtain a typology based on observed variables and to further investigate how the encountered classes might be related to external variables, where the effectiveness of classification process and the unbiased estimations of parameters become the main concern. In the step-wise LCA, the class membership is assigned and subsequently its relationship with covariates is established. This leading-edge modeling approach suffers from severe downward-biased estimations. The illustration of LCA is focused on alternative bias correction approaches and demonstrates the effect of modal and proportional class-membership assignment along with BCH and ML correction procedures. The illustration of LCA is presented with three covariates, which are psychometric variables operationalizing formal reasoning, divergent thinking and field dependence-independence, respectively. Moreover, taxometric analysis, a method designed to detect the type of the latent structural model, categorical or dimensional, is introduced, along with the relevant basic concepts and tools. TA was applied complementarily in the same data sets to answer the fundamental hypothesis about children's naïve knowledge on the matters under study and it comprises an additional asset in building theory which is fundamental for educational practices. Taxometric analysis provided results that were ambiguous as far as the type of the latent structure. This finding initiates further discussion and sets a problematization within this framework rethinking fundamental assumptions and epistemological issues.
Westgate, Philip M
2016-01-01
When generalized estimating equations (GEE) incorporate an unstructured working correlation matrix, the variances of regression parameter estimates can inflate due to the estimation of the correlation parameters. In previous work, an approximation for this inflation that results in a corrected version of the sandwich formula for the covariance matrix of regression parameter estimates was derived. Use of this correction for correlation structure selection also reduces the over-selection of the unstructured working correlation matrix. In this manuscript, we conduct a simulation study to demonstrate that an increase in variances of regression parameter estimates can occur when GEE incorporates structured working correlation matrices as well. Correspondingly, we show the ability of the corrected version of the sandwich formula to improve the validity of inference and correlation structure selection. We also study the relative influences of two popular corrections to a different source of bias in the empirical sandwich covariance estimator.
Zhang S
2014-07-01
Full Text Available Shiyuan Zhang,1 James Paul,2 Manyat Nantha-Aree,2 Norman Buckley,2 Uswa Shahzad,2 Ji Cheng,2 Justin DeBeer,5 Mitchell Winemaker,5 David Wismer,5 Dinshaw Punthakee,5 Victoria Avram,5 Lehana Thabane1–41Department of Clinical Epidemiology and Biostatistics, 2Department of Anesthesia, McMaster University, Hamilton, ON, Canada; 3Biostatistics Unit/Centre for Evaluation of Medicines, St Joseph's Healthcare - Hamilton, Hamilton, ON, Canada; 4Population Health Research Institute, Hamilton Health Science/McMaster University, 5Department of Surgery, Division of Orthopaedics, McMaster University, Hamilton, ON, CanadaBackground: Although seemingly straightforward, the statistical comparison of a continuous variable in a randomized controlled trial that has both a pre- and posttreatment score presents an interesting challenge for trialists. We present here empirical application of four statistical methods (posttreatment scores with analysis of variance, analysis of covariance, change in scores, and percent change in scores, using data from a randomized controlled trial of postoperative pain in patients following total joint arthroplasty (the Morphine COnsumption in Joint Replacement Patients, With and Without GaBapentin Treatment, a RandomIzed ControlLEd Study [MOBILE] trials.Methods: Analysis of covariance (ANCOVA was used to adjust for baseline measures and to provide an unbiased estimate of the mean group difference of the 1-year postoperative knee flexion scores in knee arthroplasty patients. Robustness tests were done by comparing ANCOVA with three comparative methods: the posttreatment scores, change in scores, and percentage change from baseline.Results: All four methods showed similar direction of effect; however, ANCOVA (-3.9; 95% confidence interval [CI]: -9.5, 1.6; P=0.15 and the posttreatment score (-4.3; 95% CI: -9.8, 1.2; P=0.12 method provided the highest precision of estimate compared with the change score (-3.0; 95% CI: -9.9, 3.8; P=0
Corrado Sandini
2018-05-01
Full Text Available Background: Schizophrenia is currently considered a neurodevelopmental disorder of connectivity. Still few studies have investigated how brain networks develop in children and adolescents who are at risk for developing psychosis. 22q11.2 Deletion Syndrome (22q11DS offers a unique opportunity to investigate the pathogenesis of schizophrenia from a neurodevelopmental perspective. Structural covariance (SC is a powerful approach to explore morphometric relations between brain regions that can furthermore detect biomarkers of psychosis, both in 22q11DS and in the general population.Methods: Here we implement a state-of-the-art sliding-window approach to characterize maturation of SC network architecture in a large longitudinal cohort of patients with 22q11DS (110 with 221 visits and healthy controls (117 with 211 visits. We furthermore propose a new clustering-based approach to group regions according to trajectories of structural connectivity maturation. We correlate measures of SC with development of working memory, a core executive function that is highly affected in both idiopathic psychosis and 22q11DS. Finally, in 22q11DS we explore correlations between SC dysconnectivity and severity of internalizing psychopathology.Results: In HCs network architecture underwent a quadratic developmental trajectory maturing up to mid-adolescence. Late-childhood maturation was particularly evident for fronto-parietal cortices, while Default-Mode-Network-related regions showed a more protracted linear development. Working memory performance was positively correlated with network segregation and fronto-parietal connectivity. In 22q11DS, we demonstrate aberrant maturation of SC with disturbed architecture selectively emerging during adolescence and correlating more severe internalizing psychopathology. Patients also presented a lack of typical network development during late-childhood, that was particularly prominent for frontal connectivity.Conclusions: Our
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.
Cross-covariance functions for multivariate geostatistics
Genton, Marc G.; Kleiber, William
2015-01-01
Continuously indexed datasets with multiple variables have become ubiquitous in the geophysical, ecological, environmental and climate sciences, and pose substantial analysis challenges to scientists and statisticians. For many years, scientists developed models that aimed at capturing the spatial behavior for an individual process; only within the last few decades has it become commonplace to model multiple processes jointly. The key difficulty is in specifying the cross-covariance function, that is, the function responsible for the relationship between distinct variables. Indeed, these cross-covariance functions must be chosen to be consistent with marginal covariance functions in such a way that the second-order structure always yields a nonnegative definite covariance matrix. We review the main approaches to building cross-covariance models, including the linear model of coregionalization, convolution methods, the multivariate Matérn and nonstationary and space-time extensions of these among others. We additionally cover specialized constructions, including those designed for asymmetry, compact support and spherical domains, with a review of physics-constrained models. We illustrate select models on a bivariate regional climate model output example for temperature and pressure, along with a bivariate minimum and maximum temperature observational dataset; we compare models by likelihood value as well as via cross-validation co-kriging studies. The article closes with a discussion of unsolved problems. © Institute of Mathematical Statistics, 2015.
López, J.; Francés, F.
2013-08-01
Recent evidences of the impact of persistent modes of regional climate variability, coupled with the intensification of human activities, have led hydrologists to study flood regime without applying the hypothesis of stationarity. In this study, a framework for flood frequency analysis is developed on the basis of a tool that enables us to address the modelling of non-stationary time series, namely, the "generalized additive models for location, scale and shape" (GAMLSS). Two approaches to non-stationary modelling in GAMLSS were applied to the annual maximum flood records of 20 continental Spanish rivers. The results of the first approach, in which the parameters of the selected distributions were modelled as a function of time only, show the presence of clear non-stationarities in the flood regime. In a second approach, the parameters of the flood distributions are modelled as functions of climate indices (Arctic Oscillation, North Atlantic Oscillation, Mediterranean Oscillation and the Western Mediterranean Oscillation) and a reservoir index that is proposed in this paper. The results when incorporating external covariates in the study highlight the important role of interannual variability in low-frequency climate forcings when modelling the flood regime in continental Spanish rivers. Also, with this approach it is possible to properly introduce the impact on the flood regime of intensified reservoir regulation strategies. The inclusion of external covariates permits the use of these models as predictive tools. Finally, the application of non-stationary analysis shows that the differences between the non-stationary quantiles and their stationary equivalents may be important over long periods of time.
J. López
2013-08-01
Full Text Available Recent evidences of the impact of persistent modes of regional climate variability, coupled with the intensification of human activities, have led hydrologists to study flood regime without applying the hypothesis of stationarity. In this study, a framework for flood frequency analysis is developed on the basis of a tool that enables us to address the modelling of non-stationary time series, namely, the "generalized additive models for location, scale and shape" (GAMLSS. Two approaches to non-stationary modelling in GAMLSS were applied to the annual maximum flood records of 20 continental Spanish rivers. The results of the first approach, in which the parameters of the selected distributions were modelled as a function of time only, show the presence of clear non-stationarities in the flood regime. In a second approach, the parameters of the flood distributions are modelled as functions of climate indices (Arctic Oscillation, North Atlantic Oscillation, Mediterranean Oscillation and the Western Mediterranean Oscillation and a reservoir index that is proposed in this paper. The results when incorporating external covariates in the study highlight the important role of interannual variability in low-frequency climate forcings when modelling the flood regime in continental Spanish rivers. Also, with this approach it is possible to properly introduce the impact on the flood regime of intensified reservoir regulation strategies. The inclusion of external covariates permits the use of these models as predictive tools. Finally, the application of non-stationary analysis shows that the differences between the non-stationary quantiles and their stationary equivalents may be important over long periods of time.
Generally covariant gauge theories
Capovilla, R.
1992-01-01
A new class of generally covariant gauge theories in four space-time dimensions is investigated. The field variables are taken to be a Lie algebra valued connection 1-form and a scalar density. Modulo an important degeneracy, complex [euclidean] vacuum general relativity corresponds to a special case in this class. A canonical analysis of the generally covariant gauge theories with the same gauge group as general relativity shows that they describe two degrees of freedom per space point, qualifying therefore as a new set of neighbors of general relativity. The modification of the algebra of the constraints with respect to the general relativity case is computed; this is used in addressing the question of how general relativity stands out from its neighbors. (orig.)
Ali, M. Sanni|info:eu-repo/dai/nl/345709497; Groenwold, Rolf H.H.; Belitser, S.|info:eu-repo/dai/nl/304843865; Pestman, Wiebe R.; Hoes, Arno W.; Roes, Kit C.B.; Boer, Anthonius De|info:eu-repo/dai/nl/075097346; Klungel, Olaf H.|info:eu-repo/dai/nl/181447649
2015-01-01
Objectives To assess the current practice of propensity score (PS) analysis in the medical literature, particularly the assessment and reporting of balance on confounders. Study Design and Setting A PubMed search identified studies using PS methods from December 2011 through May 2012. For each
A simple sample size formula for analysis of covariance in cluster randomized trials.
Teerenstra, S.; Eldridge, S.; Graff, M.J.; Hoop, E. de; Borm, G.F.
2012-01-01
For cluster randomized trials with a continuous outcome, the sample size is often calculated as if an analysis of the outcomes at the end of the treatment period (follow-up scores) would be performed. However, often a baseline measurement of the outcome is available or feasible to obtain. An
Bhadra, Anindya
2013-04-22
We describe a Bayesian technique to (a) perform a sparse joint selection of significant predictor variables and significant inverse covariance matrix elements of the response variables in a high-dimensional linear Gaussian sparse seemingly unrelated regression (SSUR) setting and (b) perform an association analysis between the high-dimensional sets of predictors and responses in such a setting. To search the high-dimensional model space, where both the number of predictors and the number of possibly correlated responses can be larger than the sample size, we demonstrate that a marginalization-based collapsed Gibbs sampler, in combination with spike and slab type of priors, offers a computationally feasible and efficient solution. As an example, we apply our method to an expression quantitative trait loci (eQTL) analysis on publicly available single nucleotide polymorphism (SNP) and gene expression data for humans where the primary interest lies in finding the significant associations between the sets of SNPs and possibly correlated genetic transcripts. Our method also allows for inference on the sparse interaction network of the transcripts (response variables) after accounting for the effect of the SNPs (predictor variables). We exploit properties of Gaussian graphical models to make statements concerning conditional independence of the responses. Our method compares favorably to existing Bayesian approaches developed for this purpose. © 2013, The International Biometric Society.
Gong, Maozhen
Selecting an appropriate prior distribution is a fundamental issue in Bayesian Statistics. In this dissertation, under the framework provided by Berger and Bernardo, I derive the reference priors for several models which include: Analysis of Variance (ANOVA)/Analysis of Covariance (ANCOVA) models with a categorical variable under common ordering constraints, the conditionally autoregressive (CAR) models and the simultaneous autoregressive (SAR) models with a spatial autoregression parameter rho considered. The performances of reference priors for ANOVA/ANCOVA models are evaluated by simulation studies with comparisons to Jeffreys' prior and Least Squares Estimation (LSE). The priors are then illustrated in a Bayesian model of the "Risk of Type 2 Diabetes in New Mexico" data, where the relationship between the type 2 diabetes risk (through Hemoglobin A1c) and different smoking levels is investigated. In both simulation studies and real data set modeling, the reference priors that incorporate internal order information show good performances and can be used as default priors. The reference priors for the CAR and SAR models are also illustrated in the "1999 SAT State Average Verbal Scores" data with a comparison to a Uniform prior distribution. Due to the complexity of the reference priors for both CAR and SAR models, only a portion (12 states in the Midwest) of the original data set is considered. The reference priors can give a different marginal posterior distribution compared to a Uniform prior, which provides an alternative for prior specifications for areal data in Spatial statistics.
Voronin I.
2016-01-01
Full Text Available Structural equation modelling (SEM has become an important tool in behaviour genetic research. The application of SEM for multivariate twin analysis allows revealing the structure of genetic and environmental factors underlying individual differences in human traits. We outline the framework of twin method and SEM, describe SEM implementation of a multivariate twin model and provide an example of a multivariate twin study. The study included 901 adolescent twin pairs from Russia. We measured general cognitive ability and characteristics of working memory and planning. The individual differences in working memory and planning were explained mostly by person-specific environment. The variability of intelligence is related to genes, family environment, and person specific environment. Moderate and weak associations between intelligence, working memory, and planning were entirely explained by shared environmental effects.
Covariance Manipulation for Conjunction Assessment
Hejduk, M. D.
2016-01-01
The manipulation of space object covariances to try to provide additional or improved information to conjunction risk assessment is not an uncommon practice. Types of manipulation include fabricating a covariance when it is missing or unreliable to force the probability of collision (Pc) to a maximum value ('PcMax'), scaling a covariance to try to improve its realism or see the effect of covariance volatility on the calculated Pc, and constructing the equivalent of an epoch covariance at a convenient future point in the event ('covariance forecasting'). In bringing these methods to bear for Conjunction Assessment (CA) operations, however, some do not remain fully consistent with best practices for conducting risk management, some seem to be of relatively low utility, and some require additional information before they can contribute fully to risk analysis. This study describes some basic principles of modern risk management (following the Kaplan construct) and then examines the PcMax and covariance forecasting paradigms for alignment with these principles; it then further examines the expected utility of these methods in the modern CA framework. Both paradigms are found to be not without utility, but only in situations that are somewhat carefully circumscribed.
Covariate measurement error correction methods in mediation analysis with failure time data.
Zhao, Shanshan; Prentice, Ross L
2014-12-01
Mediation analysis is important for understanding the mechanisms whereby one variable causes changes in another. Measurement error could obscure the ability of the potential mediator to explain such changes. This article focuses on developing correction methods for measurement error in the mediator with failure time outcomes. We consider a broad definition of measurement error, including technical error, and error associated with temporal variation. The underlying model with the "true" mediator is assumed to be of the Cox proportional hazards model form. The induced hazard ratio for the observed mediator no longer has a simple form independent of the baseline hazard function, due to the conditioning event. We propose a mean-variance regression calibration approach and a follow-up time regression calibration approach, to approximate the partial likelihood for the induced hazard function. Both methods demonstrate value in assessing mediation effects in simulation studies. These methods are generalized to multiple biomarkers and to both case-cohort and nested case-control sampling designs. We apply these correction methods to the Women's Health Initiative hormone therapy trials to understand the mediation effect of several serum sex hormone measures on the relationship between postmenopausal hormone therapy and breast cancer risk. © 2014, The International Biometric Society.
Probabilistic Structural Analysis Program
Pai, Shantaram S.; Chamis, Christos C.; Murthy, Pappu L. N.; Stefko, George L.; Riha, David S.; Thacker, Ben H.; Nagpal, Vinod K.; Mital, Subodh K.
2010-01-01
NASA/NESSUS 6.2c is a general-purpose, probabilistic analysis program that computes probability of failure and probabilistic sensitivity measures of engineered systems. Because NASA/NESSUS uses highly computationally efficient and accurate analysis techniques, probabilistic solutions can be obtained even for extremely large and complex models. Once the probabilistic response is quantified, the results can be used to support risk-informed decisions regarding reliability for safety-critical and one-of-a-kind systems, as well as for maintaining a level of quality while reducing manufacturing costs for larger-quantity products. NASA/NESSUS has been successfully applied to a diverse range of problems in aerospace, gas turbine engines, biomechanics, pipelines, defense, weaponry, and infrastructure. This program combines state-of-the-art probabilistic algorithms with general-purpose structural analysis and lifting methods to compute the probabilistic response and reliability of engineered structures. Uncertainties in load, material properties, geometry, boundary conditions, and initial conditions can be simulated. The structural analysis methods include non-linear finite-element methods, heat-transfer analysis, polymer/ceramic matrix composite analysis, monolithic (conventional metallic) materials life-prediction methodologies, boundary element methods, and user-written subroutines. Several probabilistic algorithms are available such as the advanced mean value method and the adaptive importance sampling method. NASA/NESSUS 6.2c is structured in a modular format with 15 elements.
Lim, Ki Chun; Kim, J. S.; Kim, C. Y.; Lee, H. K.; Moon, D. H. [Ulsan University, Seoul (Korea, Republic of)
2002-07-01
We investigated the relations between rCBF and psychopathology in schizophrenic patients using a SPM99. Thirty-two patients(M/F:22/10, 25{+-}5,6yr) with active symptoms of schizophrenia and 15 age matched normal controls underwent Tc-99m ECD brain perfusion SPECT. Psychopathology of all patients were also assessed according to PANSS (positive and negative syndrome scale in schizophrenia). By covariate SPM analysis, specific areas where rCBF correlated with sum scores of positive/negative synptoms were identified. Regional CBF of schizophrenics was different in several cortical regions from normal controls. Sum scores of positive symptoms were positively correlated with rCBF of both rectal and inferior frontal gyri and right transverse temporal gyrus, and negatively correlated with rCBF of left lingual and right middle temporal gyri (p<0.01). Sum scores of negative symptoms were positively correlated with rCBF of both middle temporal gyri and negatively correlated with rCBF of right superior parietal lobule and medial frontal gyrus (p<0.01). Positive and negative symptoms of schizophrenia were correlated with rCBF change in different regions of cerebral association cortex.
Lim, Ki Chun; Kim, J. S.; Kim, C. Y.; Lee, H. K.; Moon, D. H.
2002-01-01
We investigated the relations between rCBF and psychopathology in schizophrenic patients using a SPM99. Thirty-two patients(M/F:22/10, 25±5,6yr) with active symptoms of schizophrenia and 15 age matched normal controls underwent Tc-99m ECD brain perfusion SPECT. Psychopathology of all patients were also assessed according to PANSS (positive and negative syndrome scale in schizophrenia). By covariate SPM analysis, specific areas where rCBF correlated with sum scores of positive/negative synptoms were identified. Regional CBF of schizophrenics was different in several cortical regions from normal controls. Sum scores of positive symptoms were positively correlated with rCBF of both rectal and inferior frontal gyri and right transverse temporal gyrus, and negatively correlated with rCBF of left lingual and right middle temporal gyri (p<0.01). Sum scores of negative symptoms were positively correlated with rCBF of both middle temporal gyri and negatively correlated with rCBF of right superior parietal lobule and medial frontal gyrus (p<0.01). Positive and negative symptoms of schizophrenia were correlated with rCBF change in different regions of cerebral association cortex
Large Covariance Estimation by Thresholding Principal Orthogonal Complements
Fan, Jianqing; Liao, Yuan; Mincheva, Martina
2012-01-01
This paper deals with the estimation of a high-dimensional covariance with a conditional sparsity structure and fast-diverging eigenvalues. By assuming sparse error covariance matrix in an approximate factor model, we allow for the presence of some cross-sectional correlation even after taking out common but unobservable factors. We introduce the Principal Orthogonal complEment Thresholding (POET) method to explore such an approximate factor structure with sparsity. The POET estimator includes the sample covariance matrix, the factor-based covariance matrix (Fan, Fan, and Lv, 2008), the thresholding estimator (Bickel and Levina, 2008) and the adaptive thresholding estimator (Cai and Liu, 2011) as specific examples. We provide mathematical insights when the factor analysis is approximately the same as the principal component analysis for high-dimensional data. The rates of convergence of the sparse residual covariance matrix and the conditional sparse covariance matrix are studied under various norms. It is shown that the impact of estimating the unknown factors vanishes as the dimensionality increases. The uniform rates of convergence for the unobserved factors and their factor loadings are derived. The asymptotic results are also verified by extensive simulation studies. Finally, a real data application on portfolio allocation is presented. PMID:24348088
Large Covariance Estimation by Thresholding Principal Orthogonal Complements.
Fan, Jianqing; Liao, Yuan; Mincheva, Martina
2013-09-01
This paper deals with the estimation of a high-dimensional covariance with a conditional sparsity structure and fast-diverging eigenvalues. By assuming sparse error covariance matrix in an approximate factor model, we allow for the presence of some cross-sectional correlation even after taking out common but unobservable factors. We introduce the Principal Orthogonal complEment Thresholding (POET) method to explore such an approximate factor structure with sparsity. The POET estimator includes the sample covariance matrix, the factor-based covariance matrix (Fan, Fan, and Lv, 2008), the thresholding estimator (Bickel and Levina, 2008) and the adaptive thresholding estimator (Cai and Liu, 2011) as specific examples. We provide mathematical insights when the factor analysis is approximately the same as the principal component analysis for high-dimensional data. The rates of convergence of the sparse residual covariance matrix and the conditional sparse covariance matrix are studied under various norms. It is shown that the impact of estimating the unknown factors vanishes as the dimensionality increases. The uniform rates of convergence for the unobserved factors and their factor loadings are derived. The asymptotic results are also verified by extensive simulation studies. Finally, a real data application on portfolio allocation is presented.
On discriminant analysis techniques and correlation structures in high dimensions
Clemmensen, Line Katrine Harder
This paper compares several recently proposed techniques for performing discriminant analysis in high dimensions, and illustrates that the various sparse methods dier in prediction abilities depending on their underlying assumptions about the correlation structures in the data. The techniques...... the methods in two: Those who assume independence between the variables and thus use a diagonal estimate of the within-class covariance matrix, and those who assume dependence between the variables and thus use an estimate of the within-class covariance matrix, which also estimates the correlations between...... variables. The two groups of methods are compared and the pros and cons are exemplied using dierent cases of simulated data. The results illustrate that the estimate of the covariance matrix is an important factor with respect to choice of method, and the choice of method should thus be driven by the nature...
Covariant effective action for loop quantum cosmology from order reduction
Sotiriou, Thomas P.
2009-01-01
Loop quantum cosmology (LQC) seems to be predicting modified effective Friedmann equations without extra degrees of freedom. A puzzle arises if one decides to seek for a covariant effective action which would lead to the given Friedmann equation: The Einstein-Hilbert action is the only action that leads to second order field equations and, hence, there exists no covariant action which, under metric variation, leads to a modified Friedmann equation without extra degrees of freedom. It is shown that, at least for isotropic models in LQC, this issue is naturally resolved and a covariant effective action can be found if one considers higher order theories of gravity but faithfully follows effective field theory techniques. However, our analysis also raises doubts on whether a covariant description without background structures can be found for anisotropic models.
Gaskins, J T; Daniels, M J
2016-01-02
The estimation of the covariance matrix is a key concern in the analysis of longitudinal data. When data consists of multiple groups, it is often assumed the covariance matrices are either equal across groups or are completely distinct. We seek methodology to allow borrowing of strength across potentially similar groups to improve estimation. To that end, we introduce a covariance partition prior which proposes a partition of the groups at each measurement time. Groups in the same set of the partition share dependence parameters for the distribution of the current measurement given the preceding ones, and the sequence of partitions is modeled as a Markov chain to encourage similar structure at nearby measurement times. This approach additionally encourages a lower-dimensional structure of the covariance matrices by shrinking the parameters of the Cholesky decomposition toward zero. We demonstrate the performance of our model through two simulation studies and the analysis of data from a depression study. This article includes Supplementary Material available online.
Choi, Seung Hoan; Labadorf, Adam T; Myers, Richard H; Lunetta, Kathryn L; Dupuis, Josée; DeStefano, Anita L
2017-02-06
Next generation sequencing provides a count of RNA molecules in the form of short reads, yielding discrete, often highly non-normally distributed gene expression measurements. Although Negative Binomial (NB) regression has been generally accepted in the analysis of RNA sequencing (RNA-Seq) data, its appropriateness has not been exhaustively evaluated. We explore logistic regression as an alternative method for RNA-Seq studies designed to compare cases and controls, where disease status is modeled as a function of RNA-Seq reads using simulated and Huntington disease data. We evaluate the effect of adjusting for covariates that have an unknown relationship with gene expression. Finally, we incorporate the data adaptive method in order to compare false positive rates. When the sample size is small or the expression levels of a gene are highly dispersed, the NB regression shows inflated Type-I error rates but the Classical logistic and Bayes logistic (BL) regressions are conservative. Firth's logistic (FL) regression performs well or is slightly conservative. Large sample size and low dispersion generally make Type-I error rates of all methods close to nominal alpha levels of 0.05 and 0.01. However, Type-I error rates are controlled after applying the data adaptive method. The NB, BL, and FL regressions gain increased power with large sample size, large log2 fold-change, and low dispersion. The FL regression has comparable power to NB regression. We conclude that implementing the data adaptive method appropriately controls Type-I error rates in RNA-Seq analysis. Firth's logistic regression provides a concise statistical inference process and reduces spurious associations from inaccurately estimated dispersion parameters in the negative binomial framework.
Kawano, Toshihiko; Shibata, Keiichi.
1997-09-01
A covariance evaluation system for the evaluated nuclear data library was established. The parameter estimation method and the least squares method with a spline function are used to generate the covariance data. Uncertainties of nuclear reaction model parameters are estimated from experimental data uncertainties, then the covariance of the evaluated cross sections is calculated by means of error propagation. Computer programs ELIESE-3, EGNASH4, ECIS, and CASTHY are used. Covariances of 238 U reaction cross sections were calculated with this system. (author)
Structural analysis for Diagnosis
Izadi-Zamanabadi, Roozbeh; Blanke, M.
2001-01-01
Aiming at design of algorithms for fault diagnosis, structural analysis of systems offers concise yet easy overall analysis. Graph-based matching, which is the essential technique to obtain redundant information for diagnosis, is re-considered in this paper. Matching is re-formulated as a problem...... of relating faults to known parameters and measurements of a system. Using explicit fault modelling, minimal over-determined subsystems are shown to provide necessary redundancy relations from the matching. Details of the method are presented and a realistic example used to clearly describe individual steps...
Structural analysis for diagnosis
Izadi-Zamanabadi, Roozbeh; Blanke, M.
2002-01-01
Aiming at design of algorithms for fault diagnosis, structural analysis of systems offers concise yet easy overall analysis. Graph-based matching, which is the essential tech-nique to obtain redundant information for diagnosis, is reconsidered in this paper. Matching is reformulated as a problem...... of relating faults to known parameters and measurements of a system. Using explicit fault modelling, minimal overdetermined subsystems are shown to provide necessary redundancy relations from the matching. Details of the method are presented and a realistic example used to clearly describe individual steps....
Generalized structured component analysis a component-based approach to structural equation modeling
Hwang, Heungsun
2014-01-01
Winner of the 2015 Sugiyama Meiko Award (Publication Award) of the Behaviormetric Society of Japan Developed by the authors, generalized structured component analysis is an alternative to two longstanding approaches to structural equation modeling: covariance structure analysis and partial least squares path modeling. Generalized structured component analysis allows researchers to evaluate the adequacy of a model as a whole, compare a model to alternative specifications, and conduct complex analyses in a straightforward manner. Generalized Structured Component Analysis: A Component-Based Approach to Structural Equation Modeling provides a detailed account of this novel statistical methodology and its various extensions. The authors present the theoretical underpinnings of generalized structured component analysis and demonstrate how it can be applied to various empirical examples. The book enables quantitative methodologists, applied researchers, and practitioners to grasp the basic concepts behind this new a...
Poulsen, Mikael Zebbelin
2002-01-01
, by the implementation of the Simpy tool box. This is an object oriented system implemented in the Python language. It can be used for analysis of DAEs, ODEs and non-linear equation and uses e.g. symbolic representations of expressions and equations. The presentations of theory and algorithms for structural index......Differential algebraic equations (DAEs) constitute a fundamental model class for many modelling purposes in engineering and other sciences, especially for dynamical simulation of component based systems. This thesis describes a practical methodology and approach for analysing general DAE...... analysis of DAE is original in the sense that it is based on a new matrix representation of the structural information of a general DAE system instead of a graph oriented representation. Also the presentation of the theory is found to be more complete compared to other presentations, since it e.g. proves...
Determination of k{sub 0} for {sup 63}Cu(n,γ){sup 64}Cu reaction with covariance analysis
Barros, Livia F.; Dias, Mauro S.; Koskinas, Marina F.; Yamazaki, Ione M.; Semmler, Renato, E-mail: lfbarros@ipen.br [Instituto de Pesquisas Energeticas e Nucleares (IPEN/CNEN-SP), Sao Paulo, SP (Brazil)
2015-07-01
Nowadays the k{sub 0} Method is one of the most used procedures on Neutron Activation Analysis (NAA). For an element of interest, the parameter k{sub 0} can be used to determine its mass concentration. The recommended nuclear data has been investigated, and the measurement of this parameter for {sup 63}Cu(n,γ){sup 64}Cu reaction was motivated by some discrepancies that were observed in the literature. The irradiations were performed near the core of the IEA-R1 4.5 MW nuclear research reactor of the Nuclear and Energy Research Institute – IPEN-CNEN/SP, in São Paulo, Brazil. Two irradiations were carried out in sequence, using two sets of samples: the first one with a cadmium cover around the samples and the second one without it. The activity measurements were carried out in a previously calibrated HPGe gamma-ray spectrometer. Standard sources of {sup 152}Eu, {sup 133}Ba, {sup 60}Co and {sup 13}'7Cs supplied by the IAEA with gamma transitions ranging from 121 keV to 1408 keV were used in order to obtain the HPGe gamma-ray peak efficiency as a function of the energy. The covariance matrix methodology was applied to all uncertainties involved. The resulting value of k{sub 0} for {sup 63}Cu(n,γ){sup 64}Cu reaction for the gamma transition energy of the formed isotope {sup 64}Cu 1345.77 keV was 4.99 x 10{sup -4} (78).This final value for k{sub 0} has been compared with the literature. (author)
Batson, Sarah; Score, Robert; Sutton, Alex J
2017-06-01
The aim of the study was to develop the three-dimensional (3D) evidence network plot system-a novel web-based interactive 3D tool to facilitate the visualization and exploration of covariate distributions and imbalances across evidence networks for network meta-analysis (NMA). We developed the 3D evidence network plot system within an AngularJS environment using a third party JavaScript library (Three.js) to create the 3D element of the application. Data used to enable the creation of the 3D element for a particular topic are inputted via a Microsoft Excel template spreadsheet that has been specifically formatted to hold these data. We display and discuss the findings of applying the tool to two NMA examples considering multiple covariates. These two examples have been previously identified as having potentially important covariate effects and allow us to document the various features of the tool while illustrating how it can be used. The 3D evidence network plot system provides an immediate, intuitive, and accessible way to assess the similarity and differences between the values of covariates for individual studies within and between each treatment contrast in an evidence network. In this way, differences between the studies, which may invalidate the usual assumptions of an NMA, can be identified for further scrutiny. Hence, the tool facilitates NMA feasibility/validity assessments and aids in the interpretation of NMA results. The 3D evidence network plot system is the first tool designed specifically to visualize covariate distributions and imbalances across evidence networks in 3D. This will be of primary interest to systematic review and meta-analysis researchers and, more generally, those assessing the validity and robustness of an NMA to inform reimbursement decisions. Copyright © 2017 Elsevier Inc. All rights reserved.
Visualization and assessment of spatio-temporal covariance properties
Huang, Huang
2017-11-23
Spatio-temporal covariances are important for describing the spatio-temporal variability of underlying random fields in geostatistical data. For second-order stationary random fields, there exist subclasses of covariance functions that assume a simpler spatio-temporal dependence structure with separability and full symmetry. However, it is challenging to visualize and assess separability and full symmetry from spatio-temporal observations. In this work, we propose a functional data analysis approach that constructs test functions using the cross-covariances from time series observed at each pair of spatial locations. These test functions of temporal lags summarize the properties of separability or symmetry for the given spatial pairs. We use functional boxplots to visualize the functional median and the variability of the test functions, where the extent of departure from zero at all temporal lags indicates the degree of non-separability or asymmetry. We also develop a rank-based nonparametric testing procedure for assessing the significance of the non-separability or asymmetry. Essentially, the proposed methods only require the analysis of temporal covariance functions. Thus, a major advantage over existing approaches is that there is no need to estimate any covariance matrix for selected spatio-temporal lags. The performances of the proposed methods are examined by simulations with various commonly used spatio-temporal covariance models. To illustrate our methods in practical applications, we apply it to real datasets, including weather station data and climate model outputs.
HAALAND, DAVID M.; VAN BENTHEM, MARK H.; WEHLBURG, CHRISTINE M.; KOEHLER, IV FREDERICK W.
2002-01-01
Hyperspectral Fourier transform infrared images have been obtained from a neoprene sample aged in air at elevated temperatures. The massive amount of spectra available from this heterogeneous sample provides the opportunity to perform quantitative analysis of the spectral data without the need for calibration standards. Multivariate curve resolution (MCR) methods with non-negativity constraints applied to the iterative alternating least squares analysis of the spectral data has been shown to achieve the goal of quantitative image analysis without the use of standards. However, the pure-component spectra and the relative concentration maps were heavily contaminated by the presence of system artifacts in the spectral data. We have demonstrated that the detrimental effects of these artifacts can be minimized by adding an estimate of the error covariance structure of the spectral image data to the MCR algorithm. The estimate is added by augmenting the concentration and pure-component spectra matrices with scores and eigenvectors obtained from the mean-centered repeat image differences of the sample. The implementation of augmentation is accomplished by employing efficient equality constraints on the MCR analysis. Augmentation with the scores from the repeat images is found to primarily improve the pure-component spectral estimates while augmentation with the corresponding eigenvectors primarily improves the concentration maps. Augmentation with both scores and eigenvectors yielded the best result by generating less noisy pure-component spectral estimates and relative concentration maps that were largely free from a striping artifact that is present due to system errors in the FT-IR images. The MCR methods presented are general and can also be applied productively to non-image spectral data
Kozlov, Daniil
2014-05-01
The topographical, soil and vegetation maps of FLUXNET study areas are widely used for interpretation of eddy covariance measurements, for calibration of biogeochemical models and for making regional assessments of carbon balance. The poster presents methodological problems and results of ecosystem mapping using GIS, remote sensing, statistical and field methods on the example of two RusFluxNet sites in the Central Forest (33° E, 56°30'N) and Central Chernozem (36°10' E, 51°36'N) reserves. In the Central Forest reserve tacheometric measurements were used for topographical and peat surveys of bogged sphagnum spruce forest of 20-hectare area. Its common borders and its areas affected by windfall were determined. The supplies and spatial distribution of organic matter were obtained. The datasets of groundwater monitoring measurements on ten wells were compared with each other and the analysis of spatial and temporal groundwater variability was performed. The map of typical ecosystems of the reserve and its surroundings was created on the basis of analysis of multi-temporal Landsat images. In the Central Chernozem reserve the GNSS topographical survey was used for flux tower footprint mapping (22 ha). The features of microrelief predetermine development of different soils within the footprint. Close relationship between soil (73 drilling site) and terrain attributes (DEM with 2.5 m) allowed to build maps of soils and soil properties: carbon content, bulk density, upper boundary of secondary carbonates. Position for chamber-based soil respiration measurements was defined on the basis of these maps. The detailed geodetic and soil surveys of virgin lands and plowland were performed in order to estimate the effect of agrogenic processes such as dehumification, compaction and erosion on soils during the whole period of agricultural use of Central Chernozem reserve area and around. The choice of analogous soils was based on the similarity of their position within the
Liao, Hstau Y; Hashem, Yaser; Frank, Joachim
2015-06-02
Single-particle cryogenic electron microscopy (cryo-EM) is a powerful tool for the study of macromolecular structures at high resolution. Classification allows multiple structural states to be extracted and reconstructed from the same sample. One classification approach is via the covariance matrix, which captures the correlation between every pair of voxels. Earlier approaches employ computing-intensive resampling and estimate only the eigenvectors of the matrix, which are then used in a separate fast classification step. We propose an iterative scheme to explicitly estimate the covariance matrix in its entirety. In our approach, the flexibility in choosing the solution domain allows us to examine a part of the molecule in greater detail. Three-dimensional covariance maps obtained in this way from experimental data (cryo-EM images of the eukaryotic pre-initiation complex) prove to be in excellent agreement with conclusions derived by using traditional approaches, revealing in addition the interdependencies of ligand bindings and structural changes. Copyright © 2015 Elsevier Ltd. All rights reserved.
Networks of myelin covariance.
Melie-Garcia, Lester; Slater, David; Ruef, Anne; Sanabria-Diaz, Gretel; Preisig, Martin; Kherif, Ferath; Draganski, Bogdan; Lutti, Antoine
2018-04-01
Networks of anatomical covariance have been widely used to study connectivity patterns in both normal and pathological brains based on the concurrent changes of morphometric measures (i.e., cortical thickness) between brain structures across subjects (Evans, ). However, the existence of networks of microstructural changes within brain tissue has been largely unexplored so far. In this article, we studied in vivo the concurrent myelination processes among brain anatomical structures that gathered together emerge to form nonrandom networks. We name these "networks of myelin covariance" (Myelin-Nets). The Myelin-Nets were built from quantitative Magnetization Transfer data-an in-vivo magnetic resonance imaging (MRI) marker of myelin content. The synchronicity of the variations in myelin content between anatomical regions was measured by computing the Pearson's correlation coefficient. We were especially interested in elucidating the effect of age on the topological organization of the Myelin-Nets. We therefore selected two age groups: Young-Age (20-31 years old) and Old-Age (60-71 years old) and a pool of participants from 48 to 87 years old for a Myelin-Nets aging trajectory study. We found that the topological organization of the Myelin-Nets is strongly shaped by aging processes. The global myelin correlation strength, between homologous regions and locally in different brain lobes, showed a significant dependence on age. Interestingly, we also showed that the aging process modulates the resilience of the Myelin-Nets to damage of principal network structures. In summary, this work sheds light on the organizational principles driving myelination and myelin degeneration in brain gray matter and how such patterns are modulated by aging. © 2017 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.
Eekhout, I.; Wiel, M.A. van de; Heymans, M.W.
2017-01-01
Background. Multiple imputation is a recommended method to handle missing data. For significance testing after multiple imputation, Rubin’s Rules (RR) are easily applied to pool parameter estimates. In a logistic regression model, to consider whether a categorical covariate with more than two levels
ANL Critical Assembly Covariance Matrix Generation - Addendum
McKnight, Richard D. [Argonne National Lab. (ANL), Argonne, IL (United States); Grimm, Karl N. [Argonne National Lab. (ANL), Argonne, IL (United States)
2014-01-13
In March 2012, a report was issued on covariance matrices for Argonne National Laboratory (ANL) critical experiments. That report detailed the theory behind the calculation of covariance matrices and the methodology used to determine the matrices for a set of 33 ANL experimental set-ups. Since that time, three new experiments have been evaluated and approved. This report essentially updates the previous report by adding in these new experiments to the preceding covariance matrix structure.
Ottowitz, William E.; Derro, David; Dougherty, Darin D.; Lindquist, Martin A.; Fischman, Alan J.; Hall, Janet E.
2014-01-01
Objectives 1.) Expand the scope of neuroendocrine applications of functional neuroimaging techniques. 2.) Compare the covariance of amygdalar activity with that of the rest of the brain during pre- and post-menopausal levels of estrogen (E2). Based on the distribution of cortical E2 receptors and the neocortical regions where E2 has been shown to preferentially accumulate, we predict that E2 infusion will increase covariance of amygdalar activity with that of the temporal and frontal cortices. Design This basic physiology study employed a within-subject design. All participants were post-menopausal women (n =7). Analysis of covariance between whole brain and amygdalar regional cerebral glucose consumption (CMRglc) was conducted in a voxel-wise manner by means of the basic regression option in SPM2 and was applied to FDG-PET scans acquired at baseline and after a 24 hour graded E2 infusion. Setting an academic medical center; Massachusetts General Hospital, Boston, Massachusetts. Results E2 levels (mean ± sem) were significantly greater at 24 hours (257.9 pg/mL ± 29.7) than at 0 hours (28.1 pg/mL ± 3.4). Right amygdalar CMRglc showed a significant covariance with activity of three different regions of the temporal cortex during E2 infusion, but none at baseline. In addition, right amygdalar CMRglc covaried with that of the right medial and superior frontal gyri only during E2 infusion. Conclusions In addition to suggesting changes in amygdalar-cortical network connectivity as a result of short-term E2 exposure, these analyses provide evidence that basic neuroendocrine research may benefit from further use of FDG-PET and other functional neuroimaging modalities for network level analyses. PMID:18766152
Larsen, Michael Holm
1999-01-01
This note introduces the IDEF0 modelling language (semantics and syntax), and associated rules and techniques, for developing structured graphical representations of a system or enterprise. Use of this standard for IDEF0 permits the construction of models comprising system functions (activities...... that require a modelling technique for the analysis, development, re-engineering, integration, or acquisition of information systems; and incorporate a systems or enterprise modelling technique into a business process analysis or software engineering methodology.This note is a summary of the Standard...... for Integration Definition for Function Modelling (IDEF0). I.e. the Draft Federal Information Processing Standards Publication 183, 1993, December 21, Announcing the Standard for Integration Definition for Function Modelling (IDEF0)....
Competing risks and time-dependent covariates
Cortese, Giuliana; Andersen, Per K
2010-01-01
Time-dependent covariates are frequently encountered in regression analysis for event history data and competing risks. They are often essential predictors, which cannot be substituted by time-fixed covariates. This study briefly recalls the different types of time-dependent covariates......, as classified by Kalbfleisch and Prentice [The Statistical Analysis of Failure Time Data, Wiley, New York, 2002] with the intent of clarifying their role and emphasizing the limitations in standard survival models and in the competing risks setting. If random (internal) time-dependent covariates...
Activities of covariance utilization working group
Tsujimoto, Kazufumi
2013-01-01
During the past decade, there has been a interest in the calculational uncertainties induced by nuclear data uncertainties in the neutronics design of advanced nuclear system. The covariance nuclear data is absolutely essential for the uncertainty analysis. In the latest version of JENDL, JENDL-4.0, the covariance data for many nuclides, especially actinide nuclides, was substantialy enhanced. The growing interest in the uncertainty analysis and the covariance data has led to the organisation of the working group for covariance utilization under the JENDL committee. (author)
Wang, Hui-Mei; Wang, Wen-Jie; Chen, Huanfeng; Zhang, Zhonghua; Mao, Zijun; Zu, Yuan-Gang
2014-04-01
Soil physic-chemical properties differ at different depths; however, differences in afforestation-induced temporal changes at different soil depths are seldom reported. By examining 19 parameters, the temporal changes and their interactions with soil depth in a large chronosequence dataset (159 plots; 636 profiles; 2544 samples) of larch plantations were checked by multivariate analysis of covariance (MANCOVA). No linear temporal changes were found in 9 parameters (N, K, N:P, available forms of N, P, K and ratios of N: available N, P: available P and K: available K), while marked linear changes were found in the rest 10 parameters. Four of them showed divergent temporal changes between surface and deep soils. At surface soils, changing rates were 262.1 g·kg(-1)·year(-1) for SOM, 438.9 mg·g(-1)·year(-1) for C:P, 5.3 mg·g(-1)·year(-1) for C:K, and -3.23 mg·cm(-3)·year(-1) for bulk density, while contrary tendencies were found in deeper soils. These divergences resulted in much moderated or no changes in the overall 80-cm soil profile. The other six parameters showed significant temporal changes for overall 0-80-cm soil profile (P: -4.10 mg·kg(-1)·year(-1); pH: -0.0061 unit·year(-1); C:N: 167.1 mg·g(-1)·year(-1); K:P: 371.5 mg·g(-1) year(-1); N:K: -0.242 mg·g(-1)·year(-1); EC: 0.169 μS·cm(-1)·year(-1)), but without significant differences at different soil depths (P > 0.05). Our findings highlight the importance of deep soils in studying physic-chemical changes of soil properties, and the temporal changes occurred in both surface and deep soils should be fully considered for forest management and soil nutrient balance.
Székely, Gábor J.; Rizzo, Maria L.
2010-01-01
Distance correlation is a new class of multivariate dependence coefficients applicable to random vectors of arbitrary and not necessarily equal dimension. Distance covariance and distance correlation are analogous to product-moment covariance and correlation, but generalize and extend these classical bivariate measures of dependence. Distance correlation characterizes independence: it is zero if and only if the random vectors are independent. The notion of covariance with...
Ginelli, Francesco; Politi, Antonio; Chaté, Hugues; Livi, Roberto
2013-01-01
Recent years have witnessed a growing interest in covariant Lyapunov vectors (CLVs) which span local intrinsic directions in the phase space of chaotic systems. Here, we review the basic results of ergodic theory, with a specific reference to the implications of Oseledets’ theorem for the properties of the CLVs. We then present a detailed description of a ‘dynamical’ algorithm to compute the CLVs and show that it generically converges exponentially in time. We also discuss its numerical performance and compare it with other algorithms presented in the literature. We finally illustrate how CLVs can be used to quantify deviations from hyperbolicity with reference to a dissipative system (a chain of Hénon maps) and a Hamiltonian model (a Fermi–Pasta–Ulam chain). This article is part of a special issue of Journal of Physics A: Mathematical and Theoretical devoted to ‘Lyapunov analysis: from dynamical systems theory to applications’. (paper)
Deriving covariant holographic entanglement
Dong, Xi [School of Natural Sciences, Institute for Advanced Study, Princeton, NJ 08540 (United States); Lewkowycz, Aitor [Jadwin Hall, Princeton University, Princeton, NJ 08544 (United States); Rangamani, Mukund [Center for Quantum Mathematics and Physics (QMAP), Department of Physics, University of California, Davis, CA 95616 (United States)
2016-11-07
We provide a gravitational argument in favour of the covariant holographic entanglement entropy proposal. In general time-dependent states, the proposal asserts that the entanglement entropy of a region in the boundary field theory is given by a quarter of the area of a bulk extremal surface in Planck units. The main element of our discussion is an implementation of an appropriate Schwinger-Keldysh contour to obtain the reduced density matrix (and its powers) of a given region, as is relevant for the replica construction. We map this contour into the bulk gravitational theory, and argue that the saddle point solutions of these replica geometries lead to a consistent prescription for computing the field theory Rényi entropies. In the limiting case where the replica index is taken to unity, a local analysis suffices to show that these saddles lead to the extremal surfaces of interest. We also comment on various properties of holographic entanglement that follow from this construction.
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 ℓ 1 regularization. In this paper, different from existing works, we study the inverse covariance estimation problem from another perspective by efficiently modeling the low-rank structure in the inverse covariance, which is assumed to be a combination of a low-rank part and a diagonal matrix. One motivation for this assumption is that the low-rank structure is common in many applications including the climate and financial analysis, and another one is that such assumption can reduce the computational complexity when computing its inverse. Specifically, we propose an efficient COmponent Pursuit (COP) method to obtain the low-rank part, where each component can be sparse. For optimization, the COP method greedily learns a rank-one component in each iteration by maximizing the log-likelihood. Moreover, the COP algorithm enjoys several appealing properties including the existence of an efficient solution in each iteration and the theoretical guarantee on the convergence of this greedy approach. Experiments on large-scale synthetic and real-world datasets including thousands of millions variables show that the COP method is faster than the state-of-the-art techniques for the inverse covariance estimation problem when achieving comparable log-likelihood on test data.
Covariant diagrams for one-loop matching
Zhang, Zhengkang [Michigan Center for Theoretical Physics (MCTP), University of Michigan,450 Church Street, Ann Arbor, MI 48109 (United States); Deutsches Elektronen-Synchrotron (DESY),Notkestraße 85, 22607 Hamburg (Germany)
2017-05-30
We present a diagrammatic formulation of recently-revived covariant functional approaches to one-loop matching from an ultraviolet (UV) theory to a low-energy effective field theory. Various terms following from a covariant derivative expansion (CDE) are represented by diagrams which, unlike conventional Feynman diagrams, involve gauge-covariant quantities and are thus dubbed “covariant diagrams.” The use of covariant diagrams helps organize and simplify one-loop matching calculations, which we illustrate with examples. Of particular interest is the derivation of UV model-independent universal results, which reduce matching calculations of specific UV models to applications of master formulas. We show how such derivation can be done in a more concise manner than the previous literature, and discuss how additional structures that are not directly captured by existing universal results, including mixed heavy-light loops, open covariant derivatives, and mixed statistics, can be easily accounted for.
Covariant diagrams for one-loop matching
Zhang, Zhengkang
2017-01-01
We present a diagrammatic formulation of recently-revived covariant functional approaches to one-loop matching from an ultraviolet (UV) theory to a low-energy effective field theory. Various terms following from a covariant derivative expansion (CDE) are represented by diagrams which, unlike conventional Feynman diagrams, involve gauge-covariant quantities and are thus dubbed “covariant diagrams.” The use of covariant diagrams helps organize and simplify one-loop matching calculations, which we illustrate with examples. Of particular interest is the derivation of UV model-independent universal results, which reduce matching calculations of specific UV models to applications of master formulas. We show how such derivation can be done in a more concise manner than the previous literature, and discuss how additional structures that are not directly captured by existing universal results, including mixed heavy-light loops, open covariant derivatives, and mixed statistics, can be easily accounted for.
Sandini, Corrado; Scariati, Elisa; Padula, Maria Carmela; Schneider, Maude; Schaer, Marie; Van De Ville, Dimitri; Eliez, Stephan
2018-05-01
22q11.2 deletion syndrome (22q11DS) is the third-largest known genetic risk factor for the development of psychosis. Dysconnectivity has consistently been implicated in the physiopathology of psychosis. Structural covariance of cortical morphology is a method of exploring connectivity among brain regions that to date has not been employed in 22q11DS. In the present study we employed structural covariance of cortical thickness to explore connectivity alterations in a group of 108 patients with 22q11DS compared with 96 control subjects. We subsequently divided patients into two subgroups of 31 subjects each according to the presence of attenuated psychotic symptoms. FreeSurfer software was used to obtain the mean cortical thickness in 148 brain regions from T1-weighted 3T images. For each population we reconstructed a brain graph using Pearson correlation between the average thickness of each couple of brain regions, which we characterized in terms of mean correlation strength and in terms of network architecture using graph theory. Patients with 22q11DS presented increased mean correlation strength, but there was no difference in global architecture compared with control subjects. However, symptomatic patients presented increased mean correlation strength coupled with increased segregation and decreased integration compared with both control subjects and nonsymptomatic patients. They also presented increased centrality for a cluster of anterior cingulate and dorsomedial prefrontal regions. These results confirm the importance of cortical dysconnectivity in the physiopathology of psychosis. Moreover they support the significance of aberrant anterior cingulate connectivity. Copyright © 2017 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.
Tarai, Madhumita; Kumar, Keshav; Divya, O.; Bairi, Partha; Mishra, Kishor Kumar; Mishra, Ashok Kumar
2017-09-01
The present work compares the dissimilarity and covariance based unsupervised chemometric classification approaches by taking the total synchronous fluorescence spectroscopy data sets acquired for the cumin and non-cumin based herbal preparations. The conventional decomposition method involves eigenvalue-eigenvector analysis of the covariance of the data set and finds the factors that can explain the overall major sources of variation present in the data set. The conventional approach does this irrespective of the fact that the samples belong to intrinsically different groups and hence leads to poor class separation. The present work shows that classification of such samples can be optimized by performing the eigenvalue-eigenvector decomposition on the pair-wise dissimilarity matrix.
Tarai, Madhumita; Kumar, Keshav; Divya, O; Bairi, Partha; Mishra, Kishor Kumar; Mishra, Ashok Kumar
2017-09-05
The present work compares the dissimilarity and covariance based unsupervised chemometric classification approaches by taking the total synchronous fluorescence spectroscopy data sets acquired for the cumin and non-cumin based herbal preparations. The conventional decomposition method involves eigenvalue-eigenvector analysis of the covariance of the data set and finds the factors that can explain the overall major sources of variation present in the data set. The conventional approach does this irrespective of the fact that the samples belong to intrinsically different groups and hence leads to poor class separation. The present work shows that classification of such samples can be optimized by performing the eigenvalue-eigenvector decomposition on the pair-wise dissimilarity matrix. Copyright © 2017 Elsevier B.V. All rights reserved.
Uncertainty covariances in robotics applications
Smith, D.L.
1984-01-01
The application of uncertainty covariance matrices in the analysis of robot trajectory errors is explored. First, relevant statistical concepts are reviewed briefly. Then, a simple, hypothetical robot model is considered to illustrate methods for error propagation and performance test data evaluation. The importance of including error correlations is emphasized
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.
Zheng, Xueying; Qin, Guoyou; Tu, Dongsheng
2017-05-30
Motivated by the analysis of quality of life data from a clinical trial on early breast cancer, we propose in this paper a generalized partially linear mean-covariance regression model for longitudinal proportional data, which are bounded in a closed interval. Cholesky decomposition of the covariance matrix for within-subject responses and generalized estimation equations are used to estimate unknown parameters and the nonlinear function in the model. Simulation studies are performed to evaluate the performance of the proposed estimation procedures. Our new model is also applied to analyze the data from the cancer clinical trial that motivated this research. In comparison with available models in the literature, the proposed model does not require specific parametric assumptions on the density function of the longitudinal responses and the probability function of the boundary values and can capture dynamic changes of time or other interested variables on both mean and covariance of the correlated proportional responses. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.
Andersson, C. David; Hillgren, J. Mikael; Lindgren, Cecilia; Qian, Weixing; Akfur, Christine; Berg, Lotta; Ekström, Fredrik; Linusson, Anna
2015-03-01
Scientific disciplines such as medicinal- and environmental chemistry, pharmacology, and toxicology deal with the questions related to the effects small organic compounds exhort on biological targets and the compounds' physicochemical properties responsible for these effects. A common strategy in this endeavor is to establish structure-activity relationships (SARs). The aim of this work was to illustrate benefits of performing a statistical molecular design (SMD) and proper statistical analysis of the molecules' properties before SAR and quantitative structure-activity relationship (QSAR) analysis. Our SMD followed by synthesis yielded a set of inhibitors of the enzyme acetylcholinesterase (AChE) that had very few inherent dependencies between the substructures in the molecules. If such dependencies exist, they cause severe errors in SAR interpretation and predictions by QSAR-models, and leave a set of molecules less suitable for future decision-making. In our study, SAR- and QSAR models could show which molecular sub-structures and physicochemical features that were advantageous for the AChE inhibition. Finally, the QSAR model was used for the prediction of the inhibition of AChE by an external prediction set of molecules. The accuracy of these predictions was asserted by statistical significance tests and by comparisons to simple but relevant reference models.
Parameters of the covariance function of galaxies
Fesenko, B.I.; Onuchina, E.V.
1988-01-01
The two-point angular covariance functions for two samples of galaxies are considered using quick methods of analysis. It is concluded that in the previous investigations the amplitude of the covariance function in the Lick counts was overestimated and the rate of decrease of the function underestimated
Covariance Function for Nearshore Wave Assimilation Systems
2018-01-30
which is applicable for any spectral wave model. The four dimensional variational (4DVar) assimilation methods are based on the mathematical ...covariance can be modeled by a parameterized Gaussian function, for nearshore wave assimilation applications , the covariance function depends primarily on...SPECTRAL ACTION DENSITY, RESPECTIVELY. ............................ 5 FIGURE 2. TOP ROW: STATISTICAL ANALYSIS OF THE WAVE-FIELD PROPERTIES AT THE
Covariance data evaluation for experimental data
Liu Tingjin
1993-01-01
Some methods and codes have been developed and utilized for covariance data evaluation of experimental data, including parameter analysis, physical analysis, Spline fitting etc.. These methods and codes can be used in many different cases
Analysis of fMRI data using noise-diffusion network models: a new covariance-coding perspective.
Gilson, Matthieu
2018-04-01
Since the middle of the 1990s, studies of resting-state fMRI/BOLD data have explored the correlation patterns of activity across the whole brain, which is referred to as functional connectivity (FC). Among the many methods that have been developed to interpret FC, a recently proposed model-based approach describes the propagation of fluctuating BOLD activity within the recurrently connected brain network by inferring the effective connectivity (EC). In this model, EC quantifies the strengths of directional interactions between brain regions, viewed from the proxy of BOLD activity. In addition, the tuning procedure for the model provides estimates for the local variability (input variances) to explain how the observed FC is generated. Generalizing, the network dynamics can be studied in the context of an input-output mapping-determined by EC-for the second-order statistics of fluctuating nodal activities. The present paper focuses on the following detection paradigm: observing output covariances, how discriminative is the (estimated) network model with respect to various input covariance patterns? An application with the model fitted to experimental fMRI data-movie viewing versus resting state-illustrates that changes in local variability and changes in brain coordination go hand in hand.
The Structures Panel of the Aeronautics Research and Development Board of India ... A great variety of topics was covered, including themes such as nonlinear finite ... or shell structures, and three are on the composite form of construction, ...
2014-01-01
Background Analysis of variance (ANOVA), change-score analysis (CSA) and analysis of covariance (ANCOVA) respond differently to baseline imbalance in randomized controlled trials. However, no empirical studies appear to have quantified the differential bias and precision of estimates derived from these methods of analysis, and their relative statistical power, in relation to combinations of levels of key trial characteristics. This simulation study therefore examined the relative bias, precision and statistical power of these three analyses using simulated trial data. Methods 126 hypothetical trial scenarios were evaluated (126 000 datasets), each with continuous data simulated by using a combination of levels of: treatment effect; pretest-posttest correlation; direction and magnitude of baseline imbalance. The bias, precision and power of each method of analysis were calculated for each scenario. Results Compared to the unbiased estimates produced by ANCOVA, both ANOVA and CSA are subject to bias, in relation to pretest-posttest correlation and the direction of baseline imbalance. Additionally, ANOVA and CSA are less precise than ANCOVA, especially when pretest-posttest correlation ≥ 0.3. When groups are balanced at baseline, ANCOVA is at least as powerful as the other analyses. Apparently greater power of ANOVA and CSA at certain imbalances is achieved in respect of a biased treatment effect. Conclusions Across a range of correlations between pre- and post-treatment scores and at varying levels and direction of baseline imbalance, ANCOVA remains the optimum statistical method for the analysis of continuous outcomes in RCTs, in terms of bias, precision and statistical power. PMID:24712304
Measuring covariation in RNA alignments: Physical realism improves information measures
Lindgreen, Stinus; Gardner, Paul Phillip; Krogh, Anders
2006-01-01
Motivation: The importance of non-coding RNAs is becoming increasingly evident, and often the function of these molecules depends on the structure. It is common to use alignments of related RNA sequences to deduce the consensus secondary structure by detecting patterns of co-evolution. A central...... part of such an analysis is to measure covariation between two positions in an alignment. Here, we rank various measures ranging from simple mutual information to more advanced covariation measures. Results: Mutual information is still used for secondary structure prediction, but the results...... of this study indicate which measures are useful. Incorporating more structural information by considering e.g. indels and stacking improves accuracy, suggesting that physically realistic measures yield improved predictions. This can be used to improve both current and future programs for secondary structure...
Bry, X; Verron, T; Cazes, P
2009-05-29
In this work, we consider chemical and physical variable groups describing a common set of observations (cigarettes). One of the groups, minor smoke compounds (minSC), is assumed to depend on the others (minSC predictors). PLS regression (PLSR) of m inSC on the set of all predictors appears not to lead to a satisfactory analytic model, because it does not take into account the expert's knowledge. PLS path modeling (PLSPM) does not use the multidimensional structure of predictor groups. Indeed, the expert needs to separate the influence of several pre-designed predictor groups on minSC, in order to see what dimensions this influence involves. To meet these needs, we consider a multi-group component-regression model, and propose a method to extract from each group several strong uncorrelated components that fit the model. Estimation is based on a global multiple covariance criterion, used in combination with an appropriate nesting approach. Compared to PLSR and PLSPM, the structural equation exploratory regression (SEER) we propose fully uses predictor group complementarity, both conceptually and statistically, to predict the dependent group.
Covariant representations of nuclear *-algebras
Moore, S.M.
1978-01-01
Extensions of the Csup(*)-algebra theory for covariant representations to nuclear *-algebra are considered. Irreducible covariant representations are essentially unique, an invariant state produces a covariant representation with stable vacuum, and the usual relation between ergodic states and covariant representations holds. There exist construction and decomposition theorems and a possible relation between derivations and covariant representations
Correa Londoño Guillermo
2013-08-01
group homogeneous experimental units. In such cases, it would be feasible to evaluate the use of analysis of covariance to achieve the same objectives that blocking does. In order to apply an adequate correction via analysis of covariance it is necessary to fulfill two conditions: viability and pertinence. Viability refers to the possibility to relate, by means of a regression model, a fraction of the variability of the response to the covariate. Pertinence has to do with the adequacy of the applied correction, taking into account that the elimination of the effect of the covariate doesn’t extract some part of the treatment’s effect. Viability is usually evaluated with the assistance of some statistical software. Pertinence, on the other hand, requires a conceptual approach.
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.
Sparse reduced-rank regression with covariance estimation
Chen, Lisha; Huang, Jianhua Z.
2014-01-01
Improving the predicting performance of the multiple response regression compared with separate linear regressions is a challenging question. On the one hand, it is desirable to seek model parsimony when facing a large number of parameters. On the other hand, for certain applications it is necessary to take into account the general covariance structure for the errors of the regression model. We assume a reduced-rank regression model and work with the likelihood function with general error covariance to achieve both objectives. In addition we propose to select relevant variables for reduced-rank regression by using a sparsity-inducing penalty, and to estimate the error covariance matrix simultaneously by using a similar penalty on the precision matrix. We develop a numerical algorithm to solve the penalized regression problem. In a simulation study and real data analysis, the new method is compared with two recent methods for multivariate regression and exhibits competitive performance in prediction and variable selection.
Slater, David; Ruef, Anne; Sanabria‐Diaz, Gretel; Preisig, Martin; Kherif, Ferath; Draganski, Bogdan; Lutti, Antoine
2017-01-01
Abstract Networks of anatomical covariance have been widely used to study connectivity patterns in both normal and pathological brains based on the concurrent changes of morphometric measures (i.e., cortical thickness) between brain structures across subjects (Evans, 2013). However, the existence of networks of microstructural changes within brain tissue has been largely unexplored so far. In this article, we studied in vivo the concurrent myelination processes among brain anatomical structures that gathered together emerge to form nonrandom networks. We name these “networks of myelin covariance” (Myelin‐Nets). The Myelin‐Nets were built from quantitative Magnetization Transfer data—an in‐vivo magnetic resonance imaging (MRI) marker of myelin content. The synchronicity of the variations in myelin content between anatomical regions was measured by computing the Pearson's correlation coefficient. We were especially interested in elucidating the effect of age on the topological organization of the Myelin‐Nets. We therefore selected two age groups: Young‐Age (20–31 years old) and Old‐Age (60–71 years old) and a pool of participants from 48 to 87 years old for a Myelin‐Nets aging trajectory study. We found that the topological organization of the Myelin‐Nets is strongly shaped by aging processes. The global myelin correlation strength, between homologous regions and locally in different brain lobes, showed a significant dependence on age. Interestingly, we also showed that the aging process modulates the resilience of the Myelin‐Nets to damage of principal network structures. In summary, this work sheds light on the organizational principles driving myelination and myelin degeneration in brain gray matter and how such patterns are modulated by aging. PMID:29271053
Structural Covariates of Homicide Rates : A European City Cross-National Comparative Analysis
McCall, Patricia L.; Nieuwbeerta, Paul
2007-01-01
Most previous empirical comparative studies of homicide examine homicide rates across nations or subnational units within a single country. This study is the first in which a European cross-national city comparison is made. The article aims to provide insight into the extent that the homicide rates
Laser Covariance Vibrometry for Unsymmetrical Mode Detection
Kobold, Michael C
2006-01-01
Simulated cross - spectral covariance (CSC) from optical return from simulated surface vibration indicates CW phase modulation may be an appropriate phenomenology for adequate classification of vehicles by structural mode...
Covariant Noncommutative Field Theory
Estrada-Jimenez, S [Licenciaturas en Fisica y en Matematicas, Facultad de Ingenieria, Universidad Autonoma de Chiapas Calle 4a Ote. Nte. 1428, Tuxtla Gutierrez, Chiapas (Mexico); Garcia-Compean, H [Departamento de Fisica, Centro de Investigacion y de Estudios Avanzados del IPN P.O. Box 14-740, 07000 Mexico D.F., Mexico and Centro de Investigacion y de Estudios Avanzados del IPN, Unidad Monterrey Via del Conocimiento 201, Parque de Investigacion e Innovacion Tecnologica (PIIT) Autopista nueva al Aeropuerto km 9.5, Lote 1, Manzana 29, cp. 66600 Apodaca Nuevo Leon (Mexico); Obregon, O [Instituto de Fisica de la Universidad de Guanajuato P.O. Box E-143, 37150 Leon Gto. (Mexico); Ramirez, C [Facultad de Ciencias Fisico Matematicas, Universidad Autonoma de Puebla, P.O. Box 1364, 72000 Puebla (Mexico)
2008-07-02
The covariant approach to noncommutative field and gauge theories is revisited. In the process the formalism is applied to field theories invariant under diffeomorphisms. Local differentiable forms are defined in this context. The lagrangian and hamiltonian formalism is consistently introduced.
Covariant Noncommutative Field Theory
Estrada-Jimenez, S.; Garcia-Compean, H.; Obregon, O.; Ramirez, C.
2008-01-01
The covariant approach to noncommutative field and gauge theories is revisited. In the process the formalism is applied to field theories invariant under diffeomorphisms. Local differentiable forms are defined in this context. The lagrangian and hamiltonian formalism is consistently introduced
Ehler, Martin; Rajapakse, Vinodh; Zeeberg, Barry; Brooks, Brian; Brown, Jacob; Czaja, Wojciech; Bonner, Robert F.
The gene networks underlying closure of the optic fissure during vertebrate eye development are poorly understood. We used a novel clustering method based on Laplacian Eigenmaps, a nonlinear dimension reduction method, to analyze microarray data from laser capture microdissected (LCM) cells at the site and developmental stages (days 10.5 to 12.5) of optic fissure closure. Our new method provided greater biological specificity than classical clustering algorithms in terms of identifying more biological processes and functions related to eye development as defined by Gene Ontology at lower false discovery rates. This new methodology builds on the advantages of LCM to isolate pure phenotypic populations within complex tissues and allows improved ability to identify critical gene products expressed at lower copy number. The combination of LCM of embryonic organs, gene expression microarrays, and extracting spatial and temporal co-variations appear to be a powerful approach to understanding the gene regulatory networks that specify mammalian organogenesis.
Phenotypic covariance at species' borders.
Caley, M Julian; Cripps, Edward; Game, Edward T
2013-05-28
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. 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. 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.
Covariant diagrams for one-loop matching
Zhang, Zhengkang
2016-10-01
We present a diagrammatic formulation of recently-revived covariant functional approaches to one-loop matching from an ultraviolet (UV) theory to a low-energy effective field theory. Various terms following from a covariant derivative expansion (CDE) are represented by diagrams which, unlike conventional Feynman diagrams, involve gaugecovariant quantities and are thus dubbed ''covariant diagrams.'' The use of covariant diagrams helps organize and simplify one-loop matching calculations, which we illustrate with examples. Of particular interest is the derivation of UV model-independent universal results, which reduce matching calculations of specific UV models to applications of master formulas. We show how such derivation can be done in a more concise manner than the previous literature, and discuss how additional structures that are not directly captured by existing universal results, including mixed heavy-light loops, open covariant derivatives, and mixed statistics, can be easily accounted for.
Covariant diagrams for one-loop matching
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.
Covariance data processing code. ERRORJ
Kosako, Kazuaki
2001-01-01
The covariance data processing code, ERRORJ, was developed to process the covariance data of JENDL-3.2. ERRORJ has the processing functions of covariance data for cross sections including resonance parameters, angular distribution and energy distribution. (author)
Fuchs, Maurice Bernard
2016-01-01
Addressing structures, this book presents a classic discipline in a modern setting by combining illustrated examples with insights into the solutions. It is the fruit of the author’s many years of teaching the subject and of just as many years of research into the design of optimal structures. Although intended for an advanced level of instruction it has an undergraduate course at its core. Further, the book was written with the advantage of having massive computer power in the background, an aspect which changes the entire approach to many engineering disciplines and in particular to structures. This paradigm shift has dislodged the force (flexibility) method from its former prominence and paved the way for the displacement (stiffness) method, despite the multitude of linear equations it spawns. In this book, however, both methods are taught: the force method offers a perfect vehicle for understanding structural behavior, bearing in mind that it is the displacement method which does the heavy number crunch...
Fábio Luiz Buranelo Toral
2006-10-01
Full Text Available Este trabalho foi realizado com o objetivo de selecionar o arranjo para efeitos fixos e a estrutura de (covariância residual que melhor representam a variabilidade dos pesos dentro do rebanho e dentro dos indivíduos, considerando-se dados de pesos de bovinos analisados como medidas repetidas. Foram utilizados dados de peso de 3.690 bovinos Canchim, obtidos ao nascimento, à desmama, aos 12 e aos 18 meses de idade. Analisaram-se diferentes arranjos para os efeitos fixos (grupos de contemporâneos e/ou efeitos principais de ano, mês ou época de nascimento e sexo do bezerro e diferentes estruturas de (covariâncias para os resíduos, considerando-se ou não alteração da variância residual ao longo da vida do animal e alteração da correlação entre as medidas tomadas em intervalos diferentes. Os resultados indicaram que o arranjo mais adequado dos efeitos fixos para representar a variabilidade dos pesos dos animais dentro do rebanho foi o grupo de contemporâneos formado por ano, mês e sexo do bezerro e que as melhores estruturas de (covariâncias residuais foram a Fator Analítico de Primeira Ordem e a Não Estruturada, que consideram o aumento das variâncias com o aumento da idade do indivíduo e as correlações diferentes para cada par de medidas de peso.The aim of this work was to evaluate arrays of fixed effects and residual covariance structures that best fit the herd and the animal variability to weights at birth, weaning, twelve and eighteen months of 3,690 Canchim animals. Different arrays of fixed effects (contemporary groups and, or the main effects of year, month or season of birth and sex and different residual covariance structures (considering or not change of variance and of correlation between weights at different ages were studied. The results indicated that the most adequate array of fixed effects to fit herd variability was the contemporary group of year, month and sex. The best residual covariance structures were
Flanagan, L. B.; Geske, N.; Emrick, C.; Johnson, B. G.
2006-12-01
Grassland ecosystems typically exhibit very large annual fluctuations in above-ground biomass production and net ecosystem productivity (NEP). Eddy covariance flux measurements, plant stable isotope analyses, and canopy spectral reflectance techniques have been applied to study environmental constraints on grassland ecosystem productivity and the acclimation responses of the ecosystem at a site near Lethbridge, Alberta, Canada. We have observed substantial interannual variation in grassland productivity during 1999-2005. In addition, there was a strong correlation between peak above-ground biomass production and NEP calculated from eddy covariance measurements. Interannual variation in NEP was strongly controlled by the total amount of precipitation received during the growing season (April-August). We also observed significant positive correlations between a multivariate ENSO index and total growing season precipitation, and between the ENSO index and annual NEP values. This suggested that a significant fraction of the annual variability in grassland productivity was associated with ENSO during 1999-2005. Grassland productivity varies asymmetrically in response to changes in precipitation with increases in productivity during wet years being much more pronounced than reductions during dry years. Strong increases in plant water-use efficiency, based on carbon and oxygen stable isotope analyses, contribute to the resilience of productivity during times of drought. Within a growing season increased stomatal limitation of photosynthesis, associated with improved water-use efficiency, resulted in apparent shifts in leaf xanthophyll cycle pigments and changes to the Photochemical Reflectance Index (PRI) calculated from hyper-spectral reflectance measurements conducted at the canopy-scale. These shifts in PRI were apparent before seasonal drought caused significant reductions in leaf area index (LAI) and changes to canopy-scale "greenness" based on NDVI values. With
Sekulic, Damir; Tahiraj, Enver; Zvan, Milan; Zenic, Natasa; Uljevic, Ognjen; Lesnik, Blaz
2016-01-01
Team sports are rarely studied with regard to doping behaviour and doping-related factors regardless of their global popularity. This study aimed to investigate doping factors and covariates of potential doping behaviour in high-level team-sport athletes. The subjects were 457 high-performing, national- and international-level athletes (21.9 ± 3.4 years of age; 179 females) involved in volleyball (n = 77), soccer (n = 163), basketball (n = 114) and handball (n = 103). Previously validated self-administered questionnaires aimed at evidencing sport factors, doping-related factors, knowledge on sport nutrition and doping, and attitudes to performance enhancement were used. The results indicated a higher doping likelihood in male athletes, with a significant gender difference for basketball and handball. In males, a higher doping likelihood is found for athletes who had achieved better results at junior-age level, those who regularly consume dietary supplements, and who perceive their sport as being contaminated by doping. A higher sport achievement at senior-age level is protective against potential doping behaviour in males. In females, a higher likelihood of doping is evidenced in those athletes involved in binge drinking, while a lower tendency for doping is evidenced in female athletes who possess better knowledge on sport nutrition. Knowledge about doping is very low and thus education about doping is urgently needed. An improvement of knowledge on sport nutrition might be a potentially effective method for reducing the tendency for doping in females. Future studies should consider other approaches and theories, such as theory of planned behaviour and/or social-cognitive theory, in studying the problem of doping behaviour in team-sports. Key points The doping knowledge among Kosovar team-sport athletes is very low and systematic anti-doping education is urgently needed. The highest risk of doping behaviour in males is found for those athletes who had been
Error estimation for ADS nuclear properties by using nuclear data covariances
Tsujimoto, Kazufumi
2005-01-01
Error for nuclear properties of accelerator-driven subcritical system by the uncertainties of nuclear data was performed. An uncertainty analysis was done using the sensitivity coefficients based on the generalized perturbation theory and the variance matrix data. For major actinides and structural material, the covariance data in JENDL-3.3 library were used. For MA, newly evaluated covariance data was used since there had been no reliable data in all libraries. (author)
Covariance and sensitivity data generation at ORNL
Leal, L. C.; Derrien, H.; Larson, N. M.; Alpan, A.
2005-01-01
Covariance data are required to assess uncertainties in design parameters in several nuclear applications. The error estimation of calculated quantities relies on the nuclear data uncertainty information available in the basic nuclear data libraries, such as the US Evaluated Nuclear Data Library, ENDF/B. The uncertainty files in the ENDF/B library are obtained from the analysis of experimental data and are stored as variance and covariance data. In this paper we address the generation of covariance data in the resonance region done with the computer code SAMMY. SAMMY is used in the evaluation of the experimental data in the resolved and unresolved resonance energy regions. The data fitting of cross sections is based on the generalised least-squares formalism (Bayesian theory) together with the resonance formalism described by R-matrix theory. Two approaches are used in SAMMY for the generation of resonance parameter covariance data. In the evaluation process SAMMY generates a set of resonance parameters that fit the data, and, it provides the resonance parameter covariances. For resonance parameter evaluations where there are no resonance parameter covariance data available, the alternative is to use an approach called the 'retroactive' resonance parameter covariance generation. In this paper, we describe the application of the retroactive covariance generation approach for the gadolinium isotopes. (authors)
Collapse Analysis of Timber Structures
Kirkegaard, Poul Henning; Sørensen, John Dalsgaard
2008-01-01
of Structures and a probabilistic modelling of the timber material proposed in the Probabilistic Model Code (PMC) of the Joint Committee on Structural Safety (JCSS). Due to the framework in the Danish Code the timber structure has to be evaluated with respect to the following criteria where at least one shall...... to criteria a) and b) the timber frame structure has one column with a reliability index a bit lower than an assumed target level. By removal three columns one by one no significant extensive failure of the entire structure or significant parts of it are obtained. Therefore the structure can be considered......A probabilistic based collapse analysis has been performed for a glulam frame structure supporting the roof over the main court in a Norwegian sports centre. The robustness analysis is based on the framework for robustness analysis introduced in the Danish Code of Practice for the Safety...
Structural systems reliability analysis
Frangopol, D.
1975-01-01
For an exact evaluation of the reliability of a structure it appears necessary to determine the distribution densities of the loads and resistances and to calculate the correlation coefficients between loads and between resistances. These statistical characteristics can be obtained only on the basis of a long activity period. In case that such studies are missing the statistical properties formulated here give upper and lower bounds of the reliability. (orig./HP) [de
Pozsgay, Victor; Hirsch, Flavien; Branciard, Cyril; Brunner, Nicolas
2017-12-01
We introduce Bell inequalities based on covariance, one of the most common measures of correlation. Explicit examples are discussed, and violations in quantum theory are demonstrated. A crucial feature of these covariance Bell inequalities is their nonlinearity; this has nontrivial consequences for the derivation of their local bound, which is not reached by deterministic local correlations. For our simplest inequality, we derive analytically tight bounds for both local and quantum correlations. An interesting application of covariance Bell inequalities is that they can act as "shared randomness witnesses": specifically, the value of the Bell expression gives device-independent lower bounds on both the dimension and the entropy of the shared random variable in a local model.
Progress on Nuclear Data Covariances: AFCI-1.2 Covariance Library
Oblozinsky, P.; Oblozinsky, P.; Mattoon, C.M.; Herman, M.; Mughabghab, S.F.; Pigni, M.T.; Talou, P.; Hale, G.M.; Kahler, A.C.; Kawano, T.; Little, R.C.; Young, P.G
2009-01-01
Improved neutron cross section covariances were produced for 110 materials including 12 light nuclei (coolants and moderators), 78 structural materials and fission products, and 20 actinides. Improved covariances were organized into AFCI-1.2 covariance library in 33-energy groups, from 10 -5 eV to 19.6 MeV. BNL contributed improved covariance data for the following materials: 23 Na and 55 Mn where more detailed evaluation was done; improvements in major structural materials 52 Cr, 56 Fe and 58 Ni; improved estimates for remaining structural materials and fission products; improved covariances for 14 minor actinides, and estimates of mubar covariances for 23 Na and 56 Fe. LANL contributed improved covariance data for 235 U and 239 Pu including prompt neutron fission spectra and completely new evaluation for 240 Pu. New R-matrix evaluation for 16 O including mubar covariances is under completion. BNL assembled the library and performed basic testing using improved procedures including inspection of uncertainty and correlation plots for each material. The AFCI-1.2 library was released to ANL and INL in August 2009.
Damir Sekulic, Enver Tahiraj, Milan Zvan, Natasa Zenic, Ognjen Uljevic, Blaz Lesnik
2016-12-01
Full Text Available Team sports are rarely studied with regard to doping behaviour and doping-related factors regardless of their global popularity. This study aimed to investigate doping factors and covariates of potential doping behaviour in high-level team-sport athletes. The subjects were 457 high-performing, national- and international-level athletes (21.9 ± 3.4 years of age; 179 females involved in volleyball (n = 77, soccer (n = 163, basketball (n = 114 and handball (n = 103. Previously validated self-administered questionnaires aimed at evidencing sport factors, doping-related factors, knowledge on sport nutrition and doping, and attitudes to performance enhancement were used. The results indicated a higher doping likelihood in male athletes, with a significant gender difference for basketball and handball. In males, a higher doping likelihood is found for athletes who had achieved better results at junior-age level, those who regularly consume dietary supplements, and who perceive their sport as being contaminated by doping. A higher sport achievement at senior-age level is protective against potential doping behaviour in males. In females, a higher likelihood of doping is evidenced in those athletes involved in binge drinking, while a lower tendency for doping is evidenced in female athletes who possess better knowledge on sport nutrition. Knowledge about doping is very low and thus education about doping is urgently needed. An improvement of knowledge on sport nutrition might be a potentially effective method for reducing the tendency for doping in females. Future studies should consider other approaches and theories, such as theory of planned behaviour and/or social-cognitive theory, in studying the problem of doping behaviour in team-sports.
Bourget, Antoine; Troost, Jan [Laboratoire de Physique Théorique, École Normale Supérieure, 24 rue Lhomond, 75005 Paris (France)
2016-03-23
We construct a covariant generating function for the spectrum of chiral primaries of symmetric orbifold conformal field theories with N=(4,4) supersymmetry in two dimensions. For seed target spaces K3 and T{sup 4}, the generating functions capture the SO(21) and SO(5) representation theoretic content of the chiral ring respectively. Via string dualities, we relate the transformation properties of the chiral ring under these isometries of the moduli space to the Lorentz covariance of perturbative string partition functions in flat space.
Dimension from covariance matrices.
Carroll, T L; Byers, J M
2017-02-01
We describe a method to estimate embedding dimension from a time series. This method includes an estimate of the probability that the dimension estimate is valid. Such validity estimates are not common in algorithms for calculating the properties of dynamical systems. The algorithm described here compares the eigenvalues of covariance matrices created from an embedded signal to the eigenvalues for a covariance matrix of a Gaussian random process with the same dimension and number of points. A statistical test gives the probability that the eigenvalues for the embedded signal did not come from the Gaussian random process.
High-dimensional covariance estimation with high-dimensional data
Pourahmadi, Mohsen
2013-01-01
Methods for estimating sparse and large covariance matrices Covariance and correlation matrices play fundamental roles in every aspect of the analysis of multivariate data collected from a variety of fields including business and economics, health care, engineering, and environmental and physical sciences. High-Dimensional Covariance Estimation provides accessible and comprehensive coverage of the classical and modern approaches for estimating covariance matrices as well as their applications to the rapidly developing areas lying at the intersection of statistics and mac
Donald R Cantrell
2010-10-01
Full Text Available The functional separation of ON and OFF pathways, one of the fundamental features of the visual system, starts in the retina. During postnatal development, some retinal ganglion cells (RGCs whose dendrites arborize in both ON and OFF sublaminae of the inner plexiform layer transform into RGCs with dendrites that monostratify in either the ON or OFF sublamina, acquiring final dendritic morphology in a subtype-dependent manner. Little is known about how the receptive field (RF properties of ON, OFF, and ON-OFF RGCs mature during this time because of the lack of a reliable and efficient method to classify RGCs into these subtypes. To address this deficiency, we developed an innovative variant of Spike Triggered Covariance (STC analysis, which we term Spike Triggered Covariance - Non-Centered (STC-NC analysis. Using a multi-electrode array (MEA, we recorded the responses of a large population of mouse RGCs to a Gaussian white noise stimulus. As expected, the Spike-Triggered Average (STA fails to identify responses driven by symmetric static nonlinearities such as those that underlie ON-OFF center RGC behavior. The STC-NC technique, in contrast, provides an efficient means to identify ON-OFF responses and quantify their RF center sizes accurately. Using this new tool, we find that RGCs gradually develop sensitivity to focal stimulation after eye opening, that the percentage of ON-OFF center cells decreases with age, and that RF centers of ON and ON-OFF cells become smaller. Importantly, we demonstrate for the first time that neurotrophin-3 (NT-3 regulates the development of physiological properties of ON-OFF center RGCs. Overexpression of NT-3 leads to the precocious maturation of RGC responsiveness and accelerates the developmental decrease of RF center size in ON-OFF cells. In summary, our study introduces STC-NC analysis which successfully identifies subtype RGCs and demonstrates how RF development relates to a neurotrophic driver in the retina.
Siti Hidayati
2015-05-01
Full Text Available The aims of this study were (1 to analyze the phenotypic performance of Ettawa Grade (EG goat; (2to estimate the heritability of birth weight (BW, weaning weight (WW, yearling weight (YW, and geneticcorrelation between two body weights on the third different period; and (3 to analyze the variance andcovariance component of body weight. The material used were the exiting records of 437 EG goats in BalaiPembibitan Ternak Unggul dan Hijauan Pakan Ternak Pelaihari, South Kalimantan. These goats originatedfrom the crossing between 19 males and 216 females from periods of 2009 - 2012. Nested Design methodwas used to etimate the phenotypic correlation, heritability and genetic correlation. Variance componentswere determined from heritability estimation, while covariance components were determined from geneticcerrelation estimation. Phenotypic correlation between BW and WW, between BW and YW, and betweenWW and YW were 0.19 (low; 0.31 (medium; 0.65 (high; respectively. Heritability of BW, WW, and YW were0.43±0.23 (high; WW 0.27±0.19 (medium; and YW 1.01±0.38 (excludeof the h2 value, respectively.Genetic correlation between BW and WW, between BW and YW, and between WW and YW were -0.04(negative low; 0.49 (positive medium; and -0.41 (negative medium, respectively. Variance components ofbuck, ewes, and kid for BW were 10.76%; 37.16%; and 52.09%, respectively, for WW were 6.67%; 38.52%;and 54.81%, respectively, and for YW were 25.15%; 58.37%; and 16.43%, respectively. Covariancecomponents of buck, ewes, and kid between BW and WW were -3.91%; 66.45%; and 37.46%, respectively,between BW and YW were 65.68%; 16.50%; and 17.82, and between WW and YW were -5.14%; 83.87%; and21.28%, respectively. In conclusions variance component of ewes and kid were high in body weight at birthand weaning time. Therefore, selection should be conducted for body weight at birth and weaning time.
Are your covariates under control? How normalization can re-introduce covariate effects.
Pain, Oliver; Dudbridge, Frank; Ronald, Angelica
2018-04-30
Many statistical tests rely on the assumption that the residuals of a model are normally distributed. Rank-based inverse normal transformation (INT) of the dependent variable is one of the most popular approaches to satisfy the normality assumption. When covariates are included in the analysis, a common approach is to first adjust for the covariates and then normalize the residuals. This study investigated the effect of regressing covariates against the dependent variable and then applying rank-based INT to the residuals. The correlation between the dependent variable and covariates at each stage of processing was assessed. An alternative approach was tested in which rank-based INT was applied to the dependent variable before regressing covariates. Analyses based on both simulated and real data examples demonstrated that applying rank-based INT to the dependent variable residuals after regressing out covariates re-introduces a linear correlation between the dependent variable and covariates, increasing type-I errors and reducing power. On the other hand, when rank-based INT was applied prior to controlling for covariate effects, residuals were normally distributed and linearly uncorrelated with covariates. This latter approach is therefore recommended in situations were normality of the dependent variable is required.
The analysis of cracked structures
Davidson, I.
1974-01-01
A brief review of the general problem of stable crack systems in many classes of structures, notably reinforced concrete structures, is made. Very simple methods of analysis are derived and some elaboration is described, as well as methods of optimising the calculations. Analytical methods are compared with experiments
Effect of neural connectivity on autocovariance and cross covariance estimates
Stecker Mark M
2007-01-01
Full Text Available Abstract Background Measurements of auto and cross covariance functions are frequently used to investigate neural systems. In interpreting this data, it is commonly assumed that the largest contribution to the recordings comes from sources near the electrode. However, the potential recorded at an electrode represents the superimposition of the potentials generated by large numbers of active neural structures. This creates situations under which the measured auto and cross covariance functions are dominated by the activity in structures far from the electrode and in which the distance dependence of the cross-covariance function differs significantly from that describing the activity in the actual neural structures. Methods Direct application of electrostatics to calculate the theoretical auto and cross covariance functions that would be recorded from electrodes immersed in a large volume filled with active neural structures with specific statistical properties. Results It is demonstrated that the potentials recorded from a monopolar electrode surrounded by dipole sources in a uniform medium are predominantly due to activity in neural structures far from the electrode when neuronal correlations drop more slowly than 1/r3 or when the size of the neural system is much smaller than a known correlation distance. Recordings from quadrupolar sources are strongly dependent on distant neurons when correlations drop more slowly than 1/r or the size of the system is much smaller than the correlation distance. Differences between bipolar and monopolar recordings are discussed. It is also demonstrated that the cross covariance of the recorded in two spatially separated electrodes declines as a power-law function of the distance between them even when the electrical activity from different neuronal structures is uncorrelated. Conclusion When extracellular electrophysiologic recordings are made from systems containing large numbers of neural structures, it is
Structural analysis of NPP components and structures
Saarenheimo, A.; Keinaenen, H.; Talja, H.
1998-01-01
Capabilities for effective structural integrity assessment have been created and extended in several important cases. In the paper presented applications deal with pressurised thermal shock loading, PTS, and severe dynamic loading cases of containment, reinforced concrete structures and piping components. Hydrogen combustion within the containment is considered in some severe accident scenarios. Can a steel containment withstand the postulated hydrogen detonation loads and still maintain its integrity? This is the topic of Chapter 2. The following Chapter 3 deals with a reinforced concrete floor subjected to jet impingement caused by a postulated rupture of a near-by high-energy pipe and Chapter 4 deals with dynamic loading resistance of the pipe lines under postulated pressure transients due to water hammer. The reliability of the structural integrity analysing methods and capabilities which have been developed for application in NPP component assessment, shall be evaluated and verified. The resources available within the RATU2 programme alone cannot allow performing of the large scale experiments needed for that purpose. Thus, the verification of the PTS analysis capabilities has been conducted by participation in international co-operative programmes. Participation to the European Network for Evaluating Steel Components (NESC) is the topic of a parallel paper in this symposium. The results obtained in two other international programmes are summarised in Chapters 5 and 6 of this paper, where PTS tests with a model vessel and benchmark assessment of a RPV nozzle integrity are described. (author)
Bijleveld, F. D.
In this study some statistical issues involved in the simultaneous analysis of accident related outcomes of the road traffic process are investigated. Since accident related outcomes like the number of victims, fatalities or accidents show interdependencies, their simultaneous analysis requires that
Probabilistic Structural Analysis Theory Development
Burnside, O. H.
1985-01-01
The objective of the Probabilistic Structural Analysis Methods (PSAM) project is to develop analysis techniques and computer programs for predicting the probabilistic response of critical structural components for current and future space propulsion systems. This technology will play a central role in establishing system performance and durability. The first year's technical activity is concentrating on probabilistic finite element formulation strategy and code development. Work is also in progress to survey critical materials and space shuttle mian engine components. The probabilistic finite element computer program NESSUS (Numerical Evaluation of Stochastic Structures Under Stress) is being developed. The final probabilistic code will have, in the general case, the capability of performing nonlinear dynamic of stochastic structures. It is the goal of the approximate methods effort to increase problem solving efficiency relative to finite element methods by using energy methods to generate trial solutions which satisfy the structural boundary conditions. These approximate methods will be less computer intensive relative to the finite element approach.
Pelloni, Sandro
2014-01-01
several times until convergence is reached for the analytical values and their uncertainties. An important result of the study is that the asymptotic analytical values of the integral parameters are closer to the experimental values as compared to the standard first adjustment results. Moreover, the asymptotic analytical values seem rather independent of the specific a priori variance/covariance data used in the analysis, namely COMMARA-2.0 or BOLNA, despite different a priori analytical values respectively obtained with JEFF-3.1 or ENDF/B-VI.8 data. The asymptotic uncertainties obtained on the basis of the two libraries are also similar
Covariant field equations in supergravity
Vanhecke, Bram [KU Leuven, Institute for Theoretical Physics, Leuven (Belgium); Ghent University, Faculty of Physics, Gent (Belgium); Proeyen, Antoine van [KU Leuven, Institute for Theoretical Physics, Leuven (Belgium)
2017-12-15
Covariance is a useful property for handling supergravity theories. In this paper, we prove a covariance property of supergravity field equations: under reasonable conditions, field equations of supergravity are covariant modulo other field equations. We prove that for any supergravity there exist such covariant equations of motion, other than the regular equations of motion, that are equivalent to the latter. The relations that we find between field equations and their covariant form can be used to obtain multiplets of field equations. In practice, the covariant field equations are easily found by simply covariantizing the ordinary field equations. (copyright 2017 WILEY-VCH Verlag GmbH and Co. KGaA, Weinheim)
Covariant field equations in supergravity
Vanhecke, Bram; Proeyen, Antoine van
2017-01-01
Covariance is a useful property for handling supergravity theories. In this paper, we prove a covariance property of supergravity field equations: under reasonable conditions, field equations of supergravity are covariant modulo other field equations. We prove that for any supergravity there exist such covariant equations of motion, other than the regular equations of motion, that are equivalent to the latter. The relations that we find between field equations and their covariant form can be used to obtain multiplets of field equations. In practice, the covariant field equations are easily found by simply covariantizing the ordinary field equations. (copyright 2017 WILEY-VCH Verlag GmbH and Co. KGaA, Weinheim)
Bhadra, Anindya; Mallick, Bani K.
2013-01-01
our method to an expression quantitative trait loci (eQTL) analysis on publicly available single nucleotide polymorphism (SNP) and gene expression data for humans where the primary interest lies in finding the significant associations between the sets
The Bayesian Covariance Lasso.
Khondker, Zakaria S; Zhu, Hongtu; Chu, Haitao; Lin, Weili; Ibrahim, Joseph G
2013-04-01
Estimation of sparse covariance matrices and their inverse subject to positive definiteness constraints has drawn a lot of attention in recent years. The abundance of high-dimensional data, where the sample size ( n ) is less than the dimension ( d ), requires shrinkage estimation methods since the maximum likelihood estimator is not positive definite in this case. Furthermore, when n is larger than d but not sufficiently larger, shrinkage estimation is more stable than maximum likelihood as it reduces the condition number of the precision matrix. Frequentist methods have utilized penalized likelihood methods, whereas Bayesian approaches rely on matrix decompositions or Wishart priors for shrinkage. In this paper we propose a new method, called the Bayesian Covariance Lasso (BCLASSO), for the shrinkage estimation of a precision (covariance) matrix. We consider a class of priors for the precision matrix that leads to the popular frequentist penalties as special cases, develop a Bayes estimator for the precision matrix, and propose an efficient sampling scheme that does not precalculate boundaries for positive definiteness. The proposed method is permutation invariant and performs shrinkage and estimation simultaneously for non-full rank data. Simulations show that the proposed BCLASSO performs similarly as frequentist methods for non-full rank data.
Arah Onyebuchi A
2008-02-01
Full Text Available Abstract Tu et al present an analysis of the equivalence of three paradoxes, namely, Simpson's, Lord's, and the suppression phenomena. They conclude that all three simply reiterate the occurrence of a change in the association of any two variables when a third variable is statistically controlled for. This is not surprising because reversal or change in magnitude is common in conditional analysis. At the heart of the phenomenon of change in magnitude, with or without reversal of effect estimate, is the question of which to use: the unadjusted (combined table or adjusted (sub-table estimate. Hence, Simpson's paradox and related phenomena are a problem of covariate selection and adjustment (when to adjust or not in the causal analysis of non-experimental data. It cannot be overemphasized that although these paradoxes reveal the perils of using statistical criteria to guide causal analysis, they hold neither the explanations of the phenomenon they depict nor the pointers on how to avoid them. The explanations and solutions lie in causal reasoning which relies on background knowledge, not statistical criteria.
Flexible Bayesian Dynamic Modeling of Covariance and Correlation Matrices
Lan, Shiwei
2017-11-08
Modeling covariance (and correlation) matrices is a challenging problem due to the large dimensionality and positive-definiteness constraint. In this paper, we propose a novel Bayesian framework based on decomposing the covariance matrix into variance and correlation matrices. The highlight is that the correlations are represented as products of vectors on unit spheres. We propose a variety of distributions on spheres (e.g. the squared-Dirichlet distribution) to induce flexible prior distributions for covariance matrices that go beyond the commonly used inverse-Wishart prior. To handle the intractability of the resulting posterior, we introduce the adaptive $\\\\Delta$-Spherical Hamiltonian Monte Carlo. We also extend our structured framework to dynamic cases and introduce unit-vector Gaussian process priors for modeling the evolution of correlation among multiple time series. Using an example of Normal-Inverse-Wishart problem, a simulated periodic process, and an analysis of local field potential data (collected from the hippocampus of rats performing a complex sequence memory task), we demonstrated the validity and effectiveness of our proposed framework for (dynamic) modeling covariance and correlation matrices.
Integrated piping structural analysis system
Motoi, Toshio; Yamadera, Masao; Horino, Satoshi; Idehata, Takamasa
1979-01-01
Structural analysis of the piping system for nuclear power plants has become larger in scale and in quantity. In addition, higher quality analysis is regarded as of major importance nowadays from the point of view of nuclear plant safety. In order to fulfill to the above requirements, an integrated piping structural analysis system (ISAP-II) has been developed. Basic philosophy of this system is as follows: 1. To apply the date base system. All information is concentrated. 2. To minimize the manual process in analysis, evaluation and documentation. Especially to apply the graphic system as much as possible. On the basis of the above philosophy four subsystems were made. 1. Data control subsystem. 2. Analysis subsystem. 3. Plotting subsystem. 4. Report subsystem. Function of the data control subsystem is to control all information of the data base. Piping structural analysis can be performed by using the analysis subsystem. Isometric piping drawing and mode shape, etc. can be plotted by using the plotting subsystem. Total analysis report can be made without the manual process through the reporting subsystem. (author)
Mäntyniemi, Samu; Uusitalo, Laura; Peltonen, Heikki
2013-01-01
We developed a generic, age-structured, state-space stock assessment model that can be used as a platform for including information elicited from stakeholders. The model tracks the mean size-at-age and then uses it to explain rates of natural and ﬁshing mortality. The ﬁshery selectivity is divided...... to two components, which makes it possible to model the active seeking of the ﬂeet for certain sizes of ﬁsh, as well as the selectivity of the gear itself. The model can account for uncertainties that are not currently accounted for in state-of-the-art models for integrated assessments: (i) The form...... of the stock–recruitment function is considered uncertain and is accounted for by using Bayesian model averaging. (ii) In addition to recruitment variation, process variation in natural mortality, growth parameters, and ﬁshing mortality can also be treated as uncertain parameters...
Adachi, Kohei
2013-01-01
Rubin and Thayer ("Psychometrika," 47:69-76, 1982) proposed the EM algorithm for exploratory and confirmatory maximum likelihood factor analysis. In this paper, we prove the following fact: the EM algorithm always gives a proper solution with positive unique variances and factor correlations with absolute values that do not exceed one,…
N.A.H. van Hest; C.J.P.A. Hoebe (Christian); J.W. Boer, den; J.K. Vermunt (Jeroen); E.P.F. IJzerman (Ed); W.G. Boersma (Wim); J.H. Richardus (Jan Hendrik)
2008-01-01
textabstractTo estimate incidence and completeness of notification of Legionnaires' disease (LD) in The Netherlands in 2000 and 2001, we performed a capture–recapture analysis using three registers: Notifications, Laboratory results and Hospital admissions. After record-linkage, 373 of the 780 LD
Lorentz Covariance of Langevin Equation
Koide, T.; Denicol, G.S.; Kodama, T.
2008-01-01
Relativistic covariance of a Langevin type equation is discussed. The requirement of Lorentz invariance generates an entanglement between the force and noise terms so that the noise itself should not be a covariant quantity. (author)
ERRORJ. Covariance processing code system for JENDL. Version 2
Chiba, Gou
2003-09-01
ERRORJ is the covariance processing code system for Japanese Evaluated Nuclear Data Library (JENDL) that can produce group-averaged covariance data to apply it to the uncertainty analysis of nuclear characteristics. ERRORJ can treat the covariance data for cross sections including resonance parameters as well as angular distributions and energy distributions of secondary neutrons which could not be dealt with by former covariance processing codes. In addition, ERRORJ can treat various forms of multi-group cross section and produce multi-group covariance file with various formats. This document describes an outline of ERRORJ and how to use it. (author)
Distance covariance for stochastic processes
Matsui, Muneya; Mikosch, Thomas Valentin; Samorodnitsky, Gennady
2017-01-01
The distance covariance of two random vectors is a measure of their dependence. The empirical distance covariance and correlation can be used as statistical tools for testing whether two random vectors are independent. We propose an analog of the distance covariance for two stochastic processes...
Structural analysis for LMFBR applications
1983-01-01
Firstly, we discuss the use of elastic analysis for structural design of LMFBR components. The elastic analysis methods have been used for structural design of the Fast Breeder Test Reactor as well as the proposed prototype Test Breeder Reactor. The design of Fast Breeder Test Reactor which is nearing completion is the same as that of Rapsodie. Nevertheless, the design had to he checked against the latest design codes available, namely the ASME Code case 1592. This paper however, is confined to Structural analysis of PFBR components. The problems faced in the design of some of the components, in particular, the inner vessel (plenum separator) are discussed. As far as design codes are concerned, we make use of ASME Code Section III and the Code Case N-47, for high temperature design. The problem faced in the use of these rules are also described along with the description of analysis. Studies in the field of cyclic loading include extension of Bree's breakdown and plastic cycling criteria for ratchet free operation to biaxial stress fields. In other fields, namely, inelastic analysis, piping analysis in the creep regime etc. we are only at a start
Shakedown analysis of elastoplastic structures
Koenig, J.A.
1981-01-01
Classical shakedown analysis rests on the assumptions of perfectly plastic, associative temperature-independent constitutive laws, negligible inertia and damping forces and negligible geometric effects. This paper provides a survey of the recent literature on the structural behaviour under variable repeated loads, with emphasis on the developments which relaxed some of the above assumptions, but preserved the character of generalization of limit analysis typical of the 'classical' shakedown theory and methods of analysis and design (in contrast to evolutive, step-by-step approaches of incremental plasticity). (orig.)
Structural analysis of syndiotactic polystyrene
Mitani, Masahiro
1988-09-01
Since the stereostructure of a high-molecular compound includes three types of isotactic, atactic and sydiotactic structures, a high-molecular compound with excellent properties can be produced by controlling the stereogularity of the compound with the identical composition. The stereoregularity of a stereogular polystyrene, or syndiotactic polystyrene (SPS), which had been successfully synthesized recently was quantitatively determined and the open chain structure by polymerization was investigated by nuclear magnetic resonance spectroscopy. Two SPSs were synthesized from cis-beta-d/sub/1-styrene and trans-beta-d/sub/1-styrene with alpha, beta, beta-d/sub/3-styrene. The results of spectral analysis of these two SPSs indicate that the former is of trans-conformation and the latter is of gauche conformation and that accordingly the open chain structure by polymerization of SPS is of cis-open chain and SPS has a planar zigzag structure even in the solution. (5 figs, 9 refs)
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.
Covariance as input to and output from resonance analyses
Larson, N.M.
1992-01-01
Accurate data analysis requires understanding of the roles played by both data and parameter covariance matrices. In this paper the entire data reduction/analysis process is examined, for neutron-induced reactions in the resonance region. Interrelationships between data and parameter covariance matrices are examined and alternative reduction/analysis methods discussed
Spatiotemporal noise covariance estimation from limited empirical magnetoencephalographic data
Jun, Sung C; Plis, Sergey M; Ranken, Doug M; Schmidt, David M
2006-01-01
The performance of parametric magnetoencephalography (MEG) and electroencephalography (EEG) source localization approaches can be degraded by the use of poor background noise covariance estimates. In general, estimation of the noise covariance for spatiotemporal analysis is difficult mainly due to the limited noise information available. Furthermore, its estimation requires a large amount of storage and a one-time but very large (and sometimes intractable) calculation or its inverse. To overcome these difficulties, noise covariance models consisting of one pair or a sum of multi-pairs of Kronecker products of spatial covariance and temporal covariance have been proposed. However, these approaches cannot be applied when the noise information is very limited, i.e., the amount of noise information is less than the degrees of freedom of the noise covariance models. A common example of this is when only averaged noise data are available for a limited prestimulus region (typically at most a few hundred milliseconds duration). For such cases, a diagonal spatiotemporal noise covariance model consisting of sensor variances with no spatial or temporal correlation has been the common choice for spatiotemporal analysis. In this work, we propose a different noise covariance model which consists of diagonal spatial noise covariance and Toeplitz temporal noise covariance. It can easily be estimated from limited noise information, and no time-consuming optimization and data-processing are required. Thus, it can be used as an alternative choice when one-pair or multi-pair noise covariance models cannot be estimated due to lack of noise information. To verify its capability we used Bayesian inference dipole analysis and a number of simulated and empirical datasets. We compared this covariance model with other existing covariance models such as conventional diagonal covariance, one-pair and multi-pair noise covariance models, when noise information is sufficient to estimate them. We
Structural analysis for LMFBR applications
Vaze, M.K.K.
1983-01-01
The use of elastic analysis for structural design of LMFBR components is discussed. The elastic analysis methods have been used for structural design of the Fast Breeder Test Reactor as well as the proposed Prototype Fast Breeder Reactor. The design of Fast Breeder Test Reactor which is nearing completion is same as that of Rapsodie. Nevertheless, the design had to be checked against the latest design codes available, namely the ASME Code case 1592. This paper however, is confined to Structural analysis of PFBR components. The problems faced in the design of some of the components, in particular, the inner vessel (plenum separator) are discussed. As far as design codes are concerned, ASME Code Section III and the Code Case N-47 are used for high temperature design. The problems faced in the use of these rules are also described along with the description of analysis. Studies in the field of cyclic loading include extension of Bree's shakedown and plastic cycling criteria for ratchet free operation to biaxial stress fields
Structural Analysis of Complex Networks
Dehmer, Matthias
2011-01-01
Filling a gap in literature, this self-contained book presents theoretical and application-oriented results that allow for a structural exploration of complex networks. The work focuses not only on classical graph-theoretic methods, but also demonstrates the usefulness of structural graph theory as a tool for solving interdisciplinary problems. Applications to biology, chemistry, linguistics, and data analysis are emphasized. The book is suitable for a broad, interdisciplinary readership of researchers, practitioners, and graduate students in discrete mathematics, statistics, computer science,
Cluster analysis of track structure
Michalik, V.
1991-01-01
One of the possibilities of classifying track structures is application of conventional partition techniques of analysis of multidimensional data to the track structure. Using these cluster algorithms this paper attempts to find characteristics of radiation reflecting the spatial distribution of ionizations in the primary particle track. An absolute frequency distribution of clusters of ionizations giving the mean number of clusters produced by radiation per unit of deposited energy can serve as this characteristic. General computation techniques used as well as methods of calculations of distributions of clusters for different radiations are discussed. 8 refs.; 5 figs
Total Analysis System for Ship Structural Strength
Takuya, Yoneya; Hiroyuki, Kobayashi; Abdul M., Rahim; Yoshimichi, Sasaki; Masaki, Irisawa; Technical Investigation and Information Department, Research Center; Technical Investigation and Information Department, Research Center; Singapore Office; Technical Investigation and Information Department, Research Center; Technical Investigation and Information Department, Research Center
2001-01-01
This paper outlines a total analysis system for ship hull structures, which integrates a wide variety of analysis functions to realise practical applications of rational methods for assessing ship structural strength. It is based on direct calculation of wave-induced loads as well as three-dimensional structural analysis of an entire-ship or hold structure. Three major analysis functions of the total system are ship motion and wave load analysis, ship structural analysis and statistical analy...
Chen, Fei; Loizou, Philipos C
2010-12-01
The normalized covariance measure (NCM) has been shown previously to predict reliably the intelligibility of noise-suppressed speech containing non-linear distortions. This study analyzes a simplified NCM measure that requires only a small number of bands (not necessarily contiguous) and uses simple binary (1 or 0) weighting functions. The rationale behind the use of a small number of bands is to account for the fact that the spectral information contained in contiguous or nearby bands is correlated and redundant. The modified NCM measure was evaluated with speech intelligibility scores obtained by normal-hearing listeners in 72 noisy conditions involving noise-suppressed speech corrupted by four different types of maskers (car, babble, train, and street interferences). High correlation (r = 0.8) was obtained with the modified NCM measure even when only one band was used. Further analysis revealed a masker-specific pattern of correlations when only one band was used, and bands with low correlation signified the corresponding envelopes that have been severely distorted by the noise-suppression algorithm and/or the masker. Correlation improved to r = 0.84 when only two disjoint bands (centered at 325 and 1874 Hz) were used. Even further improvements in correlation (r = 0.85) were obtained when three or four lower-frequency (<700 Hz) bands were selected.
Stereological analysis of spatial structures
Hansen, Linda Vadgård
The thesis deals with stereological analysis of spatial structures. One area of focus has been to improve the precision of well-known stereological estimators by including information that is available via automatic image analysis. Furthermore, the thesis presents a stochastic model for star......-shaped three-dimensional objects using the radial function. It appears that the model is highly fleksiblel in the sense that it can be used to describe an object with arbitrary irregular surface. Results on the distribution of well-known local stereological volume estimators are provided....
Non-Critical Covariant Superstrings
Grassi, P A
2005-01-01
We construct a covariant description of non-critical superstrings in even dimensions. We construct explicitly supersymmetric hybrid type variables in a linear dilaton background, and study an underlying N=2 twisted superconformal algebra structure. We find similarities between non-critical superstrings in 2n+2 dimensions and critical superstrings compactified on CY_(4-n) manifolds. We study the spectrum of the non-critical strings, and in particular the Ramond-Ramond massless fields. We use the supersymmetric variables to construct the non-critical superstrings sigma-model action in curved target space backgrounds with coupling to the Ramond-Ramond fields. We consider as an example non-critical type IIA strings on AdS_2 background with Ramond-Ramond 2-form flux.
Nonlinear realization of general covariance group
Hamamoto, Shinji
1979-01-01
The structure of the theory resulting from the nonlinear realization of general covariance group is analysed. We discuss the general form of free Lagrangian for Goldstone fields, and propose as a special choice one reasonable form which is shown to describe a gravitational theory with massless tensor graviton and massive vector tordion. (author)
P2 : A random effects model with covariates for directed graphs
van Duijn, M.A.J.; Snijders, T.A.B.; Zijlstra, B.J.H.
A random effects model is proposed for the analysis of binary dyadic data that represent a social network or directed graph, using nodal and/or dyadic attributes as covariates. The network structure is reflected by modeling the dependence between the relations to and from the same actor or node.
Impact of the 235U Covariance Data in Benchmark Calculations
Leal, Luiz C.; Mueller, D.; Arbanas, G.; Wiarda, D.; Derrien, H.
2008-01-01
The error estimation for calculated quantities relies on nuclear data uncertainty information available in the basic nuclear data libraries such as the U.S. Evaluated Nuclear Data File (ENDF/B). The uncertainty files (covariance matrices) in the ENDF/B library are generally obtained from analysis of experimental data. In the resonance region, the computer code SAMMY is used for analyses of experimental data and generation of resonance parameters. In addition to resonance parameters evaluation, SAMMY also generates resonance parameter covariance matrices (RPCM). SAMMY uses the generalized least-squares formalism (Bayes method) together with the resonance formalism (R-matrix theory) for analysis of experimental data. Two approaches are available for creation of resonance-parameter covariance data. (1) During the data-evaluation process, SAMMY generates both a set of resonance parameters that fit the experimental data and the associated resonance-parameter covariance matrix. (2) For existing resonance-parameter evaluations for which no resonance-parameter covariance data are available, SAMMY can retroactively create a resonance-parameter covariance matrix. The retroactive method was used to generate covariance data for 235U. The resulting 235U covariance matrix was then used as input to the PUFF-IV code, which processed the covariance data into multigroup form, and to the TSUNAMI code, which calculated the uncertainty in the multiplication factor due to uncertainty in the experimental cross sections. The objective of this work is to demonstrate the use of the 235U covariance data in calculations of critical benchmark systems
Extensive set of low-fidelity cross sections covariances in fast neutron region
Pigni, M.T.; Herman, M.; Oblozinsky, P.
2008-01-01
We produced a large set of neutron cross section covariances in the energy range of 5 keV - 20 MeV. The covariance matrices were calculated for 307 isotopes divided into three major regions: structural materials, fission products, and heavy nuclei. These results have been developed to provide initial, but consistent estimates of covariance data for nuclear criticality safety applications. The methodology for the determination of such covariance matrices is presented. It combines the nuclear reaction model code EMPIRE which calculates sensitivity of cross sections to nuclear reaction model parameters, and the Bayesian code KALMAN that propagates uncertainties of the model parameters to cross sections. Taking into account large number of materials, only marginal reference to experimental data was made. The covariances were derived from the perturbation of several key model parameters selected by the sensitivity analysis. These parameters refer to the optical model potential, the level densities and the strength of the pre-equilibrium emission. This work represents the first try ever to generate nuclear data covariances on such a large scale. (authors)
General Galilei Covariant Gaussian Maps
Gasbarri, Giulio; Toroš, Marko; Bassi, Angelo
2017-09-01
We characterize general non-Markovian Gaussian maps which are covariant under Galilean transformations. In particular, we consider translational and Galilean covariant maps and show that they reduce to the known Holevo result in the Markovian limit. We apply the results to discuss measures of macroscopicity based on classicalization maps, specifically addressing dissipation, Galilean covariance and non-Markovianity. We further suggest a possible generalization of the macroscopicity measure defined by Nimmrichter and Hornberger [Phys. Rev. Lett. 110, 16 (2013)].
Fast Computing for Distance Covariance
Huo, Xiaoming; Szekely, Gabor J.
2014-01-01
Distance covariance and distance correlation have been widely adopted in measuring dependence of a pair of random variables or random vectors. If the computation of distance covariance and distance correlation is implemented directly accordingly to its definition then its computational complexity is O($n^2$) which is a disadvantage compared to other faster methods. In this paper we show that the computation of distance covariance and distance correlation of real valued random variables can be...
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.
Bayesian hierarchical model for large-scale covariance matrix estimation.
Zhu, Dongxiao; Hero, Alfred O
2007-12-01
Many bioinformatics problems implicitly depend on estimating large-scale covariance matrix. The traditional approaches tend to give rise to high variance and low accuracy due to "overfitting." We cast the large-scale covariance matrix estimation problem into the Bayesian hierarchical model framework, and introduce dependency between covariance parameters. We demonstrate the advantages of our approaches over the traditional approaches using simulations and OMICS data analysis.
Convex Banding of the Covariance Matrix.
Bien, Jacob; Bunea, Florentina; Xiao, Luo
2016-01-01
We introduce a new sparse estimator of the covariance matrix for high-dimensional models in which the variables have a known ordering. Our estimator, which is the solution to a convex optimization problem, is equivalently expressed as an estimator which tapers the sample covariance matrix by a Toeplitz, sparsely-banded, data-adaptive matrix. As a result of this adaptivity, the convex banding estimator enjoys theoretical optimality properties not attained by previous banding or tapered estimators. In particular, our convex banding estimator is minimax rate adaptive in Frobenius and operator norms, up to log factors, over commonly-studied classes of covariance matrices, and over more general classes. Furthermore, it correctly recovers the bandwidth when the true covariance is exactly banded. Our convex formulation admits a simple and efficient algorithm. Empirical studies demonstrate its practical effectiveness and illustrate that our exactly-banded estimator works well even when the true covariance matrix is only close to a banded matrix, confirming our theoretical results. Our method compares favorably with all existing methods, in terms of accuracy and speed. We illustrate the practical merits of the convex banding estimator by showing that it can be used to improve the performance of discriminant analysis for classifying sound recordings.
Structural Analysis of Fungal Cerebrosides
Eliana eBarreto-Bergter
2011-12-01
Full Text Available Of the ceramide monohexosides (CMHs, gluco- and galactosylceramides are the main neutral glycosphingolipids expressed in fungal cells. Their structural determination is greatly dependent on the use of mass spectrometric techniques, including fast atom bombardment-mass spectrometry (FAB-MS, electrospray ionization (ESI-MS, and energy collision-induced dissociation mass spectrometry (ESI-MS/CID-MS. Nuclear magnetic resonance (NMR has also been used successfully. Such a combination of techniques, combined with classical analytical separation, such as HPTLC and column chromatography, has led to the structural elucidation of a great number of fungal CMHs. The structure of fungal CMH is conserved among fungal species and consists of a glucose or galactose residue attached to a ceramide moiety containing 9-methyl-4,8-sphingadienine with an amidic linkage to hydroxylated fatty acids, most commonly having 16 or 18 carbon atoms and unsaturation between C-3 and C-4. Along with their unique structural characteristics, fungal CMHs have a peculiar subcellular distribution and striking biological properties. Fungal cerebrosides were also characterized as antigenic molecules directly or indirectly involved in cell growth or differentiation in Schizophyllum commune, Cryptococcus neoformans, Pseudallescheria boydii, Candida albicans, Aspergillus nidulans, A.fumigatus and Colletotrichum gloeosporioides. Besides classical techniques for cerebroside (CMH analysis, we now describe new approaches, combining conventional TLC and mass spectrometry, as well as emerging technologies for subcellular localization and distribution of glycosphingolipids by SIMS and imaging MALDI TOF .
Parametric number covariance in quantum chaotic spectra.
Vinayak; Kumar, Sandeep; Pandey, Akhilesh
2016-03-01
We study spectral parametric correlations in quantum chaotic systems and introduce the number covariance as a measure of such correlations. We derive analytic results for the classical random matrix ensembles using the binary correlation method and obtain compact expressions for the covariance. We illustrate the universality of this measure by presenting the spectral analysis of the quantum kicked rotors for the time-reversal invariant and time-reversal noninvariant cases. A local version of the parametric number variance introduced earlier is also investigated.
Structural analysis of nuclear components
Ikonen, K.; Hyppoenen, P.; Mikkola, T.; Noro, H.; Raiko, H.; Salminen, P.; Talja, H.
1983-05-01
THe report describes the activities accomplished in the project 'Structural Analysis Project of Nuclear Power Plant Components' during the years 1974-1982 in the Nuclear Engineering Laboratory at the Technical Research Centre of Finland. The objective of the project has been to develop Finnish expertise in structural mechanics related to nuclear engineering. The report describes the starting point of the research work, the organization of the project and the research activities on various subareas. Further the work done with computer codes is described and also the problems which the developed expertise has been applied to. Finally, the diploma works, publications and work reports, which are mainly in Finnish, are listed to give a view of the content of the project. (author)
ACORNS, Covariance and Correlation Matrix Diagonalization
Szondi, E.J.
1990-01-01
1 - Description of program or function: The program allows the user to verify the different types of covariance/correlation matrices used in the activation neutron spectrometry. 2 - Method of solution: The program performs the diagonalization of the input covariance/relative covariance/correlation matrices. The Eigen values are then analyzed to determine the rank of the matrices. If the Eigen vectors of the pertinent correlation matrix have also been calculated, the program can perform a complete factor analysis (generation of the factor matrix and its rotation in Kaiser's 'varimax' sense to select the origin of the correlations). 3 - Restrictions on the complexity of the problem: Matrix size is limited to 60 on PDP and to 100 on IBM PC/AT
Zero curvature conditions and conformal covariance
Akemann, G.; Grimm, R.
1992-05-01
Two-dimensional zero curvature conditions were investigated in detail, with special emphasis on conformal properties, and the appearance of covariant higher order differential operators constructed in terms of a projective connection was elucidated. The analysis is based on the Kostant decomposition of simple Lie algebras in terms of representations with respect to their 'principal' SL(2) subalgebra. (author) 27 refs
Roldan-Valadez, Ernesto; Piña-Jimenez, Carlos; Favila, Rafael; Rios, Camilo
2013-01-01
Introduction: There is an age-related conversion of red to yellow bone marrow in the axial skeleton, with a gender-related difference less well established. Our purpose was to clarify the variability of bone marrow fat fraction (FF) in the lumbar spine due to the interaction of gender and age groups. Methods: 44 healthy volunteers (20 males, 30–65 years old and 24 females, 30–69 years old) underwent 3T magnetic resonance spectroscopy (MRS) and conventional MRI examination of the lumbar spine; single-voxel spectrum was acquired for each vertebral body (VB). After controlling body mass index (BMI), a two-way between-groups multivariate analysis of covariance (MANCOVA) assessed the gender and age group differences in FF quantification for each lumbar VB. Results: There was a significant interaction between gender and age group, p = .017, with a large effect size (partial η 2 = .330). However the interaction explained only 33% of the observed variance. Main effects were not statistically significant. BMI was non-significantly related to FF quantification. Conclusions: Young males showed a high FF content, which declined in the 4th decade, then increased the next 3 decades to reach a FF content just below the initial FF means. Females’ FF were low in the 3rd decade, depicted an accelerated increase in the 4th decade, then a gradual increase the next 3 decades to reach a FF content similar to males’ values. Our findings suggest that quantification of bone marrow FF using MRS might be used as a surrogate biomarker of bone marrow activity in clinical settings
Roldan-Valadez, Ernesto, E-mail: ernest.roldan@usa.net [Magnetic Resonance Unit, Medica Sur Clinic and Foundation, Mexico City (Mexico); Piña-Jimenez, Carlos [Magnetic Resonance Unit, Medica Sur Clinic and Foundation, Mexico City (Mexico); Favila, Rafael [GE Healthcare, Mexico City (Mexico); Rios, Camilo [Neurochemistry Department, Mexican National Institute of Neurology and Neurosurgery, Mexico City (Mexico)
2013-11-01
Introduction: There is an age-related conversion of red to yellow bone marrow in the axial skeleton, with a gender-related difference less well established. Our purpose was to clarify the variability of bone marrow fat fraction (FF) in the lumbar spine due to the interaction of gender and age groups. Methods: 44 healthy volunteers (20 males, 30–65 years old and 24 females, 30–69 years old) underwent 3T magnetic resonance spectroscopy (MRS) and conventional MRI examination of the lumbar spine; single-voxel spectrum was acquired for each vertebral body (VB). After controlling body mass index (BMI), a two-way between-groups multivariate analysis of covariance (MANCOVA) assessed the gender and age group differences in FF quantification for each lumbar VB. Results: There was a significant interaction between gender and age group, p = .017, with a large effect size (partial η{sup 2} = .330). However the interaction explained only 33% of the observed variance. Main effects were not statistically significant. BMI was non-significantly related to FF quantification. Conclusions: Young males showed a high FF content, which declined in the 4th decade, then increased the next 3 decades to reach a FF content just below the initial FF means. Females’ FF were low in the 3rd decade, depicted an accelerated increase in the 4th decade, then a gradual increase the next 3 decades to reach a FF content similar to males’ values. Our findings suggest that quantification of bone marrow FF using MRS might be used as a surrogate biomarker of bone marrow activity in clinical settings.
Nuclear data covariances in the Indian context
Ganesan, S.
2014-01-01
The topic of covariances is recognized as an important part of several ongoing nuclear data science activities, since 2007, in the Nuclear Data Physics Centre of India (NDPCI). A Phase-1 project in collaboration with the Statistics department in Manipal University, Karnataka (Prof. K.M. Prasad and Prof. S. Nair) on nuclear data covariances was executed successfully during 2007-2011 period. In Phase-I, the NDPCI has conducted three national Theme meetings sponsored by the DAE-BRNS in 2008, 2010 and 2013 on nuclear data covariances. In Phase-1, the emphasis was on a thorough basic understanding of the concept of covariances including assigning uncertainties to experimental data in terms of partial errors and micro correlations, through a study and a detailed discussion of open literature. Towards the end of Phase-1, measurements and a first time covariance analysis of cross-sections for 58 Ni (n, p) 58 Co reaction measured in Mumbai Pelletron accelerator using 7 Li (p,n) reactions as neutron source in the MeV energy region were performed under a PhD programme on nuclear data covariances in which enrolled are two students, Shri B.S. Shivashankar and Ms. Shanti Sheela. India is also successfully evolving a team of young researchers to code nuclear data of uncertainties, with the perspectives on covariances, in the IAEA-EXFOR format. A Phase-II DAE-BRNS-NDPCI proposal of project at Manipal has been submitted and the proposal is undergoing a peer-review at this time. In Phase-2, modern nuclear data evaluation techniques that including covariances will be further studied as a research and development effort, as a first time effort. These efforts include the use of techniques such as that of the Kalman filter. Presently, a 48 hours lecture series on treatment of errors and their propagation is being formulated under auspices of the Homi Bhabha National Institute. The talk describes the progress achieved thus far in the learning curve of the above-mentioned and exciting
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.
Functional Generalized Structured Component Analysis.
Suk, Hye Won; Hwang, Heungsun
2016-12-01
An extension of Generalized Structured Component Analysis (GSCA), called Functional GSCA, is proposed to analyze functional data that are considered to arise from an underlying smooth curve varying over time or other continua. GSCA has been geared for the analysis of multivariate data. Accordingly, it cannot deal with functional data that often involve different measurement occasions across participants and a large number of measurement occasions that exceed the number of participants. Functional GSCA addresses these issues by integrating GSCA with spline basis function expansions that represent infinite-dimensional curves onto a finite-dimensional space. For parameter estimation, functional GSCA minimizes a penalized least squares criterion by using an alternating penalized least squares estimation algorithm. The usefulness of functional GSCA is illustrated with gait data.
Beetsma, R.M.W.J.; de Jong, Frank; Giuliodori, M.; Widijanto, D.
We use realized variances and covariances based on intraday data to measure the dependence structure of eurozone sovereign yields. Our analysis focuses on the impact of news, obtained from the Eurointelligence newsflash, on the dependence structure. More news tends to raise the volatility of yields
The impact of news ans the SMP on realized (co)variances in the Eurozone sovereign debt market
Beetsma, R.; de Jong, F.; Giuliodori, M.; Widijanto, D.
2014-01-01
We use realized variances and covariances based on intraday data from Eurozone sovereign bond market to measure the dependence structure of eurozone sovereign yields. Our analysis focuses on the impact of news, obtained from the Eurointelligence newsash, on the dependence structure. More news raises
Dynamic analysis of embedded structures
Kausel, E.; Whitman, R.V.; Morray, J.P.
1977-01-01
The paper presents simplified rules to account for embeddment and soil layering in the soil-structure interaction problem, to be used in dynamic analysis. The relationship between the spring method, and a direct solution (in which both soil and structure are modeled with finite elements and linear members) is first presented. It is shown that for consistency of the results with the two solution methods the spring method should be performed in the following three steps: 1. Determination of the motion of the massless foundation (having the same shape as the actual one) when subjected to the same input motion as the direct solution. 2. Determination of the frequency dependent subgrade stiffness for the relevant degrees of freedom. 3. Computations of the response of the real structure supported on frequency dependent soil springs and subjected at the base of these springs to the motion computed in step 1. The first two steps require, in general, finite element methods, which would make the procedure not attractive. It is shown in the paper, however, that excellent approximations can be obtained, on the basis of 1-dimensional wave propagation theory for the solution of step 1, and correction factors modifying for embeddment the corresponding springs of a surface footing on a layered stratum, for the solution of step 2. (Auth.)
Covariation in Natural Causal Induction.
Cheng, Patricia W.; Novick, Laura R.
1991-01-01
Biases and models usually offered by cognitive and social psychology and by philosophy to explain causal induction are evaluated with respect to focal sets (contextually determined sets of events over which covariation is computed). A probabilistic contrast model is proposed as underlying covariation computation in natural causal induction. (SLD)
Do current cosmological observations rule out all covariant Galileons?
Peirone, Simone; Frusciante, Noemi; Hu, Bin; Raveri, Marco; Silvestri, Alessandra
2018-03-01
We revisit the cosmology of covariant Galileon gravity in view of the most recent cosmological data sets, including weak lensing. As a higher derivative theory, covariant Galileon models do not have a Λ CDM limit and predict a very different structure formation pattern compared with the standard Λ CDM scenario. Previous cosmological analyses suggest that this model is marginally disfavored, yet cannot be completely ruled out. In this work we use a more recent and extended combination of data, and we allow for more freedom in the cosmology, by including a massive neutrino sector with three different mass hierarchies. We use the Planck measurements of cosmic microwave background temperature and polarization; baryonic acoustic oscillations measurements by BOSS DR12; local measurements of H0; the joint light-curve analysis supernovae sample; and, for the first time, weak gravitational lensing from the KiDS Collaboration. We find, that in order to provide a reasonable fit, a nonzero neutrino mass is indeed necessary, but we do not report any sizable difference among the three neutrino hierarchies. Finally, the comparison of the Bayesian evidence to the Λ CDM one shows that in all the cases considered, covariant Galileon models are statistically ruled out by cosmological data.
QUANTITATIVE ANALYSIS OF BANDED STRUCTURES IN DUAL-PHASE STEELS
Benoit Krebs
2011-05-01
Full Text Available Dual-Phase (DP steels are composed of martensite islands dispersed in a ductile ferrite matrix, which provides a good balance between strength and ductility. Current processing conditions (continuous casting followed by hot and cold rolling generate 'banded structures' i.e., irregular, parallel and alternating bands of ferrite and martensite, which are detrimental to mechanical properties and especially for in-use properties. We present an original and simple method to quantify the intensity and wavelength of these bands. This method, based on the analysis of covariance function of binary images, is firstly tested on model images. It is compared with ASTM E-1268 standard and appears to be more robust. Then it is applied on real DP steel microstructures and proves to be sufficiently sensitive to discriminate samples resulting from different thermo-mechanical routes.
Analysis of Pumphouse RCC Frame Structure for Soil Structure Interaction
Mr A.S. Thombare; Prof. V.P. Kumbhar; Prof. A.H. Kumbhar
2016-01-01
When structure is built on ground some elements of structure are direct contact with soil. When loads are applied on structure internal forces are developed in both the structure as well as in soil. It results in deformation of both the components which are independent to each other. This are called soil structure interaction. The analysis is done by using (Bentley STAAD.Pro V8i Version 2007) software. The analysis carried out been pump house structure R.C.C. frame structure find ...
Sensitivity Analysis of Viscoelastic Structures
A.M.G. de Lima
2006-01-01
Full Text Available In the context of control of sound and vibration of mechanical systems, the use of viscoelastic materials has been regarded as a convenient strategy in many types of industrial applications. Numerical models based on finite element discretization have been frequently used in the analysis and design of complex structural systems incorporating viscoelastic materials. Such models must account for the typical dependence of the viscoelastic characteristics on operational and environmental parameters, such as frequency and temperature. In many applications, including optimal design and model updating, sensitivity analysis based on numerical models is a very usefull tool. In this paper, the formulation of first-order sensitivity analysis of complex frequency response functions is developed for plates treated with passive constraining damping layers, considering geometrical characteristics, such as the thicknesses of the multi-layer components, as design variables. Also, the sensitivity of the frequency response functions with respect to temperature is introduced. As an example, response derivatives are calculated for a three-layer sandwich plate and the results obtained are compared with first-order finite-difference approximations.
Nespolo, Roberto F; Castañeda, Luis E; Roff, Derek A
2005-08-01
Energy metabolism in animals has been largely studied in relation to exogenous sources of variation. However, because they give insight into the relationship between whole metabolism and lower organizational levels such as organs and tissues, examination of endogenous determinants of metabolism other than body mass is itself very important. We studied the multivariate association of body parts and several aspects of energy metabolism in an insect, the nymphs of the sand cricket, Gryllus firmus. By using a variety of both univariate and multivariate techniques, we explored the resultant variance-covariance matrix to build a path diagram with latent variables. After controlling for body mass, we found a significant canonical correlation between metabolism and morphology. According to the factor loadings and path coefficients, the most important contributions of morphology to the correlation were thorax and abdomen size measures, whereas the most important metabolic contribution was resting metabolism. Activity metabolism was mostly explained by body mass rather than body parts, which could be a result of resting rates being chronic consequences of the functioning of the metabolic machinery that the insect must maintain.
Soil Retaining Structures : Development of models for structural analysis
Bakker, K.J.
2000-01-01
The topic of this thesis is the development of models for the structural analysis of soil retaining structures. The soil retaining structures being looked at are; block revetments, flexible retaining walls and bored tunnels in soft soil. Within this context typical structural behavior of these
A three domain covariance framework for EEG/MEG data
Ros, B.P.; Bijma, F.; de Gunst, M.C.M.; de Munck, J.C.
2015-01-01
In this paper we introduce a covariance framework for the analysis of single subject EEG and MEG data that takes into account observed temporal stationarity on small time scales and trial-to-trial variations. We formulate a model for the covariance matrix, which is a Kronecker product of three
Covariance matrices of experimental data
Perey, F.G.
1978-01-01
A complete statement of the uncertainties in data is given by its covariance matrix. It is shown how the covariance matrix of data can be generated using the information available to obtain their standard deviations. Determination of resonance energies by the time-of-flight method is used as an example. The procedure for combining data when the covariance matrix is non-diagonal is given. The method is illustrated by means of examples taken from the recent literature to obtain an estimate of the energy of the first resonance in carbon and for five resonances of 238 U
Evaluation and processing of covariance data
Wagner, M.
1993-01-01
These proceedings of a specialists'meeting on evaluation and processing of covariance data is divided into 4 parts bearing on: part 1- Needs for evaluated covariance data (2 Papers), part 2- generation of covariance data (15 Papers), part 3- Processing of covariance files (2 Papers), part 4-Experience in the use of evaluated covariance data (2 Papers)
More on Estimation of Banded and Banded Toeplitz Covariance Matrices
Berntsson, Fredrik; Ohlson, Martin
2017-01-01
In this paper we consider two different linear covariance structures, e.g., banded and bended Toeplitz, and how to estimate them using different methods, e.g., by minimizing different norms. One way to estimate the parameters in a linear covariance structure is to use tapering, which has been shown to be the solution to a universal least squares problem. We know that tapering not always guarantee the positive definite constraints on the estimated covariance matrix and may not be a suitable me...
On Galilean covariant quantum mechanics
Horzela, A.; Kapuscik, E.; Kempczynski, J.; Joint Inst. for Nuclear Research, Dubna
1991-08-01
Formalism exhibiting the Galilean covariance of wave mechanics is proposed. A new notion of quantum mechanical forces is introduced. The formalism is illustrated on the example of the harmonic oscillator. (author)
Zarco-Tejada, P.J.; Morales Sierra, A.; Testi, L.; Villalobos, F.
2013-01-01
This study provides insight into the assessment of the spatio-temporal trends of chlorophyll fluorescence, narrow-band physiological indices, and structural indices acquired with a hyperspectral imager flown over a flux tower in a canopy characterized by small seasonal structural changes and a
Automated analysis and design of complex structures
Wilson, E.L.
1977-01-01
This paper discusses the following: 1. The relationship of analysis to design. 2. New methods of analysis. 3. Improved finite elements. 4. Effect of minicomputer on structural analysis methods. 5. The use of system of microprocessors for nonlinear structural analysis. 6. The role of interacting graphics systems in future analysis and design. The discussion focusses on the impact of new inexpensive computer hardware on design and analysis methods. (Auth.)
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.
Precomputing Process Noise Covariance for Onboard Sequential Filters
Olson, Corwin G.; Russell, Ryan P.; Carpenter, J. Russell
2017-01-01
Process noise is often used in estimation filters to account for unmodeled and mismodeled accelerations in the dynamics. The process noise covariance acts to inflate the state covariance over propagation intervals, increasing the uncertainty in the state. In scenarios where the acceleration errors change significantly over time, the standard process noise covariance approach can fail to provide effective representation of the state and its uncertainty. Consider covariance analysis techniques provide a method to precompute a process noise covariance profile along a reference trajectory using known model parameter uncertainties. The process noise covariance profile allows significantly improved state estimation and uncertainty representation over the traditional formulation. As a result, estimation performance on par with the consider filter is achieved for trajectories near the reference trajectory without the additional computational cost of the consider filter. The new formulation also has the potential to significantly reduce the trial-and-error tuning currently required of navigation analysts. A linear estimation problem as described in several previous consider covariance analysis studies is used to demonstrate the effectiveness of the precomputed process noise covariance, as well as a nonlinear descent scenario at the asteroid Bennu with optical navigation.
A cautionary note on generalized linear models for covariance of unbalanced longitudinal data
Huang, Jianhua Z.; Chen, Min; Maadooliat, Mehdi; Pourahmadi, Mohsen
2012-01-01
Missing data in longitudinal studies can create enormous challenges in data analysis when coupled with the positive-definiteness constraint on a covariance matrix. For complete balanced data, the Cholesky decomposition of a covariance matrix makes
Smooth individual level covariates adjustment in disease mapping.
Huque, Md Hamidul; Anderson, Craig; Walton, Richard; Woolford, Samuel; Ryan, Louise
2018-05-01
Spatial models for disease mapping should ideally account for covariates measured both at individual and area levels. The newly available "indiCAR" model fits the popular conditional autoregresssive (CAR) model by accommodating both individual and group level covariates while adjusting for spatial correlation in the disease rates. This algorithm has been shown to be effective but assumes log-linear associations between individual level covariates and outcome. In many studies, the relationship between individual level covariates and the outcome may be non-log-linear, and methods to track such nonlinearity between individual level covariate and outcome in spatial regression modeling are not well developed. In this paper, we propose a new algorithm, smooth-indiCAR, to fit an extension to the popular conditional autoregresssive model that can accommodate both linear and nonlinear individual level covariate effects while adjusting for group level covariates and spatial correlation in the disease rates. In this formulation, the effect of a continuous individual level covariate is accommodated via penalized splines. We describe a two-step estimation procedure to obtain reliable estimates of individual and group level covariate effects where both individual and group level covariate effects are estimated separately. This distributed computing framework enhances its application in the Big Data domain with a large number of individual/group level covariates. We evaluate the performance of smooth-indiCAR through simulation. Our results indicate that the smooth-indiCAR method provides reliable estimates of all regression and random effect parameters. We illustrate our proposed methodology with an analysis of data on neutropenia admissions in New South Wales (NSW), Australia. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Decision analysis for deteriorating structures
Val, Dimitri V.; Stewart, Mark G.
2005-01-01
Measures that improve durability of a structure usually increase its initial cost. Thus, in order to make a decision about a cost-effective solution the life-cycle cost of a structure including cost of structural failure needs to be considered. Due to uncertainties associated with structural properties, loads and environmental conditions the cost of structural failure is a random variable. The paper derives probability distributions of the cost of failure of a single structure and a group of identical structures when single or multiple failures are possible during the service life of a structure. The probability distributions are based on cumulative probabilities of failure of a single structure over its service life. It is assumed that failures occur at discrete points in time, the cost of failure set at the time of decision making remains constant for a particular design solution and the discount rate is a deterministic parameter not changing with time. The probability distributions can be employed to evaluate the expected life-cycle cost or the expected utility, which is then used in decision making. An example, which considers the selection of durability specifications for a reinforced concrete structure built on the coast, illustrates the use of the derived probability distributions
Hua, Hairui; Burke, Danielle L; Crowther, Michael J; Ensor, Joie; Tudur Smith, Catrin; Riley, Richard D
2017-02-28
Stratified medicine utilizes individual-level covariates that are associated with a differential treatment effect, also known as treatment-covariate interactions. When multiple trials are available, meta-analysis is used to help detect true treatment-covariate interactions by combining their data. Meta-regression of trial-level information is prone to low power and ecological bias, and therefore, individual participant data (IPD) meta-analyses are preferable to examine interactions utilizing individual-level information. However, one-stage IPD models are often wrongly specified, such that interactions are based on amalgamating within- and across-trial information. We compare, through simulations and an applied example, fixed-effect and random-effects models for a one-stage IPD meta-analysis of time-to-event data where the goal is to estimate a treatment-covariate interaction. We show that it is crucial to centre patient-level covariates by their mean value in each trial, in order to separate out within-trial and across-trial information. Otherwise, bias and coverage of interaction estimates may be adversely affected, leading to potentially erroneous conclusions driven by ecological bias. We revisit an IPD meta-analysis of five epilepsy trials and examine age as a treatment effect modifier. The interaction is -0.011 (95% CI: -0.019 to -0.003; p = 0.004), and thus highly significant, when amalgamating within-trial and across-trial information. However, when separating within-trial from across-trial information, the interaction is -0.007 (95% CI: -0.019 to 0.005; p = 0.22), and thus its magnitude and statistical significance are greatly reduced. We recommend that meta-analysts should only use within-trial information to examine individual predictors of treatment effect and that one-stage IPD models should separate within-trial from across-trial information to avoid ecological bias. © 2016 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd
Covariate-adjusted measures of discrimination for survival data
White, Ian R; Rapsomaniki, Eleni; Frikke-Schmidt, Ruth
2015-01-01
by the study design (e.g. age and sex) influence discrimination and can make it difficult to compare model discrimination between studies. Although covariate adjustment is a standard procedure for quantifying disease-risk factor associations, there are no covariate adjustment methods for discrimination...... statistics in censored survival data. OBJECTIVE: To develop extensions of the C-index and D-index that describe the prognostic ability of a model adjusted for one or more covariate(s). METHOD: We define a covariate-adjusted C-index and D-index for censored survival data, propose several estimators......, and investigate their performance in simulation studies and in data from a large individual participant data meta-analysis, the Emerging Risk Factors Collaboration. RESULTS: The proposed methods perform well in simulations. In the Emerging Risk Factors Collaboration data, the age-adjusted C-index and D-index were...
Parametric Covariance Model for Horizon-Based Optical Navigation
Hikes, Jacob; Liounis, Andrew J.; Christian, John A.
2016-01-01
This Note presents an entirely parametric version of the covariance for horizon-based optical navigation measurements. The covariance can be written as a function of only the spacecraft position, two sensor design parameters, the illumination direction, the size of the observed planet, the size of the lit arc to be used, and the total number of observed horizon points. As a result, one may now more clearly understand the sensitivity of horizon-based optical navigation performance as a function of these key design parameters, which is insight that was obscured in previous (and nonparametric) versions of the covariance. Finally, the new parametric covariance is shown to agree with both the nonparametric analytic covariance and results from a Monte Carlo analysis.
Asset allocation with different covariance/correlation estimators
Μανταφούνη, Σοφία
2007-01-01
The subject of the study is to test whether the use of different covariance – correlation estimators than the historical covariance matrix that is widely used, would help in portfolio optimization through the mean-variance analysis. In other words, if an investor would like to use the mean-variance analysis in order to invest in assets like stocks or indices, would it be of some help to use more sophisticated estimators for the covariance matrix of the returns of his portfolio? The procedure ...
PLASTIC ANALYSIS OF STEEL FRAME STRUCTURE
M. Rogac
2013-05-01
Full Text Available This paper presents the plastic analysis of steel frame structure loaded by gravity loads. By applying the cinematic theorem of ultimate analysis, the ultimate load for the case of elastic - ideally plastic material is calculated. The identical structure was treated in the computer program SAP2000 where the zone of material reinforcement in the plastic area was covered. Keywords: Steel frame structure, plastic analysis, ultimate gravity load, material reinforcement.
Robustness Analysis of Kinetic Structures
Kirkegaard, Poul Henning; Sørensen, John Dalsgaard
2009-01-01
Kinetic structures in architecture follows a new trend which is emerging in responsive architecture coined by Nicholas Negroponte when he proposed that architecture may benefit from the integration of computing power into built spaces and structures, and that better performing, more rational...
Impact of the 235U covariance data in benchmark calculations
Leal, Luiz; Mueller, Don; Arbanas, Goran; Wiarda, Dorothea; Derrien, Herve
2008-01-01
The error estimation for calculated quantities relies on nuclear data uncertainty information available in the basic nuclear data libraries such as the U.S. Evaluated Nuclear Data File (ENDF/B). The uncertainty files (covariance matrices) in the ENDF/B library are generally obtained from analysis of experimental data. In the resonance region, the computer code SAMMY is used for analyses of experimental data and generation of resonance parameters. In addition to resonance parameters evaluation, SAMMY also generates resonance parameter covariance matrices (RPCM). SAMMY uses the generalized least-squares formalism (Bayes' method) together with the resonance formalism (R-matrix theory) for analysis of experimental data. Two approaches are available for creation of resonance-parameter covariance data. (1) During the data-evaluation process, SAMMY generates both a set of resonance parameters that fit the experimental data and the associated resonance-parameter covariance matrix. (2) For existing resonance-parameter evaluations for which no resonance-parameter covariance data are available, SAMMY can retroactively create a resonance-parameter covariance matrix. The retroactive method was used to generate covariance data for 235 U. The resulting 235 U covariance matrix was then used as input to the PUFF-IV code, which processed the covariance data into multigroup form, and to the TSUNAMI code, which calculated the uncertainty in the multiplication factor due to uncertainty in the experimental cross sections. The objective of this work is to demonstrate the use of the 235 U covariance data in calculations of critical benchmark systems. (authors)
A New Approach for Nuclear Data Covariance and Sensitivity Generation
Leal, L.C.; Larson, N.M.; Derrien, H.; Kawano, T.; Chadwick, M.B.
2005-01-01
Covariance data are required to correctly assess uncertainties in design parameters in nuclear applications. The error estimation of calculated quantities relies on the nuclear data uncertainty information available in the basic nuclear data libraries, such as the U.S. Evaluated Nuclear Data File, ENDF/B. The uncertainty files in the ENDF/B library are obtained from the analysis of experimental data and are stored as variance and covariance data. The computer code SAMMY is used in the analysis of the experimental data in the resolved and unresolved resonance energy regions. The data fitting of cross sections is based on generalized least-squares formalism (Bayes' theory) together with the resonance formalism described by R-matrix theory. Two approaches are used in SAMMY for the generation of resonance-parameter covariance data. In the evaluation process SAMMY generates a set of resonance parameters that fit the data, and, in addition, it also provides the resonance-parameter covariances. For existing resonance-parameter evaluations where no resonance-parameter covariance data are available, the alternative is to use an approach called the 'retroactive' resonance-parameter covariance generation. In the high-energy region the methodology for generating covariance data consists of least-squares fitting and model parameter adjustment. The least-squares fitting method calculates covariances directly from experimental data. The parameter adjustment method employs a nuclear model calculation such as the optical model and the Hauser-Feshbach model, and estimates a covariance for the nuclear model parameters. In this paper we describe the application of the retroactive method and the parameter adjustment method to generate covariance data for the gadolinium isotopes
Aspects of second-order analysis of structured inhomogeneous spatio-temporal processes
Møller, Jesper; Ghorbani, Mohammad
2012-01-01
Statistical methodology for spatio-temporal point processes is in its infancy. We consider second-order analysis based on pair correlation functions and K-functions for general inhomogeneous spatio-temporal point processes and for inhomogeneous spatio-temporal Cox processes. Assuming spatio......-temporal separability of the intensity function, we clarify different meanings of second-order spatio-temporal separability. One is second-order spatio-temporal independence and relates to log-Gaussian Cox processes with an additive covariance structure of the underlying spatio-temporal Gaussian process. Another...... concerns shot-noise Cox processes with a separable spatio-temporal covariance density. We propose diagnostic procedures for checking hypotheses of second-order spatio-temporal separability, which we apply on simulated and real data....
Second-order analysis of structured inhomogeneous spatio-temporal point processes
Møller, Jesper; Ghorbani, Mohammad
Statistical methodology for spatio-temporal point processes is in its infancy. We consider second-order analysis based on pair correlation functions and K-functions for first general inhomogeneous spatio-temporal point processes and second inhomogeneous spatio-temporal Cox processes. Assuming...... spatio-temporal separability of the intensity function, we clarify different meanings of second-order spatio-temporal separability. One is second-order spatio-temporal independence and relates e.g. to log-Gaussian Cox processes with an additive covariance structure of the underlying spatio......-temporal Gaussian process. Another concerns shot-noise Cox processes with a separable spatio-temporal covariance density. We propose diagnostic procedures for checking hypotheses of second-order spatio-temporal separability, which we apply on simulated and real data (the UK 2001 epidemic foot and mouth disease data)....
Tiilikainen, J; Tilli, J-M; Bosund, V; Mattila, M; Hakkarainen, T; Airaksinen, V-M; Lipsanen, H
2007-01-01
Two novel genetic algorithms implementing principal component analysis and an adaptive nonlinear fitness-space-structure technique are presented and compared with conventional algorithms in x-ray reflectivity analysis. Principal component analysis based on Hessian or interparameter covariance matrices is used to rotate a coordinate frame. The nonlinear adaptation applies nonlinear estimates to reshape the probability distribution of the trial parameters. The simulated x-ray reflectivity of a realistic model of a periodic nanolaminate structure was used as a test case for the fitting algorithms. The novel methods had significantly faster convergence and less stagnation than conventional non-adaptive genetic algorithms. The covariance approach needs no additional curve calculations compared with conventional methods, and it had better convergence properties than the computationally expensive Hessian approach. These new algorithms can also be applied to other fitting problems where tight interparameter dependence is present
Modeling, Analysis, and Optimization Issues for Large Space Structures
Pinson, L. D. (Compiler); Amos, A. K. (Compiler); Venkayya, V. B. (Compiler)
1983-01-01
Topics concerning the modeling, analysis, and optimization of large space structures are discussed including structure-control interaction, structural and structural dynamics modeling, thermal analysis, testing, and design.
Autodesk Robot Structural Analysis Professional 2016 essentials
Marsh, Ken
2016-01-01
Autodesk Robot Structural Analysis Professional 2016 - Essentials is an excellent introduction to the essential features, functions, and workflows of Autodesk Robot Structural Analysis Professional. Master the tools you will need to make Robot work for you: Go from zero to proficiency with this thorough and detailed introduction to the essential concepts and workflows of Robot Structural Analysis Professional 2016. - Demystify the interface - Manipulate and manage Robot tables like a pro - Learn how to use Robot's modeling tools - Master loading techniques - Harness Robot automated load combinations - Decipher simplified seismic loading - Discover workflows for steel and concrete design - Gain insights to help troubleshoot issues Guided exercises are provided to help cement fundamental concepts in Robot Structural Analysis and drive home key functions. Get up to speed quickly with this essential text and add Robot Structural Analysis Professional 2016 to your analysis and design toolbox. New in 2016: AWC-NDS ...
GLq(N)-covariant quantum algebras and covariant differential calculus
Isaev, A.P.; Pyatov, P.N.
1992-01-01
GL q (N)-covariant quantum algebras with generators satisfying quadratic polynomial relations are considered. It is that, up to some innessential arbitrariness, there are only two kinds of such quantum algebras, namely, the algebras with q-deformed commutation and q-deformed anticommutation relations. 25 refs
GLq(N)-covariant quantum algebras and covariant differential calculus
Isaev, A.P.; Pyatov, P.N.
1993-01-01
We consider GL q (N)-covariant quantum algebras with generators satisfying quadratic polynomial relations. We show that, up to some inessential arbitrariness, there are only two kinds of such quantum algebras, namely, the algebras with q-deformed commutation and q-deformed anticommutation relations. The connection with the bicovariant differential calculus on the linear quantum groups is discussed. (orig.)
Automated analysis and design of complex structures
Wilson, E.L.
1977-01-01
The present application of optimum design appears to be restricted to components of the structure rather than to the total structural system. Since design normally involved many analysis of the system any improvement in the efficiency of the basic methods of analysis will allow more complicated systems to be designed by optimum methods. The evaluation of the risk and reliability of a structural system can be extremely important. Reliability studies have been made of many non-structural systems for which the individual components have been extensively tested and the service environment is known. For such systems the reliability studies are valid. For most structural systems, however, the properties of the components can only be estimated and statistical data associated with the potential loads is often minimum. Also, a potentially critical loading condition may be completely neglected in the study. For these reasons and the previous problems associated with the reliability of both linear and nonlinear analysis computer programs it appears to be premature to place a significant value on such studies for complex structures. With these comments as background the purpose of this paper is to discuss the following: the relationship of analysis to design; new methods of analysis; new of improved finite elements; effect of minicomputer on structural analysis methods; the use of system of microprocessors for nonlinear structural analysis; the role of interacting graphics systems in future analysis and design. This discussion will focus on the impact of new, inexpensive computer hardware on design and analysis methods
Structure of polysaccharide and structural analysis by x-ray
Yuguchi, Yoshiaki
2010-01-01
Polysaccharides occur in plants and the living body in the solid, gel, or liquid. They have a highly structural diversity and possess the potential to be used for development of new materials and energy sources. So it is very important to understand their molecular structure under various conditions. This review introduces the structural characteristics of polysaccharides and the examples of their analysis by the X-ray scattering method. (author)
Cosmic censorship conjecture revisited: covariantly
Hamid, Aymen I M; Goswami, Rituparno; 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. (paper)
Covariance expressions for eigenvalue and eigenvector problems
Liounis, Andrew J.
There are a number of important scientific and engineering problems whose solutions take the form of an eigenvalue--eigenvector problem. Some notable examples include solutions to linear systems of ordinary differential equations, controllability of linear systems, finite element analysis, chemical kinetics, fitting ellipses to noisy data, and optimal estimation of attitude from unit vectors. In many of these problems, having knowledge of the eigenvalue and eigenvector Jacobians is either necessary or is nearly as important as having the solution itself. For instance, Jacobians are necessary to find the uncertainty in a computed eigenvalue or eigenvector estimate. This uncertainty, which is usually represented as a covariance matrix, has been well studied for problems similar to the eigenvalue and eigenvector problem, such as singular value decomposition. There has been substantially less research on the covariance of an optimal estimate originating from an eigenvalue-eigenvector problem. In this thesis we develop two general expressions for the Jacobians of eigenvalues and eigenvectors with respect to the elements of their parent matrix. The expressions developed make use of only the parent matrix and the eigenvalue and eigenvector pair under consideration. In addition, they are applicable to any general matrix (including complex valued matrices, eigenvalues, and eigenvectors) as long as the eigenvalues are simple. Alongside this, we develop expressions that determine the uncertainty in a vector estimate obtained from an eigenvalue-eigenvector problem given the uncertainty of the terms of the matrix. The Jacobian expressions developed are numerically validated with forward finite, differencing and the covariance expressions are validated using Monte Carlo analysis. Finally, the results from this work are used to determine covariance expressions for a variety of estimation problem examples and are also applied to the design of a dynamical system.
Dynamic analysis program for frame structure
Ando, Kozo; Chiba, Toshio
1975-01-01
A general purpose computer program named ISTRAN/FD (Isub(HI) STRucture ANalysis/Frame structure, Dynamic analysis) has been developed for dynamic analysis of three-dimensional frame structures. This program has functions of free vibration analysis, seismic response analysis, graphic display by plotter and CRT, etc. This paper introduces ISTRAN/FD; examples of its application are shown with various problems : idealization of the cantilever, dynamic analysis of the main tower of the suspension bridge, three-dimensional vibration in the plate girder bridge, seismic response in the boiler steel structure, and dynamic properties of the underground LNG tank. In this last example, solid elements, in addition to beam elements, are especially used for the analysis. (auth.)
FINITE ELEMENT ANALYSIS OF STRUCTURES
PECINGINA OLIMPIA-MIOARA
2015-05-01
Full Text Available The application of finite element method is analytical when solutions can not be applied for deeper study analyzes static, dynamic or other types of requirements in different points of the structures .In practice it is necessary to know the behavior of the structure or certain parts components of the machine under the influence of certain factors static and dynamic . The application of finite element in the optimization of components leads to economic growth , to increase reliability and durability organs studied, thus the machine itself.
Analysis and design of SSC underground structures
Clark, G.T.
1993-01-01
This paper describes the analysis and design of underground structures for the Superconducting Super Collider (SSC) Project. A brief overview of the SSC Project and the types of underground structures are presented. Engineering properties and non-linear behavior of the geologic materials are reviewed. The three-dimensional sequential finite element rock-structure interaction analysis techniques developed by the author are presented and discussed. Several examples of how the method works, specific advantages, and constraints are presented. Finally, the structural designs that resulted from the sequential interaction analysis are presented
Petersen, Lone K; Restrepo, Jaime; Moreira, Edson D
2017-01-01
were seronegative for that type at day 1. CONCLUSIONS: 9vHPV vaccine immunogenicity was robust among subjects with differing baseline characteristics. It was generally comparable across subjects of different races and from different regions. Greater immunogenicity in girls and boys versus young women...... as geometric mean titers (GMTs). Covariates examined were age, gender, race, region of residence, and HPV serostatus and PCR status at day 1. RESULTS: GMTs to all 9 vaccine HPV types decreased with age at vaccination initiation, and were otherwise generally similar among the demographic subgroups defined...... by gender, race and region of residence. For all subgroups defined by race or region of residence, GMTs were higher in girls and boys than in young women. Vaccination of subjects who were seropositive at day 1 to a vaccine HPV type resulted in higher GMTs to that type, compared with those in subjects who...
Petersen, Lone K; Restrepo, Jaime; Moreira, Edson D
2017-01-01
BACKGROUND: The immunogenicity profile of the 9-valent HPV (9vHPV) vaccine was evaluated across five phase III clinical studies conducted in girls and boys 9-15 years of age and young women 16-26 years of age. The effect of baseline characteristics of subjects on vaccine-induced HPV antibody...... responses was assessed. METHODS: Immunogenicity data from 11,304 subjects who received ≥1 dose of 9vHPV vaccine in five Phase III studies were analyzed. Vaccine was administered as a 3-dose regimen. HPV antibody titers were assessed 1 month after dose 3 using a competitive Luminex immunoassay and summarized...... as geometric mean titers (GMTs). Covariates examined were age, gender, race, region of residence, and HPV serostatus and PCR status at day 1. RESULTS: GMTs to all 9 vaccine HPV types decreased with age at vaccination initiation, and were otherwise generally similar among the demographic subgroups defined...
Covariant chronogeometry and extreme distances
Segal, I.E.
1981-01-01
A theory for the analysis of major features of the fundamental physical structure of the universe, from micro- to macroscopic is proposed. It indicates that gravity is essentially the transform of the aggregate of the basic microscopic forces under conformal inversion. The theory also suggests a natural form for elementary particle structure that implies a nonparametric cosmological effect and indicates an intrinsic hierarchy among the microscopic forces. (author)
Empirical Likelihood in Nonignorable Covariate-Missing Data Problems.
Xie, Yanmei; Zhang, Biao
2017-04-20
Missing covariate data occurs often in regression analysis, which frequently arises in the health and social sciences as well as in survey sampling. We study methods for the analysis of a nonignorable covariate-missing data problem in an assumed conditional mean function when some covariates are completely observed but other covariates are missing for some subjects. We adopt the semiparametric perspective of Bartlett et al. (Improving upon the efficiency of complete case analysis when covariates are MNAR. Biostatistics 2014;15:719-30) on regression analyses with nonignorable missing covariates, in which they have introduced the use of two working models, the working probability model of missingness and the working conditional score model. In this paper, we study an empirical likelihood approach to nonignorable covariate-missing data problems with the objective of effectively utilizing the two working models in the analysis of covariate-missing data. We propose a unified approach to constructing a system of unbiased estimating equations, where there are more equations than unknown parameters of interest. One useful feature of these unbiased estimating equations is that they naturally incorporate the incomplete data into the data analysis, making it possible to seek efficient estimation of the parameter of interest even when the working regression function is not specified to be the optimal regression function. We apply the general methodology of empirical likelihood to optimally combine these unbiased estimating equations. We propose three maximum empirical likelihood estimators of the underlying regression parameters and compare their efficiencies with other existing competitors. We present a simulation study to compare the finite-sample performance of various methods with respect to bias, efficiency, and robustness to model misspecification. The proposed empirical likelihood method is also illustrated by an analysis of a data set from the US National Health and
Analysis of Nonlinear Dynamic Structures
Bheema
work a two degrees of freedom nonlinear system with zero memory was ... FRF is the most widely used method in structural dynamics which gives information about the ..... 3.6, which is the waterfall diagram of the same response, as well.
Covariance matrices and applications to the field of nuclear data
Smith, D.L.
1981-11-01
A student's introduction to covariance error analysis and least-squares evaluation of data is provided. It is shown that the basic formulas used in error propagation can be derived from a consideration of the geometry of curvilinear coordinates. Procedures for deriving covariances for scaler and vector functions of several variables are presented. Proper methods for reporting experimental errors and for deriving covariance matrices from these errors are indicated. The generalized least-squares method for evaluating experimental data is described. Finally, the use of least-squares techniques in data fitting applications is discussed. Specific examples of the various procedures are presented to clarify the concepts
Covariance matrix estimation for stationary time series
Xiao, Han; Wu, Wei Biao
2011-01-01
We obtain a sharp convergence rate for banded covariance matrix estimates of stationary processes. A precise order of magnitude is derived for spectral radius of sample covariance matrices. We also consider a thresholded covariance matrix estimator that can better characterize sparsity if the true covariance matrix is sparse. As our main tool, we implement Toeplitz [Math. Ann. 70 (1911) 351–376] idea and relate eigenvalues of covariance matrices to the spectral densities or Fourier transforms...
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
Covariant Gauss law commutator anomaly
Dunne, G.V.; Trugenberger, C.A.; Massachusetts Inst. of Tech., Cambridge
1990-01-01
Using a (fixed-time) hamiltonian formalism we derive a covariant form for the anomaly in the commutator algebra of Gauss law generators for chiral fermions interacting with a dynamical non-abelian gauge field in 3+1 dimensions. (orig.)
Covariant gauges for constrained systems
Gogilidze, S.A.; Khvedelidze, A.M.; Pervushin, V.N.
1995-01-01
The method of constructing of extended phase space for singular theories which permits the consideration of covariant gauges without the introducing of a ghost fields, is proposed. The extension of the phase space is carried out by the identification of the initial theory with an equivalent theory with higher derivatives and applying to it the Ostrogradsky method of Hamiltonian description. 7 refs
Evans, Nathan J; Steyvers, Mark; Brown, Scott D
2018-06-05
Understanding individual differences in cognitive performance is an important part of understanding how variations in underlying cognitive processes can result in variations in task performance. However, the exploration of individual differences in the components of the decision process-such as cognitive processing speed, response caution, and motor execution speed-in previous research has been limited. Here, we assess the heritability of the components of the decision process, with heritability having been a common aspect of individual differences research within other areas of cognition. Importantly, a limitation of previous work on cognitive heritability is the underlying assumption that variability in response times solely reflects variability in the speed of cognitive processing. This assumption has been problematic in other domains, due to the confounding effects of caution and motor execution speed on observed response times. We extend a cognitive model of decision-making to account for relatedness structure in a twin study paradigm. This approach can separately quantify different contributions to the heritability of response time. Using data from the Human Connectome Project, we find strong evidence for the heritability of response caution, and more ambiguous evidence for the heritability of cognitive processing speed and motor execution speed. Our study suggests that the assumption made in previous studies-that the heritability of cognitive ability is based on cognitive processing speed-may be incorrect. More generally, our methodology provides a useful avenue for future research in complex data that aims to analyze cognitive traits across different sources of related data, whether the relation is between people, tasks, experimental phases, or methods of measurement. © 2018 Cognitive Science Society, Inc.
Structural analysis in medical imaging
Dellepiane, S.; Serpico, S.B.; Venzano, L.; Vernazza, G.
1987-01-01
The conventional techniques in Pattern Recognition (PR) have been greatly improved by the introduction of Artificial Intelligence (AI) approaches, in particular for knowledge representation, inference mechanism and control structure. The purpose of this paper is to describe an image understanding system, based on the integrated approach (AI - PR), developed in the author's Department to interpret Nuclear Magnetic Resonance (NMR) images. The system is characterized by a heterarchical control structure and a blackboard model for the global data-base. The major aspects of the system are pointed out, with particular reference to segmentation, knowledge representation and error recovery (backtracking). The eye slices obtained in the case of two patients have been analyzed and the related results are discussed
Eulerian fluid-structure analysis of BWR
McMaster, W.H.
1979-05-01
A fluid-structure-interaction algorithm is developed for the analysis of the dynamic response of a BWR pressure-suppression pool and containment structure. The method is incorporated into a two-dimensional semi-implicit Eulerian hydrodynamics code, PELE-IC, for the solution of incompressible flow coupled to flexible structures. The fluid, structure, and coupling algorithms have been verified by calculation of solved problems from the literature and by comparison with air and steam blowdown experiments
Structural Analysis of Natural Products
Přichystal, Jakub; Schug, K. A.; Lemr, Karel; Novák, Jiří; Havlíček, Vladimír
2016-01-01
Roč. 88, č. 21 (2016), s. 10338-10346 ISSN 0003-2700 R&D Projects: GA MŠk(CZ) LO1509; GA MŠk(CZ) LH14064; GA ČR(CZ) GA16-20229S Institutional support: RVO:61388971 Keywords : IONIZATION-MASS-SPECTROMETRY * BIOSYNTHETIC GENE CLUSTERS * STRUCTURE ELUCIDATION Subject RIV: EE - Microbiology, Virology Impact factor: 6.320, year: 2016
Reliability analysis and assessment of structural systems
Yao, J.T.P.; Anderson, C.A.
1977-01-01
The study of structural reliability deals with the probability of having satisfactory performance of the structure under consideration within any specific time period. To pursue this study, it is necessary to apply available knowledge and methodology in structural analysis (including dynamics) and design, behavior of materials and structures, experimental mechanics, and the theory of probability and statistics. In addition, various severe loading phenomena such as strong motion earthquakes and wind storms are important considerations. For three decades now, much work has been done on reliability analysis of structures, and during this past decade, certain so-called 'Level I' reliability-based design codes have been proposed and are in various stages of implementation. These contributions will be critically reviewed and summarized in this paper. Because of the undesirable consequences resulting from the failure of nuclear structures, it is important and desirable to consider the structural reliability in the analysis and design of these structures. Moreover, after these nuclear structures are constructed, it is desirable for engineers to be able to assess the structural reliability periodically as well as immediately following the occurrence of severe loading conditions such as a strong-motion earthquake. During this past decade, increasing use has been made of techniques of system identification in structural engineering. On the basis of non-destructive test results, various methods have been developed to obtain an adequate mathematical model (such as the equations of motion with more realistic parameters) to represent the structural system
Structure analysis - chiromancy of the rock
Huber, A.; Huber, M.
1989-01-01
The reader may initially be surprised by a comparison between structure analysis and palmistry which is, in effect, a comparison between a scientific research method on the one hand and art which is equated with magical powers on the other. In the figurative sense, however, these two fields have some points in common which should help us to obtain a first impression of the nature of geological structure analysis. Chiromancy uses the lines and the form of the hand to predict the character and the future of the person in question. In the same way, geologists use rocks and rock forms to obtain information on structure and behaviour of different formations. Structure analysis is a specialised field of geological investigation in which traces of deformation are interpreted as expressions of rockforming forces. This article discusses how and why the character of a rock formation as well as its past, present and even future behaviour can be determined using structure analysis. (author) 11 figs
Using machine learning to assess covariate balance in matching studies.
Linden, Ariel; Yarnold, Paul R
2016-12-01
In order to assess the effectiveness of matching approaches in observational studies, investigators typically present summary statistics for each observed pre-intervention covariate, with the objective of showing that matching reduces the difference in means (or proportions) between groups to as close to zero as possible. In this paper, we introduce a new approach to distinguish between study groups based on their distributions of the covariates using a machine-learning algorithm called optimal discriminant analysis (ODA). Assessing covariate balance using ODA as compared with the conventional method has several key advantages: the ability to ascertain how individuals self-select based on optimal (maximum-accuracy) cut-points on the covariates; the application to any variable metric and number of groups; its insensitivity to skewed data or outliers; and the use of accuracy measures that can be widely applied to all analyses. Moreover, ODA accepts analytic weights, thereby extending the assessment of covariate balance to any study design where weights are used for covariate adjustment. By comparing the two approaches using empirical data, we are able to demonstrate that using measures of classification accuracy as balance diagnostics produces highly consistent results to those obtained via the conventional approach (in our matched-pairs example, ODA revealed a weak statistically significant relationship not detected by the conventional approach). Thus, investigators should consider ODA as a robust complement, or perhaps alternative, to the conventional approach for assessing covariate balance in matching studies. © 2016 John Wiley & Sons, Ltd.
Covariant differential complexes of quantum linear groups
Isaev, A.P.; Pyatov, P.N.
1993-01-01
We consider the possible covariant external algebra structures for Cartan's 1-forms (Ω) on G L q (N) and S L q (N). Our starting point is that Ω s realize an adjoint representation of quantum group and all monomials of Ω s possess the unique ordering. For the obtained external algebras we define the differential mapping d possessing the usual nilpotence condition, and the generally deformed version of Leibnitz rules. The status of the known examples of G L q (N)-differential calculi in the proposed classification scheme and the problems of S L q (N)-reduction are discussed. (author.). 26 refs
Structural analysis consultation using artificial intelligence
Melosh, R. J.; Marcal, P. V.; Berke, L.
1978-01-01
The primary goal of consultation is definition of the best strategy to deal with a structural engineering analysis objective. The knowledge base to meet the need is designed to identify the type of numerical analysis, the needed modeling detail, and specific analysis data required. Decisions are constructed on the basis of the data in the knowledge base - material behavior, relations between geometry and structural behavior, measures of the importance of time and temperature changes - and user supplied specifics characteristics of the spectrum of analysis types, the relation between accuracy and model detail on the structure, its mechanical loadings, and its temperature states. Existing software demonstrated the feasibility of the approach, encompassing the 36 analysis classes spanning nonlinear, temperature affected, incremental analyses which track the behavior of structural systems.
Lorentz covariant canonical symplectic algorithms for dynamics of charged particles
Wang, Yulei; Liu, Jian; Qin, Hong
2016-12-01
In this paper, the Lorentz covariance of algorithms is introduced. Under Lorentz transformation, both the form and performance of a Lorentz covariant algorithm are invariant. To acquire the advantages of symplectic algorithms and Lorentz covariance, a general procedure for constructing Lorentz covariant canonical symplectic algorithms (LCCSAs) is provided, based on which an explicit LCCSA for dynamics of relativistic charged particles is built. LCCSA possesses Lorentz invariance as well as long-term numerical accuracy and stability, due to the preservation of a discrete symplectic structure and the Lorentz symmetry of the system. For situations with time-dependent electromagnetic fields, which are difficult to handle in traditional construction procedures of symplectic algorithms, LCCSA provides a perfect explicit canonical symplectic solution by implementing the discretization in 4-spacetime. We also show that LCCSA has built-in energy-based adaptive time steps, which can optimize the computation performance when the Lorentz factor varies.
Robustness Analysis of Timber Truss Structure
Rajčić, Vlatka; Čizmar, Dean; Kirkegaard, Poul Henning
2010-01-01
The present paper discusses robustness of structures in general and the robustness requirements given in the codes. Robustness of timber structures is also an issues as this is closely related to Working group 3 (Robustness of systems) of the COST E55 project. Finally, an example of a robustness...... evaluation of a widespan timber truss structure is presented. This structure was built few years ago near Zagreb and has a span of 45m. Reliability analysis of the main members and the system is conducted and based on this a robustness analysis is preformed....
Structural Analysis Algorithms for Nanomaterials
Larsen, Peter Mahler
the existing factorial-time bound. This method is subsequently extended to two-dimensional monolayers. A method is presented for the identication of ordered crystalline phases in molecular dynamics simulations. A robust classication is obtained by the use of template matching, also formulated as a bipartite......-strain interfaces. The stable, low-energy interfaces which are found as a result are intended for use in the design and construction of topological superconductors, which have important applications in quantum computing. Cluster expansion models are used to nd ground-state structures in gold-silver nanoparticles......, which are used in a variety of catalysis processes. In addition to this concrete application, theoretical methods are developed for the optimal construction of cluster expansion models, the exact determination of ground states in a large model, and the exhaustive determination of all possible ground...
Structural Dynamics and Data Analysis
Luthman, Briana L.
2013-01-01
This project consists of two parts, the first will be the post-flight analysis of data from a Delta IV launch vehicle, and the second will be a Finite Element Analysis of a CubeSat. Shock and vibration data was collected on WGS-5 (Wideband Global SATCOM- 5) which was launched on a Delta IV launch vehicle. Using CAM (CAlculation with Matrices) software, the data is to be plotted into Time History, Shock Response Spectrum, and SPL (Sound Pressure Level) curves. In this format the data is to be reviewed and compared to flight instrumentation data from previous flights of the same launch vehicle. This is done to ensure the current mission environments, such as shock, random vibration, and acoustics, are not out of family with existing flight experience. In family means the peaks on the SRS curve for WGS-5 are similar to the peaks from the previous flights and there are no major outliers. The curves from the data will then be compiled into a useful format so that is can be peer reviewed then presented before an engineering review board if required. Also, the reviewed data will be uploaded to the Engineering Review Board Information System (ERBIS) to archive. The second part of this project is conducting Finite Element Analysis of a CubeSat. In 2010, Merritt Island High School partnered with NASA to design, build and launch a CubeSat. The team is now called StangSat in honor of their mascot, the mustang. Over the past few years, the StangSat team has built a satellite and has now been manifested for flight on a SpaceX Falcon 9 launch in 2014. To prepare for the final launch, a test flight was conducted in Mojave, California. StangSat was launched on a Prospector 18D, a high altitude rocket made by Garvey Spacecraft Corporation, along with their sister satellite CP9 built by California Polytechnic University. However, StangSat was damaged during an off nominal landing and this project will give beneficial insights into what loads the CubeSat experienced during the crash
Structural analysis of fuel handling systems
Lee, L S.S. [Atomic Energy of Canada Ltd., Mississauga, ON (Canada)
1997-12-31
The purpose of this paper has three aspects: (i) to review `why` and `what` types of structural analysis, testing and report are required for the fuel handling systems according to the codes, or needed for design of a product, (ii) to review the input requirements for analysis and the analysis procedures, and (iii) to improve the communication between the analysis and other elements of the product cycle. The required or needed types of analysis and report may be categorized into three major groups: (i) Certified Stress Reports for design by analysis, (ii) Design Reports not required for certification and registration, but are still required by codes, and (iii) Design Calculations required by codes or needed for design. Input requirements for structural analysis include: design, code classification, loadings, and jurisdictionary boundary. Examples of structural analysis for the fueling machine head and support structure are given. For improving communication between the structural analysis and the other elements of the product cycle, some areas in the specification of design requirements and load rating are discussed. (author). 6 refs., 1 tab., 4 figs.
Structural analysis of fuel handling systems
Lee, L.S.S.
1996-01-01
The purpose of this paper has three aspects: (i) to review 'why' and 'what' types of structural analysis, testing and report are required for the fuel handling systems according to the codes, or needed for design of a product, (ii) to review the input requirements for analysis and the analysis procedures, and (iii) to improve the communication between the analysis and other elements of the product cycle. The required or needed types of analysis and report may be categorized into three major groups: (i) Certified Stress Reports for design by analysis, (ii) Design Reports not required for certification and registration, but are still required by codes, and (iii) Design Calculations required by codes or needed for design. Input requirements for structural analysis include: design, code classification, loadings, and jurisdictionary boundary. Examples of structural analysis for the fueling machine head and support structure are given. For improving communication between the structural analysis and the other elements of the product cycle, some areas in the specification of design requirements and load rating are discussed. (author). 6 refs., 1 tab., 4 figs
Massive data compression for parameter-dependent covariance matrices
Heavens, Alan F.; Sellentin, Elena; de Mijolla, Damien; Vianello, Alvise
2017-12-01
We show how the massive data compression algorithm MOPED can be used to reduce, by orders of magnitude, the number of simulated data sets which are required to estimate the covariance matrix required for the analysis of Gaussian-distributed data. This is relevant when the covariance matrix cannot be calculated directly. The compression is especially valuable when the covariance matrix varies with the model parameters. In this case, it may be prohibitively expensive to run enough simulations to estimate the full covariance matrix throughout the parameter space. This compression may be particularly valuable for the next generation of weak lensing surveys, such as proposed for Euclid and Large Synoptic Survey Telescope, for which the number of summary data (such as band power or shear correlation estimates) is very large, ∼104, due to the large number of tomographic redshift bins which the data will be divided into. In the pessimistic case where the covariance matrix is estimated separately for all points in an Monte Carlo Markov Chain analysis, this may require an unfeasible 109 simulations. We show here that MOPED can reduce this number by a factor of 1000, or a factor of ∼106 if some regularity in the covariance matrix is assumed, reducing the number of simulations required to a manageable 103, making an otherwise intractable analysis feasible.
NAPS: Network Analysis of Protein Structures
Chakrabarty, Broto; Parekh, Nita
2016-01-01
Traditionally, protein structures have been analysed by the secondary structure architecture and fold arrangement. An alternative approach that has shown promise is modelling proteins as a network of non-covalent interactions between amino acid residues. The network representation of proteins provide a systems approach to topological analysis of complex three-dimensional structures irrespective of secondary structure and fold type and provide insights into structure-function relationship. We have developed a web server for network based analysis of protein structures, NAPS, that facilitates quantitative and qualitative (visual) analysis of residue–residue interactions in: single chains, protein complex, modelled protein structures and trajectories (e.g. from molecular dynamics simulations). The user can specify atom type for network construction, distance range (in Å) and minimal amino acid separation along the sequence. NAPS provides users selection of node(s) and its neighbourhood based on centrality measures, physicochemical properties of amino acids or cluster of well-connected residues (k-cliques) for further analysis. Visual analysis of interacting domains and protein chains, and shortest path lengths between pair of residues are additional features that aid in functional analysis. NAPS support various analyses and visualization views for identifying functional residues, provide insight into mechanisms of protein folding, domain-domain and protein–protein interactions for understanding communication within and between proteins. URL:http://bioinf.iiit.ac.in/NAPS/. PMID:27151201
Dynamic analysis and design of offshore structures
Chandrasekaran, Srinivasan
2015-01-01
This book attempts to provide readers with an overall idea of various types of offshore platform geometries. It covers the various environmental loads encountered by these structures, a detailed description of the fundamentals of structural dynamics in a class-room style, estimate of damping in offshore structures and their applications in the preliminary analysis and design. Basic concepts of structural dynamics are emphasized through simple illustrative examples and exercises. Design methodologies and guidelines, which are FORM based concepts are explained through a few applied example structures. Each chapter also has tutorials and exercises for self-learning. A dedicated chapter on stochastic dynamics will help the students to extend the basic concepts of structural dynamics to this advanced domain of research. Hydrodynamic response of offshore structures with perforated members is one of the recent research applications, which is found to be one of the effective manner of retrofitting offshore structur...
Group covariance and metrical theory
Halpern, L.
1983-01-01
The a priori introduction of a Lie group of transformations into a physical theory has often proved to be useful; it usually serves to describe special simplified conditions before a general theory can be worked out. Newton's assumptions of absolute space and time are examples where the Euclidian group and translation group have been introduced. These groups were extended to the Galilei group and modified in the special theory of relativity to the Poincare group to describe physics under the given conditions covariantly in the simplest way. The criticism of the a priori character leads to the formulation of the general theory of relativity. The general metric theory does not really give preference to a particular invariance group - even the principle of equivalence can be adapted to a whole family of groups. The physical laws covariantly inserted into the metric space are however adapted to the Poincare group. 8 references
Kodeli, Ivan-Alexander
2005-01-01
The new cross-section covariance matrix library ZZ-VITAMIN-J/COVA/EFF3 intended to simplify and encourage sensitivity and uncertainty analysis was prepared and is available from the NEA Data Bank. The library is organised in a ready-to-use form including both the covariance matrix data as well as processing tools:-Cross-section covariance matrices from the EFF-3 evaluation for five materials: 9 Be, 28 Si, 56 Fe, 58 Ni and 60 Ni. Other data will be included when available. -FORTRAN program ANGELO-2 to extrapolate/interpolate the covariance matrices to a users' defined energy group structure. -FORTRAN program LAMBDA to verify the mathematical properties of the covariance matrices, like symmetry, positive definiteness, etc. The preparation, testing and use of the covariance matrix library are presented. The uncertainties based on the cross-section covariance data were compared with those based on other evaluations, like ENDF/B-VI. The collapsing procedure used in the ANGELO-2 code was compared and validated with the one used in the NJOY system
Deffner, Veronika; Küchenhoff, Helmut; Breitner, Susanne; Schneider, Alexandra; Cyrys, Josef; Peters, Annette
2018-03-13
The ultrafine particle measurements in the Augsburger Umweltstudie, a panel study conducted in Augsburg, Germany, exhibit measurement error from various sources. Measurements of mobile devices show classical possibly individual-specific measurement error; Berkson-type error, which may also vary individually, occurs, if measurements of fixed monitoring stations are used. The combination of fixed site and individual exposure measurements results in a mixture of the two error types. We extended existing bias analysis approaches to linear mixed models with a complex error structure including individual-specific error components, autocorrelated errors, and a mixture of classical and Berkson error. Theoretical considerations and simulation results show, that autocorrelation may severely change the attenuation of the effect estimations. Furthermore, unbalanced designs and the inclusion of confounding variables influence the degree of attenuation. Bias correction with the method of moments using data with mixture measurement error partially yielded better results compared to the usage of incomplete data with classical error. Confidence intervals (CIs) based on the delta method achieved better coverage probabilities than those based on Bootstrap samples. Moreover, we present the application of these new methods to heart rate measurements within the Augsburger Umweltstudie: the corrected effect estimates were slightly higher than their naive equivalents. The substantial measurement error of ultrafine particle measurements has little impact on the results. The developed methodology is generally applicable to longitudinal data with measurement error. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Clan structure analysis and rapidity gap probability
Lupia, S.; Giovannini, A.; Ugoccioni, R.
1995-01-01
Clan structure analysis in rapidity intervals is generalized from negative binomial multiplicity distribution to the wide class of compound Poisson distributions. The link of generalized clan structure analysis with correlation functions is also established. These theoretical results are then applied to minimum bias events and evidentiate new interesting features, which can be inspiring and useful in order to discuss data on rapidity gap probability at TEVATRON and HERA. (orig.)
Clan structure analysis and rapidity gap probability
Lupia, S. [Turin Univ. (Italy). Ist. di Fisica Teorica]|[Istituto Nazionale di Fisica Nucleare, Turin (Italy); Giovannini, A. [Turin Univ. (Italy). Ist. di Fisica Teorica]|[Istituto Nazionale di Fisica Nucleare, Turin (Italy); Ugoccioni, R. [Turin Univ. (Italy). Ist. di Fisica Teorica]|[Istituto Nazionale di Fisica Nucleare, Turin (Italy)
1995-03-01
Clan structure analysis in rapidity intervals is generalized from negative binomial multiplicity distribution to the wide class of compound Poisson distributions. The link of generalized clan structure analysis with correlation functions is also established. These theoretical results are then applied to minimum bias events and evidentiate new interesting features, which can be inspiring and useful in order to discuss data on rapidity gap probability at TEVATRON and HERA. (orig.)
Non-evaluation applications for covariance matrices
Smith, D.L.
1982-05-01
The possibility for application of covariance matrix techniques to a variety of common research problems other than formal data evaluation are demonstrated by means of several examples. These examples deal with such matters as fitting spectral data, deriving uncertainty estimates for results calculated from experimental data, obtaining the best values for plurally-measured quantities, and methods for analysis of cross section errors based on properties of the experiment. The examples deal with realistic situations encountered in the laboratory, and they are treated in sufficient detail to enable a careful reader to extrapolate the methods to related problems.
Nonlinear structural analysis using integrated force method
A new formulation termed the Integrated Force Method (IFM) was proposed by Patnaik ... nated ``Structure (nY m)'' where (nY m) are the force and displacement degrees of ..... Patnaik S N, Yadagiri S 1976 Frequency analysis of structures.
Crystallographic Analysis and Structural Revision of a ...
ABSTRACT. Single crystal X-ray analysis of a spiroterpenoid rearrangement product has revealed that its structure is, in fact, isomeric with the structure proposed previously – an observation that has significant mechanistic implications. KEYWORDS. Spiroterpenoid, rearrangement, X-ray crystallography, camphor derivative.
Crystallographic Analysis and Structural Revision of a ...
Single crystal X-ray analysis of a spiroterpenoid rearrangement product has revealed that its structure is, in fact, isomeric with the structure proposed previously – an observation that has significant mechanistic implications. Keywords: Spiroterpenoid, rearrangement, X-ray crystallography, camphor derivative.
Entity Authentication:Analysis using Structured Intuition
Ahmed, Naveed; Jensen, Christian D.
2010-01-01
In this paper, we propose a new method for the analysis that uses intuition of the analyst in a structured way. First we define entity authentication in terms of fine level authentication goals (FLAGs). Then we use some relevant structures in protocol narrations and use them to justify FLAGs...
Bayesian tests on components of the compound symmetry covariance matrix
Mulder, J.; Fox, J.P.
2013-01-01
Complex dependency structures are often conditionally modeled, where random effects parameters are used to specify the natural heterogeneity in the population. When interest is focused on the dependency structure, inferences can be made from a complex covariance matrix using a marginal modeling
Business strategy and financial structure: an empirical analysis of acute care hospitals.
Ginn, G O; Young, G J; Beekun, R I
1995-01-01
This study investigated the relationship between business strategy and financial structure in the U.S. hospital industry. We studied two dimensions of financial structure--liquidity and leverage. Liquidity was assessed by the acid ratio, and leverage was assessed using the equity funding ratio. Drawing from managerial, finance, and resource dependence perspectives, we developed and tested hypotheses about the relationship between Miles and Snow strategy types and financial structure. Relevant contextual financial and organizational variables were controlled for statistically through the Multivariate Analysis of Covariance technique. The relationship between business strategy and financial structure was found to be significant. Among the Miles and Snow strategy types, defenders were found to have relatively high liquidity and low leverage. Prospectors typically had low liquidity and high leverage. Implications for financial planning, competitive assessment, and reimbursement policy are discussed.
Xu, L.; Chanton, J.; McDermitt, D. K.; Li, J.; Green, R. B.
2015-12-01
Methane plays a critical role in the radiation balance and chemistry of the atmosphere. Globally, landfill methane emission contributes about 10-19% of the anthropogenic methane burden into the atmosphere. In the United States, 18% of annual anthropogenic methane emissions come from landfills, which represent the third largest source of anthropogenic methane emissions, behind enteric fermentation and natural gas and oil production. One uncertainty in estimating landfill methane emissions is the fraction of methane oxidized when methane produced under anaerobic conditions passes through the cover soil. We developed a simple stoichiometric model to estimate methane oxidation fraction when the anaerobic CO2 / CH4 production ratio is known, or can be estimated. The model predicts a linear relationship between CO2 emission rates and CH4 emission rates, where the slope depends on anaerobic CO2 / CH4 production ratio and the fraction of methane oxidized, and the intercept depends on non-methane-dependent oxidation processes. The model was tested using carbon dioxide emission rates (fluxes) and methane emission rates (fluxes) measured using the eddy covariance method over a one year period at the Turkey Run landfill in Georgia, USA. The CO2 / CH4 production ratio was estimated by measuring CO2 and CH4 concentrations in air sampled under anaerobic conditions deep inside the landfill. We also used a mass balance approach to independently estimate fractional oxidation based on stable isotope measurements (δ13C of methane) of gas samples taken from deep inside the landfill and just above the landfill surface. Results from the two independent methods agree well. The model will be described and methane oxidation will be discussed in relation to wind direction, location at the landfill, and age of the deposited refuse.
Agustín Ruiz
Full Text Available Plasma amyloid beta (Aβ levels are being investigated as potential biomarkers for Alzheimer's disease. In AB128 cross-sectional study, a number of medical relevant correlates of blood Aβ40 or Aβ42 were analyzed in 140 subjects (51 Alzheimer's disease patients, 53 healthy controls and 36 individuals diagnosed with mild cognitive impairment. We determined the association between multiple variables with Aβ40 and Aβ42 levels measured in three different blood compartments called i Aβ directly accessible (DA in the plasma, ii Aβ recovered from the plasma matrix (RP after diluting the plasma sample in a formulated buffer, and iii associated with the remaining cellular pellet (CP. We confirmed that diastolic blood pressure (DBP is consistently correlated with blood DA Aβ40 levels (r=-0.19, P=0.032. These results were consistent in the three phenotypic groups studied. Importantly, the observation resisted covariation with age, gender or creatinine levels. Observed effect size and direction of Aβ40 levels/DBP correlation are in accordance with previous reports. Of note, DA Aβ40 and the RP Aβ40 were also strongly associated with creatinine levels (r=0.599, P<<0.001 and to a lesser extent to urea, age, hematocrit, uric acid and homocysteine (p<0.001. DBP and the rest of statistical significant correlates identified should be considered as potential confounder factors in studies investigating blood Aβ levels as potential AD biomarker. Remarkably, the factors affecting Aβ levels in plasma (DA, RP and blood cell compartments (CP seem completely different.
AFCI-2.0 Neutron Cross Section Covariance Library
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
Herman, M.; Oblozinsky, P.; Mattoon, C.M.; Pigni, M.; Hoblit, S.; Mughabghab, S.F.; Sonzogni, A.; Talou, P.; Chadwick, M.B.; Hale, G.M.; Kahler, A.C.; Kawano, T.; Little, R.C.; Yount, P.G.
2011-01-01
The cross section covariance library has been under development by BNL-LANL collaborative effort over the last three years. The project builds on two covariance libraries developed earlier, with considerable input from BNL and LANL. In 2006, international effort under WPEC Subgroup 26 produced BOLNA covariance library by putting together data, often preliminary, from various sources for most important materials for nuclear reactor technology. This was followed in 2007 by collaborative effort of four US national laboratories to produce covariances, often of modest quality - hence the name low-fidelity, for virtually complete set of materials included in ENDF/B-VII.0. The present project is focusing on covariances of 4-5 major reaction channels for 110 materials of importance for power reactors. The work started under Global Nuclear Energy Partnership (GNEP) in 2008, which changed to Advanced Fuel Cycle Initiative (AFCI) in 2009. With the 2011 release the name has changed to the Covariance Multigroup Matrix for Advanced Reactor Applications (COMMARA) version 2.0. The primary purpose of the library is to provide covariances for AFCI data adjustment project, which is focusing on the needs of fast advanced burner reactors. Responsibility of BNL was defined as developing covariances for structural materials and fission products, management of the library and coordination of the work; LANL responsibility was defined as covariances for light nuclei and actinides. The COMMARA-2.0 covariance library has been developed by BNL-LANL collaboration for Advanced Fuel Cycle Initiative applications over the period of three years, 2008-2010. It contains covariances for 110 materials relevant to fast reactor R and D. The library is to be used together with the ENDF/B-VII.0 central values of the latest official release of US files of evaluated neutron cross sections. COMMARA-2.0 library contains neutron cross section covariances for 12 light nuclei (coolants and moderators), 78
Structural Analysis in a Conceptual Design Framework
Padula, Sharon L.; Robinson, Jay H.; Eldred, Lloyd B.
2012-01-01
Supersonic aircraft designers must shape the outer mold line of the aircraft to improve multiple objectives, such as mission performance, cruise efficiency, and sonic-boom signatures. Conceptual designers have demonstrated an ability to assess these objectives for a large number of candidate designs. Other critical objectives and constraints, such as weight, fuel volume, aeroelastic effects, and structural soundness, are more difficult to address during the conceptual design process. The present research adds both static structural analysis and sizing to an existing conceptual design framework. The ultimate goal is to include structural analysis in the multidisciplinary optimization of a supersonic aircraft. Progress towards that goal is discussed and demonstrated.
Nonparametric Bayesian models for a spatial covariance.
Reich, Brian J; Fuentes, Montserrat
2012-01-01
A crucial step in the analysis of spatial data is to estimate the spatial correlation function that determines the relationship between a spatial process at two locations. The standard approach to selecting the appropriate correlation function is to use prior knowledge or exploratory analysis, such as a variogram analysis, to select the correct parametric correlation function. Rather that selecting a particular parametric correlation function, we treat the covariance function as an unknown function to be estimated from the data. We propose a flexible prior for the correlation function to provide robustness to the choice of correlation function. We specify the prior for the correlation function using spectral methods and the Dirichlet process prior, which is a common prior for an unknown distribution function. Our model does not require Gaussian data or spatial locations on a regular grid. The approach is demonstrated using a simulation study as well as an analysis of California air pollution data.
RNA structural constraints in the evolution of the influenza A virus genome NP segment
A.P. Gultyaev (Alexander); A. Tsyganov-Bodounov (Anton); M.I. Spronken (Monique); S. Van Der Kooij (Sander); R.A.M. Fouchier (Ron); R.C.L. Olsthoorn (René)
2014-01-01
textabstractConserved RNA secondary structures were predicted in the nucleoprotein (NP) segment of the influenza A virus genome using comparative sequence and structure analysis. A number of structural elements exhibiting nucleotide covariations were identified over the whole segment length,
Alterations in Anatomical Covariance in the Prematurely Born.
Scheinost, Dustin; Kwon, Soo Hyun; Lacadie, Cheryl; Vohr, Betty R; Schneider, Karen C; Papademetris, Xenophon; Constable, R Todd; Ment, Laura R
2017-01-01
Preterm (PT) birth results in long-term alterations in functional and structural connectivity, but the related changes in anatomical covariance are just beginning to be explored. To test the hypothesis that PT birth alters patterns of anatomical covariance, we investigated brain volumes of 25 PTs and 22 terms at young adulthood using magnetic resonance imaging. Using regional volumetrics, seed-based analyses, and whole brain graphs, we show that PT birth is associated with reduced volume in bilateral temporal and inferior frontal lobes, left caudate, left fusiform, and posterior cingulate for prematurely born subjects at young adulthood. Seed-based analyses demonstrate altered patterns of anatomical covariance for PTs compared with terms. PTs exhibit reduced covariance with R Brodmann area (BA) 47, Broca's area, and L BA 21, Wernicke's area, and white matter volume in the left prefrontal lobe, but increased covariance with R BA 47 and left cerebellum. Graph theory analyses demonstrate that measures of network complexity are significantly less robust in PTs compared with term controls. Volumes in regions showing group differences are significantly correlated with phonological awareness, the fundamental basis for reading acquisition, for the PTs. These data suggest both long-lasting and clinically significant alterations in the covariance in the PTs at young adulthood. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.
Covariant description of Hamiltonian form for field dynamics
Ozaki, Hiroshi
2005-01-01
Hamiltonian form of field dynamics is developed on a space-like hypersurface in space-time. A covariant Poisson bracket on the space-like hypersurface is defined and it plays a key role to describe every algebraic relation into a covariant form. It is shown that the Poisson bracket has the same symplectic structure that was brought in the covariant symplectic approach. An identity invariant under the canonical transformations is obtained. The identity follows a canonical equation in which the interaction Hamiltonian density generates a deformation of the space-like hypersurface. The equation just corresponds to the Yang-Feldman equation in the Heisenberg pictures in quantum field theory. By converting the covariant Poisson bracket on the space-like hypersurface to four-dimensional commutator, we can pass over to quantum field theory in the Heisenberg picture without spoiling the explicit relativistic covariance. As an example the canonical QCD is displayed in a covariant way on a space-like hypersurface
Khan, Jahidur Rahman; Islam, Md Mazharul; Awan, Nabil; Muurlink, Olav
2018-03-06
Low birth weight (LBW) remains a leading global cause of childhood morbidity and mortality. This study leverages a large national survey to determine current prevalence and socioeconomic, demographic and heath related factors associated with LBW in Bangladesh. Data from the Multiple Indicator Cluster Survey (MICS) 2012-13 of Bangladesh were analyzed. A total of 2319 women for whom contemporaneous birth weight data was available and who had a live birth in the two years preceding the survey were sampled for this study. However, this analysis only was able to take advantage of 29% of the total sample with 71% missing birth weight for newborns. The indicator, LBW (rates observed in Rajshahi (11%) and highest rates in Rangpur (28%). Education of mothers (adjusted odds ratio [AOR] 0.52, 95% confidence interval [CI] 0.39-0.68 for secondary or higher educated mother) and poor antenatal care (ANC) (AOR 1.40, 95% CI 1.04-1.90) were associated with LBW after adjusting for mother's age, parity and cluster effects. Mothers from wealthier families were less likely to give birth to an LBW infant. Further indicators that wealth continues to play a role in LBW were that place of delivery, ANC and delivery assistance by quality health workers were significantly associated with LBW. However there has been a notable fall in LBW prevalence in Bangladesh since the last comparable survey (prevalence 36%), and an evidence of possible elimination of rural/urban disparities. Low birth weight remains associated with key indicators not just of maternal poverty (notably adequate maternal education) but also markers of structural poverty in health care (notably quality ANC). Results based on this sub-sample indicate LBW is still a public health concern in Bangladesh and an integrated effort from all stakeholders should be continued and interventions based on the study findings should be devised to further reduce the risk of LBW.
Impact analysis of composite aircraft structures
Pifko, Allan B.; Kushner, Alan S.
1993-01-01
The impact analysis of composite aircraft structures is discussed. Topics discussed include: background remarks on aircraft crashworthiness; comments on modeling strategies for crashworthiness simulation; initial study of simulation of progressive failure of an aircraft component constructed of composite material; and research direction in composite characterization for impact analysis.
Cheung, Mike W.-L.; Cheung, Shu Fai
2016-01-01
Meta-analytic structural equation modeling (MASEM) combines the techniques of meta-analysis and structural equation modeling for the purpose of synthesizing correlation or covariance matrices and fitting structural equation models on the pooled correlation or covariance matrix. Both fixed-effects and random-effects models can be defined in MASEM.…
Structural analysis of polycrystalline (graphitized) materials
Efremenko, M.M.; Kravchik, A.E.; Osmakov, A.S.
1993-01-01
Specific features of the structure of polycrystal carbon materials (CM), characterized by high enough degree of structural perfection and different genesis are analyzed. From the viewpoint of fine and supercrystallite structure analysis of the most characteristic groups of graphitized CM: artificial graphites, and natural graphites, as well, has been carried out. It is ascertained that in paracrystal CM a monolayer of hexagonally-bound carbon atoms is the basic element of the structure, and in graphitized CM - a microlayer. The importance of the evaluation of the degree of three-dimensional ordering of the microlayer is shown
Modeling Covariance Breakdowns in Multivariate GARCH
Jin, Xin; Maheu, John M
2014-01-01
This paper proposes a flexible way of modeling dynamic heterogeneous covariance breakdowns in multivariate GARCH (MGARCH) models. During periods of normal market activity, volatility dynamics are governed by an MGARCH specification. A covariance breakdown is any significant temporary deviation of the conditional covariance matrix from its implied MGARCH dynamics. This is captured through a flexible stochastic component that allows for changes in the conditional variances, covariances and impl...
Covariance and correlation estimation in electron-density maps.
Altomare, Angela; Cuocci, Corrado; Giacovazzo, Carmelo; Moliterni, Anna; Rizzi, Rosanna
2012-03-01
Quite recently two papers have been published [Giacovazzo & Mazzone (2011). Acta Cryst. A67, 210-218; Giacovazzo et al. (2011). Acta Cryst. A67, 368-382] which calculate the variance in any point of an electron-density map at any stage of the phasing process. The main aim of the papers was to associate a standard deviation to each pixel of the map, in order to obtain a better estimate of the map reliability. This paper deals with the covariance estimate between points of an electron-density map in any space group, centrosymmetric or non-centrosymmetric, no matter the correlation between the model and target structures. The aim is as follows: to verify if the electron density in one point of the map is amplified or depressed as an effect of the electron density in one or more other points of the map. High values of the covariances are usually connected with undesired features of the map. The phases are the primitive random variables of our probabilistic model; the covariance changes with the quality of the model and therefore with the quality of the phases. The conclusive formulas show that the covariance is also influenced by the Patterson map. Uncertainty on measurements may influence the covariance, particularly in the final stages of the structure refinement; a general formula is obtained taking into account both phase and measurement uncertainty, valid at any stage of the crystal structure solution.
Bayes Factor Covariance Testing in Item Response Models.
Fox, Jean-Paul; Mulder, Joris; Sinharay, Sandip
2017-12-01
Two marginal one-parameter item response theory models are introduced, by integrating out the latent variable or random item parameter. It is shown that both marginal response models are multivariate (probit) models with a compound symmetry covariance structure. Several common hypotheses concerning the underlying covariance structure are evaluated using (fractional) Bayes factor tests. The support for a unidimensional factor (i.e., assumption of local independence) and differential item functioning are evaluated by testing the covariance components. The posterior distribution of common covariance components is obtained in closed form by transforming latent responses with an orthogonal (Helmert) matrix. This posterior distribution is defined as a shifted-inverse-gamma, thereby introducing a default prior and a balanced prior distribution. Based on that, an MCMC algorithm is described to estimate all model parameters and to compute (fractional) Bayes factor tests. Simulation studies are used to show that the (fractional) Bayes factor tests have good properties for testing the underlying covariance structure of binary response data. The method is illustrated with two real data studies.
Structural Analysis of Kufasat Using Ansys Program
Al-Maliky, Firas T.; AlBermani, Mohamed J.
2018-03-01
The current work focuses on vibration and modal analysis of KufaSat structure using ANSYS 16 program. Three types of Aluminum alloys (5052-H32, 6061-T6 and 7075-T6) were selected for investigation of the structure under design loads. Finite element analysis (FEA) in design static load of 51 g was performed. The natural frequencies for five modes were estimated using modal analysis. In order to ensure that KufaSat could withstand with various conditions during launch, the Margin of safety was calculated. The results of deformation and Von Mises stress for linear buckling analysis were also performed. The comparison of data was done to select the optimum material for KufaSat structures.
Numerical Limit Analysis of Reinforced Concrete Structures
Larsen, Kasper Paaske
For more than half a century, limit state analysis based on the extremum principles have been used to assess the load bearing capacity of reinforced concrete structures. Extensi- ve research within the field has lead to several techniques for performing such analysis manually. While these manual...... methods provide engineers with valuable tools for limit sta- te analysis, their application becomes difficult with increased structural complexity. The main challenge is to solve the optimization problem posed by the extremum principles. This thesis is a study of how numerical methods can be used to solve...... limit state analysis problems. The work focuses on determination of the load bearing capacity of reinforced concrete structures by employing the lower bound theorem and a finite element method using equilibrium elements is developed. The recent year’s development within the field of convex optimization...
Seismic analysis and design of NPP structures
de Carvalho Santos, S.H.; da Silva, R.E.
1989-01-01
Numerical methods for static and dynamic analysis of structures, as well as for the design of individual structural elements under the applied loads are under continuous development, being very sophisticated methods nowadays available for the engineering practice. Nevertheless, this sophistication will be useless if some important aspects necessary to assure full compatability between analysis and design are disregarded. Some of these aspects are discussed herein. This paper presents an integrated approach for the seismic analysis and design of NPP structures: the development of models for the seismic analysis, the distribution of the global seismic forces among the seismic-resistant elements and the criteria for the design of the individual elements for combined static and dynamic forces are the main topics to be discussed herein. The proposed methodology is illustrated. Some examples taken from the project practice are presented for illustration the exposed concepts
Proofs of Contracted Length Non-covariance
Strel'tsov, V.N.
1994-01-01
Different proofs of contracted length non covariance are discussed. The way based on the establishment of interval inconstancy (dependence on velocity) seems to be the most convincing one. It is stressed that the known non covariance of the electromagnetic field energy and momentum of a moving charge ('the problem 4/3') is a direct consequence of contracted length non covariance. 8 refs
Construction of covariance matrix for experimental data
Liu Tingjin; Zhang Jianhua
1992-01-01
For evaluators and experimenters, the information is complete only in the case when the covariance matrix is given. The covariance matrix of the indirectly measured data has been constructed and discussed. As an example, the covariance matrix of 23 Na(n, 2n) cross section is constructed. A reasonable result is obtained
Global plastic models for computerized structural analysis
Roche, R.L.; Hoffmann, A.
1977-01-01
In many types of structures, it is possible to use generalized stresses (like membrane forces, bending moment, torsion moment...) to define a yield surface for a part of the structure. Analysis can be achieved by using the HILL's principle and a hardening rule. The whole formulation is said 'Global Plastic Model'. Two different global models are used in the CEASEMT system for structural analysis, one for shell analysis and the other for piping analysis (in plastic or creep field). In shell analysis the generalized stresses chosen are the membrane forces and bending (including torsion) moments. There is only one yield condition for a normal to the middle surface and no integration along the thickness is required. In piping analysis, the choice of generalized stresses is bending moments, torsional moment, hoop stress and tension stress. There is only a set of stresses for a cross section and no integration over the cross section area is needed. Connected strains are axis curvature, torsion, uniform strains. The definition of the yield surface is the most important item. A practical way is to use a diagonal quadratic function of the stress components. But the coefficients are depending of the shape of the pipe element, especially for curved segments. Indications will be given on the yield functions used. Some examples of applications in structural analysis are added to the text
Jansen, E.; Schaefer, W.; Will, G.; Kernforschungsanlage Juelich G.m.b.H.
1988-01-01
An outline and an application of the two-stage method in neutron powder diffractometry are presented. Stage (1): Individual reflection data like position, half-width and integrated intensity are analysed by profile fitting. The profile analysis is based on an experimentally determined instrument function and can be applied without prior knowledge of a structural model. A mathematical procedure is described which results in a variance-covariance matrix containing standard deviations and correlations of the refined reflection parameters. Stage (2): The individual reflection data derived from the profile fitting procedure can be used for appropriate purposes either in structure determination or in texture and strain or stress analysis. The integrated intensities are used in the non-diagonal weighted least-squares routine POWLS for structure refinement. The weight matrix is given by the inverted variance-covariance matrix of stage (1). This procedure is the basis for reliable and real Bragg R values and for a realistic estimation of standard deviations of structural parameters. In the case of texture analysis the integrated intensities are compiled into pole figures representing the intensity distribution for all sample orientations of individual hkl. Various examples for the wide application of the two-stage method in structure and texture analysis are given: Structure refinement of a standard quartz specimen, magnetic ordering in the system Tb x Y 1-x Ag, preferred orientation effects in deformed marble and texture investigations of a triclinic plagioclase. (orig.)
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.
A three domain covariance framework for EEG/MEG data.
Roś, Beata P; Bijma, Fetsje; de Gunst, Mathisca C M; de Munck, Jan C
2015-10-01
In this paper we introduce a covariance framework for the analysis of single subject EEG and MEG data that takes into account observed temporal stationarity on small time scales and trial-to-trial variations. We formulate a model for the covariance matrix, which is a Kronecker product of three components that correspond to space, time and epochs/trials, and consider maximum likelihood estimation of the unknown parameter values. An iterative algorithm that finds approximations of the maximum likelihood estimates is proposed. Our covariance model is applicable in a variety of cases where spontaneous EEG or MEG acts as source of noise and realistic noise covariance estimates are needed, such as in evoked activity studies, or where the properties of spontaneous EEG or MEG are themselves the topic of interest, like in combined EEG-fMRI experiments in which the correlation between EEG and fMRI signals is investigated. We use a simulation study to assess the performance of the estimator and investigate the influence of different assumptions about the covariance factors on the estimated covariance matrix and on its components. We apply our method to real EEG and MEG data sets. Copyright © 2015 Elsevier Inc. All rights reserved.
De Roover, K.; Vermunt, J.K.; Timmerman, Marieke E.; Ceulemans, Eva
2017-01-01
Given multivariate data, many research questions pertain to the covariance structure: whether and how the variables (for example, personality measures) covary. Exploratory factor analysis (EFA) is often used to look for latent variables that may explain the covariances among variables; for example,
Lorentz covariant theory of gravitation
Fagundes, H.V.
1974-12-01
An alternative method for the calculation of second order effects, like the secular shift of Mercury's perihelium is developed. This method uses the basic ideas of thirring combined with the more mathematical approach of Feyman. In the case of a static source, the treatment used is greatly simplified. Besides, Einstein-Infeld-Hoffmann's Lagrangian for a system of two particles and spin-orbit and spin-spin interactions of two particles with classical spin, ie, internal angular momentum in Moller's sense, are obtained from the Lorentz covariant theory
Sebestyen, A.
1975-07-01
The principle of covariance is extended to coordinates corresponding to internal degrees of freedom. The conditions for a system to be isolated are given. It is shown how internal forces arise in such systems. Equations for internal fields are derived. By an interpretation of the generalized coordinates based on group theory it is shown how particles of the ordinary sense enter into the model and as a simple application the gravitational interaction of two pointlike particles is considered and the shift of the perihelion is deduced. (Sz.Z.)
Covariant gauges at finite temperature
Landshoff, Peter V
1992-01-01
A prescription is presented for real-time finite-temperature perturbation theory in covariant gauges, in which only the two physical degrees of freedom of the gauge-field propagator acquire thermal parts. The propagators for the unphysical degrees of freedom of the gauge field, and for the Faddeev-Popov ghost field, are independent of temperature. This prescription is applied to the calculation of the one-loop gluon self-energy and the two-loop interaction pressure, and is found to be simpler to use than the conventional one.
Using Fit Indexes to Select a Covariance Model for Longitudinal Data
Liu, Siwei; Rovine, Michael J.; Molenaar, Peter C. M.
2012-01-01
This study investigated the performance of fit indexes in selecting a covariance structure for longitudinal data. Data were simulated to follow a compound symmetry, first-order autoregressive, first-order moving average, or random-coefficients covariance structure. We examined the ability of the likelihood ratio test (LRT), root mean square error…