Evidence for bivariate linkage of obesity and HDL-C levels in the Framingham Heart Study.

Science.gov (United States)

Arya, Rector; Lehman, Donna; Hunt, Kelly J; Schneider, Jennifer; Almasy, Laura; Blangero, John; Stern, Michael P; Duggirala, Ravindranath

2003-12-31

Epidemiological studies have indicated that obesity and low high-density lipoprotein (HDL) levels are strong cardiovascular risk factors, and that these traits are inversely correlated. Despite the belief that these traits are correlated in part due to pleiotropy, knowledge on specific genes commonly affecting obesity and dyslipidemia is very limited. To address this issue, we first conducted univariate multipoint linkage analysis for body mass index (BMI) and HDL-C to identify loci influencing variation in these phenotypes using Framingham Heart Study data relating to 1702 subjects distributed across 330 pedigrees. Subsequently, we performed bivariate multipoint linkage analysis to detect common loci influencing covariation between these two traits. We scanned the genome and identified a major locus near marker D6S1009 influencing variation in BMI (LOD = 3.9) using the program SOLAR. We also identified a major locus for HDL-C near marker D2S1334 on chromosome 2 (LOD = 3.5) and another region near marker D6S1009 on chromosome 6 with suggestive evidence for linkage (LOD = 2.7). Since these two phenotypes have been independently mapped to the same region on chromosome 6q, we used the bivariate multipoint linkage approach using SOLAR. The bivariate linkage analysis of BMI and HDL-C implicated the genetic region near marker D6S1009 as harboring a major gene commonly influencing these phenotypes (bivariate LOD = 6.2; LODeq = 5.5) and appears to improve power to map the correlated traits to a region, precisely. We found substantial evidence for a quantitative trait locus with pleiotropic effects, which appears to influence both BMI and HDL-C phenotypes in the Framingham data.

Ordinal Bivariate Inequality

DEFF Research Database (Denmark)

Sonne-Schmidt, Christoffer Scavenius; Tarp, Finn; Østerdal, Lars Peter Raahave

2016-01-01

This paper introduces a concept of inequality comparisons with ordinal bivariate categorical data. In our model, one population is more unequal than another when they have common arithmetic median outcomes and the first can be obtained from the second by correlation-increasing switches and....../or median-preserving spreads. For the canonical 2 × 2 case (with two binary indicators), we derive a simple operational procedure for checking ordinal inequality relations in practice. As an illustration, we apply the model to childhood deprivation in Mozambique....

Modeling animal-vehicle collisions using diagonal inflated bivariate Poisson regression.

Science.gov (United States)

Lao, Yunteng; Wu, Yao-Jan; Corey, Jonathan; Wang, Yinhai

2011-01-01

Two types of animal-vehicle collision (AVC) data are commonly adopted for AVC-related risk analysis research: reported AVC data and carcass removal data. One issue with these two data sets is that they were found to have significant discrepancies by previous studies. In order to model these two types of data together and provide a better understanding of highway AVCs, this study adopts a diagonal inflated bivariate Poisson regression method, an inflated version of bivariate Poisson regression model, to fit the reported AVC and carcass removal data sets collected in Washington State during 2002-2006. The diagonal inflated bivariate Poisson model not only can model paired data with correlation, but also handle under- or over-dispersed data sets as well. Compared with three other types of models, double Poisson, bivariate Poisson, and zero-inflated double Poisson, the diagonal inflated bivariate Poisson model demonstrates its capability of fitting two data sets with remarkable overlapping portions resulting from the same stochastic process. Therefore, the diagonal inflated bivariate Poisson model provides researchers a new approach to investigating AVCs from a different perspective involving the three distribution parameters (λ(1), λ(2) and λ(3)). The modeling results show the impacts of traffic elements, geometric design and geographic characteristics on the occurrences of both reported AVC and carcass removal data. It is found that the increase of some associated factors, such as speed limit, annual average daily traffic, and shoulder width, will increase the numbers of reported AVCs and carcass removals. Conversely, the presence of some geometric factors, such as rolling and mountainous terrain, will decrease the number of reported AVCs. Published by Elsevier Ltd.

Multifractal detrended cross-correlation analysis on gold, crude oil and foreign exchange rate time series

Science.gov (United States)

Pal, Mayukha; Madhusudana Rao, P.; Manimaran, P.

2014-12-01

We apply the recently developed multifractal detrended cross-correlation analysis method to investigate the cross-correlation behavior and fractal nature between two non-stationary time series. We analyze the daily return price of gold, West Texas Intermediate and Brent crude oil, foreign exchange rate data, over a period of 18 years. The cross correlation has been measured from the Hurst scaling exponents and the singularity spectrum quantitatively. From the results, the existence of multifractal cross-correlation between all of these time series is found. We also found that the cross correlation between gold and oil prices possess uncorrelated behavior and the remaining bivariate time series possess persistent behavior. It was observed for five bivariate series that the cross-correlation exponents are less than the calculated average generalized Hurst exponents (GHE) for q0 and for one bivariate series the cross-correlation exponent is greater than GHE for all q values.

A bivariate model for analyzing recurrent multi-type automobile failures

Science.gov (United States)

Sunethra, A. A.; Sooriyarachchi, M. R.

2017-09-01

The failure mechanism in an automobile can be defined as a system of multi-type recurrent failures where failures can occur due to various multi-type failure modes and these failures are repetitive such that more than one failure can occur from each failure mode. In analysing such automobile failures, both the time and type of the failure serve as response variables. However, these two response variables are highly correlated with each other since the timing of failures has an association with the mode of the failure. When there are more than one correlated response variables, the fitting of a multivariate model is more preferable than separate univariate models. Therefore, a bivariate model of time and type of failure becomes appealing for such automobile failure data. When there are multiple failure observations pertaining to a single automobile, such data cannot be treated as independent data because failure instances of a single automobile are correlated with each other while failures among different automobiles can be treated as independent. Therefore, this study proposes a bivariate model consisting time and type of failure as responses adjusted for correlated data. The proposed model was formulated following the approaches of shared parameter models and random effects models for joining the responses and for representing the correlated data respectively. The proposed model is applied to a sample of automobile failures with three types of failure modes and up to five failure recurrences. The parametric distributions that were suitable for the two responses of time to failure and type of failure were Weibull distribution and multinomial distribution respectively. The proposed bivariate model was programmed in SAS Procedure Proc NLMIXED by user programming appropriate likelihood functions. The performance of the bivariate model was compared with separate univariate models fitted for the two responses and it was identified that better performance is secured by

A New Measure Of Bivariate Asymmetry And Its Evaluation

International Nuclear Information System (INIS)

Ferreira, Flavio Henn; Kolev, Nikolai Valtchev

2008-01-01

In this paper we propose a new measure of bivariate asymmetry, based on conditional correlation coefficients. A decomposition of the Pearson correlation coefficient in terms of its conditional versions is studied and an example of application of the proposed measure is given.

Comparison between two bivariate Poisson distributions through the ...

African Journals Online (AJOL)

These two models express themselves by their probability mass function. ... To remedy this problem, Berkhout and Plug proposed a bivariate Poisson distribution accepting the correlation as well negative, equal to zero, that positive.

A comparison between multivariate and bivariate analysis used in marketing research

Directory of Open Access Journals (Sweden)

Constantin, C.

2012-01-01

Full Text Available This paper is about an instrumental research conducted in order to compare the information given by two multivariate data analysis in comparison with the usual bivariate analysis. The outcomes of the research reveal that sometimes the multivariate methods use more information from a certain variable, but sometimes they use only a part of the information considered the most important for certain associations. For this reason, a researcher should use both categories of data analysis in order to obtain entirely useful information.

A bivariate contaminated binormal model for robust fitting of proper ROC curves to a pair of correlated, possibly degenerate, ROC datasets.

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Zhai, Xuetong; Chakraborty, Dev P

2017-06-01

The objective was to design and implement a bivariate extension to the contaminated binormal model (CBM) to fit paired receiver operating characteristic (ROC) datasets-possibly degenerate-with proper ROC curves. Paired datasets yield two correlated ratings per case. Degenerate datasets have no interior operating points and proper ROC curves do not inappropriately cross the chance diagonal. The existing method, developed more than three decades ago utilizes a bivariate extension to the binormal model, implemented in CORROC2 software, which yields improper ROC curves and cannot fit degenerate datasets. CBM can fit proper ROC curves to unpaired (i.e., yielding one rating per case) and degenerate datasets, and there is a clear scientific need to extend it to handle paired datasets. In CBM, nondiseased cases are modeled by a probability density function (pdf) consisting of a unit variance peak centered at zero. Diseased cases are modeled with a mixture distribution whose pdf consists of two unit variance peaks, one centered at positive μ with integrated probability α, the mixing fraction parameter, corresponding to the fraction of diseased cases where the disease was visible to the radiologist, and one centered at zero, with integrated probability (1-α), corresponding to disease that was not visible. It is shown that: (a) for nondiseased cases the bivariate extension is a unit variances bivariate normal distribution centered at (0,0) with a specified correlation ρ 1 ; (b) for diseased cases the bivariate extension is a mixture distribution with four peaks, corresponding to disease not visible in either condition, disease visible in only one condition, contributing two peaks, and disease visible in both conditions. An expression for the likelihood function is derived. A maximum likelihood estimation (MLE) algorithm, CORCBM, was implemented in the R programming language that yields parameter estimates and the covariance matrix of the parameters, and other statistics

A power study of bivariate LOD score analysis of a complex trait and fear/discomfort with strangers.

Science.gov (United States)

Ji, Fei; Lee, Dayoung; Mendell, Nancy Role

2005-12-30

Complex diseases are often reported along with disease-related traits (DRT). Sometimes investigators consider both disease and DRT phenotypes separately and sometimes they consider individuals as affected if they have either the disease or the DRT, or both. We propose instead to consider the joint distribution of the disease and the DRT and do a linkage analysis assuming a pleiotropic model. We evaluated our results through analysis of the simulated datasets provided by Genetic Analysis Workshop 14. We first conducted univariate linkage analysis of the simulated disease, Kofendrerd Personality Disorder and one of its simulated associated traits, phenotype b (fear/discomfort with strangers). Subsequently, we considered the bivariate phenotype, which combined the information on Kofendrerd Personality Disorder and fear/discomfort with strangers. We developed a program to perform bivariate linkage analysis using an extension to the Elston-Stewart peeling method of likelihood calculation. Using this program we considered the microsatellites within 30 cM of the gene pleiotropic for this simulated disease and DRT. Based on 100 simulations of 300 families we observed excellent power to detect linkage within 10 cM of the disease locus using the DRT and the bivariate trait.

Regression analysis for bivariate gap time with missing first gap time data.

Science.gov (United States)

Huang, Chia-Hui; Chen, Yi-Hau

2017-01-01

We consider ordered bivariate gap time while data on the first gap time are unobservable. This study is motivated by the HIV infection and AIDS study, where the initial HIV contracting time is unavailable, but the diagnosis times for HIV and AIDS are available. We are interested in studying the risk factors for the gap time between initial HIV contraction and HIV diagnosis, and gap time between HIV and AIDS diagnoses. Besides, the association between the two gap times is also of interest. Accordingly, in the data analysis we are faced with two-fold complexity, namely data on the first gap time is completely missing, and the second gap time is subject to induced informative censoring due to dependence between the two gap times. We propose a modeling framework for regression analysis of bivariate gap time under the complexity of the data. The estimating equations for the covariate effects on, as well as the association between, the two gap times are derived through maximum likelihood and suitable counting processes. Large sample properties of the resulting estimators are developed by martingale theory. Simulations are performed to examine the performance of the proposed analysis procedure. An application of data from the HIV and AIDS study mentioned above is reported for illustration.

Analysis of input variables of an artificial neural network using bivariate correlation and canonical correlation

International Nuclear Information System (INIS)

Costa, Valter Magalhaes

2011-01-01

was trained and the results were satisfactory since the IEA-R1 Data Acquisition System reactor monitors 64 variables and, with a set of 9 input variables resulting from the correlation analysis, it was possible to monitor 51 variables using neural networks. (author)

Multifractal detrended cross-correlation analysis in the MENA area

Science.gov (United States)

El Alaoui, Marwane; Benbachir, Saâd

2013-12-01

In this paper, we investigated multifractal cross-correlations qualitatively and quantitatively using a cross-correlation test and the Multifractal detrended cross-correlation analysis method (MF-DCCA) for markets in the MENA area. We used cross-correlation coefficients to measure the level of this correlation. The analysis concerns four stock market indices of Morocco, Tunisia, Egypt and Jordan. The countries chosen are signatory of the Agadir agreement concerning the establishment of a free trade area comprising Arab Mediterranean countries. We computed the bivariate generalized Hurst exponent, Rényi exponent and spectrum of singularity for each pair of indices to measure quantitatively the cross-correlations. By analyzing the results, we found the existence of multifractal cross-correlations between all of these markets. We compared the spectrum width of these indices; we also found which pair of indices has a strong multifractal cross-correlation.

Bivariate genome-wide association meta-analysis of pediatric musculoskeletal traits reveals pleiotropic effects at the SREBF1/TOM1L2 locus

DEFF Research Database (Denmark)

Medina-Gomez, Carolina; Kemp, John P; Dimou, Niki L

2017-01-01

Bone mineral density is known to be a heritable, polygenic trait whereas genetic variants contributing to lean mass variation remain largely unknown. We estimated the shared SNP heritability and performed a bivariate GWAS meta-analysis of total-body lean mass (TB-LM) and total-body less head bone...... as in human muscle tissue. This is the first bivariate GWAS meta-analysis to demonstrate genetic factors with pleiotropic effects on bone mineral density and lean mass.Bone mineral density and lean skeletal mass are heritable traits. Here, Medina-Gomez and colleagues perform bivariate GWAS analyses of total...

Spectrum-based estimators of the bivariate Hurst exponent

Czech Academy of Sciences Publication Activity Database

Krištoufek, Ladislav

2014-01-01

Roč. 90, č. 6 (2014), art. 062802 ISSN 1539-3755 R&D Projects: GA ČR(CZ) GP14-11402P Institutional support: RVO:67985556 Keywords : bivariate Hurst exponent * power- law cross-correlations * estimation Subject RIV: AH - Economics Impact factor: 2.288, year: 2014 http://library.utia.cas.cz/separaty/2014/E/kristoufek-0436818.pdf

Bivariate Rainfall and Runoff Analysis Using Shannon Entropy Theory

Science.gov (United States)

Rahimi, A.; Zhang, L.

2012-12-01

Rainfall-Runoff analysis is the key component for many hydrological and hydraulic designs in which the dependence of rainfall and runoff needs to be studied. It is known that the convenient bivariate distribution are often unable to model the rainfall-runoff variables due to that they either have constraints on the range of the dependence or fixed form for the marginal distributions. Thus, this paper presents an approach to derive the entropy-based joint rainfall-runoff distribution using Shannon entropy theory. The distribution derived can model the full range of dependence and allow different specified marginals. The modeling and estimation can be proceeded as: (i) univariate analysis of marginal distributions which includes two steps, (a) using the nonparametric statistics approach to detect modes and underlying probability density, and (b) fitting the appropriate parametric probability density functions; (ii) define the constraints based on the univariate analysis and the dependence structure; (iii) derive and validate the entropy-based joint distribution. As to validate the method, the rainfall-runoff data are collected from the small agricultural experimental watersheds located in semi-arid region near Riesel (Waco), Texas, maintained by the USDA. The results of unviariate analysis show that the rainfall variables follow the gamma distribution, whereas the runoff variables have mixed structure and follow the mixed-gamma distribution. With this information, the entropy-based joint distribution is derived using the first moments, the first moments of logarithm transformed rainfall and runoff, and the covariance between rainfall and runoff. The results of entropy-based joint distribution indicate: (1) the joint distribution derived successfully preserves the dependence between rainfall and runoff, and (2) the K-S goodness of fit statistical tests confirm the marginal distributions re-derived reveal the underlying univariate probability densities which further

Detrended partial cross-correlation analysis of two nonstationary time series influenced by common external forces

Science.gov (United States)

Qian, Xi-Yuan; Liu, Ya-Min; Jiang, Zhi-Qiang; Podobnik, Boris; Zhou, Wei-Xing; Stanley, H. Eugene

2015-06-01

When common factors strongly influence two power-law cross-correlated time series recorded in complex natural or social systems, using detrended cross-correlation analysis (DCCA) without considering these common factors will bias the results. We use detrended partial cross-correlation analysis (DPXA) to uncover the intrinsic power-law cross correlations between two simultaneously recorded time series in the presence of nonstationarity after removing the effects of other time series acting as common forces. The DPXA method is a generalization of the detrended cross-correlation analysis that takes into account partial correlation analysis. We demonstrate the method by using bivariate fractional Brownian motions contaminated with a fractional Brownian motion. We find that the DPXA is able to recover the analytical cross Hurst indices, and thus the multiscale DPXA coefficients are a viable alternative to the conventional cross-correlation coefficient. We demonstrate the advantage of the DPXA coefficients over the DCCA coefficients by analyzing contaminated bivariate fractional Brownian motions. We calculate the DPXA coefficients and use them to extract the intrinsic cross correlation between crude oil and gold futures by taking into consideration the impact of the U.S. dollar index. We develop the multifractal DPXA (MF-DPXA) method in order to generalize the DPXA method and investigate multifractal time series. We analyze multifractal binomial measures masked with strong white noises and find that the MF-DPXA method quantifies the hidden multifractal nature while the multifractal DCCA method fails.

Stress-strength reliability for general bivariate distributions

Directory of Open Access Journals (Sweden)

Alaa H. Abdel-Hamid

2016-10-01

Full Text Available An expression for the stress-strength reliability R=P(X1bivariate distribution. Such distribution includes bivariate compound Weibull, bivariate compound Gompertz, bivariate compound Pareto, among others. In the parametric case, the maximum likelihood estimates of the parameters and reliability function R are obtained. In the non-parametric case, point and interval estimates of R are developed using Govindarajulu's asymptotic distribution-free method when X1 and X2 are dependent. An example is given when the population distribution is bivariate compound Weibull. Simulation is performed, based on different sample sizes to study the performance of estimates.

Bivariate extreme value with application to PM10 concentration analysis

Science.gov (United States)

Amin, Nor Azrita Mohd; Adam, Mohd Bakri; Ibrahim, Noor Akma; Aris, Ahmad Zaharin

2015-05-01

This study is focus on a bivariate extreme of renormalized componentwise maxima with generalized extreme value distribution as a marginal function. The limiting joint distribution of several parametric models are presented. Maximum likelihood estimation is employed for parameter estimations and the best model is selected based on the Akaike Information Criterion. The weekly and monthly componentwise maxima series are extracted from the original observations of daily maxima PM10 data for two air quality monitoring stations located in Pasir Gudang and Johor Bahru. The 10 years data are considered for both stations from year 2001 to 2010. The asymmetric negative logistic model is found as the best fit bivariate extreme model for both weekly and monthly maxima componentwise series. However the dependence parameters show that the variables for weekly maxima series is more dependence to each other compared to the monthly maxima.

Univariate and Bivariate Empirical Mode Decomposition for Postural Stability Analysis

Directory of Open Access Journals (Sweden)

Jacques Duchêne

2008-05-01

Full Text Available The aim of this paper was to compare empirical mode decomposition (EMD and two new extended methods of  EMD named complex empirical mode decomposition (complex-EMD and bivariate empirical mode decomposition (bivariate-EMD. All methods were used to analyze stabilogram center of pressure (COP time series. The two new methods are suitable to be applied to complex time series to extract complex intrinsic mode functions (IMFs before the Hilbert transform is subsequently applied on the IMFs. The trace of the analytic IMF in the complex plane has a circular form, with each IMF having its own rotation frequency. The area of the circle and the average rotation frequency of IMFs represent efficient indicators of the postural stability status of subjects. Experimental results show the effectiveness of these indicators to identify differences in standing posture between groups.

Using bivariate signal analysis to characterize the epileptic focus: the benefit of surrogates.

Science.gov (United States)

Andrzejak, R G; Chicharro, D; Lehnertz, K; Mormann, F

2011-04-01

The disease epilepsy is related to hypersynchronous activity of networks of neurons. While acute epileptic seizures are the most extreme manifestation of this hypersynchronous activity, an elevated level of interdependence of neuronal dynamics is thought to persist also during the seizure-free interval. In multichannel recordings from brain areas involved in the epileptic process, this interdependence can be reflected in an increased linear cross correlation but also in signal properties of higher order. Bivariate time series analysis comprises a variety of approaches, each with different degrees of sensitivity and specificity for interdependencies reflected in lower- or higher-order properties of pairs of simultaneously recorded signals. Here we investigate which approach is best suited to detect putatively elevated interdependence levels in signals recorded from brain areas involved in the epileptic process. For this purpose, we use the linear cross correlation that is sensitive to lower-order signatures of interdependence, a nonlinear interdependence measure that integrates both lower- and higher-order properties, and a surrogate-corrected nonlinear interdependence measure that aims to specifically characterize higher-order properties. We analyze intracranial electroencephalographic recordings of the seizure-free interval from 29 patients with an epileptic focus located in the medial temporal lobe. Our results show that all three approaches detect higher levels of interdependence for signals recorded from the brain hemisphere containing the epileptic focus as compared to signals recorded from the opposite hemisphere. For the linear cross correlation, however, these differences are not significant. For the nonlinear interdependence measure, results are significant but only of moderate accuracy with regard to the discriminative power for the focal and nonfocal hemispheres. The highest significance and accuracy is obtained for the surrogate-corrected nonlinear

Bivariate discrete beta Kernel graduation of mortality data.

Science.gov (United States)

Mazza, Angelo; Punzo, Antonio

2015-07-01

Various parametric/nonparametric techniques have been proposed in literature to graduate mortality data as a function of age. Nonparametric approaches, as for example kernel smoothing regression, are often preferred because they do not assume any particular mortality law. Among the existing kernel smoothing approaches, the recently proposed (univariate) discrete beta kernel smoother has been shown to provide some benefits. Bivariate graduation, over age and calendar years or durations, is common practice in demography and actuarial sciences. In this paper, we generalize the discrete beta kernel smoother to the bivariate case, and we introduce an adaptive bandwidth variant that may provide additional benefits when data on exposures to the risk of death are available; furthermore, we outline a cross-validation procedure for bandwidths selection. Using simulations studies, we compare the bivariate approach proposed here with its corresponding univariate formulation and with two popular nonparametric bivariate graduation techniques, based on Epanechnikov kernels and on P-splines. To make simulations realistic, a bivariate dataset, based on probabilities of dying recorded for the US males, is used. Simulations have confirmed the gain in performance of the new bivariate approach with respect to both the univariate and the bivariate competitors.

Which global stock indices trigger stronger contagion risk in the Vietnamese stock market? Evidence using a bivariate analysis

OpenAIRE

Wang Kuan-Min; Lai Hung-Cheng

2013-01-01

This paper extends recent investigations into risk contagion effects on stock markets to the Vietnamese stock market. Daily data spanning October 9, 2006 to May 3, 2012 are sourced to empirically validate the contagion effects between stock markets in Vietnam, and China, Japan, Singapore, and the US. To facilitate the validation of contagion effects with market-related coefficients, this paper constructs a bivariate EGARCH model of dynamic conditional correlation coefficients. Using the...

Applied Statistics: From Bivariate through Multivariate Techniques [with CD-ROM

Science.gov (United States)

Warner, Rebecca M.

2007-01-01

This book provides a clear introduction to widely used topics in bivariate and multivariate statistics, including multiple regression, discriminant analysis, MANOVA, factor analysis, and binary logistic regression. The approach is applied and does not require formal mathematics; equations are accompanied by verbal explanations. Students are asked…

Smoothing of the bivariate LOD score for non-normal quantitative traits.

Science.gov (United States)

Buil, Alfonso; Dyer, Thomas D; Almasy, Laura; Blangero, John

2005-12-30

Variance component analysis provides an efficient method for performing linkage analysis for quantitative traits. However, type I error of variance components-based likelihood ratio testing may be affected when phenotypic data are non-normally distributed (especially with high values of kurtosis). This results in inflated LOD scores when the normality assumption does not hold. Even though different solutions have been proposed to deal with this problem with univariate phenotypes, little work has been done in the multivariate case. We present an empirical approach to adjust the inflated LOD scores obtained from a bivariate phenotype that violates the assumption of normality. Using the Collaborative Study on the Genetics of Alcoholism data available for the Genetic Analysis Workshop 14, we show how bivariate linkage analysis with leptokurtotic traits gives an inflated type I error. We perform a novel correction that achieves acceptable levels of type I error.

IDF relationships using bivariate copula for storm events in Peninsular Malaysia

Science.gov (United States)

Ariff, N. M.; Jemain, A. A.; Ibrahim, K.; Wan Zin, W. Z.

2012-11-01

SummaryIntensity-duration-frequency (IDF) curves are used in many hydrologic designs for the purpose of water managements and flood preventions. The IDF curves available in Malaysia are those obtained from univariate analysis approach which only considers the intensity of rainfalls at fixed time intervals. As several rainfall variables are correlated with each other such as intensity and duration, this paper aims to derive IDF points for storm events in Peninsular Malaysia by means of bivariate frequency analysis. This is achieved through utilizing the relationship between storm intensities and durations using the copula method. Four types of copulas; namely the Ali-Mikhail-Haq (AMH), Frank, Gaussian and Farlie-Gumbel-Morgenstern (FGM) copulas are considered because the correlation between storm intensity, I, and duration, D, are negative and these copulas are appropriate when the relationship between the variables are negative. The correlations are attained by means of Kendall's τ estimation. The analysis was performed on twenty rainfall stations with hourly data across Peninsular Malaysia. Using Akaike's Information Criteria (AIC) for testing goodness-of-fit, both Frank and Gaussian copulas are found to be suitable to represent the relationship between I and D. The IDF points found by the copula method are compared to the IDF curves yielded based on the typical IDF empirical formula of the univariate approach. This study indicates that storm intensities obtained from both methods are in agreement with each other for any given storm duration and for various return periods.

Can the bivariate Hurst exponent be higher than an average of the separate Hurst exponents?

Czech Academy of Sciences Publication Activity Database

Krištoufek, Ladislav

2015-01-01

Roč. 431, č. 1 (2015), s. 124-127 ISSN 0378-4371 R&D Projects: GA ČR(CZ) GP14-11402P Institutional support: RVO:67985556 Keywords : Correlations * Power- law cross-correlations * Bivariate Hurst exponent * Spectrum coherence Subject RIV: AH - Economics Impact factor: 1.785, year: 2015 http://library.utia.cas.cz/separaty/2015/E/kristoufek-0452314.pdf

Bivariate functional data clustering: grouping streams based on a varying coefficient model of the stream water and air temperature relationship

Science.gov (United States)

H. Li; X. Deng; Andy Dolloff; E. P. Smith

2015-01-01

A novel clustering method for bivariate functional data is proposed to group streams based on their water–air temperature relationship. A distance measure is developed for bivariate curves by using a time-varying coefficient model and a weighting scheme. This distance is also adjusted by spatial correlation of streams via the variogram. Therefore, the proposed...

Probability distributions with truncated, log and bivariate extensions

CERN Document Server

Thomopoulos, Nick T

2018-01-01

This volume presents a concise and practical overview of statistical methods and tables not readily available in other publications. It begins with a review of the commonly used continuous and discrete probability distributions. Several useful distributions that are not so common and less understood are described with examples and applications in full detail: discrete normal, left-partial, right-partial, left-truncated normal, right-truncated normal, lognormal, bivariate normal, and bivariate lognormal. Table values are provided with examples that enable researchers to easily apply the distributions to real applications and sample data. The left- and right-truncated normal distributions offer a wide variety of shapes in contrast to the symmetrically shaped normal distribution, and a newly developed spread ratio enables analysts to determine which of the three distributions best fits a particular set of sample data. The book will be highly useful to anyone who does statistical and probability analysis. This in...

Using bivariate latent basis growth curve analysis to better understand treatment outcome in youth with anorexia nervosa.

Science.gov (United States)

Byrne, Catherine E; Wonderlich, Joseph A; Curby, Timothy; Fischer, Sarah; Lock, James; Le Grange, Daniel

2018-04-25

This study explored the relation between eating-related obsessionality and weight restoration utilizing bivariate latent basis growth curve modelling. Eating-related obsessionality is a moderator of treatment outcome for adolescents with anorexia nervosa (AN). This study examined the degree to which the rate of change in eating-related obsessionality was associated with the rate of change in weight over time in family-based treatment (FBT) and individual therapy for AN. Data were drawn from a 2-site randomized controlled trial that compared FBT and adolescent focused therapy for AN. Bivariate latent basis growth curves were used to examine the differences of the relations between trajectories of body weight and symptoms associated with eating and weight obsessionality. In the FBT group, the slope of eating-related obsessionality scores and the slope of weight were significantly (negatively) correlated. This finding indicates that a decrease in overall eating-relating obsessionality is significantly associated with an increase in weight for individuals who received FBT. However, there was no relation between change in obsessionality scores and change in weight in the adolescent focused therapy group. Results suggest that FBT has a specific impact on both weight gain and obsessive compulsive behaviour that is distinct from individual therapy. Copyright © 2018 John Wiley & Sons, Ltd and Eating Disorders Association.

An Affine Invariant Bivariate Version of the Sign Test.

Science.gov (United States)

1987-06-01

words: affine invariance, bivariate quantile, bivariate symmetry, model,. generalized median, influence function , permutation test, normal efficiency...calculate a bivariate version of the influence function , and the resulting form is bounded, as is the case for the univartate sign test, and shows the...terms of a blvariate analogue of IHmpel’s (1974) influence function . The latter, though usually defined as a von-Mises derivative of certain

Parameter estimation and statistical test of geographically weighted bivariate Poisson inverse Gaussian regression models

Science.gov (United States)

Amalia, Junita; Purhadi, Otok, Bambang Widjanarko

2017-11-01

Poisson distribution is a discrete distribution with count data as the random variables and it has one parameter defines both mean and variance. Poisson regression assumes mean and variance should be same (equidispersion). Nonetheless, some case of the count data unsatisfied this assumption because variance exceeds mean (over-dispersion). The ignorance of over-dispersion causes underestimates in standard error. Furthermore, it causes incorrect decision in the statistical test. Previously, paired count data has a correlation and it has bivariate Poisson distribution. If there is over-dispersion, modeling paired count data is not sufficient with simple bivariate Poisson regression. Bivariate Poisson Inverse Gaussian Regression (BPIGR) model is mix Poisson regression for modeling paired count data within over-dispersion. BPIGR model produces a global model for all locations. In another hand, each location has different geographic conditions, social, cultural and economic so that Geographically Weighted Regression (GWR) is needed. The weighting function of each location in GWR generates a different local model. Geographically Weighted Bivariate Poisson Inverse Gaussian Regression (GWBPIGR) model is used to solve over-dispersion and to generate local models. Parameter estimation of GWBPIGR model obtained by Maximum Likelihood Estimation (MLE) method. Meanwhile, hypothesis testing of GWBPIGR model acquired by Maximum Likelihood Ratio Test (MLRT) method.

Global assessment of predictability of water availability: A bivariate probabilistic Budyko analysis

Science.gov (United States)

Wang, Weiguang; Fu, Jianyu

2018-02-01

Estimating continental water availability is of great importance for water resources management, in terms of maintaining ecosystem integrity and sustaining society development. To more accurately quantify the predictability of water availability, on the basis of univariate probabilistic Budyko framework, a bivariate probabilistic Budyko approach was developed using copula-based joint distribution model for considering the dependence between parameter ω of Wang-Tang's equation and the Normalized Difference Vegetation Index (NDVI), and was applied globally. The results indicate the predictive performance in global water availability is conditional on the climatic condition. In comparison with simple univariate distribution, the bivariate one produces the lower interquartile range under the same global dataset, especially in the regions with higher NDVI values, highlighting the importance of developing the joint distribution by taking into account the dependence structure of parameter ω and NDVI, which can provide more accurate probabilistic evaluation of water availability.

Bivariate spatial analysis of temperature and precipitation from general circulation models and observation proxies

KAUST Repository

Philbin, R.

2015-05-22

This study validates the near-surface temperature and precipitation output from decadal runs of eight atmospheric ocean general circulation models (AOGCMs) against observational proxy data from the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis temperatures and Global Precipitation Climatology Project (GPCP) precipitation data. We model the joint distribution of these two fields with a parsimonious bivariate Matérn spatial covariance model, accounting for the two fields\\' spatial cross-correlation as well as their own smoothnesses. We fit output from each AOGCM (30-year seasonal averages from 1981 to 2010) to a statistical model on each of 21 land regions. Both variance and smoothness values agree for both fields over all latitude bands except southern mid-latitudes. Our results imply that temperature fields have smaller smoothness coefficients than precipitation fields, while both have decreasing smoothness coefficients with increasing latitude. Models predict fields with smaller smoothness coefficients than observational proxy data for the tropics. The estimated spatial cross-correlations of these two fields, however, are quite different for most GCMs in mid-latitudes. Model correlation estimates agree well with those for observational proxy data for Australia, at high northern latitudes across North America, Europe and Asia, as well as across the Sahara, India, and Southeast Asia, but elsewhere, little consistent agreement exists.

Bivariate spatial analysis of temperature and precipitation from general circulation models and observation proxies

KAUST Repository

Philbin, R.; Jun, M.

2015-01-01

This study validates the near-surface temperature and precipitation output from decadal runs of eight atmospheric ocean general circulation models (AOGCMs) against observational proxy data from the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis temperatures and Global Precipitation Climatology Project (GPCP) precipitation data. We model the joint distribution of these two fields with a parsimonious bivariate Matérn spatial covariance model, accounting for the two fields' spatial cross-correlation as well as their own smoothnesses. We fit output from each AOGCM (30-year seasonal averages from 1981 to 2010) to a statistical model on each of 21 land regions. Both variance and smoothness values agree for both fields over all latitude bands except southern mid-latitudes. Our results imply that temperature fields have smaller smoothness coefficients than precipitation fields, while both have decreasing smoothness coefficients with increasing latitude. Models predict fields with smaller smoothness coefficients than observational proxy data for the tropics. The estimated spatial cross-correlations of these two fields, however, are quite different for most GCMs in mid-latitudes. Model correlation estimates agree well with those for observational proxy data for Australia, at high northern latitudes across North America, Europe and Asia, as well as across the Sahara, India, and Southeast Asia, but elsewhere, little consistent agreement exists.

STUDI PERBANDINGAN ANTARA ALGORITMA BIVARIATE MARGINAL DISTRIBUTION DENGAN ALGORITMA GENETIKA

Directory of Open Access Journals (Sweden)

Chastine Fatichah

2006-01-01

Full Text Available Bivariate Marginal Distribution Algorithm is extended from Estimation of Distribution Algorithm. This heuristic algorithm proposes the new approach for recombination of generate new individual that without crossover and mutation process such as genetic algorithm. Bivariate Marginal Distribution Algorithm uses connectivity variable the pair gene for recombination of generate new individual. Connectivity between variable is doing along optimization process. In this research, genetic algorithm performance with one point crossover is compared with Bivariate Marginal Distribution Algorithm performance in case Onemax, De Jong F2 function, and Traveling Salesman Problem. In this research, experimental results have shown performance the both algorithm is dependence of parameter respectively and also population size that used. For Onemax case with size small problem, Genetic Algorithm perform better with small number of iteration and more fast for get optimum result. However, Bivariate Marginal Distribution Algorithm perform better of result optimization for case Onemax with huge size problem. For De Jong F2 function, Genetic Algorithm perform better from Bivariate Marginal Distribution Algorithm of a number of iteration and time. For case Traveling Salesman Problem, Bivariate Marginal Distribution Algorithm have shown perform better from Genetic Algorithm of optimization result. Abstract in Bahasa Indonesia : Bivariate Marginal Distribution Algorithm merupakan perkembangan lebih lanjut dari Estimation of Distribution Algorithm. Algoritma heuristik ini mengenalkan pendekatan baru dalam melakukan rekombinasi untuk membentuk individu baru, yaitu tidak menggunakan proses crossover dan mutasi seperti pada Genetic Algorithm. Bivariate Marginal Distribution Algorithm menggunakan keterkaitan pasangan variabel dalam melakukan rekombinasi untuk membentuk individu baru. Keterkaitan antar variabel tersebut ditemukan selama proses optimasi berlangsung. Aplikasi yang

Recurrent major depression and right hippocampal volume: A bivariate linkage and association study.

Science.gov (United States)

Mathias, Samuel R; Knowles, Emma E M; Kent, Jack W; McKay, D Reese; Curran, Joanne E; de Almeida, Marcio A A; Dyer, Thomas D; Göring, Harald H H; Olvera, Rene L; Duggirala, Ravi; Fox, Peter T; Almasy, Laura; Blangero, John; Glahn, David C

2016-01-01

Previous work has shown that the hippocampus is smaller in the brains of individuals suffering from major depressive disorder (MDD) than those of healthy controls. Moreover, right hippocampal volume specifically has been found to predict the probability of subsequent depressive episodes. This study explored the utility of right hippocampal volume as an endophenotype of recurrent MDD (rMDD). We observed a significant genetic correlation between the two traits in a large sample of Mexican American individuals from extended pedigrees (ρg = -0.34, p = 0.013). A bivariate linkage scan revealed a significant pleiotropic quantitative trait locus on chromosome 18p11.31-32 (LOD = 3.61). Bivariate association analysis conducted under the linkage peak revealed a variant (rs574972) within an intron of the gene SMCHD1 meeting the corrected significance level (χ(2) = 19.0, p = 7.4 × 10(-5)). Univariate association analyses of each phenotype separately revealed that the same variant was significant for right hippocampal volume alone, and also revealed a suggestively significant variant (rs12455524) within the gene DLGAP1 for rMDD alone. The results implicate right-hemisphere hippocampal volume as a possible endophenotype of rMDD, and in so doing highlight a potential gene of interest for rMDD risk. © 2015 Wiley Periodicals, Inc.

Comparison of Model Reliabilities from Single-Step and Bivariate Blending Methods

DEFF Research Database (Denmark)

Taskinen, Matti; Mäntysaari, Esa; Lidauer, Martin

2013-01-01

Model based reliabilities in genetic evaluation are compared between three methods: animal model BLUP, single-step BLUP, and bivariate blending after genomic BLUP. The original bivariate blending is revised in this work to better account animal models. The study data is extracted from...... be calculated. Model reliabilities by the single-step and the bivariate blending methods were higher than by animal model due to genomic information. Compared to the single-step method, the bivariate blending method reliability estimates were, in general, lower. Computationally bivariate blending method was......, on the other hand, lighter than the single-step method....

Personality Traits as Predictors of Shopping Motivations and Behaviors: A Canonical Correlation Analysis

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Ali Gohary

2014-10-01

Full Text Available This study examines the relationship between Big Five personality traits with shopping motivation variables consisting of compulsive and impulsive buying, hedonic and utilitarian shopping values. Two hundred forty seven college students were recruited to participate in this research. Bivariate correlation demonstrates an overlap between personality traits; consequently, canonical correlation was performed to prevent this phenomenon. The results of multiple regression analysis suggested conscientiousness, neuroticism and openness as predictors of compulsive buying, impulsive buying and utilitarian shopping values. In addition, the results showed significant differences between males and females on conscientiousness, neuroticism, openness, compulsive buying and hedonic shopping value. Besides, using hierarchical regression analysis, we examined sex as moderator between Big Five personality traits and shopping variables, but we didn’t find sufficient evidence to prove it.

Reliability for some bivariate beta distributions

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Nadarajah Saralees

2005-01-01

Full Text Available In the area of stress-strength models there has been a large amount of work as regards estimation of the reliability R=Pr( Xbivariate distribution with dependence between X and Y . In particular, we derive explicit expressions for R when the joint distribution is bivariate beta. The calculations involve the use of special functions.

Reliability for some bivariate gamma distributions

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Nadarajah Saralees

2005-01-01

Full Text Available In the area of stress-strength models, there has been a large amount of work as regards estimation of the reliability R=Pr( Xbivariate distribution with dependence between X and Y . In particular, we derive explicit expressions for R when the joint distribution is bivariate gamma. The calculations involve the use of special functions.

Ordinal bivariate inequality

DEFF Research Database (Denmark)

Sonne-Schmidt, Christoffer Scavenius; Tarp, Finn; Østerdal, Lars Peter Raahave

This paper introduces a concept of inequality comparisons with ordinal bivariate categorical data. In our model, one population is more unequal than another when they have common arithmetic median outcomes and the first can be obtained from the second by correlationincreasing switches and/or median......-preserving spreads. For the canonical 2x2 case (with two binary indicators), we derive a simple operational procedure for checking ordinal inequality relations in practice. As an illustration, we apply the model to childhood deprivation in Mozambique....

Bivariate Correlation Analysis of the Chemometric Profiles of Chinese Wild Salvia miltiorrhiza Based on UPLC-Qqq-MS and Antioxidant Activities

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Xiaodan Zhang

2018-02-01

Full Text Available To better understand the mechanisms underlying the pharmacological actions of Salvia miltiorrhiza, correlation between the chemical profiles and in vitro antioxidant activities in 50 batches of wild S. miltiorrhiza samples was analyzed. Our ultra-performance liquid chromatography–tandem mass spectrometry analysis detected twelve phenolic acids and five tanshinones and obtained various chemical profiles from different origins. In a principal component analysis (PCA and cluster analysis, the tanshinones cryptotanshinone, tanshinone IIA and dihydrotanshinone I exhibited higher weights in PC1, whereas the phenolic acids danshensu, salvianolic acids A and B and lithospermic acid were highly loaded in PC2. All components could be optimized as markers of different locations and might be suitable for S. miltiorrhiza quality analyses. Additionally, the DPPH and ABTS assays used to comprehensively evaluate antioxidant activities indicated large variations, with mean DPPH and ABTS scavenging potencies of 32.24 and 23.39 μg/mL, respectively, among S. miltiorrhiza extract solutions. Notably, samples that exceeded the mean IC50 values had higher phenolic acid contents. A correlation analysis indicated a strong correlation between the antioxidant activities and phenolic acid contents. Caffeic acid, danshensu, rosmarinic acid, lithospermic acid and salvianolic acid B were major contributors to antioxidant activity. In conclusion, phenolic compounds were the predominant antioxidant components in the investigated plant species. These plants may be sources of potent natural antioxidants and beneficial chemopreventive agents.

Which global stock indices trigger stronger contagion risk in the Vietnamese stock market? Evidence using a bivariate analysis

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Wang Kuan-Min

2013-01-01

Full Text Available This paper extends recent investigations into risk contagion effects on stock markets to the Vietnamese stock market. Daily data spanning October 9, 2006 to May 3, 2012 are sourced to empirically validate the contagion effects between stock markets in Vietnam, and China, Japan, Singapore, and the US. To facilitate the validation of contagion effects with market-related coefficients, this paper constructs a bivariate EGARCH model of dynamic conditional correlation coefficients. Using the correlation contagion test and Dungey et al.’s (2005 contagion test, we find contagion effects between the Vietnamese and four other stock markets, namely Japan, Singapore, China, and the US. Second, we show that the Japanese stock market causes stronger contagion risk in the Vietnamese stock market compared to the stock markets of China, Singapore, and the US. Finally, we show that the Chinese and US stock markets cause weaker contagion effects in the Vietnamese stock market because of stronger interdependence effects between the former two markets.

Bivariate frequency analysis of rainfall intensity and duration for urban stormwater infrastructure design

Science.gov (United States)

Jun, Changhyun; Qin, Xiaosheng; Gan, Thian Yew; Tung, Yeou-Koung; De Michele, Carlo

2017-10-01

This study presents a storm-event based bivariate frequency analysis approach to determine design rainfalls in which, the number, intensity and duration of actual rainstorm events were considered. To derive more realistic design storms, the occurrence probability of an individual rainstorm event was determined from the joint distribution of storm intensity and duration through a copula model. Hourly rainfall data were used at three climate stations respectively located in Singapore, South Korea and Canada. It was found that the proposed approach could give a more realistic description of rainfall characteristics of rainstorm events and design rainfalls. As results, the design rainfall quantities from actual rainstorm events at the three studied sites are consistently lower than those obtained from the conventional rainfall depth-duration-frequency (DDF) method, especially for short-duration storms (such as 1-h). It results from occurrence probabilities of each rainstorm event and a different angle for rainfall frequency analysis, and could offer an alternative way of describing extreme rainfall properties and potentially help improve the hydrologic design of stormwater management facilities in urban areas.

Bivariate copula in fitting rainfall data

Science.gov (United States)

Yee, Kong Ching; Suhaila, Jamaludin; Yusof, Fadhilah; Mean, Foo Hui

2014-07-01

The usage of copula to determine the joint distribution between two variables is widely used in various areas. The joint distribution of rainfall characteristic obtained using the copula model is more ideal than the standard bivariate modelling where copula is belief to have overcome some limitation. Six copula models will be applied to obtain the most suitable bivariate distribution between two rain gauge stations. The copula models are Ali-Mikhail-Haq (AMH), Clayton, Frank, Galambos, Gumbel-Hoogaurd (GH) and Plackett. The rainfall data used in the study is selected from rain gauge stations which are located in the southern part of Peninsular Malaysia, during the period from 1980 to 2011. The goodness-of-fit test in this study is based on the Akaike information criterion (AIC).

A Bivariate Mixture Model for Natural Antibody Levels to Human Papillomavirus Types 16 and 18: Baseline Estimates for Monitoring the Herd Effects of Immunization.

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Margaretha A Vink

Full Text Available Post-vaccine monitoring programs for human papillomavirus (HPV have been introduced in many countries, but HPV serology is still an underutilized tool, partly owing to the weak antibody response to HPV infection. Changes in antibody levels among non-vaccinated individuals could be employed to monitor herd effects of immunization against HPV vaccine types 16 and 18, but inference requires an appropriate statistical model. The authors developed a four-component bivariate mixture model for jointly estimating vaccine-type seroprevalence from correlated antibody responses against HPV16 and -18 infections. This model takes account of the correlation between HPV16 and -18 antibody concentrations within subjects, caused e.g. by heterogeneity in exposure level and immune response. The model was fitted to HPV16 and -18 antibody concentrations as measured by a multiplex immunoassay in a large serological survey (3,875 females carried out in the Netherlands in 2006/2007, before the introduction of mass immunization. Parameters were estimated by Bayesian analysis. We used the deviance information criterion for model selection; performance of the preferred model was assessed through simulation. Our analysis uncovered elevated antibody concentrations in doubly as compared to singly seropositive individuals, and a strong clustering of HPV16 and -18 seropositivity, particularly around the age of sexual debut. The bivariate model resulted in a more reliable classification of singly and doubly seropositive individuals than achieved by a combination of two univariate models, and suggested a higher pre-vaccine HPV16 seroprevalence than previously estimated. The bivariate mixture model provides valuable baseline estimates of vaccine-type seroprevalence and may prove useful in seroepidemiologic assessment of the herd effects of HPV vaccination.

Chain Plot: A Tool for Exploiting Bivariate Temporal Structures

OpenAIRE

Taylor, CC; Zempeni, A

2004-01-01

In this paper we present a graphical tool useful for visualizing the cyclic behaviour of bivariate time series. We investigate its properties and link it to the asymmetry of the two variables concerned. We also suggest adding approximate confidence bounds to the points on the plot and investigate the effect of lagging to the chain plot. We conclude our paper by some standard Fourier analysis, relating and comparing this to the chain plot.

Obtaining DDF Curves of Extreme Rainfall Data Using Bivariate Copula and Frequency Analysis

DEFF Research Database (Denmark)

Sadri, Sara; Madsen, Henrik; Mikkelsen, Peter Steen

2009-01-01

, situated near Copenhagen in Denmark. For rainfall extracted using method 2, the marginal distribution of depth was found to fit the Generalized Pareto distribution while duration was found to fit the Gamma distribution, using the method of L-moments. The volume was fit with a generalized Pareto...... with duration for a given return period and name them DDF (depth-duration-frequency) curves. The copula approach does not assume the rainfall variables are independent or jointly normally distributed. Rainfall series are extracted in three ways: (1) by maximum mean intensity; (2) by depth and duration...... distribution and the duration was fit with a Pearson type III distribution for rainfall extracted using method 3. The Clayton copula was found to be appropriate for bivariate analysis of rainfall depth and duration for both methods 2 and 3. DDF curves derived using the Clayton copula for depth and duration...

Building Bivariate Tables: The compareGroups Package for R

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Isaac Subirana

2014-05-01

Full Text Available The R package compareGroups provides functions meant to facilitate the construction of bivariate tables (descriptives of several variables for comparison between groups and generates reports in several formats (LATEX, HTML or plain text CSV. Moreover, bivariate tables can be viewed directly on the R console in a nice format. A graphical user interface (GUI has been implemented to build the bivariate tables more easily for those users who are not familiar with the R software. Some new functions and methods have been incorporated in the newest version of the compareGroups package (version 1.x to deal with time-to-event variables, stratifying tables, merging several tables, and revising the statistical methods used. The GUI interface also has been improved, making it much easier and more intuitive to set the inputs for building the bivariate tables. The ?rst version (version 0.x and this version were presented at the 2010 useR! conference (Sanz, Subirana, and Vila 2010 and the 2011 useR! conference (Sanz, Subirana, and Vila 2011, respectively. Package compareGroups is available from the Comprehensive R Archive Network at http://CRAN.R-project.org/package=compareGroups.

The relative performance of bivariate causality tests in small samples

NARCIS (Netherlands)

Bult, J..R.; Leeflang, P.S.H.; Wittink, D.R.

1997-01-01

Causality tests have been applied to establish directional effects and to reduce the set of potential predictors, For the latter type of application only bivariate tests can be used, In this study we compare bivariate causality tests. Although the problem addressed is general and could benefit

Effect of catchment properties and flood generation regime on copula selection for bivariate flood frequency analysis

Science.gov (United States)

Filipova, Valeriya; Lawrence, Deborah; Klempe, Harald

2018-02-01

Applying copula-based bivariate flood frequency analysis is advantageous because the results provide information on both the flood peak and volume. More data are, however, required for such an analysis, and it is often the case that only data series with a limited record length are available. To overcome this issue of limited record length, data regarding climatic and geomorphological properties can be used to complement statistical methods. In this paper, we present a study of 27 catchments located throughout Norway, in which we assess whether catchment properties, flood generation processes and flood regime have an effect on the correlation between flood peak and volume and, in turn, on the selection of copulas. To achieve this, the annual maximum flood events were first classified into events generated primarily by rainfall, snowmelt or a combination of these. The catchments were then classified into flood regime, depending on the predominant flood generation process producing the annual maximum flood events. A contingency table and Fisher's exact test were used to determine the factors that affect the selection of copulas in the study area. The results show that the two-parameter copulas BB1 and BB7 are more commonly selected in catchments with high steepness, high mean annual runoff and rainfall flood regime. These findings suggest that in these types of catchments, the dependence structure between flood peak and volume is more complex and cannot be modeled effectively using a one-parameter copula. The results illustrate that by relating copula types to flood regime and catchment properties, additional information can be supplied for selecting copulas in catchments with limited data.

The Role of Wealth and Health in Insurance Choice: Bivariate Probit Analysis in China

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Yiding Yue

2014-01-01

Full Text Available This paper captures the correlation between the choices of health insurance and pension insurance using the bivariate probit model and then studies the effect of wealth and health on insurance choice. Our empirical evidence shows that people who participate in a health care program are more likely to participate in a pension plan at the same time, while wealth and health have different effects on the choices of the health care program and the pension program. Generally, the higher an individual’s wealth level is, the more likelihood he will participate in a health care program; but wealth has no effect on the participation of pension. Health status has opposite effects on choices of health care programs and pension plans; the poorer an individual’s health is, the more likely he is to participate in health care programs, while the better health he enjoys, the more likely he is to participate in pension plans. When the investigation scope narrows down to commercial insurance, there is only a significant effect of health status on commercial health insurance. The commercial insurance choice and the insurance choice of the agricultural population are more complicated.

Detrended fluctuation analysis made flexible to detect range of cross-correlated fluctuations

Science.gov (United States)

Kwapień, Jarosław; Oświecimka, Paweł; DroŻdŻ, Stanisław

2015-11-01

The detrended cross-correlation coefficient ρDCCA has recently been proposed to quantify the strength of cross-correlations on different temporal scales in bivariate, nonstationary time series. It is based on the detrended cross-correlation and detrended fluctuation analyses (DCCA and DFA, respectively) and can be viewed as an analog of the Pearson coefficient in the case of the fluctuation analysis. The coefficient ρDCCA works well in many practical situations but by construction its applicability is limited to detection of whether two signals are generally cross-correlated, without the possibility to obtain information on the amplitude of fluctuations that are responsible for those cross-correlations. In order to introduce some related flexibility, here we propose an extension of ρDCCA that exploits the multifractal versions of DFA and DCCA: multifractal detrended fluctuation analysis and multifractal detrended cross-correlation analysis, respectively. The resulting new coefficient ρq not only is able to quantify the strength of correlations but also allows one to identify the range of detrended fluctuation amplitudes that are correlated in two signals under study. We show how the coefficient ρq works in practical situations by applying it to stochastic time series representing processes with long memory: autoregressive and multiplicative ones. Such processes are often used to model signals recorded from complex systems and complex physical phenomena like turbulence, so we are convinced that this new measure can successfully be applied in time-series analysis. In particular, we present an example of such application to highly complex empirical data from financial markets. The present formulation can straightforwardly be extended to multivariate data in terms of the q -dependent counterpart of the correlation matrices and then to the network representation.

A Bivariate Extension to Traditional Empirical Orthogonal Function Analysis

DEFF Research Database (Denmark)

Nielsen, Allan Aasbjerg; Hilger, Klaus Baggesen; Andersen, Ole Baltazar

2002-01-01

This paper describes the application of canonical correlations analysis to the joint analysis of global monthly mean values of 1996-1997 sea surface temperature (SST) and height (SSH) data. The SST data are considered as one set and the SSH data as another set of multivariate observations, both w...... as for example an increase in the SST will lead to an increase in the SSH. The analysis clearly shows the build-up of one of the largest El Niño events on record. Also the analysis indicates a phase lag of approximately one month between the SST and SSH fields....

An efficient algorithm for generating random number pairs drawn from a bivariate normal distribution

Science.gov (United States)

Campbell, C. W.

1983-01-01

An efficient algorithm for generating random number pairs from a bivariate normal distribution was developed. Any desired value of the two means, two standard deviations, and correlation coefficient can be selected. Theoretically the technique is exact and in practice its accuracy is limited only by the quality of the uniform distribution random number generator, inaccuracies in computer function evaluation, and arithmetic. A FORTRAN routine was written to check the algorithm and good accuracy was obtained. Some small errors in the correlation coefficient were observed to vary in a surprisingly regular manner. A simple model was developed which explained the qualities aspects of the errors.

A Bivariate return period for levee failure monitoring

Science.gov (United States)

Isola, M.; Caporali, E.

2017-12-01

Levee breaches are strongly linked with the interaction processes among water, soil and structure, thus many are the factors that affect the breach development. One of the main is the hydraulic load, characterized by intensity and duration, i.e. by the flood event hydrograph. On the magnitude of the hydraulic load is based the levee design, generally without considering the fatigue failure due to the load duration. Moreover, many are the cases in which the levee breach are characterized by flood of magnitude lower than the design one. In order to implement the strategies of flood risk management, we built here a procedure based on a multivariate statistical analysis of flood peak and volume together with the analysis of the past levee failure events. Particularly, in order to define the probability of occurrence of the hydraulic load on a levee, a bivariate copula model is used to obtain the bivariate joint distribution of flood peak and volume. Flood peak is the expression of the load magnitude, while the volume is the expression of the stress over time. We consider the annual flood peak and the relative volume. The volume is given by the hydrograph area between the beginning and the end of event. The beginning of the event is identified as an abrupt rise of the discharge by more than 20%. The end is identified as the point from which the receding limb is characterized by the baseflow, using a nonlinear reservoir algorithm as baseflow separation technique. By this, with the aim to define warning thresholds we consider the past levee failure events and the relative bivariate return period (BTr) compared with the estimation of a traditional univariate model. The discharge data of 30 hydrometric stations of Arno River in Tuscany, Italy, in the period 1995-2016 are analysed. The database of levee failure events, considering for each event the location as well as the failure mode, is also created. The events were registered in the period 2000-2014 by EEA

Effectiveness of enforcement levels of speed limit and drink driving laws and associated factors – Exploratory empirical analysis using a bivariate ordered probit model

Directory of Open Access Journals (Sweden)

Behram Wali

2017-06-01

Full Text Available The contemporary traffic safety research comprises little information on quantifying the simultaneous association between drink driving and speeding among fatally injured drivers. Potential correlation between driver's drink driving and speeding behavior poses a substantial methodological concern which needs investigation. This study therefore focused on investigating the simultaneous impact of socioeconomic factors, fatalities, vehicle ownership, health services and highway agency road safety policies on enforcement levels of speed limit and drink driving laws. The effectiveness of enforcement levels of speed limit and drink driving laws has been investigated through development of bivariate ordered probit model using data extricated from WHO's global status report on road safety in 2013. The consistent and intuitive parameter estimates along with statistically significant correlation between response outcomes validates the statistical supremacy of bivariate ordered probit model. The results revealed that fatalities per thousand registered vehicles, hospital beds per hundred thousand population and road safety policies are associated with a likely medium or high effectiveness of enforcement levels of speed limit and drink driving laws, respectively. Also, the model encapsulates the effect of several other agency related variables and socio-economic status on the response outcomes. Marginal effects are reported for analyzing the impact of such factors on intermediate categories of response outcomes. The results of this study are expected to provide necessary insights to elemental enforcement programs. Also, marginal effects of explanatory variables may provide useful directions for formulating effective policy countermeasures for overcoming driver's speeding and drink driving behavior.

A dynamic bivariate Poisson model for analysing and forecasting match results in the English Premier League

NARCIS (Netherlands)

Koopman, S.J.; Lit, R.

2015-01-01

Summary: We develop a statistical model for the analysis and forecasting of football match results which assumes a bivariate Poisson distribution with intensity coefficients that change stochastically over time. The dynamic model is a novelty in the statistical time series analysis of match results

A generalized right truncated bivariate Poisson regression model with applications to health data.

Science.gov (United States)

Islam, M Ataharul; Chowdhury, Rafiqul I

2017-01-01

A generalized right truncated bivariate Poisson regression model is proposed in this paper. Estimation and tests for goodness of fit and over or under dispersion are illustrated for both untruncated and right truncated bivariate Poisson regression models using marginal-conditional approach. Estimation and test procedures are illustrated for bivariate Poisson regression models with applications to Health and Retirement Study data on number of health conditions and the number of health care services utilized. The proposed test statistics are easy to compute and it is evident from the results that the models fit the data very well. A comparison between the right truncated and untruncated bivariate Poisson regression models using the test for nonnested models clearly shows that the truncated model performs significantly better than the untruncated model.

Unadjusted Bivariate Two-Group Comparisons: When Simpler is Better.

Science.gov (United States)

Vetter, Thomas R; Mascha, Edward J

2018-01-01

Hypothesis testing involves posing both a null hypothesis and an alternative hypothesis. This basic statistical tutorial discusses the appropriate use, including their so-called assumptions, of the common unadjusted bivariate tests for hypothesis testing and thus comparing study sample data for a difference or association. The appropriate choice of a statistical test is predicated on the type of data being analyzed and compared. The unpaired or independent samples t test is used to test the null hypothesis that the 2 population means are equal, thereby accepting the alternative hypothesis that the 2 population means are not equal. The unpaired t test is intended for comparing dependent continuous (interval or ratio) data from 2 study groups. A common mistake is to apply several unpaired t tests when comparing data from 3 or more study groups. In this situation, an analysis of variance with post hoc (posttest) intragroup comparisons should instead be applied. Another common mistake is to apply a series of unpaired t tests when comparing sequentially collected data from 2 study groups. In this situation, a repeated-measures analysis of variance, with tests for group-by-time interaction, and post hoc comparisons, as appropriate, should instead be applied in analyzing data from sequential collection points. The paired t test is used to assess the difference in the means of 2 study groups when the sample observations have been obtained in pairs, often before and after an intervention in each study subject. The Pearson chi-square test is widely used to test the null hypothesis that 2 unpaired categorical variables, each with 2 or more nominal levels (values), are independent of each other. When the null hypothesis is rejected, 1 concludes that there is a probable association between the 2 unpaired categorical variables. When comparing 2 groups on an ordinal or nonnormally distributed continuous outcome variable, the 2-sample t test is usually not appropriate. The

GIS-based bivariate statistical techniques for groundwater potential analysis (an example of Iran)

Science.gov (United States)

Haghizadeh, Ali; Moghaddam, Davoud Davoudi; Pourghasemi, Hamid Reza

2017-12-01

Groundwater potential analysis prepares better comprehension of hydrological settings of different regions. This study shows the potency of two GIS-based data driven bivariate techniques namely statistical index (SI) and Dempster-Shafer theory (DST) to analyze groundwater potential in Broujerd region of Iran. The research was done using 11 groundwater conditioning factors and 496 spring positions. Based on the ground water potential maps (GPMs) of SI and DST methods, 24.22% and 23.74% of the study area is covered by poor zone of groundwater potential, and 43.93% and 36.3% of Broujerd region is covered by good and very good potential zones, respectively. The validation of outcomes displayed that area under the curve (AUC) of SI and DST techniques are 81.23% and 79.41%, respectively, which shows SI method has slightly a better performance than the DST technique. Therefore, SI and DST methods are advantageous to analyze groundwater capacity and scrutinize the complicated relation between groundwater occurrence and groundwater conditioning factors, which permits investigation of both systemic and stochastic uncertainty. Finally, it can be realized that these techniques are very beneficial for groundwater potential analyzing and can be practical for water-resource management experts.

Preparation and bivariate analysis of suspensions of human chromosomes

Energy Technology Data Exchange (ETDEWEB)

van den Engh, G.J.; Trask, B.J.; Gray, J.W.; Langlois, R.G.; Yu, L.C.

1985-01-01

Chromosomes were isolated from a variety of human cell types using a HEPES-buffered hypotonic solution (pH 8.0) containing KCl, MgSO/sub 4/ dithioerythritol, and RNase. The chromosomes isolated by this procedure could be stained with a variety of fluorescent stains including propidium iodide, chromomycin A3, and Hoeschst 33258. Addition of sodium citrate to the stained chromosomes was found to improve the total fluorescence resolution. High-quality bivariate Hoeschst vs. chromomycin fluorescence distributions were obtained for chromosomes isolated from a human fibroblast cell strain, a human colon carcinoma cell line, and human peripheral blood lymphocyte cultures. Good flow karyotypes were also obtained from primary amniotic cell cultures. The Hoeschst vs. chromomycin flow karyotypes of a given cell line, made at different times and at dye concentrations varying over fourfold ranges, show little variation in the relative peak positions of the chromosomes. The size of the DNA in chromosomes isolated using this procedure ranges from 20 to 50 kilobases. The described isolation procedure is simple, it yields high-quality flow karyotypes, and it can be used to prepare chromosomes from clinical samples. 22 references, 7 figures, 1 table.

Bivariate value-at-risk

Directory of Open Access Journals (Sweden)

Giuseppe Arbia

2007-10-01

Full Text Available In this paper we extend the concept of Value-at-risk (VaR to bivariate return distributions in order to obtain measures of the market risk of an asset taking into account additional features linked to downside risk exposure. We first present a general definition of risk as the probability of an adverse event over a random distribution and we then introduce a measure of market risk (b-VaR that admits the traditional b of an asset in portfolio management as a special case when asset returns are normally distributed. Empirical evidences are provided by using Italian stock market data.

A non-stationary cost-benefit based bivariate extreme flood estimation approach

Science.gov (United States)

Qi, Wei; Liu, Junguo

2018-02-01

Cost-benefit analysis and flood frequency analysis have been integrated into a comprehensive framework to estimate cost effective design values. However, previous cost-benefit based extreme flood estimation is based on stationary assumptions and analyze dependent flood variables separately. A Non-Stationary Cost-Benefit based bivariate design flood estimation (NSCOBE) approach is developed in this study to investigate influence of non-stationarities in both the dependence of flood variables and the marginal distributions on extreme flood estimation. The dependence is modeled utilizing copula functions. Previous design flood selection criteria are not suitable for NSCOBE since they ignore time changing dependence of flood variables. Therefore, a risk calculation approach is proposed based on non-stationarities in both marginal probability distributions and copula functions. A case study with 54-year observed data is utilized to illustrate the application of NSCOBE. Results show NSCOBE can effectively integrate non-stationarities in both copula functions and marginal distributions into cost-benefit based design flood estimation. It is also found that there is a trade-off between maximum probability of exceedance calculated from copula functions and marginal distributions. This study for the first time provides a new approach towards a better understanding of influence of non-stationarities in both copula functions and marginal distributions on extreme flood estimation, and could be beneficial to cost-benefit based non-stationary bivariate design flood estimation across the world.

Powerful bivariate genome-wide association analyses suggest the SOX6 gene influencing both obesity and osteoporosis phenotypes in males.

Directory of Open Access Journals (Sweden)

Yao-Zhong Liu

2009-08-01

Full Text Available Current genome-wide association studies (GWAS are normally implemented in a univariate framework and analyze different phenotypes in isolation. This univariate approach ignores the potential genetic correlation between important disease traits. Hence this approach is difficult to detect pleiotropic genes, which may exist for obesity and osteoporosis, two common diseases of major public health importance that are closely correlated genetically.To identify such pleiotropic genes and the key mechanistic links between the two diseases, we here performed the first bivariate GWAS of obesity and osteoporosis. We searched for genes underlying co-variation of the obesity phenotype, body mass index (BMI, with the osteoporosis risk phenotype, hip bone mineral density (BMD, scanning approximately 380,000 SNPs in 1,000 unrelated homogeneous Caucasians, including 499 males and 501 females. We identified in the male subjects two SNPs in intron 1 of the SOX6 (SRY-box 6 gene, rs297325 and rs4756846, which were bivariately associated with both BMI and hip BMD, achieving p values of 6.82x10(-7 and 1.47x10(-6, respectively. The two SNPs ranked at the top in significance for bivariate association with BMI and hip BMD in the male subjects among all the approximately 380,000 SNPs examined genome-wide. The two SNPs were replicated in a Framingham Heart Study (FHS cohort containing 3,355 Caucasians (1,370 males and 1,985 females from 975 families. In the FHS male subjects, the two SNPs achieved p values of 0.03 and 0.02, respectively, for bivariate association with BMI and femoral neck BMD. Interestingly, SOX6 was previously found to be essential to both cartilage formation/chondrogenesis and obesity-related insulin resistance, suggesting the gene's dual role in both bone and fat.Our findings, together with the prior biological evidence, suggest the SOX6 gene's importance in co-regulation of obesity and osteoporosis.

Powerful Bivariate Genome-Wide Association Analyses Suggest the SOX6 Gene Influencing Both Obesity and Osteoporosis Phenotypes in Males

Science.gov (United States)

Liu, Yao-Zhong; Pei, Yu-Fang; Liu, Jian-Feng; Yang, Fang; Guo, Yan; Zhang, Lei; Liu, Xiao-Gang; Yan, Han; Wang, Liang; Zhang, Yin-Ping; Levy, Shawn; Recker, Robert R.; Deng, Hong-Wen

2009-01-01

Background Current genome-wide association studies (GWAS) are normally implemented in a univariate framework and analyze different phenotypes in isolation. This univariate approach ignores the potential genetic correlation between important disease traits. Hence this approach is difficult to detect pleiotropic genes, which may exist for obesity and osteoporosis, two common diseases of major public health importance that are closely correlated genetically. Principal Findings To identify such pleiotropic genes and the key mechanistic links between the two diseases, we here performed the first bivariate GWAS of obesity and osteoporosis. We searched for genes underlying co-variation of the obesity phenotype, body mass index (BMI), with the osteoporosis risk phenotype, hip bone mineral density (BMD), scanning ∼380,000 SNPs in 1,000 unrelated homogeneous Caucasians, including 499 males and 501 females. We identified in the male subjects two SNPs in intron 1 of the SOX6 (SRY-box 6) gene, rs297325 and rs4756846, which were bivariately associated with both BMI and hip BMD, achieving p values of 6.82×10−7 and 1.47×10−6, respectively. The two SNPs ranked at the top in significance for bivariate association with BMI and hip BMD in the male subjects among all the ∼380,000 SNPs examined genome-wide. The two SNPs were replicated in a Framingham Heart Study (FHS) cohort containing 3,355 Caucasians (1,370 males and 1,985 females) from 975 families. In the FHS male subjects, the two SNPs achieved p values of 0.03 and 0.02, respectively, for bivariate association with BMI and femoral neck BMD. Interestingly, SOX6 was previously found to be essential to both cartilage formation/chondrogenesis and obesity-related insulin resistance, suggesting the gene's dual role in both bone and fat. Conclusions Our findings, together with the prior biological evidence, suggest the SOX6 gene's importance in co-regulation of obesity and osteoporosis. PMID:19714249

Causal networks clarify productivity-richness interrelations, bivariate plots do not

Science.gov (United States)

Grace, James B.; Adler, Peter B.; Harpole, W. Stanley; Borer, Elizabeth T.; Seabloom, Eric W.

2014-01-01

Perhaps no other pair of variables in ecology has generated as much discussion as species richness and ecosystem productivity, as illustrated by the reactions by Pierce (2013) and others to Adler et al.'s (2011) report that empirical patterns are weak and inconsistent. Adler et al. (2011) argued we need to move beyond a focus on simplistic bivariate relationships and test mechanistic, multivariate causal hypotheses. We feel the continuing debate over productivity–richness relationships (PRRs) provides a focused context for illustrating the fundamental difficulties of using bivariate relationships to gain scientific understanding.

Approximation of bivariate copulas by patched bivariate Fréchet copulas

KAUST Repository

Zheng, Yanting; Yang, Jingping; Huang, Jianhua Z.

2011-01-01

Bivariate Fréchet (BF) copulas characterize dependence as a mixture of three simple structures: comonotonicity, independence and countermonotonicity. They are easily interpretable but have limitations when used as approximations to general dependence structures. To improve the approximation property of the BF copulas and keep the advantage of easy interpretation, we develop a new copula approximation scheme by using BF copulas locally and patching the local pieces together. Error bounds and a probabilistic interpretation of this approximation scheme are developed. The new approximation scheme is compared with several existing copula approximations, including shuffle of min, checkmin, checkerboard and Bernstein approximations and exhibits better performance, especially in characterizing the local dependence. The utility of the new approximation scheme in insurance and finance is illustrated in the computation of the rainbow option prices and stop-loss premiums. © 2010 Elsevier B.V.

Approximation of bivariate copulas by patched bivariate Fréchet copulas

KAUST Repository

Zheng, Yanting

2011-03-01

Bivariate Fréchet (BF) copulas characterize dependence as a mixture of three simple structures: comonotonicity, independence and countermonotonicity. They are easily interpretable but have limitations when used as approximations to general dependence structures. To improve the approximation property of the BF copulas and keep the advantage of easy interpretation, we develop a new copula approximation scheme by using BF copulas locally and patching the local pieces together. Error bounds and a probabilistic interpretation of this approximation scheme are developed. The new approximation scheme is compared with several existing copula approximations, including shuffle of min, checkmin, checkerboard and Bernstein approximations and exhibits better performance, especially in characterizing the local dependence. The utility of the new approximation scheme in insurance and finance is illustrated in the computation of the rainbow option prices and stop-loss premiums. © 2010 Elsevier B.V.

A method of moments to estimate bivariate survival functions: the copula approach

Directory of Open Access Journals (Sweden)

Silvia Angela Osmetti

2013-05-01

Full Text Available In this paper we discuss the problem on parametric and non parametric estimation of the distributions generated by the Marshall-Olkin copula. This copula comes from the Marshall-Olkin bivariate exponential distribution used in reliability analysis. We generalize this model by the copula and different marginal distributions to construct several bivariate survival functions. The cumulative distribution functions are not absolutely continuous and they unknown parameters are often not be obtained in explicit form. In order to estimate the parameters we propose an easy procedure based on the moments. This method consist in two steps: in the first step we estimate only the parameters of marginal distributions and in the second step we estimate only the copula parameter. This procedure can be used to estimate the parameters of complex survival functions in which it is difficult to find an explicit expression of the mixed moments. Moreover it is preferred to the maximum likelihood one for its simplex mathematic form; in particular for distributions whose maximum likelihood parameters estimators can not be obtained in explicit form.

Bivariate Cointegration Analysis of Energy-Economy Interactions in Iran

Directory of Open Access Journals (Sweden)

Ismail Oladimeji Soile

2015-12-01

Full Text Available Fixing the prices of energy products below their opportunity cost for welfare and redistribution purposes is common with governments of many oil producing developing countries. This has often resulted in huge energy consumption in developing countries and the question that emerge is whether this increased energy consumption results in higher economic activities. Available statistics show that Iran’s economy growth shrunk for the first time in two decades from 2011 amidst the introduction of pricing reform in 2010 and 2014 suggesting a relationship between energy use and economic growth. Accordingly, the study examined the causality and the likelihood of a long term relationship between energy and economic growth in Iran. Unlike previous studies which have focused on the effects and effectiveness of the reform, the paper investigates the rationale for the reform. The study applied a bivariate cointegration time series econometric approach. The results reveals a one-way causality running from economic growth to energy with no feedback with evidence of long run connection. The implication of this is that energy conservation policy is not inimical to economic growth. This evidence lend further support for the ongoing subsidy reforms in Iran as a measure to check excessive and inefficient use of energy.

Application of bivariate mapping for hydrological classification and analysis of temporal change and scale effects in Switzerland

NARCIS (Netherlands)

Speich, Matthias J.R.; Bernhard, Luzi; Teuling, Ryan; Zappa, Massimiliano

2015-01-01

Hydrological classification schemes are important tools for assessing the impacts of a changing climate on the hydrology of a region. In this paper, we present bivariate mapping as a simple means of classifying hydrological data for a quantitative and qualitative assessment of temporal change.

Correlation, Regression, and Cointegration of Nonstationary Economic Time Series

DEFF Research Database (Denmark)

Johansen, Søren

), and Phillips (1986) found the limit distributions. We propose to distinguish between empirical and population correlation coefficients and show in a bivariate autoregressive model for nonstationary variables that the empirical correlation and regression coefficients do not converge to the relevant population...... values, due to the trending nature of the data. We conclude by giving a simple cointegration analysis of two interests. The analysis illustrates that much more insight can be gained about the dynamic behavior of the nonstationary variables then simply by calculating a correlation coefficient......Yule (1926) introduced the concept of spurious or nonsense correlation, and showed by simulation that for some nonstationary processes, that the empirical correlations seem not to converge in probability even if the processes were independent. This was later discussed by Granger and Newbold (1974...

The return period analysis of natural disasters with statistical modeling of bivariate joint probability distribution.

Science.gov (United States)

Li, Ning; Liu, Xueqin; Xie, Wei; Wu, Jidong; Zhang, Peng

2013-01-01

New features of natural disasters have been observed over the last several years. The factors that influence the disasters' formation mechanisms, regularity of occurrence and main characteristics have been revealed to be more complicated and diverse in nature than previously thought. As the uncertainty involved increases, the variables need to be examined further. This article discusses the importance and the shortage of multivariate analysis of natural disasters and presents a method to estimate the joint probability of the return periods and perform a risk analysis. Severe dust storms from 1990 to 2008 in Inner Mongolia were used as a case study to test this new methodology, as they are normal and recurring climatic phenomena on Earth. Based on the 79 investigated events and according to the dust storm definition with bivariate, the joint probability distribution of severe dust storms was established using the observed data of maximum wind speed and duration. The joint return periods of severe dust storms were calculated, and the relevant risk was analyzed according to the joint probability. The copula function is able to simulate severe dust storm disasters accurately. The joint return periods generated are closer to those observed in reality than the univariate return periods and thus have more value in severe dust storm disaster mitigation, strategy making, program design, and improvement of risk management. This research may prove useful in risk-based decision making. The exploration of multivariate analysis methods can also lay the foundation for further applications in natural disaster risk analysis. © 2012 Society for Risk Analysis.

Investigating the relationship between costs and outcomes for English mental health providers: a bi-variate multi-level regression analysis.

Science.gov (United States)

Moran, Valerie; Jacobs, Rowena

2018-06-01

Provider payment systems for mental health care that incentivize cost control and quality improvement have been a policy focus in a number of countries. In England, a new prospective provider payment system is being introduced to mental health that should encourage providers to control costs and improve outcomes. The aim of this research is to investigate the relationship between costs and outcomes to ascertain whether there is a trade-off between controlling costs and improving outcomes. The main data source is the Mental Health Minimum Data Set (MHMDS) for the years 2011/12 and 2012/13. Costs are calculated using NHS reference cost data while outcomes are measured using the Health of the Nation Outcome Scales (HoNOS). We estimate a bivariate multi-level model with costs and outcomes simultaneously. We calculate the correlation and plot the pairwise relationship between residual costs and outcomes at the provider level. After controlling for a range of demographic, need, social, and treatment variables, residual variation in costs and outcomes remains at the provider level. The correlation between residual costs and outcomes is negative, but very small, suggesting that cost-containment efforts by providers should not undermine outcome-improving efforts under the new payment system.

Cost-offsets of prescription drug expenditures: data analysis via a copula-based bivariate dynamic hurdle model.

Science.gov (United States)

Deb, Partha; Trivedi, Pravin K; Zimmer, David M

2014-10-01

In this paper, we estimate a copula-based bivariate dynamic hurdle model of prescription drug and nondrug expenditures to test the cost-offset hypothesis, which posits that increased expenditures on prescription drugs are offset by reductions in other nondrug expenditures. We apply the proposed methodology to data from the Medical Expenditure Panel Survey, which have the following features: (i) the observed bivariate outcomes are a mixture of zeros and continuously measured positives; (ii) both the zero and positive outcomes show state dependence and inter-temporal interdependence; and (iii) the zeros and the positives display contemporaneous association. The point mass at zero is accommodated using a hurdle or a two-part approach. The copula-based approach to generating joint distributions is appealing because the contemporaneous association involves asymmetric dependence. The paper studies samples categorized by four health conditions: arthritis, diabetes, heart disease, and mental illness. There is evidence of greater than dollar-for-dollar cost-offsets of expenditures on prescribed drugs for relatively low levels of spending on drugs and less than dollar-for-dollar cost-offsets at higher levels of drug expenditures. Copyright © 2013 John Wiley & Sons, Ltd.

GIS-Based bivariate statistical techniques for groundwater potential ...

Indian Academy of Sciences (India)

24

This study shows the potency of two GIS-based data driven bivariate techniques namely ... In the view of these weaknesses , there is a strong requirement for reassessment of .... Font color: Text 1, Not Expanded by / Condensed by , ...... West Bengal (India) using remote sensing, geographical information system and multi-.

A Bivariate Generalized Linear Item Response Theory Modeling Framework to the Analysis of Responses and Response Times.

Science.gov (United States)

Molenaar, Dylan; Tuerlinckx, Francis; van der Maas, Han L J

2015-01-01

A generalized linear modeling framework to the analysis of responses and response times is outlined. In this framework, referred to as bivariate generalized linear item response theory (B-GLIRT), separate generalized linear measurement models are specified for the responses and the response times that are subsequently linked by cross-relations. The cross-relations can take various forms. Here, we focus on cross-relations with a linear or interaction term for ability tests, and cross-relations with a curvilinear term for personality tests. In addition, we discuss how popular existing models from the psychometric literature are special cases in the B-GLIRT framework depending on restrictions in the cross-relation. This allows us to compare existing models conceptually and empirically. We discuss various extensions of the traditional models motivated by practical problems. We also illustrate the applicability of our approach using various real data examples, including data on personality and cognitive ability.

About some properties of bivariate splines with shape parameters

Science.gov (United States)

Caliò, F.; Marchetti, E.

2017-07-01

The paper presents and proves geometrical properties of a particular bivariate function spline, built and algorithmically implemented in previous papers. The properties typical of this family of splines impact the field of computer graphics in particular that of the reverse engineering.

Bivariate Gaussian bridges: directional factorization of diffusion in Brownian bridge models.

Science.gov (United States)

Kranstauber, Bart; Safi, Kamran; Bartumeus, Frederic

2014-01-01

In recent years high resolution animal tracking data has become the standard in movement ecology. The Brownian Bridge Movement Model (BBMM) is a widely adopted approach to describe animal space use from such high resolution tracks. One of the underlying assumptions of the BBMM is isotropic diffusive motion between consecutive locations, i.e. invariant with respect to the direction. Here we propose to relax this often unrealistic assumption by separating the Brownian motion variance into two directional components, one parallel and one orthogonal to the direction of the motion. Our new model, the Bivariate Gaussian bridge (BGB), tracks movement heterogeneity across time. Using the BGB and identifying directed and non-directed movement within a trajectory resulted in more accurate utilisation distributions compared to dynamic Brownian bridges, especially for trajectories with a non-isotropic diffusion, such as directed movement or Lévy like movements. We evaluated our model with simulated trajectories and observed tracks, demonstrating that the improvement of our model scales with the directional correlation of a correlated random walk. We find that many of the animal trajectories do not adhere to the assumptions of the BBMM. The proposed model improves accuracy when describing the space use both in simulated correlated random walks as well as observed animal tracks. Our novel approach is implemented and available within the "move" package for R.

Sexual Harassment Retaliation Climate DEOCS 4.1 Construct Validity Summary

Science.gov (United States)

2017-08-01

Correlations Bivariate Pearson correlation among items were examined to identify and reduce questions with overlapping content. Table 5 displays the...exploratory factor analysis, and bivariate correlations (sample 1) 2) To determine the factor structure of the remaining (final) questions via...statistics, reliability analysis, exploratory factor analysis, and bivariate correlations of the prospective Sexual Harassment Retaliation Climate

Optimizing an objective function under a bivariate probability model

NARCIS (Netherlands)

X. Brusset; N.M. Temme (Nico)

2007-01-01

htmlabstractThe motivation of this paper is to obtain an analytical closed form of a quadratic objective function arising from a stochastic decision process with bivariate exponential probability distribution functions that may be dependent. This method is applicable when results need to be

Bivariate generalized Pareto distribution for extreme atmospheric particulate matter

Science.gov (United States)

Amin, Nor Azrita Mohd; Adam, Mohd Bakri; Ibrahim, Noor Akma; Aris, Ahmad Zaharin

2015-02-01

The high particulate matter (PM10) level is the prominent issue causing various impacts to human health and seriously affecting the economics. The asymptotic theory of extreme value is apply for analyzing the relation of extreme PM10 data from two nearby air quality monitoring stations. The series of daily maxima PM10 for Johor Bahru and Pasir Gudang stations are consider for year 2001 to 2010 databases. The 85% and 95% marginal quantile apply to determine the threshold values and hence construct the series of exceedances over the chosen threshold. The logistic, asymmetric logistic, negative logistic and asymmetric negative logistic models areconsidered as the dependence function to the joint distribution of a bivariate observation. Maximum likelihood estimation is employed for parameter estimations. The best fitted model is chosen based on the Akaike Information Criterion and the quantile plots. It is found that the asymmetric logistic model gives the best fitted model for bivariate extreme PM10 data and shows the weak dependence between two stations.

Bivariate return periods of temperature and precipitation explain a large fraction of European crop yields

Science.gov (United States)

Zscheischler, Jakob; Orth, Rene; Seneviratne, Sonia I.

2017-07-01

Crops are vital for human society. Crop yields vary with climate and it is important to understand how climate and crop yields are linked to ensure future food security. Temperature and precipitation are among the key driving factors of crop yield variability. Previous studies have investigated mostly linear relationships between temperature and precipitation and crop yield variability. Other research has highlighted the adverse impacts of climate extremes, such as drought and heat waves, on crop yields. Impacts are, however, often non-linearly related to multivariate climate conditions. Here we derive bivariate return periods of climate conditions as indicators for climate variability along different temperature-precipitation gradients. We show that in Europe, linear models based on bivariate return periods of specific climate conditions explain on average significantly more crop yield variability (42 %) than models relying directly on temperature and precipitation as predictors (36 %). Our results demonstrate that most often crop yields increase along a gradient from hot and dry to cold and wet conditions, with lower yields associated with hot and dry periods. The majority of crops are most sensitive to climate conditions in summer and to maximum temperatures. The use of bivariate return periods allows the integration of non-linear impacts into climate-crop yield analysis. This offers new avenues to study the link between climate and crop yield variability and suggests that they are possibly more strongly related than what is inferred from conventional linear models.

Bivariational calculations for radiation transfer in an inhomogeneous participating media

International Nuclear Information System (INIS)

El Wakil, S.A.; Machali, H.M.; Haggag, M.H.; Attia, M.T.

1986-07-01

Equations for radiation transfer are obtained for dispersive media with space dependent albedo. Bivariational bound principle is used to calculate the reflection and transmission coefficients for such media. Numerical results are given and compared. (author)

Spectral density regression for bivariate extremes

KAUST Repository

Castro Camilo, Daniela

2016-05-11

We introduce a density regression model for the spectral density of a bivariate extreme value distribution, that allows us to assess how extremal dependence can change over a covariate. Inference is performed through a double kernel estimator, which can be seen as an extension of the Nadaraya–Watson estimator where the usual scalar responses are replaced by mean constrained densities on the unit interval. Numerical experiments with the methods illustrate their resilience in a variety of contexts of practical interest. An extreme temperature dataset is used to illustrate our methods. © 2016 Springer-Verlag Berlin Heidelberg

The use of bivariate spatial modeling of questionnaire and parasitology data to predict the distribution of Schistosoma haematobium in Coastal Kenya.

Directory of Open Access Journals (Sweden)

Hugh J W Sturrock

Full Text Available Questionnaires of reported blood in urine (BIU distributed through the existing school system provide a rapid and reliable method to classify schools according to the prevalence of Schistosoma haematobium, thereby helping in the targeting of schistosomiasis control. However, not all schools return questionnaires and it is unclear whether treatment is warranted in such schools. This study investigates the use of bivariate spatial modelling of available and multiple data sources to predict the prevalence of S. haematobium at every school along the Kenyan coast.Data from a questionnaire survey conducted by the Kenya Ministry of Education in Coast Province in 2009 were combined with available parasitological and environmental data in a Bayesian bivariate spatial model. This modeled the relationship between BIU data and environmental covariates, as well as the relationship between BIU and S. haematobium infection prevalence, to predict S. haematobium infection prevalence at all schools in the study region. Validation procedures were implemented to assess the predictive accuracy of endemicity classification.The prevalence of BIU was negatively correlated with distance to nearest river and there was considerable residual spatial correlation at small (~15 km spatial scales. There was a predictable relationship between the prevalence of reported BIU and S. haematobium infection. The final model exhibited excellent sensitivity (0.94 but moderate specificity (0.69 in identifying low (<10% prevalence schools, and had poor performance in differentiating between moderate and high prevalence schools (sensitivity 0.5, specificity 1.Schistosomiasis is highly focal and there is a need to target treatment on a school-by-school basis. The use of bivariate spatial modelling can supplement questionnaire data to identify schools requiring mass treatment, but is unable to distinguish between moderate and high prevalence schools.

Bivariate Rayleigh Distribution and its Properties

Directory of Open Access Journals (Sweden)

Ahmad Saeed Akhter

2007-01-01

Full Text Available Rayleigh (1880 observed that the sea waves follow no law because of the complexities of the sea, but it has been seen that the probability distributions of wave heights, wave length, wave induce pitch, wave and heave motions of the ships follow the Rayleigh distribution. At present, several different quantities are in use for describing the state of the sea; for example, the mean height of the waves, the root mean square height, the height of the “significant waves” (the mean height of the highest one-third of all the waves the maximum height over a given interval of the time, and so on. At present, the ship building industry knows less than any other construction industry about the service conditions under which it must operate. Only small efforts have been made to establish the stresses and motions and to incorporate the result of such studies in to design. This is due to the complexity of the problem caused by the extensive variability of the sea and the corresponding response of the ships. Although the problem appears feasible, yet it is possible to predict service conditions for ships in an orderly and relatively simple manner Rayleigh (1980 derived it from the amplitude of sound resulting from many independent sources. This distribution is also connected with one or two dimensions and is sometimes referred to as “random walk” frequency distribution. The Rayleigh distribution can be derived from the bivariate normal distribution when the variate are independent and random with equal variances. We try to construct bivariate Rayleigh distribution with marginal Rayleigh distribution function and discuss its fundamental properties.

Bivariate return periods of temperature and precipitation explain a large fraction of European crop yields

Directory of Open Access Journals (Sweden)

J. Zscheischler

2017-07-01

Full Text Available Crops are vital for human society. Crop yields vary with climate and it is important to understand how climate and crop yields are linked to ensure future food security. Temperature and precipitation are among the key driving factors of crop yield variability. Previous studies have investigated mostly linear relationships between temperature and precipitation and crop yield variability. Other research has highlighted the adverse impacts of climate extremes, such as drought and heat waves, on crop yields. Impacts are, however, often non-linearly related to multivariate climate conditions. Here we derive bivariate return periods of climate conditions as indicators for climate variability along different temperature–precipitation gradients. We show that in Europe, linear models based on bivariate return periods of specific climate conditions explain on average significantly more crop yield variability (42 % than models relying directly on temperature and precipitation as predictors (36 %. Our results demonstrate that most often crop yields increase along a gradient from hot and dry to cold and wet conditions, with lower yields associated with hot and dry periods. The majority of crops are most sensitive to climate conditions in summer and to maximum temperatures. The use of bivariate return periods allows the integration of non-linear impacts into climate–crop yield analysis. This offers new avenues to study the link between climate and crop yield variability and suggests that they are possibly more strongly related than what is inferred from conventional linear models.

Classification of Knee Joint Vibration Signals Using Bivariate Feature Distribution Estimation and Maximal Posterior Probability Decision Criterion

Directory of Open Access Journals (Sweden)

Fang Zheng

2013-04-01

Full Text Available Analysis of knee joint vibration or vibroarthrographic (VAG signals using signal processing and machine learning algorithms possesses high potential for the noninvasive detection of articular cartilage degeneration, which may reduce unnecessary exploratory surgery. Feature representation of knee joint VAG signals helps characterize the pathological condition of degenerative articular cartilages in the knee. This paper used the kernel-based probability density estimation method to model the distributions of the VAG signals recorded from healthy subjects and patients with knee joint disorders. The estimated densities of the VAG signals showed explicit distributions of the normal and abnormal signal groups, along with the corresponding contours in the bivariate feature space. The signal classifications were performed by using the Fisher’s linear discriminant analysis, support vector machine with polynomial kernels, and the maximal posterior probability decision criterion. The maximal posterior probability decision criterion was able to provide the total classification accuracy of 86.67% and the area (Az of 0.9096 under the receiver operating characteristics curve, which were superior to the results obtained by either the Fisher’s linear discriminant analysis (accuracy: 81.33%, Az: 0.8564 or the support vector machine with polynomial kernels (accuracy: 81.33%, Az: 0.8533. Such results demonstrated the merits of the bivariate feature distribution estimation and the superiority of the maximal posterior probability decision criterion for analysis of knee joint VAG signals.

Semi-automated detection of aberrant chromosomes in bivariate flow karyotypes

NARCIS (Netherlands)

Boschman, G. A.; Manders, E. M.; Rens, W.; Slater, R.; Aten, J. A.

1992-01-01

A method is described that is designed to compare, in a standardized procedure, bivariate flow karyotypes of Hoechst 33258 (HO)/Chromomycin A3 (CA) stained human chromosomes from cells with aberrations with a reference flow karyotype of normal chromosomes. In addition to uniform normalization of

Visualization of synchronization of the uterine contraction signals: running cross-correlation and wavelet running cross-correlation methods.

Science.gov (United States)

Oczeretko, Edward; Swiatecka, Jolanta; Kitlas, Agnieszka; Laudanski, Tadeusz; Pierzynski, Piotr

2006-01-01

In physiological research, we often study multivariate data sets, containing two or more simultaneously recorded time series. The aim of this paper is to present the cross-correlation and the wavelet cross-correlation methods to assess synchronization between contractions in different topographic regions of the uterus. From a medical point of view, it is important to identify time delays between contractions, which may be of potential diagnostic significance in various pathologies. The cross-correlation was computed in a moving window with a width corresponding to approximately two or three contractions. As a result, the running cross-correlation function was obtained. The propagation% parameter assessed from this function allows quantitative description of synchronization in bivariate time series. In general, the uterine contraction signals are very complicated. Wavelet transforms provide insight into the structure of the time series at various frequencies (scales). To show the changes of the propagation% parameter along scales, a wavelet running cross-correlation was used. At first, the continuous wavelet transforms as the uterine contraction signals were received and afterwards, a running cross-correlation analysis was conducted for each pair of transformed time series. The findings show that running functions are very useful in the analysis of uterine contractions.

Statistical power analyses using G*Power 3.1: tests for correlation and regression analyses.

Science.gov (United States)

Faul, Franz; Erdfelder, Edgar; Buchner, Axel; Lang, Albert-Georg

2009-11-01

G*Power is a free power analysis program for a variety of statistical tests. We present extensions and improvements of the version introduced by Faul, Erdfelder, Lang, and Buchner (2007) in the domain of correlation and regression analyses. In the new version, we have added procedures to analyze the power of tests based on (1) single-sample tetrachoric correlations, (2) comparisons of dependent correlations, (3) bivariate linear regression, (4) multiple linear regression based on the random predictor model, (5) logistic regression, and (6) Poisson regression. We describe these new features and provide a brief introduction to their scope and handling.

On the matched pairs sign test using bivariate ranked set sampling ...

African Journals Online (AJOL)

BVRSS) is introduced and investigated. We show that this test is asymptotically more efficient than its counterpart sign test based on a bivariate simple random sample (BVSRS). The asymptotic null distribution and the efficiency of the test are derived.

REGRES: A FORTRAN-77 program to calculate nonparametric and ``structural'' parametric solutions to bivariate regression equations

Science.gov (United States)

Rock, N. M. S.; Duffy, T. R.

REGRES allows a range of regression equations to be calculated for paired sets of data values in which both variables are subject to error (i.e. neither is the "independent" variable). Nonparametric regressions, based on medians of all possible pairwise slopes and intercepts, are treated in detail. Estimated slopes and intercepts are output, along with confidence limits, Spearman and Kendall rank correlation coefficients. Outliers can be rejected with user-determined stringency. Parametric regressions can be calculated for any value of λ (the ratio of the variances of the random errors for y and x)—including: (1) major axis ( λ = 1); (2) reduced major axis ( λ = variance of y/variance of x); (3) Y on Xλ = infinity; or (4) X on Y ( λ = 0) solutions. Pearson linear correlation coefficients also are output. REGRES provides an alternative to conventional isochron assessment techniques where bivariate normal errors cannot be assumed, or weighting methods are inappropriate.

Comparison of correlation analysis techniques for irregularly sampled time series

Directory of Open Access Journals (Sweden)

K. Rehfeld

2011-06-01

Full Text Available Geoscientific measurements often provide time series with irregular time sampling, requiring either data reconstruction (interpolation or sophisticated methods to handle irregular sampling. We compare the linear interpolation technique and different approaches for analyzing the correlation functions and persistence of irregularly sampled time series, as Lomb-Scargle Fourier transformation and kernel-based methods. In a thorough benchmark test we investigate the performance of these techniques.

All methods have comparable root mean square errors (RMSEs for low skewness of the inter-observation time distribution. For high skewness, very irregular data, interpolation bias and RMSE increase strongly. We find a 40 % lower RMSE for the lag-1 autocorrelation function (ACF for the Gaussian kernel method vs. the linear interpolation scheme,in the analysis of highly irregular time series. For the cross correlation function (CCF the RMSE is then lower by 60 %. The application of the Lomb-Scargle technique gave results comparable to the kernel methods for the univariate, but poorer results in the bivariate case. Especially the high-frequency components of the signal, where classical methods show a strong bias in ACF and CCF magnitude, are preserved when using the kernel methods.

We illustrate the performances of interpolation vs. Gaussian kernel method by applying both to paleo-data from four locations, reflecting late Holocene Asian monsoon variability as derived from speleothem δ¹⁸O measurements. Cross correlation results are similar for both methods, which we attribute to the long time scales of the common variability. The persistence time (memory is strongly overestimated when using the standard, interpolation-based, approach. Hence, the Gaussian kernel is a reliable and more robust estimator with significant advantages compared to other techniques and suitable for large scale application to paleo-data.

An integrated user-friendly ArcMAP tool for bivariate statistical modeling in geoscience applications

Science.gov (United States)

Jebur, M. N.; Pradhan, B.; Shafri, H. Z. M.; Yusof, Z.; Tehrany, M. S.

2014-10-01

Modeling and classification difficulties are fundamental issues in natural hazard assessment. A geographic information system (GIS) is a domain that requires users to use various tools to perform different types of spatial modeling. Bivariate statistical analysis (BSA) assists in hazard modeling. To perform this analysis, several calculations are required and the user has to transfer data from one format to another. Most researchers perform these calculations manually by using Microsoft Excel or other programs. This process is time consuming and carries a degree of uncertainty. The lack of proper tools to implement BSA in a GIS environment prompted this study. In this paper, a user-friendly tool, BSM (bivariate statistical modeler), for BSA technique is proposed. Three popular BSA techniques such as frequency ratio, weights-of-evidence, and evidential belief function models are applied in the newly proposed ArcMAP tool. This tool is programmed in Python and is created by a simple graphical user interface, which facilitates the improvement of model performance. The proposed tool implements BSA automatically, thus allowing numerous variables to be examined. To validate the capability and accuracy of this program, a pilot test area in Malaysia is selected and all three models are tested by using the proposed program. Area under curve is used to measure the success rate and prediction rate. Results demonstrate that the proposed program executes BSA with reasonable accuracy. The proposed BSA tool can be used in numerous applications, such as natural hazard, mineral potential, hydrological, and other engineering and environmental applications.

An integrated user-friendly ArcMAP tool for bivariate statistical modelling in geoscience applications

Science.gov (United States)

Jebur, M. N.; Pradhan, B.; Shafri, H. Z. M.; Yusoff, Z. M.; Tehrany, M. S.

2015-03-01

Modelling and classification difficulties are fundamental issues in natural hazard assessment. A geographic information system (GIS) is a domain that requires users to use various tools to perform different types of spatial modelling. Bivariate statistical analysis (BSA) assists in hazard modelling. To perform this analysis, several calculations are required and the user has to transfer data from one format to another. Most researchers perform these calculations manually by using Microsoft Excel or other programs. This process is time-consuming and carries a degree of uncertainty. The lack of proper tools to implement BSA in a GIS environment prompted this study. In this paper, a user-friendly tool, bivariate statistical modeler (BSM), for BSA technique is proposed. Three popular BSA techniques, such as frequency ratio, weight-of-evidence (WoE), and evidential belief function (EBF) models, are applied in the newly proposed ArcMAP tool. This tool is programmed in Python and created by a simple graphical user interface (GUI), which facilitates the improvement of model performance. The proposed tool implements BSA automatically, thus allowing numerous variables to be examined. To validate the capability and accuracy of this program, a pilot test area in Malaysia is selected and all three models are tested by using the proposed program. Area under curve (AUC) is used to measure the success rate and prediction rate. Results demonstrate that the proposed program executes BSA with reasonable accuracy. The proposed BSA tool can be used in numerous applications, such as natural hazard, mineral potential, hydrological, and other engineering and environmental applications.

Testing independence of bivariate interval-censored data using modified Kendall's tau statistic.

Science.gov (United States)

Kim, Yuneung; Lim, Johan; Park, DoHwan

2015-11-01

In this paper, we study a nonparametric procedure to test independence of bivariate interval censored data; for both current status data (case 1 interval-censored data) and case 2 interval-censored data. To do it, we propose a score-based modification of the Kendall's tau statistic for bivariate interval-censored data. Our modification defines the Kendall's tau statistic with expected numbers of concordant and disconcordant pairs of data. The performance of the modified approach is illustrated by simulation studies and application to the AIDS study. We compare our method to alternative approaches such as the two-stage estimation method by Sun et al. (Scandinavian Journal of Statistics, 2006) and the multiple imputation method by Betensky and Finkelstein (Statistics in Medicine, 1999b). © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Financialization, Crisis and Commodity Correlation Dynamics

OpenAIRE

Annastiina Silvennoinen; Susan Thorp

2010-01-01

We study bi-variate conditional volatility and correlation dynamics for individual commodity futures and financial assets from May 1990-July 2009 using DSTCC-GARCH (Silvennoinen and Terasvirta 2009). These models allow correlation to vary smoothly between extreme states via transition functions driven by indicators of market conditions. Expected stock volatility and money manager open interest in futures markets are relevant transition variables. Results point to increasing integration betwee...

Estimation of rank correlation for clustered data.

Science.gov (United States)

Rosner, Bernard; Glynn, Robert J

2017-06-30

It is well known that the sample correlation coefficient (R xy ) is the maximum likelihood estimator of the Pearson correlation (ρ xy ) for independent and identically distributed (i.i.d.) bivariate normal data. However, this is not true for ophthalmologic data where X (e.g., visual acuity) and Y (e.g., visual field) are available for each eye and there is positive intraclass correlation for both X and Y in fellow eyes. In this paper, we provide a regression-based approach for obtaining the maximum likelihood estimator of ρ xy for clustered data, which can be implemented using standard mixed effects model software. This method is also extended to allow for estimation of partial correlation by controlling both X and Y for a vector U_ of other covariates. In addition, these methods can be extended to allow for estimation of rank correlation for clustered data by (i) converting ranks of both X and Y to the probit scale, (ii) estimating the Pearson correlation between probit scores for X and Y, and (iii) using the relationship between Pearson and rank correlation for bivariate normally distributed data. The validity of the methods in finite-sized samples is supported by simulation studies. Finally, two examples from ophthalmology and analgesic abuse are used to illustrate the methods. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

Robust bivariate error detection in skewed data with application to historical radiosonde winds

KAUST Repository

Sun, Ying

2017-01-18

The global historical radiosonde archives date back to the 1920s and contain the only directly observed measurements of temperature, wind, and moisture in the upper atmosphere, but they contain many random errors. Most of the focus on cleaning these large datasets has been on temperatures, but winds are important inputs to climate models and in studies of wind climatology. The bivariate distribution of the wind vector does not have elliptical contours but is skewed and heavy-tailed, so we develop two methods for outlier detection based on the bivariate skew-t (BST) distribution, using either distance-based or contour-based approaches to flag observations as potential outliers. We develop a framework to robustly estimate the parameters of the BST and then show how the tuning parameter to get these estimates is chosen. In simulation, we compare our methods with one based on a bivariate normal distribution and a nonparametric approach based on the bagplot. We then apply all four methods to the winds observed for over 35,000 radiosonde launches at a single station and demonstrate differences in the number of observations flagged across eight pressure levels and through time. In this pilot study, the method based on the BST contours performs very well.

Robust bivariate error detection in skewed data with application to historical radiosonde winds

KAUST Repository

Sun, Ying; Hering, Amanda S.; Browning, Joshua M.

2017-01-01

The global historical radiosonde archives date back to the 1920s and contain the only directly observed measurements of temperature, wind, and moisture in the upper atmosphere, but they contain many random errors. Most of the focus on cleaning these large datasets has been on temperatures, but winds are important inputs to climate models and in studies of wind climatology. The bivariate distribution of the wind vector does not have elliptical contours but is skewed and heavy-tailed, so we develop two methods for outlier detection based on the bivariate skew-t (BST) distribution, using either distance-based or contour-based approaches to flag observations as potential outliers. We develop a framework to robustly estimate the parameters of the BST and then show how the tuning parameter to get these estimates is chosen. In simulation, we compare our methods with one based on a bivariate normal distribution and a nonparametric approach based on the bagplot. We then apply all four methods to the winds observed for over 35,000 radiosonde launches at a single station and demonstrate differences in the number of observations flagged across eight pressure levels and through time. In this pilot study, the method based on the BST contours performs very well.

Bivariate tensor product ( p , q $(p, q$ -analogue of Kantorovich-type Bernstein-Stancu-Schurer operators

Directory of Open Access Journals (Sweden)

Qing-Bo Cai

2017-11-01

Full Text Available Abstract In this paper, we construct a bivariate tensor product generalization of Kantorovich-type Bernstein-Stancu-Schurer operators based on the concept of ( p , q $(p, q$ -integers. We obtain moments and central moments of these operators, give the rate of convergence by using the complete modulus of continuity for the bivariate case and estimate a convergence theorem for the Lipschitz continuous functions. We also give some graphs and numerical examples to illustrate the convergence properties of these operators to certain functions.

Assessing the Goodness of Fit of Phylogenetic Comparative Methods: A Meta-Analysis and Simulation Study.

Directory of Open Access Journals (Sweden)

Dwueng-Chwuan Jhwueng

Full Text Available Phylogenetic comparative methods (PCMs have been applied widely in analyzing data from related species but their fit to data is rarely assessed.Can one determine whether any particular comparative method is typically more appropriate than others by examining comparative data sets?I conducted a meta-analysis of 122 phylogenetic data sets found by searching all papers in JEB, Blackwell Synergy and JSTOR published in 2002-2005 for the purpose of assessing the fit of PCMs. The number of species in these data sets ranged from 9 to 117.I used the Akaike information criterion to compare PCMs, and then fit PCMs to bivariate data sets through REML analysis. Correlation estimates between two traits and bootstrapped confidence intervals of correlations from each model were also compared.For phylogenies of less than one hundred taxa, the Independent Contrast method and the independent, non-phylogenetic models provide the best fit.For bivariate analysis, correlations from different PCMs are qualitatively similar so that actual correlations from real data seem to be robust to the PCM chosen for the analysis. Therefore, researchers might apply the PCM they believe best describes the evolutionary mechanisms underlying their data.

Quasi-bivariate variational mode decomposition as a tool of scale analysis in wall-bounded turbulence

Science.gov (United States)

Wang, Wenkang; Pan, Chong; Wang, Jinjun

2018-01-01

The identification and separation of multi-scale coherent structures is a critical task for the study of scale interaction in wall-bounded turbulence. Here, we propose a quasi-bivariate variational mode decomposition (QB-VMD) method to extract structures with various scales from instantaneous two-dimensional (2D) velocity field which has only one primary dimension. This method is developed from the one-dimensional VMD algorithm proposed by Dragomiretskiy and Zosso (IEEE Trans Signal Process 62:531-544, 2014) to cope with a quasi-2D scenario. It poses the feature of length-scale bandwidth constraint along the decomposed dimension, together with the central frequency re-balancing along the non-decomposed dimension. The feasibility of this method is tested on both a synthetic flow field and a turbulent boundary layer at moderate Reynolds number (Re_{τ } = 3458) measured by 2D particle image velocimetry (PIV). Some other popular scale separation tools, including pseudo-bi-dimensional empirical mode decomposition (PB-EMD), bi-dimensional EMD (B-EMD) and proper orthogonal decomposition (POD), are also tested for comparison. Among all these methods, QB-VMD shows advantages in both scale characterization and energy recovery. More importantly, the mode mixing problem, which degrades the performance of EMD-based methods, is avoided or minimized in QB-VMD. Finally, QB-VMD analysis of the wall-parallel plane in the log layer (at y/δ = 0.12) of the studied turbulent boundary layer shows the coexistence of large- or very large-scale motions (LSMs or VLSMs) and inner-scaled structures, which can be fully decomposed in both physical and spectral domains.

Genetic and Environmental Basis in Phenotype Correlation Between Physical Function and Cognition in Aging Chinese Twins

DEFF Research Database (Denmark)

Xu, Chunsheng; Zhang, Dongfeng; Tian, Xiaocao

2017-01-01

for cognition with handgrip strength, FTSST, near visual acuity, and number of teeth lost. Cognitive function was genetically related to pulmonary function. The FTSST and cognition shared almost the same common environmental factors but only part of the unique environmental factors, both with negative......Although the correlation between cognition and physical function has been well studied in the general population, the genetic and environmental nature of the correlation has been rarely investigated. We conducted a classical twin analysis on cognitive and physical function, including forced...... and cognitive function. Bivariate analysis showed mildly positively genetic correlations between cognition and FEV1, r G = 0.23 [95% CI: 0.03, 0.62], as well as FVC, r G = 0.35 [95% CI: 0.06, 1.00]. We found that FTSST and cognition presented very high common environmental correlation, r C = -1.00 [95% CI: -1...

application of multilinear regression analysis in modeling of soil

African Journals Online (AJOL)

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Accordingly [1, 3] in their work, they applied linear regression ... (MLRA) is a statistical technique that uses several explanatory ... order to check this, they adopted bivariate correlation analysis .... groups, namely A-1 through A-7, based on their relative expected ..... Multivariate Regression in Gorgan Province North of Iran” ...

On minimum divergence adaptation of discrete bivariate distributions to given marginals

Czech Academy of Sciences Publication Activity Database

Vajda, Igor; van der Meulen, E. C.

2005-01-01

Roč. 51, č. 1 (2005), s. 313-320 ISSN 0018-9448 R&D Projects: GA ČR GA201/02/1391; GA MŠk 1M0572 Institutional research plan: CEZ:AV0Z10750506 Keywords : approximation of contingency tables * bivariate discrete distributions * minimization of divergences Subject RIV: BD - Theory of Information Impact factor: 2.183, year: 2005

Contributory fault and level of personal injury to drivers involved in head-on collisions: Application of copula-based bivariate ordinal models.

Science.gov (United States)

Wali, Behram; Khattak, Asad J; Xu, Jingjing

2018-01-01

The main objective of this study is to simultaneously investigate the degree of injury severity sustained by drivers involved in head-on collisions with respect to fault status designation. This is complicated to answer due to many issues, one of which is the potential presence of correlation between injury outcomes of drivers involved in the same head-on collision. To address this concern, we present seemingly unrelated bivariate ordered response models by analyzing the joint injury severity probability distribution of at-fault and not-at-fault drivers. Moreover, the assumption of bivariate normality of residuals and the linear form of stochastic dependence implied by such models may be unduly restrictive. To test this, Archimedean copula structures and normal mixture marginals are integrated into the joint estimation framework, which can characterize complex forms of stochastic dependencies and non-normality in residual terms. The models are estimated using 2013 Virginia police reported two-vehicle head-on collision data, where exactly one driver is at-fault. The results suggest that both at-fault and not-at-fault drivers sustained serious/fatal injuries in 8% of crashes, whereas, in 4% of the cases, the not-at-fault driver sustained a serious/fatal injury with no injury to the at-fault driver at all. Furthermore, if the at-fault driver is fatigued, apparently asleep, or has been drinking the not-at-fault driver is more likely to sustain a severe/fatal injury, controlling for other factors and potential correlations between the injury outcomes. While not-at-fault vehicle speed affects injury severity of at-fault driver, the effect is smaller than the effect of at-fault vehicle speed on at-fault injury outcome. Contrarily, and importantly, the effect of at-fault vehicle speed on injury severity of not-at-fault driver is almost equal to the effect of not-at-fault vehicle speed on injury outcome of not-at-fault driver. Compared to traditional ordered probability

Non-Normality and Testing that a Correlation Equals Zero

Science.gov (United States)

Levy, Kenneth J.

1977-01-01

The importance of the assumption of normality for testing that a bivariate normal correlation equals zero is examined. Both empirical and theoretical evidence suggest that such tests are robust with respect to violation of the normality assumption. (Author/JKS)

A bivariate space-time downscaler under space and time misalignment.

Science.gov (United States)

Berrocal, Veronica J; Gelfand, Alan E; Holland, David M

2010-12-01

Ozone and particulate matter PM(2.5) are co-pollutants that have long been associated with increased public health risks. Information on concentration levels for both pollutants come from two sources: monitoring sites and output from complex numerical models that produce concentration surfaces over large spatial regions. In this paper, we offer a fully-model based approach for fusing these two sources of information for the pair of co-pollutants which is computationally feasible over large spatial regions and long periods of time. Due to the association between concentration levels of the two environmental contaminants, it is expected that information regarding one will help to improve prediction of the other. Misalignment is an obvious issue since the monitoring networks for the two contaminants only partly intersect and because the collection rate for PM(2.5) is typically less frequent than that for ozone.Extending previous work in Berrocal et al. (2009), we introduce a bivariate downscaler that provides a flexible class of bivariate space-time assimilation models. We discuss computational issues for model fitting and analyze a dataset for ozone and PM(2.5) for the ozone season during year 2002. We show a modest improvement in predictive performance, not surprising in a setting where we can anticipate only a small gain.

The bivariate probit model of uncomplicated control of tumor: a heuristic exposition of the methodology

International Nuclear Information System (INIS)

Herbert, Donald

1997-01-01

Purpose: To describe the concept, models, and methods for the construction of estimates of joint probability of uncomplicated control of tumors in radiation oncology. Interpolations using this model can lead to the identification of more efficient treatment regimens for an individual patient. The requirement to find the treatment regimen that will maximize the joint probability of uncomplicated control of tumors suggests a new class of evolutionary experimental designs--Response Surface Methods--for clinical trials in radiation oncology. Methods and Materials: The software developed by Lesaffre and Molenberghs is used to construct bivariate probit models of the joint probability of uncomplicated control of cancer of the oropharynx from a set of 45 patients for each of whom the presence/absence of recurrent tumor (the binary event E-bar 1 /E 1 ) and the presence/absence of necrosis (the binary event E 2 /E-bar 2 ) of the normal tissues of the target volume is recorded, together with the treatment variables dose, time, and fractionation. Results: The bivariate probit model can be used to select a treatment regime that will give a specified probability, say P(S) = 0.60, of uncomplicated control of tumor by interpolation within a set of treatment regimes with known outcomes of recurrence and necrosis. The bivariate probit model can be used to guide a sequence of clinical trials to find the maximum probability of uncomplicated control of tumor for patients in a given prognostic stratum using Response Surface methods by extrapolation from an initial set of treatment regimens. Conclusions: The design of treatments for individual patients and the design of clinical trials might be improved by use of a bivariate probit model and Response Surface Methods

A bivariate measurement error model for semicontinuous and continuous variables: Application to nutritional epidemiology.

Science.gov (United States)

Kipnis, Victor; Freedman, Laurence S; Carroll, Raymond J; Midthune, Douglas

2016-03-01

Semicontinuous data in the form of a mixture of a large portion of zero values and continuously distributed positive values frequently arise in many areas of biostatistics. This article is motivated by the analysis of relationships between disease outcomes and intakes of episodically consumed dietary components. An important aspect of studies in nutritional epidemiology is that true diet is unobservable and commonly evaluated by food frequency questionnaires with substantial measurement error. Following the regression calibration approach for measurement error correction, unknown individual intakes in the risk model are replaced by their conditional expectations given mismeasured intakes and other model covariates. Those regression calibration predictors are estimated using short-term unbiased reference measurements in a calibration substudy. Since dietary intakes are often "energy-adjusted," e.g., by using ratios of the intake of interest to total energy intake, the correct estimation of the regression calibration predictor for each energy-adjusted episodically consumed dietary component requires modeling short-term reference measurements of the component (a semicontinuous variable), and energy (a continuous variable) simultaneously in a bivariate model. In this article, we develop such a bivariate model, together with its application to regression calibration. We illustrate the new methodology using data from the NIH-AARP Diet and Health Study (Schatzkin et al., 2001, American Journal of Epidemiology 154, 1119-1125), and also evaluate its performance in a simulation study. © 2015, The International Biometric Society.

Correlation between presumed sinusitis-induced pain and paranasal sinus computed tomographic findings.

Science.gov (United States)

Mudgil, Shikha P; Wise, Scott W; Hopper, Kenneth D; Kasales, Claudia J; Mauger, David; Fornadley, John A

2002-02-01

The correlation between facial and/or head pain in patients clinically suspected of having sinusitis and actual localized findings on sinus computed tomographic (CT) imaging are poorly understood. To prospectively evaluate the relationship of paranasal sinus pain symptoms with CT imaging. Two hundred consecutive patients referred by otolaryngologists and internists for CT of the paranasal sinuses participated by completing a questionnaire immediately before undergoing CT. Three radiologists blinded to the patients' responses scored the degree of air/fluid level, mucosal thickening, bony reaction, and mucus retention cysts using a graded scale of severity (0 to 3 points). The osteomeatal complexes and nasolacrimal ducts were also evaluated for patency. Bivariate analysis was performed to evaluate the relationship between patients' localized symptoms and CT findings in the respective sinus. One hundred sixty-three patients (82%) reported having some form of facial pain or headache. The right temple/forehead was the most frequently reported region of maximal pain. On CT imaging the maxillary sinus was the most frequently involved sinus. Bivariate analysis failed to show any relationship between patient symptoms and findings on CT. Patients with a normal CT reported a mean 5.88 sites of facial or head pain versus 5.45 sites for patients with an abnormal CT. Patient-based responses of sinonasal pain symptoms fail to correlate with findings in the respective sinuses. CT should therefore be reserved for delineating the anatomy and degree of sinus disease before surgical intervention.

Technical note: Towards a continuous classification of climate using bivariate colour mapping

NARCIS (Netherlands)

Teuling, A.J.

2011-01-01

Climate is often defined in terms of discrete classes. Here I use bivariate colour mapping to show that the global distribution of K¨oppen-Geiger climate classes can largely be reproduced by combining the simple means of two key states of the climate system 5 (i.e., air temperature and relative

Childhood overweight and social correlates among school-going ...

African Journals Online (AJOL)

The purpose of this study was to investigate overweight and obesity and social correlates among school children in Dominica and Jamaica. The total sample included 3265 adolescents aged primarily 13 to 16 years from nationally representative samples from two Caribbean countries. Bivariate and multivariable analyses ...

Multiview Bayesian Correlated Component Analysis

DEFF Research Database (Denmark)

Kamronn, Simon Due; Poulsen, Andreas Trier; Hansen, Lars Kai

2015-01-01

are identical. Here we propose a hierarchical probabilistic model that can infer the level of universality in such multiview data, from completely unrelated representations, corresponding to canonical correlation analysis, to identical representations as in correlated component analysis. This new model, which...... we denote Bayesian correlated component analysis, evaluates favorably against three relevant algorithms in simulated data. A well-established benchmark EEG data set is used to further validate the new model and infer the variability of spatial representations across multiple subjects....

Functional Multiple-Set Canonical Correlation Analysis

Science.gov (United States)

Hwang, Heungsun; Jung, Kwanghee; Takane, Yoshio; Woodward, Todd S.

2012-01-01

We propose functional multiple-set canonical correlation analysis for exploring associations among multiple sets of functions. The proposed method includes functional canonical correlation analysis as a special case when only two sets of functions are considered. As in classical multiple-set canonical correlation analysis, computationally, the…

Correlation, regression, and cointegration of nonstationary economic time series

DEFF Research Database (Denmark)

Johansen, Søren

Yule (1926) introduced the concept of spurious or nonsense correlation, and showed by simulation that for some nonstationary processes, that the empirical correlations seem not to converge in probability even if the processes were independent. This was later discussed by Granger and Newbold (1974......), and Phillips (1986) found the limit distributions. We propose to distinguish between empirical and population correlation coeffients and show in a bivariate autoregressive model for nonstationary variables that the empirical correlation and regression coe¢ cients do not converge to the relevant population...

non-genetic factors and correlation studies in cattle.

African Journals Online (AJOL)

Bivariate and multivariate analyses fitting an animal model, were conducted by means of (ASREML) ... aggregate genetic improvement in beef cattle is most ..... This trend was observed in the present study. .... effects hence the prediction of correlated responses to ... the shape of the growth curve as an animal can be.

Interpreting canonical correlation analysis through biplots of stucture correlations and weights

NARCIS (Netherlands)

Braak, ter C.J.F.

1990-01-01

This paper extends the biplot technique to canonical correlation analysis and redundancy analysis. The plot of structure correlations is shown to the optimal for displaying the pairwise correlations between the variables of the one set and those of the second. The link between multivariate

Pearson's Correlation between Three Variables; Using Students' Basic Knowledge of Geometry for an Exercise in Mathematical Statistics

Science.gov (United States)

Vos, Pauline

2009-01-01

When studying correlations, how do the three bivariate correlation coefficients between three variables relate? After transforming Pearson's correlation coefficient r into a Euclidean distance, undergraduate students can tackle this problem using their secondary school knowledge of geometry (Pythagoras' theorem and similarity of triangles).…

New Exact and Asymptotic Results of Dual-Branch MRC over Correlated Nakagami-m Fading Channels

KAUST Repository

Al-Quwaiee, Hessa

2015-05-01

We present in this paper a new performance analysis results of dual-branch maximal-ratio combining over correlated Nakagami-m fading channels with arbitrary fading parameter. In particular, we derive exact closed-form expressions of the outage probability, the average bit error rate, and the ergodic capacity in terms of the extended generalized bivariate Meijer G- function. Moreover, we also provide simple closed- form asymptotic expressions in the high signal-to- noise ratio regime of these three fundamental performance measures. © 2015 IEEE.

New Exact and Asymptotic Results of Dual-Branch MRC over Correlated Nakagami-m Fading Channels

KAUST Repository

Al-Quwaiee, Hessa; Alouini, Mohamed-Slim

2015-01-01

We present in this paper a new performance analysis results of dual-branch maximal-ratio combining over correlated Nakagami-m fading channels with arbitrary fading parameter. In particular, we derive exact closed-form expressions of the outage probability, the average bit error rate, and the ergodic capacity in terms of the extended generalized bivariate Meijer G- function. Moreover, we also provide simple closed- form asymptotic expressions in the high signal-to- noise ratio regime of these three fundamental performance measures. © 2015 IEEE.

Spectral analysis by correlation

International Nuclear Information System (INIS)

Fauque, J.M.; Berthier, D.; Max, J.; Bonnet, G.

1969-01-01

The spectral density of a signal, which represents its power distribution along the frequency axis, is a function which is of great importance, finding many uses in all fields concerned with the processing of the signal (process identification, vibrational analysis, etc...). Amongst all the possible methods for calculating this function, the correlation method (correlation function calculation + Fourier transformation) is the most promising, mainly because of its simplicity and of the results it yields. The study carried out here will lead to the construction of an apparatus which, coupled with a correlator, will constitute a set of equipment for spectral analysis in real time covering the frequency range 0 to 5 MHz. (author) [fr

Two-dimensional multifractal cross-correlation analysis

International Nuclear Information System (INIS)

Xi, Caiping; Zhang, Shuning; Xiong, Gang; Zhao, Huichang; Yang, Yonghong

2017-01-01

Highlights: • We study the mathematical models of 2D-MFXPF, 2D-MFXDFA and 2D-MFXDMA. • Present the definition of the two-dimensional N 2 -partitioned multiplicative cascading process. • Do the comparative analysis of 2D-MC by 2D-MFXPF, 2D-MFXDFA and 2D-MFXDMA. • Provide a reference on the choice and parameter settings of these methods in practice. - Abstract: There are a number of situations in which several signals are simultaneously recorded in complex systems, which exhibit long-term power-law cross-correlations. This paper presents two-dimensional multifractal cross-correlation analysis based on the partition function (2D-MFXPF), two-dimensional multifractal cross-correlation analysis based on the detrended fluctuation analysis (2D-MFXDFA) and two-dimensional multifractal cross-correlation analysis based on the detrended moving average analysis (2D-MFXDMA). We apply these methods to pairs of two-dimensional multiplicative cascades (2D-MC) to do a comparative study. Then, we apply the two-dimensional multifractal cross-correlation analysis based on the detrended fluctuation analysis (2D-MFXDFA) to real images and unveil intriguing multifractality in the cross correlations of the material structures. At last, we give the main conclusions and provide a valuable reference on how to choose the multifractal algorithms in the potential applications in the field of SAR image classification and detection.

Analisis Faktor Permintaan Non Fungsional Kelompok Non-Member Terhadap Permintaan (Seri 2

Directory of Open Access Journals (Sweden)

Iskandar Putong

2011-11-01

Full Text Available Factor analysis of non functional Demand Non-Member On to Demand aimed to discover whether there is significant influence of non-functional variable on demand. Three independent variables (the effect, ie, X1 for Snob Effect (Mode, X2 for the Bandwagon effect (Prestige and X3 for the Veblen Effect (Show-off and 1 dependent variable Y for Demand. The number of respondents of 50 and 22 indicators is statistically valid. Analysis tool using the Model Correlation and simple Regression (Simple/Bivariate, Correlation and Regression Simultaneously (Multiple Regression and Partial Correlation. The results of analysis proved statistically with LOC of 95%, indicating that the bivariate, partial unison and Simultaneously there is positive and significant correlation between variables X to Y, except for variable X2 (Prestige on Bivariate correlation. 

Computational approach to Thornley's problem by bivariate operational calculus

Science.gov (United States)

Bazhlekova, E.; Dimovski, I.

2012-10-01

Thornley's problem is an initial-boundary value problem with a nonlocal boundary condition for linear onedimensional reaction-diffusion equation, used as a mathematical model of spiral phyllotaxis in botany. Applying a bivariate operational calculus we find explicit representation of the solution, containing two convolution products of special solutions and the arbitrary initial and boundary functions. We use a non-classical convolution with respect to the space variable, extending in this way the classical Duhamel principle. The special solutions involved are represented in the form of fast convergent series. Numerical examples are considered to show the application of the present technique and to analyze the character of the solution.

Prevalence and correlates of contraceptive use among female adolescents in Ghana.

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Nyarko, Samuel H

2015-08-19

Adolescence is a critical stage in the life course and evidence suggests that even though contraceptive use has been steadily increasing among women in Ghana over the past years, contraceptive prevalence and determinants among female adolescents is quite lacking. This paper examines the prevalence and correlates of contraceptive use among female adolescents in Ghana. The paper used data from the 2008 Ghana Demographic and Health survey. Bivariate analysis was carried out to determine the contraceptive prevalence among female adolescents while logistic regression analysis was applied to examine the correlates of female adolescent contraceptive use. The study founded that female adolescent contraceptive use was significantly determined by age of adolescent, education, work status, knowledge of ovulatory cycle, visit of health facility and marital status. This has implications for adolescent sexual and reproductive health programmes in Ghana. It is therefore essential to intensify girl child education and strengthen the provision of family planning information and services for female adolescents in the country.

Carbon and oxygen isotopic ratio bi-variate distribution for marble artifacts quarry assignment

International Nuclear Information System (INIS)

Pentia, M.

1995-01-01

Statistical description, by a Gaussian bi-variate probability distribution of 13 C/ 12 C and 18 O/ 16 O isotopic ratios in the ancient marble quarries has been done and the new method for obtaining the confidence level quarry assignment for marble artifacts has been presented. (author) 8 figs., 3 tabs., 4 refs

Genetic and Environmental Influences on Correlations Between Hearing and Cognitive Functions in Middle and Older Chinese Twins

DEFF Research Database (Denmark)

Xu, Chunsheng; Zhang, Dongfeng; Tian, Xiaocao

2017-01-01

, including 0.5 kHz, 1 kHz, 2 kHz, 4 kHz, 8 kHz, and 12.5 kHz, with the lower hearing thresholds indicating better hearing function. Cognitive and hearing functions were measured on 379 complete twin pairs (240 monozygotic and 139 dizygotic pairs) with a median age of 50 years (range: 40-80 years). Bivariate...... twin models were fitted to quantify the genetic and environmental components of the correlations between hearing and cognitive functions. The analysis showed significantly high genetic correlation between 2 kHz of hearing and cognition (r G = -1.00, 95% CI [-1.00, -0.46]) and moderate genetic...

Principal component regression analysis with SPSS.

Science.gov (United States)

Liu, R X; Kuang, J; Gong, Q; Hou, X L

2003-06-01

The paper introduces all indices of multicollinearity diagnoses, the basic principle of principal component regression and determination of 'best' equation method. The paper uses an example to describe how to do principal component regression analysis with SPSS 10.0: including all calculating processes of the principal component regression and all operations of linear regression, factor analysis, descriptives, compute variable and bivariate correlations procedures in SPSS 10.0. The principal component regression analysis can be used to overcome disturbance of the multicollinearity. The simplified, speeded up and accurate statistical effect is reached through the principal component regression analysis with SPSS.

Correlates of male circumcision in Eastern and Southern African countries: establishing a baseline prior to VMMC Scale-up.

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Khai Hoan Tram

Full Text Available BACKGROUND: Despite the importance of male circumcision (MC prevalence to HIV prevention efforts in Eastern and Southern Africa, there has been no systematic analysis on the correlates of male circumcision. This analysis identifies correlates of MC in 12 countries in the region with available data. METHODS: Data from the male questionnaire of DHS surveys collected between 2006-2011 in Ethiopia, Kenya, Lesotho, Malawi, Mozambique, Namibia, Rwanda, Swaziland, Tanzania, Uganda, Zambia, and Zimbabwe were analyzed. The dependent variable was self-reported male circumcision status. Independent variables included age, education, wealth quintile, place of residence, ethnicity, religion and region. Bivariate and multivariate analyses were conducted separately for each country. RESULTS: MC prevalence ranged from 8.2 percent in Swaziland to 92.2 percent in Ethiopia. Bivariate analyses showed a consistent positive association between age (being older and male circumcision. Education, wealth quintile, and place of residence were either not significantly related or differed in the direction of the relationship by country. Multivariate logistic regression showed three variables consistently associated with MC status: age (being older, religion (being Muslim and ethnicity. DISCUSSION: These data were collected prior to the scale-up of voluntary medical male circumcision (VMMC programs in 11 of the 12 countries. As the VMMC scale-up intensifies in countries across Eastern and Southern Africa, the correlates of VMMC are likely to change, with (younger age and education emerging as key correlates of VMMC performed in medical settings. The centuries-long tradition among Muslims to circumcise should continue to favor MC among this group. Non-circumcising ethnicities may become more open to MC if promoted as a health practice for decreasing HIV risk.

Diagnostic value of sTREM-1 in bronchoalveolar lavage fluid in ICU patients with bacterial lung infections: a bivariate meta-analysis.

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Shi, Jia-Xin; Li, Jia-Shu; Hu, Rong; Li, Chun-Hua; Wen, Yan; Zheng, Hong; Zhang, Feng; Li, Qin

2013-01-01

The serum soluble triggering receptor expressed on myeloid cells-1 (sTREM-1) is a useful biomarker in differentiating bacterial infections from others. However, the diagnostic value of sTREM-1 in bronchoalveolar lavage fluid (BALF) in lung infections has not been well established. We performed a meta-analysis to assess the accuracy of sTREM-1 in BALF for diagnosis of bacterial lung infections in intensive care unit (ICU) patients. We searched PUBMED, EMBASE and Web of Knowledge (from January 1966 to October 2012) databases for relevant studies that reported diagnostic accuracy data of BALF sTREM-1 in the diagnosis of bacterial lung infections in ICU patients. Pooled sensitivity, specificity, and positive and negative likelihood ratios were calculated by a bivariate regression analysis. Measures of accuracy and Q point value (Q*) were calculated using summary receiver operating characteristic (SROC) curve. The potential between-studies heterogeneity was explored by subgroup analysis. Nine studies were included in the present meta-analysis. Overall, the prevalence was 50.6%; the sensitivity was 0.87 (95% confidence interval (CI), 0.72-0.95); the specificity was 0.79 (95% CI, 0.56-0.92); the positive likelihood ratio (PLR) was 4.18 (95% CI, 1.78-9.86); the negative likelihood ratio (NLR) was 0.16 (95% CI, 0.07-0.36), and the diagnostic odds ratio (DOR) was 25.60 (95% CI, 7.28-89.93). The area under the SROC curve was 0.91 (95% CI, 0.88-0.93), with a Q* of 0.83. Subgroup analysis showed that the assay method and cutoff value influenced the diagnostic accuracy of sTREM-1. BALF sTREM-1 is a useful biomarker of bacterial lung infections in ICU patients. Further studies are needed to confirm the optimized cutoff value.

Multifractal Cross Wavelet Analysis

Science.gov (United States)

Jiang, Zhi-Qiang; Gao, Xing-Lu; Zhou, Wei-Xing; Stanley, H. Eugene

Complex systems are composed of mutually interacting components and the output values of these components usually exhibit long-range cross-correlations. Using wavelet analysis, we propose a method of characterizing the joint multifractal nature of these long-range cross correlations, a method we call multifractal cross wavelet analysis (MFXWT). We assess the performance of the MFXWT method by performing extensive numerical experiments on the dual binomial measures with multifractal cross correlations and the bivariate fractional Brownian motions (bFBMs) with monofractal cross correlations. For binomial multifractal measures, we find the empirical joint multifractality of MFXWT to be in approximate agreement with the theoretical formula. For bFBMs, MFXWT may provide spurious multifractality because of the wide spanning range of the multifractal spectrum. We also apply the MFXWT method to stock market indices, and in pairs of index returns and volatilities we find an intriguing joint multifractal behavior. The tests on surrogate series also reveal that the cross correlation behavior, particularly the cross correlation with zero lag, is the main origin of cross multifractality.

TETRA-COM: a comprehensive SPSS program for estimating the tetrachoric correlation.

Science.gov (United States)

Lorenzo-Seva, Urbano; Ferrando, Pere J

2012-12-01

We provide an SPSS program that implements descriptive and inferential procedures for estimating tetrachoric correlations. These procedures have two main purposes: (1) bivariate estimation in contingency tables and (2) constructing a correlation matrix to be used as input for factor analysis (in particular, the SPSS FACTOR procedure). In both cases, the program computes accurate point estimates, as well as standard errors and confidence intervals that are correct for any population value. For purpose (1), the program computes the contingency table together with five other measures of association. For purpose (2), the program checks the positive definiteness of the matrix, and if it is found not to be Gramian, performs a nonlinear smoothing procedure at the user's request. The SPSS syntax, a short manual, and data files related to this article are available as supplemental materials from brm.psychonomic-journals.org/content/supplemental.

A COMPARISON OF SOME ROBUST BIVARIATE CONTROL CHARTS FOR INDIVIDUAL OBSERVATIONS

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Moustafa Omar Ahmed Abu - Shawiesh

2014-06-01

Full Text Available This paper proposed and considered some bivariate control charts to monitor individual observations from a statistical process control. Usual control charts which use mean and variance-covariance estimators are sensitive to outliers. We consider the following robust alternatives to the classical Hoteling's T2: T2MedMAD, T2MCD, T2MVE a simulation study has been conducted to compare the performance of these control charts. Two real life data are analyzed to illustrate the application of these robust alternatives.

A new methodology of spatial cross-correlation analysis.

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Chen, Yanguang

2015-01-01

Spatial correlation modeling comprises both spatial autocorrelation and spatial cross-correlation processes. The spatial autocorrelation theory has been well-developed. It is necessary to advance the method of spatial cross-correlation analysis to supplement the autocorrelation analysis. This paper presents a set of models and analytical procedures for spatial cross-correlation analysis. By analogy with Moran's index newly expressed in a spatial quadratic form, a theoretical framework is derived for geographical cross-correlation modeling. First, two sets of spatial cross-correlation coefficients are defined, including a global spatial cross-correlation coefficient and local spatial cross-correlation coefficients. Second, a pair of scatterplots of spatial cross-correlation is proposed, and the plots can be used to visually reveal the causality behind spatial systems. Based on the global cross-correlation coefficient, Pearson's correlation coefficient can be decomposed into two parts: direct correlation (partial correlation) and indirect correlation (spatial cross-correlation). As an example, the methodology is applied to the relationships between China's urbanization and economic development to illustrate how to model spatial cross-correlation phenomena. This study is an introduction to developing the theory of spatial cross-correlation, and future geographical spatial analysis might benefit from these models and indexes.

Bivariate empirical mode decomposition for ECG-based biometric identification with emotional data.

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Ferdinando, Hany; Seppanen, Tapio; Alasaarela, Esko

2017-07-01

Emotions modulate ECG signals such that they might affect ECG-based biometric identification in real life application. It motivated in finding good feature extraction methods where the emotional state of the subjects has minimum impacts. This paper evaluates feature extraction based on bivariate empirical mode decomposition (BEMD) for biometric identification when emotion is considered. Using the ECG signal from the Mahnob-HCI database for affect recognition, the features were statistical distributions of dominant frequency after applying BEMD analysis to ECG signals. The achieved accuracy was 99.5% with high consistency using kNN classifier in 10-fold cross validation to identify 26 subjects when the emotional states of the subjects were ignored. When the emotional states of the subject were considered, the proposed method also delivered high accuracy, around 99.4%. We concluded that the proposed method offers emotion-independent features for ECG-based biometric identification. The proposed method needs more evaluation related to testing with other classifier and variation in ECG signals, e.g. normal ECG vs. ECG with arrhythmias, ECG from various ages, and ECG from other affective databases.

A New Methodology of Spatial Cross-Correlation Analysis

Science.gov (United States)

Chen, Yanguang

2015-01-01

Spatial correlation modeling comprises both spatial autocorrelation and spatial cross-correlation processes. The spatial autocorrelation theory has been well-developed. It is necessary to advance the method of spatial cross-correlation analysis to supplement the autocorrelation analysis. This paper presents a set of models and analytical procedures for spatial cross-correlation analysis. By analogy with Moran’s index newly expressed in a spatial quadratic form, a theoretical framework is derived for geographical cross-correlation modeling. First, two sets of spatial cross-correlation coefficients are defined, including a global spatial cross-correlation coefficient and local spatial cross-correlation coefficients. Second, a pair of scatterplots of spatial cross-correlation is proposed, and the plots can be used to visually reveal the causality behind spatial systems. Based on the global cross-correlation coefficient, Pearson’s correlation coefficient can be decomposed into two parts: direct correlation (partial correlation) and indirect correlation (spatial cross-correlation). As an example, the methodology is applied to the relationships between China’s urbanization and economic development to illustrate how to model spatial cross-correlation phenomena. This study is an introduction to developing the theory of spatial cross-correlation, and future geographical spatial analysis might benefit from these models and indexes. PMID:25993120

Generalized canonical correlation analysis with missing values

NARCIS (Netherlands)

M. van de Velden (Michel); Y. Takane

2012-01-01

textabstractGeneralized canonical correlation analysis is a versatile technique that allows the joint analysis of several sets of data matrices. The generalized canonical correlation analysis solution can be obtained through an eigenequation and distributional assumptions are not required. When

A Correlational Study of iPad Efficacy and 21st Century Teaching among Elementary School Teachers

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Shultz, Christopher F.

2017-01-01

This study was a longitudinal correlational study that applied the quantitative methodology of bi-variate correlation as well as a paired-samples "t"-test to the data. The purpose was to study the nature of the relationship between iPad efficacy, 21st century teaching, teacher efficacy, and teacher characteristics. The study also…

A non-parametric conditional bivariate reference region with an application to height/weight measurements on normal girls

DEFF Research Database (Denmark)

Petersen, Jørgen Holm

2009-01-01

A conceptually simple two-dimensional conditional reference curve is described. The curve gives a decision basis for determining whether a bivariate response from an individual is "normal" or "abnormal" when taking into account that a third (conditioning) variable may influence the bivariate...... response. The reference curve is not only characterized analytically but also by geometric properties that are easily communicated to medical doctors - the users of such curves. The reference curve estimator is completely non-parametric, so no distributional assumptions are needed about the two......-dimensional response. An example that will serve to motivate and illustrate the reference is the study of the height/weight distribution of 7-8-year-old Danish school girls born in 1930, 1950, or 1970....

Intermittency analysis of correlated data

International Nuclear Information System (INIS)

Wosiek, B.

1992-01-01

We describe the method of the analysis of the dependence of the factorial moments on the bin size in which the correlations between the moments computed for different bin sizes are taken into account. For large multiplicity nucleus-nucleus data inclusion of the correlations does not change the values of the slope parameter, but gives errors significantly reduced as compared to the case of fits with no correlations. (author)

Improving runoff risk estimates: Formulating runoff as a bivariate process using the SCS curve number method

Science.gov (United States)

Shaw, Stephen B.; Walter, M. Todd

2009-03-01

The Soil Conservation Service curve number (SCS-CN) method is widely used to predict storm runoff for hydraulic design purposes, such as sizing culverts and detention basins. As traditionally used, the probability of calculated runoff is equated to the probability of the causative rainfall event, an assumption that fails to account for the influence of variations in soil moisture on runoff generation. We propose a modification to the SCS-CN method that explicitly incorporates rainfall return periods and the frequency of different soil moisture states to quantify storm runoff risks. Soil moisture status is assumed to be correlated to stream base flow. Fundamentally, this approach treats runoff as the outcome of a bivariate process instead of dictating a 1:1 relationship between causative rainfall and resulting runoff volumes. Using data from the Fall Creek watershed in western New York and the headwaters of the French Broad River in the mountains of North Carolina, we show that our modified SCS-CN method improves frequency discharge predictions in medium-sized watersheds in the eastern United States in comparison to the traditional application of the method.

Correlation Between TNF- α and Degree of Gastritis

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Ricky Rivalino

2018-04-01

Full Text Available Background: TNF- α is a cytokine that plays an active role in the pathogenesis of gastritis. The correlation of TNF-α levels in the gastric mucosa with the severity of gastritis has long been known. However, few studies have assessed TNF-α levels in serum of gastritis patients. This study aims to evaluate correlation between serum TNF-α level with the degree of gastritis based on histopathology. Method: A cross sectional study on eighty gastritis patients that fulfilled the inclusion criteria underwent serum TNF-α examination, endoscopy, and biopsy. Rapid urease test was used for diagnosis of H. pylori infection. The severity of gastritis based on lymphocyte infiltration, neutrophil infiltration, atrophy, and intestinal metaplasia according to Updated Sydney System. Univariate and bivariate analysis (Chi-square, fisher's exact, spearman correlation, and independent T-test were done using SPSS version 22. Results: There were 41.25% patients infected with Helicobacter pylori. Serum TNF-α levels in the infected group were significantly higher compared to negative H. pylori (p < 0.05. There was significant positive correlation between serum TNF-α levels and degree of gastritis based on lymphocyte infiltration (r = 0.333; p = 0.003. Conclusion: There was a significant positive correlation between serum TNF-α level with the severity of gastritis based on lymphocyte infiltration.

Comparison of Six Methods for the Detection of Causality in a Bivariate Time Series

Czech Academy of Sciences Publication Activity Database

Krakovská, A.; Jakubík, J.; Chvosteková, M.; Coufal, David; Jajcay, Nikola; Paluš, Milan

2018-01-01

Roč. 97, č. 4 (2018), č. článku 042207. ISSN 2470-0045 R&D Projects: GA MZd(CZ) NV15-33250A Institutional support: RVO:67985807 Keywords : comparative study * causality detection * bivariate models * Granger causality * transfer entropy * convergent cross mappings Impact factor: 2.366, year: 2016 https://journals.aps.org/pre/abstract/10.1103/PhysRevE.97.042207

Bivariate- distribution for transition matrix elements in Breit-Wigner to Gaussian domains of interacting particle systems.

Science.gov (United States)

Kota, V K B; Chavda, N D; Sahu, R

2006-04-01

Interacting many-particle systems with a mean-field one-body part plus a chaos generating random two-body interaction having strength lambda exhibit Poisson to Gaussian orthogonal ensemble and Breit-Wigner (BW) to Gaussian transitions in level fluctuations and strength functions with transition points marked by lambda = lambda c and lambda = lambda F, respectively; lambda F > lambda c. For these systems a theory for the matrix elements of one-body transition operators is available, as valid in the Gaussian domain, with lambda > lambda F, in terms of orbital occupation numbers, level densities, and an integral involving a bivariate Gaussian in the initial and final energies. Here we show that, using a bivariate-t distribution, the theory extends below from the Gaussian regime to the BW regime up to lambda = lambda c. This is well tested in numerical calculations for 6 spinless fermions in 12 single-particle states.

A Study of Effects of MultiCollinearity in the Multivariable Analysis.

Science.gov (United States)

Yoo, Wonsuk; Mayberry, Robert; Bae, Sejong; Singh, Karan; Peter He, Qinghua; Lillard, James W

2014-10-01

A multivariable analysis is the most popular approach when investigating associations between risk factors and disease. However, efficiency of multivariable analysis highly depends on correlation structure among predictive variables. When the covariates in the model are not independent one another, collinearity/multicollinearity problems arise in the analysis, which leads to biased estimation. This work aims to perform a simulation study with various scenarios of different collinearity structures to investigate the effects of collinearity under various correlation structures amongst predictive and explanatory variables and to compare these results with existing guidelines to decide harmful collinearity. Three correlation scenarios among predictor variables are considered: (1) bivariate collinear structure as the most simple collinearity case, (2) multivariate collinear structure where an explanatory variable is correlated with two other covariates, (3) a more realistic scenario when an independent variable can be expressed by various functions including the other variables.

Bivariate pointing movements on large touch screens: investigating the validity of a refined Fitts' Law.

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Bützler, Jennifer; Vetter, Sebastian; Jochems, Nicole; Schlick, Christopher M

2012-01-01

On the basis of three empirical studies Fitts' Law was refined for bivariate pointing tasks on large touch screens. In the first study different target width parameters were investigated. The second study considered the effect of the motion angle. Based on the results of the two studies a refined model for movement time in human-computer interaction was formulated. A third study, which is described here in detail, concerns the validation of the refined model. For the validation study 20 subjects had to execute a bivariate pointing task on a large touch screen. In the experimental task 250 rectangular target objects were displayed at a randomly chosen position on the screen covering a broad range of ID values (ID= [1.01; 4.88]). Compared to existing refinements of Fitts' Law, the new model shows highest predictive validity. A promising field of application of the model is the ergonomic design and evaluation of project management software. By using the refined model, software designers can calculate a priori the appropriate angular position and the size of buttons, menus or icons.

A hybrid correlation analysis with application to imaging genetics

Science.gov (United States)

Hu, Wenxing; Fang, Jian; Calhoun, Vince D.; Wang, Yu-Ping

2018-03-01

Investigating the association between brain regions and genes continues to be a challenging topic in imaging genetics. Current brain region of interest (ROI)-gene association studies normally reduce data dimension by averaging the value of voxels in each ROI. This averaging may lead to a loss of information due to the existence of functional sub-regions. Pearson correlation is widely used for association analysis. However, it only detects linear correlation whereas nonlinear correlation may exist among ROIs. In this work, we introduced distance correlation to ROI-gene association analysis, which can detect both linear and nonlinear correlations and overcome the limitation of averaging operations by taking advantage of the information at each voxel. Nevertheless, distance correlation usually has a much lower value than Pearson correlation. To address this problem, we proposed a hybrid correlation analysis approach, by applying canonical correlation analysis (CCA) to the distance covariance matrix instead of directly computing distance correlation. Incorporating CCA into distance correlation approach may be more suitable for complex disease study because it can detect highly associated pairs of ROI and gene groups, and may improve the distance correlation level and statistical power. In addition, we developed a novel nonlinear CCA, called distance kernel CCA, which seeks the optimal combination of features with the most significant dependence. This approach was applied to imaging genetic data from the Philadelphia Neurodevelopmental Cohort (PNC). Experiments showed that our hybrid approach produced more consistent results than conventional CCA across resampling and both the correlation and statistical significance were increased compared to distance correlation analysis. Further gene enrichment analysis and region of interest (ROI) analysis confirmed the associations of the identified genes with brain ROIs. Therefore, our approach provides a powerful tool for finding

Selection effects in the bivariate brightness distribution for spiral galaxies

International Nuclear Information System (INIS)

Phillipps, S.; Disney, M.

1986-01-01

The joint distribution of total luminosity and characteristic surface brightness (the bivariate brightness distribution) is investigated for a complete sample of spiral galaxies in the Virgo cluster. The influence of selection and physical limits of various kinds on the apparent distribution are detailed. While the distribution of surface brightness for bright galaxies may be genuinely fairly narrow, faint galaxies exist right across the (quite small) range of accessible surface brightnesses so no statement can be made about the true extent of the distribution. The lack of high surface brightness bright galaxies in the Virgo sample relative to an overall RC2 sample (mostly field galaxies) supports the contention that the star-formation rate is reduced in the inner region of the cluster for environmental reasons. (author)

Comparing Johnson’s SBB, Weibull and Logit-Logistic bivariate distributions for modeling tree diameters and heights using copulas

Energy Technology Data Exchange (ETDEWEB)

Cardil Forradellas, A.; Molina Terrén, D.M.; Oliveres, J.; Castellnou, M.

2016-07-01

Aim of study: In this study we compare the accuracy of three bivariate distributions: Johnson’s SBB, Weibull-2P and LL-2P functions for characterizing the joint distribution of tree diameters and heights. Area of study: North-West of Spain. Material and methods: Diameter and height measurements of 128 plots of pure and even-aged Tasmanian blue gum (Eucalyptus globulus Labill.) stands located in the North-west of Spain were considered in the present study. The SBB bivariate distribution was obtained from SB marginal distributions using a Normal Copula based on a four-parameter logistic transformation. The Plackett Copula was used to obtain the bivariate models from the Weibull and Logit-logistic univariate marginal distributions. The negative logarithm of the maximum likelihood function was used to compare the results and the Wilcoxon signed-rank test was used to compare the related samples of these logarithms calculated for each sample plot and each distribution. Main results: The best results were obtained by using the Plackett copula and the best marginal distribution was the Logit-logistic. Research highlights: The copulas used in this study have shown a good performance for modeling the joint distribution of tree diameters and heights. They could be easily extended for modelling multivariate distributions involving other tree variables, such as tree volume or biomass. (Author)

Joint multifractal analysis based on wavelet leaders

Science.gov (United States)

Jiang, Zhi-Qiang; Yang, Yan-Hong; Wang, Gang-Jin; Zhou, Wei-Xing

2017-12-01

Mutually interacting components form complex systems and these components usually have long-range cross-correlated outputs. Using wavelet leaders, we propose a method for characterizing the joint multifractal nature of these long-range cross correlations; we call this method joint multifractal analysis based on wavelet leaders (MF-X-WL). We test the validity of the MF-X-WL method by performing extensive numerical experiments on dual binomial measures with multifractal cross correlations and bivariate fractional Brownian motions (bFBMs) with monofractal cross correlations. Both experiments indicate that MF-X-WL is capable of detecting cross correlations in synthetic data with acceptable estimating errors. We also apply the MF-X-WL method to pairs of series from financial markets (returns and volatilities) and online worlds (online numbers of different genders and different societies) and determine intriguing joint multifractal behavior.

Assessing characteristics related to the use of seatbelts and cell phones by drivers: application of a bivariate probit model.

Science.gov (United States)

Russo, Brendan J; Kay, Jonathan J; Savolainen, Peter T; Gates, Timothy J

2014-06-01

The effects of cell phone use and safety belt use have been an important focus of research related to driver safety. Cell phone use has been shown to be a significant source of driver distraction contributing to substantial degradations in driver performance, while safety belts have been demonstrated to play a vital role in mitigating injuries to crash-involved occupants. This study examines the prevalence of cell phone use and safety belt non-use among the driving population through direct observation surveys. A bivariate probit model is developed to simultaneously examine the factors that affect cell phone and safety belt use among motor vehicle drivers. The results show that several factors may influence drivers' decision to use cell phones and safety belts, and that these decisions are correlated. Understanding the factors that affect both cell phone use and safety belt non-use is essential to targeting policy and programs that reduce such behavior. Copyright © 2014 Elsevier Ltd. All rights reserved.

Correlates of early pregnancy serum brain-derived neurotrophic factor in a Peruvian population.

Science.gov (United States)

Yang, Na; Levey, Elizabeth; Gelaye, Bizu; Zhong, Qiu-Yue; Rondon, Marta B; Sanchez, Sixto E; Williams, Michelle A

2017-12-01

Knowledge about factors that influence serum brain-derived neurotrophic factor (BDNF) concentrations during early pregnancy is lacking. The aim of the study is to examine the correlates of early pregnancy serum BDNF concentrations. A total of 982 women attending prenatal care clinics in Lima, Peru, were recruited in early pregnancy. Pearson's correlation coefficient was calculated to evaluate the relation between BDNF concentrations and continuous covariates. Analysis of variance and generalized linear models were used to compare the unadjusted and adjusted BDNF concentrations according to categorical variables. Multivariable linear regression models were applied to determine the factors that influence early pregnancy serum BDNF concentrations. In bivariate analysis, early pregnancy serum BDNF concentrations were positively associated with maternal age (r = 0.16, P BDNF concentrations. Participants with moderate antepartum depressive symptoms (Patient Health Questionnaire-9 (PHQ-9) score ≥ 10) had lower serum BDNF concentrations compared with participants with no/mild antepartum depressive symptoms (PHQ-9 score BDNF concentrations in low-income Peruvian women. Biological changes of CRP during pregnancy may affect serum BDNF concentrations.

The Evaluation of Bivariate Mixed Models in Meta-analyses of Diagnostic Accuracy Studies with SAS, Stata and R.

Science.gov (United States)

Vogelgesang, Felicitas; Schlattmann, Peter; Dewey, Marc

2018-05-01

Meta-analyses require a thoroughly planned procedure to obtain unbiased overall estimates. From a statistical point of view not only model selection but also model implementation in the software affects the results. The present simulation study investigates the accuracy of different implementations of general and generalized bivariate mixed models in SAS (using proc mixed, proc glimmix and proc nlmixed), Stata (using gllamm, xtmelogit and midas) and R (using reitsma from package mada and glmer from package lme4). Both models incorporate the relationship between sensitivity and specificity - the two outcomes of interest in meta-analyses of diagnostic accuracy studies - utilizing random effects. Model performance is compared in nine meta-analytic scenarios reflecting the combination of three sizes for meta-analyses (89, 30 and 10 studies) with three pairs of sensitivity/specificity values (97%/87%; 85%/75%; 90%/93%). The evaluation of accuracy in terms of bias, standard error and mean squared error reveals that all implementations of the generalized bivariate model calculate sensitivity and specificity estimates with deviations less than two percentage points. proc mixed which together with reitsma implements the general bivariate mixed model proposed by Reitsma rather shows convergence problems. The random effect parameters are in general underestimated. This study shows that flexibility and simplicity of model specification together with convergence robustness should influence implementation recommendations, as the accuracy in terms of bias was acceptable in all implementations using the generalized approach. Schattauer GmbH.

Price-volume multifractal analysis of the Moroccan stock market

Science.gov (United States)

El Alaoui, Marwane

2017-11-01

In this paper, we analyzed price-volume multifractal cross-correlations of Moroccan Stock Exchange. We chose the period from January 1st 2000 to January 20th 2017 to investigate the multifractal behavior of price change and volume change series. Then, we used multifractal detrended cross-correlations analysis method (MF-DCCA) and multifractal detrended fluctuation analysis (MF-DFA) to analyze the series. We computed bivariate generalized Hurst exponent, Rényi exponent and spectrum of singularity for each pair of indices to measure quantitatively cross-correlations. Furthermore, we used detrended cross-correlations coefficient (DCCA) and cross-correlation test (Q(m)) to analyze cross-correlation quantitatively and qualitatively. By analyzing results, we found existence of price-volume multifractal cross-correlations. The spectrum width has a strong multifractal cross-correlation. We remarked that volume change series is anti-persistent when we analyzed the generalized Hurst exponent for all moments q. The cross-correlation test showed the presence of a significant cross-correlation. However, DCCA coefficient had a small positive value, which means that the level of correlation is not very significant. Finally, we analyzed sources of multifractality and their degree of contribution in the series.

Bivariate Genomic Footprinting Detects Changes in Transcription Factor Activity

Directory of Open Access Journals (Sweden)

Songjoon Baek

2017-05-01

Full Text Available In response to activating signals, transcription factors (TFs bind DNA and regulate gene expression. TF binding can be measured by protection of the bound sequence from DNase digestion (i.e., footprint. Here, we report that 80% of TF binding motifs do not show a measurable footprint, partly because of a variable cleavage pattern within the motif sequence. To more faithfully portray the effect of TFs on chromatin, we developed an algorithm that captures two TF-dependent effects on chromatin accessibility: footprinting and motif-flanking accessibility. The algorithm, termed bivariate genomic footprinting (BaGFoot, efficiently detects TF activity. BaGFoot is robust to different accessibility assays (DNase-seq, ATAC-seq, all examined peak-calling programs, and a variety of cut bias correction approaches. BaGFoot reliably predicts TF binding and provides valuable information regarding the TFs affecting chromatin accessibility in various biological systems and following various biological events, including in cases where an absolute footprint cannot be determined.

Correlation between iron deficiency anemia and intestinal parasitic infection in school-age children in Medan

Science.gov (United States)

Darlan, D. M.; Ananda, F. R.; Sari, M. I.; Arrasyid, N. K.; Sari, D. I.

2018-03-01

Anemia is an abnormal hemoglobin concentration in blood that impacts almost 40% school-age children in developing countries. Intestinal parasitic infection, along with malnutrition are contributed to influence absorption, transportation, and metabolism of iron which is the most common etiology of anemia in school-age children. The purpose of this study was to determine whether there is a correlation between iron deficiency anemia (IDA) and parasitic intestinal infection generally and protozoa infection particularly among school-age children in Medan. This was a cross-sectional study conducted from May until October 2016 in primaryschool in Medan and Hamparan Perak, Deli Serdang. Consecutive sampling was used with total 132 samples obtained. Univariate analysis and Bivariate analysis were performed.This study showed the prevalence of IDA was 7.6%, and proportion of parasitic intestinal infection was 26.5% with 19.8% protozoa infection. The correlation between IDA and intestinal parasitic infection was not significant in Chi-Square Test (p-value: 0.089), neither was between IDA and protozoa infection (p-value: 0.287). There was a correlation between MCV, MCH, and anemia with p-valueanemia, parasitic infection, and protozoa infection (p-value>0.05).

Correlation between parent-adolescent communication and adolescents' premarital sex risk.

Science.gov (United States)

Widyatuti; Hafilah Shabrina, Citra; Yuni Nursasi, Astuti

2018-02-01

Previous studies have indicated the parent-adolescent relationship has a correlation to adolescents' premarital sex behavior risk. Therefore, the aim of this study was to discover the influence of parent-adolescent communication on adolescents' risk of sexual issues. This was a quantitative study with a cross-sectional design. The population of this study consisted of students from a high school in Jakarta. A purposive sampling technique was used, which resulted in the selection of 253 students as samples. A PACS (Parent-Adolescent Communication Scale) questionnaire was applied. The results showed that 59.3% of the adolescents studied were at risk for engaging in premarital sex, while the risk for adolescents with positive communication with their parents was 56.5%. Bivariate analysis also showed a significant correlation between gender and parent-adolescent communication and the risk of adolescent premarital sex behavior (α adolescents. Communication must align with adolescents' developmental tasks. Nurses can also create a promotion program on the topic of communication for parents and adolescents. Copyright © 2018 Elsevier España, S.L.U. All rights reserved.

SNPMClust: Bivariate Gaussian Genotype Clustering and Calling for Illumina Microarrays

Directory of Open Access Journals (Sweden)

Stephen W. Erickson

2016-07-01

Full Text Available SNPMClust is an R package for genotype clustering and calling with Illumina microarrays. It was originally developed for studies using the GoldenGate custom genotyping platform but can be used with other Illumina platforms, including Infinium BeadChip. The algorithm first rescales the fluorescent signal intensity data, adds empirically derived pseudo-data to minor allele genotype clusters, then uses the package mclust for bivariate Gaussian model fitting. We compared the accuracy and sensitivity of SNPMClust to that of GenCall, Illumina's proprietary algorithm, on a data set of 94 whole-genome amplified buccal (cheek swab DNA samples. These samples were genotyped on a custom panel which included 1064 SNPs for which the true genotype was known with high confidence. SNPMClust produced uniformly lower false call rates over a wide range of overall call rates.

Anatomical analysis of human masseter using MRI

International Nuclear Information System (INIS)

Kitahara, Masaki

1990-01-01

To quantitatively elucidate individual variation in human masseter muscle, the cross sectional area of this muscle was measured in situ with MRI apparatus. The results were analyzed against age, body build and laterality in habitual mastication by bivariate correlation analysis. Materials included 52 healthy volunteers (26 males, 26 females). Right and left masseter areas and total cross sectional area of the face were measured in the same horizontal plane, and directly displayed on the console of the MRI apparatus. The areas of masseter muscles were not correlated with age or body build, but were positively correlated with the total cross sectional area of the face in the same horizontal plane. Because the left masseters tended to be larger than right, a method for evaluation of the relative size of each side of the messeters was established. This analysis revealed that the corrected dominance of the masseter corresponded, with high probability, to the habitual laterality in mastication. (author)

Anatomical analysis of human masseter using MRI

Energy Technology Data Exchange (ETDEWEB)

Kitahara, Masaki (Showa Univ., Tokyo (Japan). School of Medicine)

1990-10-01

To quantitatively elucidate individual variation in human masseter muscle, the cross sectional area of this muscle was measured in situ with MRI apparatus. The results were analyzed against age, body build and laterality in habitual mastication by bivariate correlation analysis. Materials included 52 healthy volunteers (26 males, 26 females). Right and left masseter areas and total cross sectional area of the face were measured in the same horizontal plane, and directly displayed on the console of the MRI apparatus. The areas of masseter muscles were not correlated with age or body build, but were positively correlated with the total cross sectional area of the face in the same horizontal plane. Because the left masseters tended to be larger than right, a method for evaluation of the relative size of each side of the messeters was established. This analysis revealed that the corrected dominance of the masseter corresponded, with high probability, to the habitual laterality in mastication. (author).

Parameter Optimization for Selected Correlation Analysis of Intracranial Pathophysiology

Directory of Open Access Journals (Sweden)

Rupert Faltermeier

2015-01-01

Full Text Available Recently we proposed a mathematical tool set, called selected correlation analysis, that reliably detects positive and negative correlations between arterial blood pressure (ABP and intracranial pressure (ICP. Such correlations are associated with severe impairment of the cerebral autoregulation and intracranial compliance, as predicted by a mathematical model. The time resolved selected correlation analysis is based on a windowing technique combined with Fourier-based coherence calculations and therefore depends on several parameters. For real time application of this method at an ICU it is inevitable to adjust this mathematical tool for high sensitivity and distinct reliability. In this study, we will introduce a method to optimize the parameters of the selected correlation analysis by correlating an index, called selected correlation positive (SCP, with the outcome of the patients represented by the Glasgow Outcome Scale (GOS. For that purpose, the data of twenty-five patients were used to calculate the SCP value for each patient and multitude of feasible parameter sets of the selected correlation analysis. It could be shown that an optimized set of parameters is able to improve the sensitivity of the method by a factor greater than four in comparison to our first analyses.

Parameter Optimization for Selected Correlation Analysis of Intracranial Pathophysiology.

Science.gov (United States)

Faltermeier, Rupert; Proescholdt, Martin A; Bele, Sylvia; Brawanski, Alexander

2015-01-01

Recently we proposed a mathematical tool set, called selected correlation analysis, that reliably detects positive and negative correlations between arterial blood pressure (ABP) and intracranial pressure (ICP). Such correlations are associated with severe impairment of the cerebral autoregulation and intracranial compliance, as predicted by a mathematical model. The time resolved selected correlation analysis is based on a windowing technique combined with Fourier-based coherence calculations and therefore depends on several parameters. For real time application of this method at an ICU it is inevitable to adjust this mathematical tool for high sensitivity and distinct reliability. In this study, we will introduce a method to optimize the parameters of the selected correlation analysis by correlating an index, called selected correlation positive (SCP), with the outcome of the patients represented by the Glasgow Outcome Scale (GOS). For that purpose, the data of twenty-five patients were used to calculate the SCP value for each patient and multitude of feasible parameter sets of the selected correlation analysis. It could be shown that an optimized set of parameters is able to improve the sensitivity of the method by a factor greater than four in comparison to our first analyses.

Correlation Coefficients: Appropriate Use and Interpretation.

Science.gov (United States)

Schober, Patrick; Boer, Christa; Schwarte, Lothar A

2018-05-01

Correlation in the broadest sense is a measure of an association between variables. In correlated data, the change in the magnitude of 1 variable is associated with a change in the magnitude of another variable, either in the same (positive correlation) or in the opposite (negative correlation) direction. Most often, the term correlation is used in the context of a linear relationship between 2 continuous variables and expressed as Pearson product-moment correlation. The Pearson correlation coefficient is typically used for jointly normally distributed data (data that follow a bivariate normal distribution). For nonnormally distributed continuous data, for ordinal data, or for data with relevant outliers, a Spearman rank correlation can be used as a measure of a monotonic association. Both correlation coefficients are scaled such that they range from -1 to +1, where 0 indicates that there is no linear or monotonic association, and the relationship gets stronger and ultimately approaches a straight line (Pearson correlation) or a constantly increasing or decreasing curve (Spearman correlation) as the coefficient approaches an absolute value of 1. Hypothesis tests and confidence intervals can be used to address the statistical significance of the results and to estimate the strength of the relationship in the population from which the data were sampled. The aim of this tutorial is to guide researchers and clinicians in the appropriate use and interpretation of correlation coefficients.

Multicollinearity in canonical correlation analysis in maize.

Science.gov (United States)

Alves, B M; Cargnelutti Filho, A; Burin, C

2017-03-30

The objective of this study was to evaluate the effects of multicollinearity under two methods of canonical correlation analysis (with and without elimination of variables) in maize (Zea mays L.) crop. Seventy-six maize genotypes were evaluated in three experiments, conducted in a randomized block design with three replications, during the 2009/2010 crop season. Eleven agronomic variables (number of days from sowing until female flowering, number of days from sowing until male flowering, plant height, ear insertion height, ear placement, number of plants, number of ears, ear index, ear weight, grain yield, and one thousand grain weight), 12 protein-nutritional variables (crude protein, lysine, methionine, cysteine, threonine, tryptophan, valine, isoleucine, leucine, phenylalanine, histidine, and arginine), and 6 energetic-nutritional variables (apparent metabolizable energy, apparent metabolizable energy corrected for nitrogen, ether extract, crude fiber, starch, and amylose) were measured. A phenotypic correlation matrix was first generated among the 29 variables for each of the experiments. A multicollinearity diagnosis was later performed within each group of variables using methodologies such as variance inflation factor and condition number. Canonical correlation analysis was then performed, with and without the elimination of variables, among groups of agronomic and protein-nutritional, and agronomic and energetic-nutritional variables. The canonical correlation analysis in the presence of multicollinearity (without elimination of variables) overestimates the variability of canonical coefficients. The elimination of variables is an efficient method to circumvent multicollinearity in canonical correlation analysis.

Detrended cross-correlation analysis of electroencephalogram

International Nuclear Information System (INIS)

Wang Jun; Zhao Da-Qing

2012-01-01

In the paper we use detrended cross-correlation analysis (DCCA) to study the electroencephalograms of healthy young subjects and healthy old subjects. It is found that the cross-correlation between different leads of a healthy young subject is larger than that of a healthy old subject. It was shown that the cross-correlation relationship decreases with the aging process and the phenomenon can help to diagnose whether the subject's brain function is healthy or not. (interdisciplinary physics and related areas of science and technology)

[Electroencephalogram Feature Selection Based on Correlation Coefficient Analysis].

Science.gov (United States)

Zhou, Jinzhi; Tang, Xiaofang

2015-08-01

In order to improve the accuracy of classification with small amount of motor imagery training data on the development of brain-computer interface (BCD systems, we proposed an analyzing method to automatically select the characteristic parameters based on correlation coefficient analysis. Throughout the five sample data of dataset IV a from 2005 BCI Competition, we utilized short-time Fourier transform (STFT) and correlation coefficient calculation to reduce the number of primitive electroencephalogram dimension, then introduced feature extraction based on common spatial pattern (CSP) and classified by linear discriminant analysis (LDA). Simulation results showed that the average rate of classification accuracy could be improved by using correlation coefficient feature selection method than those without using this algorithm. Comparing with support vector machine (SVM) optimization features algorithm, the correlation coefficient analysis can lead better selection parameters to improve the accuracy of classification.

Collective estimation of multiple bivariate density functions with application to angular-sampling-based protein loop modeling

KAUST Repository

Maadooliat, Mehdi

2015-10-21

This paper develops a method for simultaneous estimation of density functions for a collection of populations of protein backbone angle pairs using a data-driven, shared basis that is constructed by bivariate spline functions defined on a triangulation of the bivariate domain. The circular nature of angular data is taken into account by imposing appropriate smoothness constraints across boundaries of the triangles. Maximum penalized likelihood is used to fit the model and an alternating blockwise Newton-type algorithm is developed for computation. A simulation study shows that the collective estimation approach is statistically more efficient than estimating the densities individually. The proposed method was used to estimate neighbor-dependent distributions of protein backbone dihedral angles (i.e., Ramachandran distributions). The estimated distributions were applied to protein loop modeling, one of the most challenging open problems in protein structure prediction, by feeding them into an angular-sampling-based loop structure prediction framework. Our estimated distributions compared favorably to the Ramachandran distributions estimated by fitting a hierarchical Dirichlet process model; and in particular, our distributions showed significant improvements on the hard cases where existing methods do not work well.

Collective estimation of multiple bivariate density functions with application to angular-sampling-based protein loop modeling

KAUST Repository

Maadooliat, Mehdi; Zhou, Lan; Najibi, Seyed Morteza; Gao, Xin; Huang, Jianhua Z.

2015-01-01

This paper develops a method for simultaneous estimation of density functions for a collection of populations of protein backbone angle pairs using a data-driven, shared basis that is constructed by bivariate spline functions defined on a triangulation of the bivariate domain. The circular nature of angular data is taken into account by imposing appropriate smoothness constraints across boundaries of the triangles. Maximum penalized likelihood is used to fit the model and an alternating blockwise Newton-type algorithm is developed for computation. A simulation study shows that the collective estimation approach is statistically more efficient than estimating the densities individually. The proposed method was used to estimate neighbor-dependent distributions of protein backbone dihedral angles (i.e., Ramachandran distributions). The estimated distributions were applied to protein loop modeling, one of the most challenging open problems in protein structure prediction, by feeding them into an angular-sampling-based loop structure prediction framework. Our estimated distributions compared favorably to the Ramachandran distributions estimated by fitting a hierarchical Dirichlet process model; and in particular, our distributions showed significant improvements on the hard cases where existing methods do not work well.

Efficient estimation of semiparametric copula models for bivariate survival data

KAUST Repository

Cheng, Guang

2014-01-01

A semiparametric copula model for bivariate survival data is characterized by a parametric copula model of dependence and nonparametric models of two marginal survival functions. Efficient estimation for the semiparametric copula model has been recently studied for the complete data case. When the survival data are censored, semiparametric efficient estimation has only been considered for some specific copula models such as the Gaussian copulas. In this paper, we obtain the semiparametric efficiency bound and efficient estimation for general semiparametric copula models for possibly censored data. We construct an approximate maximum likelihood estimator by approximating the log baseline hazard functions with spline functions. We show that our estimates of the copula dependence parameter and the survival functions are asymptotically normal and efficient. Simple consistent covariance estimators are also provided. Numerical results are used to illustrate the finite sample performance of the proposed estimators. © 2013 Elsevier Inc.

Evaluation of Factors Affecting E-Bike Involved Crash and E-Bike License Plate Use in China Using a Bivariate Probit Model

Directory of Open Access Journals (Sweden)

Yanyong Guo

2017-01-01

Full Text Available The primary objective of this study is to evaluate factors affecting e-bike involved crash and license plate use in China. E-bike crashes data were collected from police database and completed through a telephone interview. Noncrash samples were collected by a questionnaire survey. A bivariate probit (BP model was developed to simultaneously examine the significant factors associated with e-bike involved crash and e-bike license plate and to account for the correlations between them. Marginal effects for contributory factors were calculated to quantify their impacts on the outcomes. The results show that several contributory factors, including gender, age, education level, driver license, car in household, experiences in using e-bike, law compliance, and aggressive driving behaviors, are found to have significant impacts on both e-bike involved crash and license plate use. Moreover, type of e-bike, frequency of using e-bike, impulse behavior, degree of riding experience, and risk perception scale are found to be associated with e-bike involved crash. It is also found that e-bike involved crash and e-bike license plate use are strongly correlated and are negative in direction. The result enhanced our comprehension of the factors related to e-bike involved crash and e-bike license plate use.

Possible antecedents and consequences of self-esteem in persons with multiple sclerosis: preliminary evidence from a cross-sectional analysis.

Science.gov (United States)

Dlugonski, Deirdre; Motl, Robert W

2012-02-01

Persons with multiple sclerosis (MS) have consistently reported lower levels of self-esteem compared with the general population. Despite this, very little is known about the antecedents and consequences of self-esteem in persons with MS. To examine (1) physical activity and social support as potentially modifiable correlates (i.e., antecedents) of self-esteem and (2) physical and psychological health-related quality of life as possible consequences of self-esteem in persons with MS. Participants (N = 46) wore an Actigraph accelerometer for 7 days and then completed a battery of questionnaires, including the Rosenberg Self-Esteem Scale (RSES), Multiple Sclerosis Impact Scale (MSIS-29), and Social Provisions Scale (SPS). The data were analyzed using PASW Statistics 18. Bivariate correlation analysis indicated that average daily step counts (r = .298, p = .026) and social support (r = .366, p = .007) were significantly correlated with self-esteem. Multiple linear regression analysis indicated that only social support was a significant predictor of self-esteem scores (β = .411, p = .004); pedometer steps approached significance as a predictor of self-esteem (β = .178, p = .112). Bivariate correlation analysis further indicated significant negative associations between self-esteem and physical (r = -.391, p = .004) and psychological (r = -.540, p = .0001) domains of health-related quality of life (HRQOL), indicating that higher self-esteem was associated with more positive HRQOL. Social support is a potentially modifiable variable that may be important to target when designing interventions to improve self-esteem and this might have implications for improving physical and psychological HRQOL in persons with MS.

Bayesian Correlation Analysis for Sequence Count Data.

Directory of Open Access Journals (Sweden)

Daniel Sánchez-Taltavull

Full Text Available Evaluating the similarity of different measured variables is a fundamental task of statistics, and a key part of many bioinformatics algorithms. Here we propose a Bayesian scheme for estimating the correlation between different entities' measurements based on high-throughput sequencing data. These entities could be different genes or miRNAs whose expression is measured by RNA-seq, different transcription factors or histone marks whose expression is measured by ChIP-seq, or even combinations of different types of entities. Our Bayesian formulation accounts for both measured signal levels and uncertainty in those levels, due to varying sequencing depth in different experiments and to varying absolute levels of individual entities, both of which affect the precision of the measurements. In comparison with a traditional Pearson correlation analysis, we show that our Bayesian correlation analysis retains high correlations when measurement confidence is high, but suppresses correlations when measurement confidence is low-especially for entities with low signal levels. In addition, we consider the influence of priors on the Bayesian correlation estimate. Perhaps surprisingly, we show that naive, uniform priors on entities' signal levels can lead to highly biased correlation estimates, particularly when different experiments have widely varying sequencing depths. However, we propose two alternative priors that provably mitigate this problem. We also prove that, like traditional Pearson correlation, our Bayesian correlation calculation constitutes a kernel in the machine learning sense, and thus can be used as a similarity measure in any kernel-based machine learning algorithm. We demonstrate our approach on two RNA-seq datasets and one miRNA-seq dataset.

Geovisualization of land use and land cover using bivariate maps and Sankey flow diagrams

Science.gov (United States)

Strode, Georgianna; Mesev, Victor; Thornton, Benjamin; Jerez, Marjorie; Tricarico, Thomas; McAlear, Tyler

2018-05-01

The terms `land use' and `land cover' typically describe categories that convey information about the landscape. Despite the major difference of land use implying some degree of anthropogenic disturbance, the two terms are commonly used interchangeably, especially when anthropogenic disturbance is ambiguous, say managed forestland or abandoned agricultural fields. Cartographically, land use and land cover are also sometimes represented interchangeably within common legends, giving with the impression that the landscape is a seamless continuum of land use parcels spatially adjacent to land cover tracts. We believe this is misleading, and feel we need to reiterate the well-established symbiosis of land uses as amalgams of land covers; in other words land covers are subsets of land use. Our paper addresses this spatially complex, and frequently ambiguous relationship, and posits that bivariate cartographic techniques are an ideal vehicle for representing both land use and land cover simultaneously. In more specific terms, we explore the use of nested symbology as ways to represent graphically land use and land cover, where land cover are circles nested with land use squares. We also investigate bivariate legends for representing statistical covariance as a means for visualizing the combinations of land use and cover. Lastly, we apply Sankey flow diagrams to further illustrate the complex, multifaceted relationships between land use and land cover. Our work is demonstrated on data representing land use and cover data for the US state of Florida.

WGCNA: an R package for weighted correlation network analysis.

Science.gov (United States)

Langfelder, Peter; Horvath, Steve

2008-12-29

Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial. The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings. The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at http://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/Rpackages/WGCNA.

Regional Analysis of Precipitation by Means of Bivariate Distribution Adjusted by Maximum Entropy; Analisis regional de precipitacion con base en una distribucion bivariada ajustada por maxima entropia

Energy Technology Data Exchange (ETDEWEB)

Escalante Sandoval, Carlos A.; Dominguez Esquivel, Jose Y. [Universidad Nacional Autonoma de Mexico (Mexico)

2001-09-01

The principle of maximum entropy (POME) is used to derive an alternative method of parameter estimation for the bivariate Gumbel distribution. A simple algorithm for this parameter estimation technique is presented. This method is applied to analyze the precipitation in a region of Mexico. Design events are compered with those obtained by the maximum likelihood procedure. According to the results, the proposed technique is a suitable option to be considered when performing frequency analysis of precipitation with small samples. [Spanish] El principio de maxima entropia, conocido como POME, es utilizado para derivar un procedimiento alternativo de estimacion de parametros de la distribucion bivariada de valores extremos con marginales Gumbel. El modelo se aplica al analisis de la precipitacion maxima en 24 horas en una region de Mexico y los eventos de diseno obtenidos son comparados con los proporcionados por la tecnica de maxima verosimilitud. De acuerdo con los resultados obtenidos, se concluye que la tecnica propuesta representa una buena opcion, sobre todo para el caso de muestras pequenas.

Association of Supply Type with Fecal Contamination of Source Water and Household Stored Drinking Water in Developing Countries: A Bivariate Meta-analysis.

Science.gov (United States)

Shields, Katherine F; Bain, Robert E S; Cronk, Ryan; Wright, Jim A; Bartram, Jamie

2015-12-01

Access to safe drinking water is essential for health. Monitoring access to drinking water focuses on water supply type at the source, but there is limited evidence on whether quality differences at the source persist in water stored in the household. We assessed the extent of fecal contamination at the source and in household stored water (HSW) and explored the relationship between contamination at each sampling point and water supply type. We performed a bivariate random-effects meta-analysis of 45 studies, identified through a systematic review, that reported either the proportion of samples free of fecal indicator bacteria and/or individual sample bacteria counts for source and HSW, disaggregated by supply type. Water quality deteriorated substantially between source and stored water. The mean percentage of contaminated samples (noncompliance) at the source was 46% (95% CI: 33, 60%), whereas mean noncompliance in HSW was 75% (95% CI: 64, 84%). Water supply type was significantly associated with noncompliance at the source (p water (OR = 0.2; 95% CI: 0.1, 0.5) and HSW (OR = 0.3; 95% CI: 0.2, 0.8) from piped supplies had significantly lower odds of contamination compared with non-piped water, potentially due to residual chlorine. Piped water is less likely to be contaminated compared with other water supply types at both the source and in HSW. A focus on upgrading water services to piped supplies may help improve safety, including for those drinking stored water.

Structural Analysis of Covariance and Correlation Matrices.

Science.gov (United States)

Joreskog, Karl G.

1978-01-01

A general approach to analysis of covariance structures is considered, in which the variances and covariances or correlations of the observed variables are directly expressed in terms of the parameters of interest. The statistical problems of identification, estimation and testing of such covariance or correlation structures are discussed.…

Dissecting the correlation structure of a bivariate phenotype ...

Indian Academy of Sciences (India)

Unknown

We use Monte-Carlo simulations to evaluate the performance of the proposed test under different trait parameters and quantitative trait distributions. An application of the method is illustrated using data on two alcohol-related phenotypes from a project on the collaborative study on the genetics of alcoholism. [Ghosh S 2005 ...

Longitudinal categorical data analysis

CERN Document Server

Sutradhar, Brajendra C

2014-01-01

This is the first book in longitudinal categorical data analysis with parametric correlation models developed based on dynamic relationships among repeated categorical responses. This book is a natural generalization of the longitudinal binary data analysis to the multinomial data setup with more than two categories. Thus, unlike the existing books on cross-sectional categorical data analysis using log linear models, this book uses multinomial probability models both in cross-sectional and longitudinal setups. A theoretical foundation is provided for the analysis of univariate multinomial responses, by developing models systematically for the cases with no covariates as well as categorical covariates, both in cross-sectional and longitudinal setups. In the longitudinal setup, both stationary and non-stationary covariates are considered. These models have also been extended to the bivariate multinomial setup along with suitable covariates. For the inferences, the book uses the generalized quasi-likelihood as w...

Gait Correlation Analysis Based Human Identification

Directory of Open Access Journals (Sweden)

Jinyan Chen

2014-01-01

Full Text Available Human gait identification aims to identify people by a sequence of walking images. Comparing with fingerprint or iris based identification, the most important advantage of gait identification is that it can be done at a distance. In this paper, silhouette correlation analysis based human identification approach is proposed. By background subtracting algorithm, the moving silhouette figure can be extracted from the walking images sequence. Every pixel in the silhouette has three dimensions: horizontal axis (x, vertical axis (y, and temporal axis (t. By moving every pixel in the silhouette image along these three dimensions, we can get a new silhouette. The correlation result between the original silhouette and the new one can be used as the raw feature of human gait. Discrete Fourier transform is used to extract features from this correlation result. Then, these features are normalized to minimize the affection of noise. Primary component analysis method is used to reduce the features’ dimensions. Experiment based on CASIA database shows that this method has an encouraging recognition performance.

Spatially varying cross-correlation coefficients in the presence of nugget effects

KAUST Repository

Kleiber, William; Genton, Marc G.

2012-01-01

We derive sufficient conditions for the cross-correlation coefficient of a multivariate spatial process to vary with location when the spatial model is augmented with nugget effects. The derived class is valid for any choice of covariance functions, and yields substantial flexibility between multiple processes. The key is to identify the cross-correlation coefficient matrix with a contraction matrix, which can be either diagonal, implying a parsimonious formulation, or a fully general contraction matrix, yielding greater flexibility but added model complexity. We illustrate the approach with a bivariate minimum and maximum temperature dataset in Colorado, allowing the two variables to be positively correlated at low elevations and nearly independent at high elevations, while still yielding a positive definite covariance matrix. © 2012 Biometrika Trust.

Spatially varying cross-correlation coefficients in the presence of nugget effects

KAUST Repository

Kleiber, William

2012-11-29

We derive sufficient conditions for the cross-correlation coefficient of a multivariate spatial process to vary with location when the spatial model is augmented with nugget effects. The derived class is valid for any choice of covariance functions, and yields substantial flexibility between multiple processes. The key is to identify the cross-correlation coefficient matrix with a contraction matrix, which can be either diagonal, implying a parsimonious formulation, or a fully general contraction matrix, yielding greater flexibility but added model complexity. We illustrate the approach with a bivariate minimum and maximum temperature dataset in Colorado, allowing the two variables to be positively correlated at low elevations and nearly independent at high elevations, while still yielding a positive definite covariance matrix. © 2012 Biometrika Trust.

Meta-Analysis of Correlations Among Usability Measures

DEFF Research Database (Denmark)

Hornbæk, Kasper Anders Søren; Effie Lai Chong, Law

2007-01-01

are generally low: effectiveness measures (e.g., errors) and efficiency measures (e.g., time) has a correlation of .247 ± .059 (Pearson's product-moment correlation with 95% confidence interval), efficiency and satisfaction (e.g., preference) one of .196 ± .064, and effectiveness and satisfaction one of .164......Understanding the relation between usability measures seems crucial to deepen our conception of usability and to select the right measures for usability studies. We present a meta-analysis of correlations among usability measures calculated from the raw data of 73 studies. Correlations...... ± .062. Changes in task complexity do not influence these correlations, but use of more complex measures attenuates them. Standard questionnaires for measuring satisfaction appear more reliable than homegrown ones. Measures of users' perceptions of phenomena are generally not correlated with objective...

Correlation analysis in chemistry: recent advances

National Research Council Canada - National Science Library

Shorter, John; Chapman, Norman Bellamy

1978-01-01

..., and applications of LFER to polycyclic arenes, heterocyclic compounds, and olefinic systems. Of particular interest is the extensive critical compilation of substituent constants and the numerous applications of correlation analysis to spectroscopy...

On the Genetic and Environmental Correlations between Trait Emotional Intelligence and Vocational Interest Factors.

Science.gov (United States)

Schermer, Julie Aitken; Petrides, Konstantinos V; Vernon, Philip A

2015-04-01

The phenotypic (observed), genetic, and environmental correlations were examined in a sample of adult twins between the four factors and global score of the trait emotional intelligence questionnaire (TEIQue) and the seven vocational interest factors of the Jackson Career Explorer (JCE). Multiple significant correlations were found involving the work style vocational interest factor (consisting of job security, stamina, accountability, planfulness, and interpersonal confidence) and the social vocational interest factor (which included interests in the social sciences, personal services, teaching, social services, and elementary education), both of which correlated significantly with all of the TEIQue variables (well-being, self-control, emotionality, sociability, and global trait EI). Following bivariate genetic analyses, most of the significant phenotypic correlations were found to also have significant genetic correlations as well as significant non-shared (unique) environmental correlations.

Quantifying the range of cross-correlated fluctuations using a q- L dependent AHXA coefficient

Science.gov (United States)

Wang, Fang; Wang, Lin; Chen, Yuming

2018-03-01

Recently, based on analogous height cross-correlation analysis (AHXA), a cross-correlation coefficient ρ×(L) has been proposed to quantify the levels of cross-correlation on different temporal scales for bivariate series. A limitation of this coefficient is that it cannot capture the full information of cross-correlations on amplitude of fluctuations. In fact, it only detects the cross-correlation at a specific order fluctuation, which might neglect some important information inherited from other order fluctuations. To overcome this disadvantage, in this work, based on the scaling of the qth order covariance and time delay L, we define a two-parameter dependent cross-correlation coefficient ρq(L) to detect and quantify the range and level of cross-correlations. This new version of ρq(L) coefficient leads to the formation of a ρq(L) surface, which not only is able to quantify the level of cross-correlations, but also allows us to identify the range of fluctuation amplitudes that are correlated in two given signals. Applications to the classical ARFIMA models and the binomial multifractal series illustrate the feasibility of this new coefficient ρq(L) . In addition, a statistical test is proposed to quantify the existence of cross-correlations between two given series. Applying our method to the real life empirical data from the 1999-2000 California electricity market, we find that the California power crisis in 2000 destroys the cross-correlation between the price and the load series but does not affect the correlation of the load series during and before the crisis.

Generalized canonical correlation analysis with missing values

NARCIS (Netherlands)

M. van de Velden (Michel); Y. Takane

2009-01-01

textabstractTwo new methods for dealing with missing values in generalized canonical correlation analysis are introduced. The first approach, which does not require iterations, is a generalization of the Test Equating method available for principal component analysis. In the second approach,

Overweight and Obesity: Prevalence and Correlates in a Large Clinical Sample of Children with Autism Spectrum Disorder

Science.gov (United States)

Zuckerman, Katharine E.; Hill, Alison P.; Guion, Kimberly; Voltolina, Lisa; Fombonne, Eric

2014-01-01

Autism Spectrum Disorders (ASDs) and childhood obesity (OBY) are rising public health concerns. This study aimed to evaluate the prevalence of overweight (OWT) and OBY in a sample of 376 Oregon children with ASD, and to assess correlates of OWT and OBY in this sample. We used descriptive statistics, bivariate, and focused multivariate analyses to…

Correlation analysis of respiratory signals by using parallel coordinate plots.

Science.gov (United States)

Saatci, Esra

2018-01-01

The understanding of the bonds and the relationships between the respiratory signals, i.e. the airflow, the mouth pressure, the relative temperature and the relative humidity during breathing may provide the improvement on the measurement methods of respiratory mechanics and sensor designs or the exploration of the several possible applications in the analysis of respiratory disorders. Therefore, the main objective of this study was to propose a new combination of methods in order to determine the relationship between respiratory signals as a multidimensional data. In order to reveal the coupling between the processes two very different methods were used: the well-known statistical correlation analysis (i.e. Pearson's correlation and cross-correlation coefficient) and parallel coordinate plots (PCPs). Curve bundling with the number intersections for the correlation analysis, Least Mean Square Time Delay Estimator (LMS-TDE) for the point delay detection and visual metrics for the recognition of the visual structures were proposed and utilized in PCP. The number of intersections was increased when the correlation coefficient changed from high positive to high negative correlation between the respiratory signals, especially if whole breath was processed. LMS-TDE coefficients plotted in PCP indicated well-matched point delay results to the findings in the correlation analysis. Visual inspection of PCB by visual metrics showed range, dispersions, entropy comparisons and linear and sinusoidal-like relationships between the respiratory signals. It is demonstrated that the basic correlation analysis together with the parallel coordinate plots perceptually motivates the visual metrics in the display and thus can be considered as an aid to the user analysis by providing meaningful views of the data. Copyright © 2017 Elsevier B.V. All rights reserved.

Cross-correlation time-of-flight analysis of molecular beam scattering

International Nuclear Information System (INIS)

Nowikow, C.V.; Grice, R.

1979-01-01

The theory of the cross-correlation method of time-of-flight analysis is presented in a form which highlights its formal similarity to the conventional method. A time-of-flight system for the analysis of crossed molecular beam scattering is described, which is based on a minicomputer interface and can operate in both the cross-correlation and conventional modes. The interface maintains the synchronisation of chopper disc rotation and channel advance indefinitely in the cross-correlation method and can acquire data in phase with the beam modulation in both methods. The shutter function of the cross-correlation method is determined and the deconvolution analysis of the data is discussed. (author)

Multiscale Detrended Cross-Correlation Analysis of STOCK Markets

Science.gov (United States)

Yin, Yi; Shang, Pengjian

2014-06-01

In this paper, we employ the detrended cross-correlation analysis (DCCA) to investigate the cross-correlations between different stock markets. We report the results of cross-correlated behaviors in US, Chinese and European stock markets in period 1997-2012 by using DCCA method. The DCCA shows the cross-correlated behaviors of intra-regional and inter-regional stock markets in the short and long term which display the similarities and differences of cross-correlated behaviors simply and roughly and the persistence of cross-correlated behaviors of fluctuations. Then, because of the limitation and inapplicability of DCCA method, we propose multiscale detrended cross-correlation analysis (MSDCCA) method to avoid "a priori" selecting the ranges of scales over which two coefficients of the classical DCCA method are identified, and employ MSDCCA to reanalyze these cross-correlations to exhibit some important details such as the existence and position of minimum, maximum and bimodal distribution which are lost if the scale structure is described by two coefficients only and essential differences and similarities in the scale structures of cross-correlation of intra-regional and inter-regional markets. More statistical characteristics of cross-correlation obtained by MSDCCA method help us to understand how two different stock markets influence each other and to analyze the influence from thus two inter-regional markets on the cross-correlation in detail, thus we get a richer and more detailed knowledge of the complex evolutions of dynamics of the cross-correlations between stock markets. The application of MSDCCA is important to promote our understanding of the internal mechanisms and structures of financial markets and helps to forecast the stock indices based on our current results demonstrated the cross-correlations between stock indices. We also discuss the MSDCCA methods of secant rolling window with different sizes and, lastly, provide some relevant implications and

Correlation analysis of the Taurus molecular cloud complex

International Nuclear Information System (INIS)

Kleiner, S.C.

1985-01-01

Autocorrelation and power spectrum methods were applied to the analysis of the density and velocity structure of the Taurus Complex and Heiles Cloud 2 as traced out by 13 CO J = 1 → 0 molecular line observations obtained with the 14m antenna of the Five College Radio Astronomy Observatory. Statistically significant correlations in the spacing of density fluctuations within the Taurus Complex and Heiles 2 were uncovered. The length scales of the observed correlations correspond in magnitude to the Jeans wavelengths characterizing gravitational instabilities with (i) interstellar atomic hydrogen gas for the case of the Taurus complex, and (ii) molecular hydrogen for Heiles 2. The observed correlations may be the signatures of past and current gravitational instabilities frozen into the structure of the molecular gas. The appendices provide a comprehensive description of the analytical and numerical methods developed for the correlation analysis of molecular clouds

Analysis of Active Components in Salvia Miltiorrhiza Injection Based on Vascular Endothelial Cell Protection

Directory of Open Access Journals (Sweden)

Shen Jie

2014-09-01

Full Text Available Correlation analysis based on chromatograms and pharmacological activities is essential for understanding the effective components in complex herbal medicines. In this report, HPLC and measurement of antioxidant properties were used to describe the active ingredients of Salvia miltiorrhiza injection (SMI. HPLC results showed that tanshinol, protocatechuic aldehyde, rosmarinic acid, salvianolic acid B, protocatechuic acid and their metabolites in rat serum may contribute to the efficacy of SMI. Assessment of antioxidant properties indicated that differences in the composition of serum powder of SMI caused differences in vascular endothelial cell protection. When bivariate correlation was carried out it was found that salvianolic acid B, tanshinol and protocatechuic aldehyde were active components of SMI because they were correlated to antioxidant properties.

International Space Station Model Correlation Analysis

Science.gov (United States)

Laible, Michael R.; Fitzpatrick, Kristin; Hodge, Jennifer; Grygier, Michael

2018-01-01

This paper summarizes the on-orbit structural dynamic data and the related modal analysis, model validation and correlation performed for the International Space Station (ISS) configuration ISS Stage ULF7, 2015 Dedicated Thruster Firing (DTF). The objective of this analysis is to validate and correlate the analytical models used to calculate the ISS internal dynamic loads and compare the 2015 DTF with previous tests. During the ISS configurations under consideration, on-orbit dynamic measurements were collected using the three main ISS instrumentation systems; Internal Wireless Instrumentation System (IWIS), External Wireless Instrumentation System (EWIS) and the Structural Dynamic Measurement System (SDMS). The measurements were recorded during several nominal on-orbit DTF tests on August 18, 2015. Experimental modal analyses were performed on the measured data to extract modal parameters including frequency, damping, and mode shape information. Correlation and comparisons between test and analytical frequencies and mode shapes were performed to assess the accuracy of the analytical models for the configurations under consideration. These mode shapes were also compared to earlier tests. Based on the frequency comparisons, the accuracy of the mathematical models is assessed and model refinement recommendations are given. In particular, results of the first fundamental mode will be discussed, nonlinear results will be shown, and accelerometer placement will be assessed.

Principal Component Analysis Based Two-Dimensional (PCA-2D) Correlation Spectroscopy: PCA Denoising for 2D Correlation Spectroscopy

International Nuclear Information System (INIS)

Jung, Young Mee

2003-01-01

Principal component analysis based two-dimensional (PCA-2D) correlation analysis is applied to FTIR spectra of polystyrene/methyl ethyl ketone/toluene solution mixture during the solvent evaporation. Substantial amount of artificial noise were added to the experimental data to demonstrate the practical noise-suppressing benefit of PCA-2D technique. 2D correlation analysis of the reconstructed data matrix from PCA loading vectors and scores successfully extracted only the most important features of synchronicity and asynchronicity without interference from noise or insignificant minor components. 2D correlation spectra constructed with only one principal component yield strictly synchronous response with no discernible a asynchronous features, while those involving at least two or more principal components generated meaningful asynchronous 2D correlation spectra. Deliberate manipulation of the rank of the reconstructed data matrix, by choosing the appropriate number and type of PCs, yields potentially more refined 2D correlation spectra

Spatial Distribution Characteristics of Healthcare Facilities in Nanjing: Network Point Pattern Analysis and Correlation Analysis

Directory of Open Access Journals (Sweden)

Jianhua Ni

2016-08-01

Full Text Available The spatial distribution of urban service facilities is largely constrained by the road network. In this study, network point pattern analysis and correlation analysis were used to analyze the relationship between road network and healthcare facility distribution. The weighted network kernel density estimation method proposed in this study identifies significant differences between the outside and inside areas of the Ming city wall. The results of network K-function analysis show that private hospitals are more evenly distributed than public hospitals, and pharmacy stores tend to cluster around hospitals along the road network. After computing the correlation analysis between different categorized hospitals and street centrality, we find that the distribution of these hospitals correlates highly with the street centralities, and that the correlations are higher with private and small hospitals than with public and large hospitals. The comprehensive analysis results could help examine the reasonability of existing urban healthcare facility distribution and optimize the location of new healthcare facilities.

Graphology and personality: a correlational analysis

OpenAIRE

2008-01-01

M.A. The title of this dissertation reads as follows: Graphology and Personality: A Correlational Analysis. The aim of this dissertation is to introduce a different projective technique (as of yet not very widely used) into the psychological arena of assessment. Graphology is a projective technique that allows the analyst to delve into the personality of the individual. Very shortly, graphology can be defined as the assessment or analysis of a person’s handwriting. When a child first attem...

Metal contamination of agricultural soils in the copper mining areas ...

Indian Academy of Sciences (India)

Soma Giri

2017-06-07

Jun 7, 2017 ... Agricultural soil; heavy metals; copper mining areas; multivariate analysis; ... multivariate statistical analysis. 2. ... sieved through standard sieve of 200 mesh size (Giri ... Pearson's correlation is a bivariate correlation ... is a variation reduction technique in which a num- ... Varimax rotation is applied to all the.

Analytic uncertainty and sensitivity analysis of models with input correlations

Science.gov (United States)

Zhu, Yueying; Wang, Qiuping A.; Li, Wei; Cai, Xu

2018-03-01

Probabilistic uncertainty analysis is a common means of evaluating mathematical models. In mathematical modeling, the uncertainty in input variables is specified through distribution laws. Its contribution to the uncertainty in model response is usually analyzed by assuming that input variables are independent of each other. However, correlated parameters are often happened in practical applications. In the present paper, an analytic method is built for the uncertainty and sensitivity analysis of models in the presence of input correlations. With the method, it is straightforward to identify the importance of the independence and correlations of input variables in determining the model response. This allows one to decide whether or not the input correlations should be considered in practice. Numerical examples suggest the effectiveness and validation of our analytic method in the analysis of general models. A practical application of the method is also proposed to the uncertainty and sensitivity analysis of a deterministic HIV model.

Bivariate least squares linear regression: Towards a unified analytic formalism. I. Functional models

Science.gov (United States)

Caimmi, R.

2011-08-01

Concerning bivariate least squares linear regression, the classical approach pursued for functional models in earlier attempts ( York, 1966, 1969) is reviewed using a new formalism in terms of deviation (matrix) traces which, for unweighted data, reduce to usual quantities leaving aside an unessential (but dimensional) multiplicative factor. Within the framework of classical error models, the dependent variable relates to the independent variable according to the usual additive model. The classes of linear models considered are regression lines in the general case of correlated errors in X and in Y for weighted data, and in the opposite limiting situations of (i) uncorrelated errors in X and in Y, and (ii) completely correlated errors in X and in Y. The special case of (C) generalized orthogonal regression is considered in detail together with well known subcases, namely: (Y) errors in X negligible (ideally null) with respect to errors in Y; (X) errors in Y negligible (ideally null) with respect to errors in X; (O) genuine orthogonal regression; (R) reduced major-axis regression. In the limit of unweighted data, the results determined for functional models are compared with their counterparts related to extreme structural models i.e. the instrumental scatter is negligible (ideally null) with respect to the intrinsic scatter ( Isobe et al., 1990; Feigelson and Babu, 1992). While regression line slope and intercept estimators for functional and structural models necessarily coincide, the contrary holds for related variance estimators even if the residuals obey a Gaussian distribution, with the exception of Y models. An example of astronomical application is considered, concerning the [O/H]-[Fe/H] empirical relations deduced from five samples related to different stars and/or different methods of oxygen abundance determination. For selected samples and assigned methods, different regression models yield consistent results within the errors (∓ σ) for both

Probabilistic modeling using bivariate normal distributions for identification of flow and displacement intervals in longwall overburden

Energy Technology Data Exchange (ETDEWEB)

Karacan, C.O.; Goodman, G.V.R. [NIOSH, Pittsburgh, PA (United States). Off Mine Safety & Health Research

2011-01-15

Gob gas ventholes (GGV) are used to control methane emissions in longwall mines by capturing it within the overlying fractured strata before it enters the work environment. In order for GGVs to effectively capture more methane and less mine air, the length of the slotted sections and their proximity to top of the coal bed should be designed based on the potential gas sources and their locations, as well as the displacements in the overburden that will create potential flow paths for the gas. In this paper, an approach to determine the conditional probabilities of depth-displacement, depth-flow percentage, depth-formation and depth-gas content of the formations was developed using bivariate normal distributions. The flow percentage, displacement and formation data as a function of distance from coal bed used in this study were obtained from a series of borehole experiments contracted by the former US Bureau of Mines as part of a research project. Each of these parameters was tested for normality and was modeled using bivariate normal distributions to determine all tail probabilities. In addition, the probability of coal bed gas content as a function of depth was determined using the same techniques. The tail probabilities at various depths were used to calculate conditional probabilities for each of the parameters. The conditional probabilities predicted for various values of the critical parameters can be used with the measurements of flow and methane percentage at gob gas ventholes to optimize their performance.

Xp21 contiguous gene syndromes: Deletion quantitation with bivariate flow karyotyping allows mapping of patient breakpoints

Energy Technology Data Exchange (ETDEWEB)

McCabe, E.R.B.; Towbin, J.A. (Baylor College of Medicine, Houston, TX (United States)); Engh, G. van den; Trask, B.J. (Lawrence Livermore National Lab., CA (United States))

1992-12-01

Bivariate flow karyotyping was used to estimate the deletion sizes for a series of patients with Xp21 contiguous gene syndromes. The deletion estimates were used to develop an approximate scale for the genomic map in Xp21. The bivariate flow karyotype results were compared with clinical and molecular genetic information on the extent of the patients' deletions, and these various types of data were consistent. The resulting map spans >15 Mb, from the telomeric interval between DXS41 (99-6) and DXS68 (1-4) to a position centromeric to the ornithine transcarbamylase locus. The deletion sizing was considered to be accurate to [plus minus]1 Mb. The map provides information on the relative localization of genes and markers within this region. For example, the map suggests that the adrenal hypoplasia congenita and glycerol kinase genes are physically close to each other, are within 1-2 Mb of the telomeric end of the Duchenne muscular dystrophy (DMD) gene, and are nearer to the DMD locus than to the more distal marker DXS28 (C7). Information of this type is useful in developing genomic strategies for positional cloning in Xp21. These investigations demonstrate that the DNA from patients with Xp21 contiguous gene syndromes can be valuable reagents, not only for ordering loci and markers but also for providing an approximate scale to the map of the Xp21 region surrounding DMD. 44 refs., 3 figs.

A comparison of the effect of 5-bromodeoxyuridine substitution on 33258 Hoechst- and DAPI-fluorescence of isolated chromosomes by bivariate flow karyotyping

NARCIS (Netherlands)

Buys, C. H.; Mesa, J.; van der Veen, A. Y.; Aten, J. A.

1986-01-01

Application of the fluorescent DNA-intercalator propidium iodide for stabilization of the mitotic chromosome structure during isolation of chromosomes from V79 Chinese hamster cells and subsequent staining with the fluorochromes 33258 Hoechst or DAPI allowed bivariate flow karyotyping of isolated

Multifractal temporally weighted detrended cross-correlation analysis to quantify power-law cross-correlation and its application to stock markets

Science.gov (United States)

Wei, Yun-Lan; Yu, Zu-Guo; Zou, Hai-Long; Anh, Vo

2017-06-01

A new method—multifractal temporally weighted detrended cross-correlation analysis (MF-TWXDFA)—is proposed to investigate multifractal cross-correlations in this paper. This new method is based on multifractal temporally weighted detrended fluctuation analysis and multifractal cross-correlation analysis (MFCCA). An innovation of the method is applying geographically weighted regression to estimate local trends in the nonstationary time series. We also take into consideration the sign of the fluctuations in computing the corresponding detrended cross-covariance function. To test the performance of the MF-TWXDFA algorithm, we apply it and the MFCCA method on simulated and actual series. Numerical tests on artificially simulated series demonstrate that our method can accurately detect long-range cross-correlations for two simultaneously recorded series. To further show the utility of MF-TWXDFA, we apply it on time series from stock markets and find that power-law cross-correlation between stock returns is significantly multifractal. A new coefficient, MF-TWXDFA cross-correlation coefficient, is also defined to quantify the levels of cross-correlation between two time series.

Data analytics using canonical correlation analysis and Monte Carlo simulation

Science.gov (United States)

Rickman, Jeffrey M.; Wang, Yan; Rollett, Anthony D.; Harmer, Martin P.; Compson, Charles

2017-07-01

A canonical correlation analysis is a generic parametric model used in the statistical analysis of data involving interrelated or interdependent input and output variables. It is especially useful in data analytics as a dimensional reduction strategy that simplifies a complex, multidimensional parameter space by identifying a relatively few combinations of variables that are maximally correlated. One shortcoming of the canonical correlation analysis, however, is that it provides only a linear combination of variables that maximizes these correlations. With this in mind, we describe here a versatile, Monte-Carlo based methodology that is useful in identifying non-linear functions of the variables that lead to strong input/output correlations. We demonstrate that our approach leads to a substantial enhancement of correlations, as illustrated by two experimental applications of substantial interest to the materials science community, namely: (1) determining the interdependence of processing and microstructural variables associated with doped polycrystalline aluminas, and (2) relating microstructural decriptors to the electrical and optoelectronic properties of thin-film solar cells based on CuInSe2 absorbers. Finally, we describe how this approach facilitates experimental planning and process control.

Metrics correlation and analysis service (MCAS)

International Nuclear Information System (INIS)

Baranovski, Andrew; Dykstra, Dave; Garzoglio, Gabriele; Hesselroth, Ted; Mhashilkar, Parag; Levshina, Tanya

2009-01-01

The complexity of Grid workflow activities and their associated software stacks inevitably involves multiple organizations, ownership, and deployment domains. In this setting, important and common tasks such as the correlation and display of metrics and debugging information (fundamental ingredients of troubleshooting) are challenged by the informational entropy inherent to independently maintained and operated software components. Because such an information 'pond' is disorganized, it a difficult environment for business intelligence analysis i.e. troubleshooting, incident investigation and trend spotting. The mission of the MCAS project is to deliver a software solution to help with adaptation, retrieval, correlation, and display of workflow-driven data and of type-agnostic events, generated by disjoint middleware.

A bivariate Chebyshev spectral collocation quasilinearization method for nonlinear evolution parabolic equations.

Science.gov (United States)

Motsa, S S; Magagula, V M; Sibanda, P

2014-01-01

This paper presents a new method for solving higher order nonlinear evolution partial differential equations (NPDEs). The method combines quasilinearisation, the Chebyshev spectral collocation method, and bivariate Lagrange interpolation. In this paper, we use the method to solve several nonlinear evolution equations, such as the modified KdV-Burgers equation, highly nonlinear modified KdV equation, Fisher's equation, Burgers-Fisher equation, Burgers-Huxley equation, and the Fitzhugh-Nagumo equation. The results are compared with known exact analytical solutions from literature to confirm accuracy, convergence, and effectiveness of the method. There is congruence between the numerical results and the exact solutions to a high order of accuracy. Tables were generated to present the order of accuracy of the method; convergence graphs to verify convergence of the method and error graphs are presented to show the excellent agreement between the results from this study and the known results from literature.

A Bivariate Chebyshev Spectral Collocation Quasilinearization Method for Nonlinear Evolution Parabolic Equations

Directory of Open Access Journals (Sweden)

S. S. Motsa

2014-01-01

Full Text Available This paper presents a new method for solving higher order nonlinear evolution partial differential equations (NPDEs. The method combines quasilinearisation, the Chebyshev spectral collocation method, and bivariate Lagrange interpolation. In this paper, we use the method to solve several nonlinear evolution equations, such as the modified KdV-Burgers equation, highly nonlinear modified KdV equation, Fisher's equation, Burgers-Fisher equation, Burgers-Huxley equation, and the Fitzhugh-Nagumo equation. The results are compared with known exact analytical solutions from literature to confirm accuracy, convergence, and effectiveness of the method. There is congruence between the numerical results and the exact solutions to a high order of accuracy. Tables were generated to present the order of accuracy of the method; convergence graphs to verify convergence of the method and error graphs are presented to show the excellent agreement between the results from this study and the known results from literature.

An assessment on the use of bivariate, multivariate and soft computing techniques for collapse susceptibility in GIS environ

Science.gov (United States)

Yilmaz, Işik; Marschalko, Marian; Bednarik, Martin

2013-04-01

The paper presented herein compares and discusses the use of bivariate, multivariate and soft computing techniques for collapse susceptibility modelling. Conditional probability (CP), logistic regression (LR) and artificial neural networks (ANN) models representing the bivariate, multivariate and soft computing techniques were used in GIS based collapse susceptibility mapping in an area from Sivas basin (Turkey). Collapse-related factors, directly or indirectly related to the causes of collapse occurrence, such as distance from faults, slope angle and aspect, topographical elevation, distance from drainage, topographic wetness index (TWI), stream power index (SPI), Normalized Difference Vegetation Index (NDVI) by means of vegetation cover, distance from roads and settlements were used in the collapse susceptibility analyses. In the last stage of the analyses, collapse susceptibility maps were produced from the models, and they were then compared by means of their validations. However, Area Under Curve (AUC) values obtained from all three models showed that the map obtained from soft computing (ANN) model looks like more accurate than the other models, accuracies of all three models can be evaluated relatively similar. The results also showed that the conditional probability is an essential method in preparation of collapse susceptibility map and highly compatible with GIS operating features.

Optical Coherence Tomography Noise Reduction Using Anisotropic Local Bivariate Gaussian Mixture Prior in 3D Complex Wavelet Domain.

Science.gov (United States)

Rabbani, Hossein; Sonka, Milan; Abramoff, Michael D

2013-01-01

In this paper, MMSE estimator is employed for noise-free 3D OCT data recovery in 3D complex wavelet domain. Since the proposed distribution for noise-free data plays a key role in the performance of MMSE estimator, a priori distribution for the pdf of noise-free 3D complex wavelet coefficients is proposed which is able to model the main statistical properties of wavelets. We model the coefficients with a mixture of two bivariate Gaussian pdfs with local parameters which are able to capture the heavy-tailed property and inter- and intrascale dependencies of coefficients. In addition, based on the special structure of OCT images, we use an anisotropic windowing procedure for local parameters estimation that results in visual quality improvement. On this base, several OCT despeckling algorithms are obtained based on using Gaussian/two-sided Rayleigh noise distribution and homomorphic/nonhomomorphic model. In order to evaluate the performance of the proposed algorithm, we use 156 selected ROIs from 650 × 512 × 128 OCT dataset in the presence of wet AMD pathology. Our simulations show that the best MMSE estimator using local bivariate mixture prior is for the nonhomomorphic model in the presence of Gaussian noise which results in an improvement of 7.8 ± 1.7 in CNR.

Optical Coherence Tomography Noise Reduction Using Anisotropic Local Bivariate Gaussian Mixture Prior in 3D Complex Wavelet Domain

Directory of Open Access Journals (Sweden)

Hossein Rabbani

2013-01-01

Full Text Available In this paper, MMSE estimator is employed for noise-free 3D OCT data recovery in 3D complex wavelet domain. Since the proposed distribution for noise-free data plays a key role in the performance of MMSE estimator, a priori distribution for the pdf of noise-free 3D complex wavelet coefficients is proposed which is able to model the main statistical properties of wavelets. We model the coefficients with a mixture of two bivariate Gaussian pdfs with local parameters which are able to capture the heavy-tailed property and inter- and intrascale dependencies of coefficients. In addition, based on the special structure of OCT images, we use an anisotropic windowing procedure for local parameters estimation that results in visual quality improvement. On this base, several OCT despeckling algorithms are obtained based on using Gaussian/two-sided Rayleigh noise distribution and homomorphic/nonhomomorphic model. In order to evaluate the performance of the proposed algorithm, we use 156 selected ROIs from 650 × 512 × 128 OCT dataset in the presence of wet AMD pathology. Our simulations show that the best MMSE estimator using local bivariate mixture prior is for the nonhomomorphic model in the presence of Gaussian noise which results in an improvement of 7.8 ± 1.7 in CNR.

Canonical correlation analysis of course and teacher evaluation

DEFF Research Database (Denmark)

Sliusarenko, Tamara; Ersbøll, Bjarne Kjær

2010-01-01

At the Technical University of Denmark course evaluations are performed by the students on a questionnaire. On one form the students are asked specific questions regarding the course. On a second form they are asked specific questions about the teacher. This study investigates the extent to which...... information obtained from the course evaluation form overlaps with information obtained from the teacher evaluation form. Employing canonical correlation analysis it was found that course and teacher evaluations are correlated. However, the structure of the canonical correlation is subject to change...

Metrics correlation and analysis service (MCAS)

International Nuclear Information System (INIS)

Baranovski, Andrew; Dykstra, Dave; Garzoglio, Gabriele; Hesselroth, Ted; Mhashilkar, Parag; Levshina, Tanya

2010-01-01

The complexity of Grid workflow activities and their associated software stacks inevitably involves multiple organizations, ownership, and deployment domains. In this setting, important and common tasks such as the correlation and display of metrics and debugging information (fundamental ingredients of troubleshooting) are challenged by the informational entropy inherent to independently maintained and operated software components. Because such an information pool is disorganized, it is a difficult environment for business intelligence analysis i.e. troubleshooting, incident investigation, and trend spotting. The mission of the MCAS project is to deliver a software solution to help with adaptation, retrieval, correlation, and display of workflow-driven data and of type-agnostic events, generated by loosely coupled or fully decoupled middleware.

Within-Subject Correlation Analysis to Detect Functional Areas Associated With Response Inhibition

Directory of Open Access Journals (Sweden)

Tomoko Yamasaki

2018-05-01

Full Text Available Functional areas in fMRI studies are often detected by brain-behavior correlation, calculating across-subject correlation between the behavioral index and the brain activity related to a function of interest. Within-subject correlation analysis is also employed in a single subject level, which utilizes cognitive fluctuations in a shorter time period by correlating the behavioral index with the brain activity across trials. In the present study, the within-subject analysis was applied to the stop-signal task, a standard task to probe response inhibition, where efficiency of response inhibition can be evaluated by the stop-signal reaction time (SSRT. Since the SSRT is estimated, by definition, not in a trial basis but from pooled trials, the correlation across runs was calculated between the SSRT and the brain activity related to response inhibition. The within-subject correlation revealed negative correlations in the anterior cingulate cortex and the cerebellum. Moreover, the dissociation pattern was observed in the within-subject analysis when earlier vs. later parts of the runs were analyzed: negative correlation was dominant in earlier runs, whereas positive correlation was dominant in later runs. Regions of interest analyses revealed that the negative correlation in the anterior cingulate cortex, but not in the cerebellum, was dominant in earlier runs, suggesting multiple mechanisms associated with inhibitory processes that fluctuate on a run-by-run basis. These results indicate that the within-subject analysis compliments the across-subject analysis by highlighting different aspects of cognitive/affective processes related to response inhibition.

Molecular Descriptors Family on Structure Activity Relationships 3. Antituberculotic Activity of some Polyhydroxyxanthones

Directory of Open Access Journals (Sweden)

Sorana BOLBOACĂ

2005-06-01

Full Text Available The antituberculotic activity of some polyhydroxyxanthones was estimated using the Molecular Descriptors Family on Structure Activity Relationships methodology. From a total number of 298110 real and distinct calculated descriptors, 94843 were significantly different and entered into multiple linear regression analysis. The best performing bi-varied model was obtained by use of all polyhydroxyxanthones. The MDF SAR model was validated splitting the molecules into training and test sets. A correlated correlations analysis was applied in other to compare the MDF SAR models with the previous SAR model. The prediction ability of antituberculotic activity of polyhydroxyxanthones with MDF SAR methodology is sustained by three arguments: leave-one-out procedure, training vs. test procedure, and the correlated correlations analysis. Looking at the bi-varied MDF SAR model, we can conclude that the antituberculotic activity of polyhydroxyxanthones is almost of geometrical nature (99% and is strongly dependent on partial atomic charge and group electronegativity.

Nonlinear canonical correlation analysis with k sets of variables

NARCIS (Netherlands)

van der Burg, Eeke; de Leeuw, Jan

1987-01-01

The multivariate technique OVERALS is introduced as a non-linear generalization of canonical correlation analysis (CCA). First, two sets CCA is introduced. Two sets CCA is a technique that computes linear combinations of sets of variables that correlate in an optimal way. Two sets CCA is then

Applying Emax model and bivariate thin plate splines to assess drug interactions.

Science.gov (United States)

Kong, Maiying; Lee, J Jack

2010-01-01

We review the semiparametric approach previously proposed by Kong and Lee and extend it to a case in which the dose-effect curves follow the Emax model instead of the median effect equation. When the maximum effects for the investigated drugs are different, we provide a procedure to obtain the additive effect based on the Loewe additivity model. Then, we apply a bivariate thin plate spline approach to estimate the effect beyond additivity along with its 95 per cent point-wise confidence interval as well as its 95 per cent simultaneous confidence interval for any combination dose. Thus, synergy, additivity, and antagonism can be identified. The advantages of the method are that it provides an overall assessment of the combination effect on the entire two-dimensional dose space spanned by the experimental doses, and it enables us to identify complex patterns of drug interaction in combination studies. In addition, this approach is robust to outliers. To illustrate this procedure, we analyzed data from two case studies.

Sparse canonical correlation analysis: new formulation and algorithm.

Science.gov (United States)

Chu, Delin; Liao, Li-Zhi; Ng, Michael K; Zhang, Xiaowei

2013-12-01

In this paper, we study canonical correlation analysis (CCA), which is a powerful tool in multivariate data analysis for finding the correlation between two sets of multidimensional variables. The main contributions of the paper are: 1) to reveal the equivalent relationship between a recursive formula and a trace formula for the multiple CCA problem, 2) to obtain the explicit characterization for all solutions of the multiple CCA problem even when the corresponding covariance matrices are singular, 3) to develop a new sparse CCA algorithm, and 4) to establish the equivalent relationship between the uncorrelated linear discriminant analysis and the CCA problem. We test several simulated and real-world datasets in gene classification and cross-language document retrieval to demonstrate the effectiveness of the proposed algorithm. The performance of the proposed method is competitive with the state-of-the-art sparse CCA algorithms.

Spatial correlations of Diceroprocta apache and its host plants: Evidence for a negative impact from Tamarix invasion

Science.gov (United States)

Ellingson, A.R.; Andersen, D.C.

2002-01-01

1. The hypothesis that the habitat-scale spatial distribution of the Apache cicada Diceroprocta apache Davis is unaffected by the presence of the invasive exotic saltcedar Tamarix ramosissima was tested using data from 205 1-m2 quadrats placed within the flood-plain of the Bill Williams River, Arizona, U.S.A. Spatial dependencies within and between cicada density and habitat variables were estimated using Moran's I and its bivariate analogue to discern patterns and associations at spatial scales from 1 to 30 m.2. Apache cicadas were spatially aggregated in high-density clusters averaging 3 m in diameter. A positive association between cicada density, estimated by exuvial density, and the per cent canopy cover of a native tree, Goodding's willow Salix gooddingii, was detected in a non-spatial correlation analysis. No non-spatial association between cicada density and saltcedar canopy cover was detected.3. Tests for spatial cross-correlation using the bivariate IYZ indicated the presence of a broad-scale negative association between cicada density and saltcedar canopy cover. This result suggests that large continuous stands of saltcedar are associated with reduced cicada density. In contrast, positive associations detected at spatial scales larger than individual quadrats suggested a spill-over of high cicada density from areas featuring Goodding's willow canopy into surrounding saltcedar monoculture.4. Taken together and considered in light of the Apache cicada's polyphagous habits, the observed spatial patterns suggest that broad-scale factors such as canopy heterogeneity affect cicada habitat use more than host plant selection. This has implications for management of lower Colorado River riparian woodlands to promote cicada presence and density through maintenance or creation of stands of native trees as well as manipulation of the characteristically dense and homogeneous saltcedar canopies.

Asymptotics of bivariate generating functions with algebraic singularities

Science.gov (United States)

Greenwood, Torin

Flajolet and Odlyzko (1990) derived asymptotic formulae the coefficients of a class of uni- variate generating functions with algebraic singularities. Gao and Richmond (1992) and Hwang (1996, 1998) extended these results to classes of multivariate generating functions, in both cases by reducing to the univariate case. Pemantle and Wilson (2013) outlined new multivariate ana- lytic techniques and used them to analyze the coefficients of rational generating functions. After overviewing these methods, we use them to find asymptotic formulae for the coefficients of a broad class of bivariate generating functions with algebraic singularities. Beginning with the Cauchy integral formula, we explicity deform the contour of integration so that it hugs a set of critical points. The asymptotic contribution to the integral comes from analyzing the integrand near these points, leading to explicit asymptotic formulae. Next, we use this formula to analyze an example from current research. In the following chapter, we apply multivariate analytic techniques to quan- tum walks. Bressler and Pemantle (2007) found a (d + 1)-dimensional rational generating function whose coefficients described the amplitude of a particle at a position in the integer lattice after n steps. Here, the minimal critical points form a curve on the (d + 1)-dimensional unit torus. We find asymptotic formulae for the amplitude of a particle in a given position, normalized by the number of steps n, as n approaches infinity. Each critical point contributes to the asymptotics for a specific normalized position. Using Groebner bases in Maple again, we compute the explicit locations of peak amplitudes. In a scaling window of size the square root of n near the peaks, each amplitude is asymptotic to an Airy function.

Genetic correlations among body condition score, yield, and fertility in first-parity cows estimated by random regression models.

Science.gov (United States)

Veerkamp, R F; Koenen, E P; De Jong, G

2001-10-01

Twenty type classifiers scored body condition (BCS) of 91,738 first-parity cows from 601 sires and 5518 maternal grandsires. Fertility data during first lactation were extracted for 177,220 cows, of which 67,278 also had a BCS observation, and first-lactation 305-d milk, fat, and protein yields were added for 180,631 cows. Heritabilities and genetic correlations were estimated using a sire-maternal grandsire model. Heritability of BCS was 0.38. Heritabilities for fertility traits were low (0.01 to 0.07), but genetic standard deviations were substantial, 9 d for days to first service and calving interval, 0.25 for number of services, and 5% for first-service conception. Phenotypic correlations between fertility and yield or BCS were small (-0.15 to 0.20). Genetic correlations between yield and all fertility traits were unfavorable (0.37 to 0.74). Genetic correlations with BCS were between -0.4 and -0.6 for calving interval and days to first service. Random regression analysis (RR) showed that correlations changed with days in milk for BCS. Little agreement was found between variances and correlations from RR, and analysis including a single month (mo 1 to 10) of data for BCS, especially during early and late lactation. However, this was due to excluding data from the conventional analysis, rather than due to the polynomials used. RR and a conventional five-traits model where BCS in mo 1, 4, 7, and 10 was treated as a separate traits (plus yield or fertility) gave similar results. Thus a parsimonious random regression model gave more realistic estimates for the (co)variances than a series of bivariate analysis on subsets of the data for BCS. A higher genetic merit for yield has unfavorable effects on fertility, but the genetic correlation suggests that BCS (at some stages of lactation) might help to alleviate the unfavorable effect of selection for higher yield on fertility.

Epileptic seizure prediction based on a bivariate spectral power methodology.

Science.gov (United States)

Bandarabadi, Mojtaba; Teixeira, Cesar A; Direito, Bruno; Dourado, Antonio

2012-01-01

The spectral power of 5 frequently considered frequency bands (Alpha, Beta, Gamma, Theta and Delta) for 6 EEG channels is computed and then all the possible pairwise combinations among the 30 features set, are used to create a 435 dimensional feature space. Two new feature selection methods are introduced to choose the best candidate features among those and to reduce the dimensionality of this feature space. The selected features are then fed to Support Vector Machines (SVMs) that classify the cerebral state in preictal and non-preictal classes. The outputs of the SVM are regularized using a method that accounts for the classification dynamics of the preictal class, also known as "Firing Power" method. The results obtained using our feature selection approaches are compared with the ones obtained using minimum Redundancy Maximum Relevance (mRMR) feature selection method. The results in a group of 12 patients of the EPILEPSIAE database, containing 46 seizures and 787 hours multichannel recording for out-of-sample data, indicate the efficiency of the bivariate approach as well as the two new feature selection methods. The best results presented sensitivity of 76.09% (35 of 46 seizures predicted) and a false prediction rate of 0.15(-1).

Correlation Between Posttraumatic Growth and Posttraumatic Stress Disorder Symptoms Based on Pearson Correlation Coefficient: A Meta-Analysis.

Science.gov (United States)

Liu, An-Nuo; Wang, Lu-Lu; Li, Hui-Ping; Gong, Juan; Liu, Xiao-Hong

2017-05-01

The literature on posttraumatic growth (PTG) is burgeoning, with the inconsistencies in the literature of the relationship between PTG and posttraumatic stress disorder (PTSD) symptoms becoming a focal point of attention. Thus, this meta-analysis aims to explore the relationship between PTG and PTSD symptoms through the Pearson correlation coefficient. A systematic search of the literature from January 1996 to November 2015 was completed. We retrieved reports on 63 studies that involved 26,951 patients. The weighted correlation coefficient revealed an effect size of 0.22 with a 95% confidence interval of 0.18 to 0.25. Meta-analysis provides evidence that PTG may be positively correlated with PTSD symptoms and that this correlation may be modified by age, trauma type, and time since trauma. Accordingly, people with high levels of PTG should not be ignored, but rather, they should continue to receive help to alleviate their PTSD symptoms.

Genome-scale cluster analysis of replicated microarrays using shrinkage correlation coefficient.

Science.gov (United States)

Yao, Jianchao; Chang, Chunqi; Salmi, Mari L; Hung, Yeung Sam; Loraine, Ann; Roux, Stanley J

2008-06-18

Currently, clustering with some form of correlation coefficient as the gene similarity metric has become a popular method for profiling genomic data. The Pearson correlation coefficient and the standard deviation (SD)-weighted correlation coefficient are the two most widely-used correlations as the similarity metrics in clustering microarray data. However, these two correlations are not optimal for analyzing replicated microarray data generated by most laboratories. An effective correlation coefficient is needed to provide statistically sufficient analysis of replicated microarray data. In this study, we describe a novel correlation coefficient, shrinkage correlation coefficient (SCC), that fully exploits the similarity between the replicated microarray experimental samples. The methodology considers both the number of replicates and the variance within each experimental group in clustering expression data, and provides a robust statistical estimation of the error of replicated microarray data. The value of SCC is revealed by its comparison with two other correlation coefficients that are currently the most widely-used (Pearson correlation coefficient and SD-weighted correlation coefficient) using statistical measures on both synthetic expression data as well as real gene expression data from Saccharomyces cerevisiae. Two leading clustering methods, hierarchical and k-means clustering were applied for the comparison. The comparison indicated that using SCC achieves better clustering performance. Applying SCC-based hierarchical clustering to the replicated microarray data obtained from germinating spores of the fern Ceratopteris richardii, we discovered two clusters of genes with shared expression patterns during spore germination. Functional analysis suggested that some of the genetic mechanisms that control germination in such diverse plant lineages as mosses and angiosperms are also conserved among ferns. This study shows that SCC is an alternative to the Pearson

Development of Test-Analysis Models (TAM) for correlation of dynamic test and analysis results

Science.gov (United States)

Angelucci, Filippo; Javeed, Mehzad; Mcgowan, Paul

1992-01-01

The primary objective of structural analysis of aerospace applications is to obtain a verified finite element model (FEM). The verified FEM can be used for loads analysis, evaluate structural modifications, or design control systems. Verification of the FEM is generally obtained as the result of correlating test and FEM models. A test analysis model (TAM) is very useful in the correlation process. A TAM is essentially a FEM reduced to the size of the test model, which attempts to preserve the dynamic characteristics of the original FEM in the analysis range of interest. Numerous methods for generating TAMs have been developed in the literature. The major emphasis of this paper is a description of the procedures necessary for creation of the TAM and the correlation of the reduced models with the FEM or the test results. Herein, three methods are discussed, namely Guyan, Improved Reduced System (IRS), and Hybrid. Also included are the procedures for performing these analyses using MSC/NASTRAN. Finally, application of the TAM process is demonstrated with an experimental test configuration of a ten bay cantilevered truss structure.

Analysis of charge-dependent azimuthal correlations with HADES

Energy Technology Data Exchange (ETDEWEB)

Kornas, Frederic [TU Darmstadt (Germany); Selyuzhenkov, Ilya [GSI (Germany); Galatyuk, Tetyana [TU Darmstadt (Germany); GSI (Germany); Collaboration: HADES-Collaboration

2016-07-01

Charge-dependent azimuthal correlations relative to the reaction plane have been proposed as a probe in the search for the chiral magnetic effect in relativistic heavy-ion collisions. These type of correlations have been measured at the RHIC BES by STAR and at the LHC by ALICE. This contribution discusses two charged particle correlations with respect to the reaction plane measured with high statistic sample of Au+Au collisions at 1.23 AGeV collected by HADES. The Forward wall detector allows to reconstruct the reaction plane using the spectator fragments. The status of the analysis with protons and charged pions will be presented.

Correlates of cannabis vape-pen use and knowledge among U.S. college students

Directory of Open Access Journals (Sweden)

Tessa Frohe

2018-06-01

Full Text Available Introduction: The proliferation of electronic devices, such as vape-pens, has provided alternative means for cannabis use. Research has found cannabis-vaping (i.e., vape-pen use is associated with lower perceived risks and higher cannabis use. Knowledge of these products may increase likelihood of subsequent use. As policies for cannabis shift, beliefs that peers and family approve of this substance use (injunctive norms increase and there has been an increase in vape-pen use among young adults (18–35year olds; however, correlates thereof remain unknown. Young adults often engage in cross-substance use with cannabis and alcohol, making alcohol a potential correlate of cannabis vape-pen use and knowledge. Therefore, we examined alcohol use and other potential correlates of vape-pen use and knowledge among a sample of university students. Methods: This secondary data analysis utilized surveys at multiple colleges in the U.S. (N=270. Alcohol use, social anxiety, cannabis expectancies, injunctive and descriptive norms and facets of impulsivity were examined as correlates of vape-pen use and knowledge using bivariate correlations and logistic regressions. Results: Alcohol use was correlated with cannabis vape-pen use and knowledge. Frequency of cannabis use, peer injunctive norms, and positive expectancies were associated with increased likelihood of vape-pen use. Lack of premeditation, a facet of impulsivity, was associated with cannabis vape-pen knowledge. Conclusions: Given the unknown nature and consequences of cannabis vape-pens, the present findings offer valuable information on correlates of this behavior. Further, correlates of knowledge of vape-pens may point to areas for education and clinical intervention to prevent heavy cannabis vape-pen use. Keywords: Marijuana, Vaporizer, College students, Substance use, Attitudes, Cannabis

CORRELATION BETWEEN SERVICE QUALITY AND PATIENT LOYALTY AT OUTPATIENT DEPARTMENT IN SPECIALITY EYE HOSPITAL OF SOUTH SUMATERA PROVINCE

Directory of Open Access Journals (Sweden)

Adelina Fitri

2016-03-01

Full Text Available Background: The increasing number of hospitals in Indonesia, which is managed by the government and private demand to provide giving the level of care, professionalism and the maximum of competence to reflect the best quality of service. Quality gives special encouragement for company to understand customer expectations which in turn can increase customer satisfaction and create customer loyalty. The objective of the study is analyse correlation between service quality and patient loyalty Method: This study used a survey of analytical methods, with the cross-sectional approach. The samples in this study were patients who were treated in outpatient installation specialty eye hospital South Sumatera Province with total 102 people based on inclusion criteria. The Sampling was taken by proportions random sampling. The data analysis was performed in univariate and bivariate statistical fisher-exact test. The data were presented in p-value, prevalence ratio(PR and 95% confidence intervals (CI. Results: Generally 46.2% respondent who are loyal aged 41-64 years, 32.3% graduate from primary school and 80.6% have distance >5 KM from hospital. From the bivariate analyzes relating to patients loyalty in specialty eye hospital South Sumatera Province are access to services (95% CI 4.3 PR (1.293-14.300 and officer interaction with patients (95% CI 6.786 PR (2.140-21.515, whereas safety (95% CI PR 1.248(0.281-5.540, convenience (95% CI PR 4.136 (1.201-14.246 and waiting times (95% CI PR 1.951 (0.259-14.717 does not have a correlation with patient loyalty. Conclusion: The loyalty of patients in Specialty Eyes Hospital South Sumatera Province have been affected by access to services and officer interaction with the patient.

A view on coupled cluster perturbation theory using a bivariational Lagrangian formulation.

Science.gov (United States)

Kristensen, Kasper; Eriksen, Janus J; Matthews, Devin A; Olsen, Jeppe; Jørgensen, Poul

2016-02-14

We consider two distinct coupled cluster (CC) perturbation series that both expand the difference between the energies of the CCSD (CC with single and double excitations) and CCSDT (CC with single, double, and triple excitations) models in orders of the Møller-Plesset fluctuation potential. We initially introduce the E-CCSD(T-n) series, in which the CCSD amplitude equations are satisfied at the expansion point, and compare it to the recently developed CCSD(T-n) series [J. J. Eriksen et al., J. Chem. Phys. 140, 064108 (2014)], in which not only the CCSD amplitude, but also the CCSD multiplier equations are satisfied at the expansion point. The computational scaling is similar for the two series, and both are term-wise size extensive with a formal convergence towards the CCSDT target energy. However, the two series are different, and the CCSD(T-n) series is found to exhibit a more rapid convergence up through the series, which we trace back to the fact that more information at the expansion point is utilized than for the E-CCSD(T-n) series. The present analysis can be generalized to any perturbation expansion representing the difference between a parent CC model and a higher-level target CC model. In general, we demonstrate that, whenever the parent parameters depend upon the perturbation operator, a perturbation expansion of the CC energy (where only parent amplitudes are used) differs from a perturbation expansion of the CC Lagrangian (where both parent amplitudes and parent multipliers are used). For the latter case, the bivariational Lagrangian formulation becomes more than a convenient mathematical tool, since it facilitates a different and faster convergent perturbation series than the simpler energy-based expansion.

Correlation of USMLE Step 1 scores with performance on dermatology in-training examinations.

Science.gov (United States)

Fening, Katherine; Vander Horst, Anthony; Zirwas, Matthew

2011-01-01

Although United States Medical Licensing Examination (USMLE) Step 1 was not designed to predict resident performance, scores are used to compare residency applicants. Multiple studies have displayed a significant correlation among Step 1 scores, in-training examination (ITE) scores, and board passage, although no such studies have been performed in dermatology. The purpose of this study is to determine if this correlation exists in dermatology, and how much of the variability in ITE scores is a result of differences in Step 1 scores. This study also seeks to determine if it is appropriate to individualize expectations for resident ITE performance. This project received institutional review board exemption. From 5 dermatology residency programs (86 residents), we collected Step 1 and ITE scores for each of the 3 years of dermatology residency, and recorded passage/failure on boards. Bivariate Pearson correlation analysis was used to assess correlation between USMLE and ITE scores. Ordinary least squares regression was computed to determine how much USMLE scores contribute to ITE variability. USMLE and ITE score correlations were highly significant (P ITE in years 1, 2, and 3, respectively. Variability in ITE scores caused by differences in USMLE scores were: ITE first-year residency = 21.8%, ITE second-year residency = 29.3%, and ITE third-year residency = 27.8%. This study had a relatively small sample size, with data from only 5 programs. There is a moderate correlation between USMLE and ITE scores, with USMLE scores explaining ∼26% of the variability in ITE scores. Copyright © 2009 American Academy of Dermatology, Inc. Published by Mosby, Inc. All rights reserved.

Occlusal force is correlated with cognitive function directly as well as indirectly via food intake in community-dwelling older Japanese: From the SONIC study.

Science.gov (United States)

Ikebe, Kazunori; Gondo, Yasuyuki; Kamide, Kei; Masui, Yukie; Ishizaki, Taturo; Arai, Yasumichi; Inagaki, Hiroki; Nakagawa, Takeshi; Kabayama, Mai; Ryuno, Hirochika; Okubo, Hitomi; Takeshita, Hajime; Inomata, Chisato; Kurushima, Yuko; Mihara, Yusuke; Hatta, Kohdai; Fukutake, Motoyoshi; Enoki, Kaori; Ogawa, Taiji; Matsuda, Ken-Ichi; Sugimoto, Ken; Oguro, Ryosuke; Takami, Yoichi; Itoh, Norihisa; Takeya, Yasushi; Yamamoto, Koichi; Rakugi, Hiromi; Murakami, Shinya; Kitamura, Masahiro; Maeda, Yoshinobu

2018-01-01

Growing evidence suggests that oral health may be an important factor associated with cognitive function in aged populations. However, many previous studies on this topic used insensitive oral indicators or did not include certain essential covariates. Thus, we examined the association between occlusal force and cognitive function in a large sample of older adults, controlling for dietary intake, vascular risk factors, inflammatory biomarkers, depression, and genetic factors. In this cross-sectional study of older community-dwelling Japanese adults, we examined data collected from 994 persons aged 70 years and 968 persons aged 80 years. Cognitive function was measured using the Japanese version of the Montreal Cognitive Assessment (MoCA-J). Oral status and function were evaluated according to the number of remaining teeth, periodontal pocket depth, and maximal occlusal force. Associations between MoCA-J scores and occlusal force were investigated via bivariate and multivariate analyses. Education level, financial status, depression score, and intake of green and yellow vegetables, as well as number of teeth and occlusal force, were significantly correlated with MoCA-J scores in both age groups. Among individuals aged 80 years, CRP and periodontal status were weakly but significantly associated with MoCA-J score. After controlling for all significant variables via bivariate analyses, the correlation between maximal occlusal force and cognitive function persisted. A path analysis confirmed the hypothesis that cognitive function is associated with occlusal force directly as well as indirectly via food intake. After controlling for possible factors, maximal occlusal force was positively associated with cognitive function directly as well as indirectly through dietary intake.

Message Correlation Analysis Tool for NOvA

International Nuclear Information System (INIS)

Lu Qiming; Biery, Kurt A; Kowalkowski, James B

2012-01-01

A complex running system, such as the NOvA online data acquisition, consists of a large number of distributed but closely interacting components. This paper describes a generic real-time correlation analysis and event identification engine, named Message Analyzer. Its purpose is to capture run time abnormalities and recognize system failures based on log messages from participating components. The initial design of analysis engine is driven by the data acquisition (DAQ) of the NOvA experiment. The Message Analyzer performs filtering and pattern recognition on the log messages and reacts to system failures identified by associated triggering rules. The tool helps the system maintain a healthy running state and to minimize data corruption. This paper also describes a domain specific language that allows the recognition patterns and correlation rules to be specified in a clear and flexible way. In addition, the engine provides a plugin mechanism for users to implement specialized patterns or rules in generic languages such as C++.

Message Correlation Analysis Tool for NOvA

CERN Multimedia

CERN. Geneva

2012-01-01

A complex running system, such as the NOvA online data acquisition, consists of a large number of distributed but closely interacting components. This paper describes a generic realtime correlation analysis and event identification engine, named Message Analyzer. Its purpose is to capture run time abnormalities and recognize system failures based on log messages from participating components. The initial design of analysis engine is driven by the DAQ of the NOvA experiment. The Message Analyzer performs filtering and pattern recognition on the log messages and reacts to system failures identified by associated triggering rules. The tool helps the system maintain a healthy running state and to minimize data corruption. This paper also describes a domain specific language that allows the recognition patterns and correlation rules to be specified in a clear and flexible way. In addition, the engine provides a plugin mechanism for users to implement specialized patterns or rules in generic languages such as C++.

International Space Station Future Correlation Analysis Improvements

Science.gov (United States)

Laible, Michael R.; Pinnamaneni, Murthy; Sugavanam, Sujatha; Grygier, Michael

2018-01-01

Ongoing modal analyses and model correlation are performed on different configurations of the International Space Station (ISS). These analyses utilize on-orbit dynamic measurements collected using four main ISS instrumentation systems: External Wireless Instrumentation System (EWIS), Internal Wireless Instrumentation System (IWIS), Space Acceleration Measurement System (SAMS), and Structural Dynamic Measurement System (SDMS). Remote Sensor Units (RSUs) are network relay stations that acquire flight data from sensors. Measured data is stored in the Remote Sensor Unit (RSU) until it receives a command to download data via RF to the Network Control Unit (NCU). Since each RSU has its own clock, it is necessary to synchronize measurements before analysis. Imprecise synchronization impacts analysis results. A study was performed to evaluate three different synchronization techniques: (i) measurements visually aligned to analytical time-response data using model comparison, (ii) Frequency Domain Decomposition (FDD), and (iii) lag from cross-correlation to align measurements. This paper presents the results of this study.

Message correlation analysis tool for NOvA

Energy Technology Data Exchange (ETDEWEB)

Lu, Qiming [Fermilab; Biery, Kurt A. [Fermilab; Kowalkowski, James B. [Fermilab

2012-01-01

A complex running system, such as the NOvA online data acquisition, consists of a large number of distributed but closely interacting components. This paper describes a generic real-time correlation analysis and event identification engine, named Message Analyzer. Its purpose is to capture run time abnormalities and recognize system failures based on log messages from participating components. The initial design of analysis engine is driven by the data acquisition (DAQ) of the NOvA experiment. The Message Analyzer performs filtering and pattern recognition on the log messages and reacts to system failures identified by associated triggering rules. The tool helps the system maintain a healthy running state and to minimize data corruption. This paper also describes a domain specific language that allows the recognition patterns and correlation rules to be specified in a clear and flexible way. In addition, the engine provides a plugin mechanism for users to implement specialized patterns or rules in generic languages such as C++.

Signal correlations in biomass combustion. An information theoretic analysis

Energy Technology Data Exchange (ETDEWEB)

Ruusunen, M.

2013-09-01

Increasing environmental and economic awareness are driving the development of combustion technologies to efficient biomass use and clean burning. To accomplish these goals, quantitative information about combustion variables is needed. However, for small-scale combustion units the existing monitoring methods are often expensive or complex. This study aimed to quantify correlations between flue gas temperatures and combustion variables, namely typical emission components, heat output, and efficiency. For this, data acquired from four small-scale combustion units and a large circulating fluidised bed boiler was studied. The fuel range varied from wood logs, wood chips, and wood pellets to biomass residue. Original signals and a defined set of their mathematical transformations were applied to data analysis. In order to evaluate the strength of the correlations, a multivariate distance measure based on information theory was derived. The analysis further assessed time-varying signal correlations and relative time delays. Ranking of the analysis results was based on the distance measure. The uniformity of the correlations in the different data sets was studied by comparing the 10-quantiles of the measured signal. The method was validated with two benchmark data sets. The flue gas temperatures and the combustion variables measured carried similar information. The strongest correlations were mainly linear with the transformed signal combinations and explicable by the combustion theory. Remarkably, the results showed uniformity of the correlations across the data sets with several signal transformations. This was also indicated by simulations using a linear model with constant structure to monitor carbon dioxide in flue gas. Acceptable performance was observed according to three validation criteria used to quantify modelling error in each data set. In general, the findings demonstrate that the presented signal transformations enable real-time approximation of the studied

Socio-economic Correlates of Malnutrition among Married Women in Bangladesh.

Science.gov (United States)

Mostafa Kamal, S M; Md Aynul, Islam

2010-12-01

This paper examines the prevalence and socio-economic correlates of malnutrition among ever married non-pregnant women of reproductive age of Bangladesh using a nationally representative weighted sample of 10,145. Body mass index was used to measure nutritional status. Both bivariate and multivariate statistical analyses were employed to assess the relationship between socio-economic characteristics and women's nutritional status. Overall, 28.5% of the women were found to be underweight. The fixed effect multivariate binary logistic regression analysis yielded significantly increased risk of underweight for the young, currently working, non-Muslim, rural residents, widowed, divorced or separated women. Significant wide variations of malnourishment prevailed in the administrative regions of the country. Wealth index and women's education were the most important determinants of underweight. The multivariate logistic regression analysis revealed that the risk of being underweight was almost seven times higher (OR=6.76, 95% CI=5.20-8.80) among women with no formal education as compared to those with higher education and the likelihood of underweight was significantly (pPoverty alleviation programmes should be strengthened targeting the poor. Effective policies, information and health education programmes for women are required to ensure adequate access to health services and for them to understand the components of a healthy diet.

CORRELATIONS BETWEEN FINDINGS OF OCCLUSAL AND MANUAL ANALYSIS IN TMD-PATIENTS

Directory of Open Access Journals (Sweden)

Mariana Dimova

2016-08-01

Full Text Available The aim of this study was to investigate and analyze the possible correlations between findings by manual functional analysis and clinical occlusal analysis in TMD-patients. Material and methods: Material of this study are 111 TMD-patients selected after visual diagnostics, functional brief review under Ahlers Jakstatt, intraoral examination and taking periodontal status. In the period September 2014 - March 2016 all patients were subjected to manual functional analysis and clinical occlusal analysis. 17 people (10 women and 7 men underwent imaging with cone-beam computed tomography. Results: There were found many statistically significant correlations between tests of the structural analysis that indicate the relationships between findings. Conclusion: The presence of statistically significant correlations between occlusal relationships, freedom in the centric and condition of the muscle complex of masticatory system and TMJ confirm the relationship between the state of occlusal components and TMD.

A Basic Bivariate Structure of Personality Attributes Evident Across Nine Languages.

Science.gov (United States)

Saucier, Gerard; Thalmayer, Amber Gayle; Payne, Doris L; Carlson, Robert; Sanogo, Lamine; Ole-Kotikash, Leonard; Church, A Timothy; Katigbak, Marcia S; Somer, Oya; Szarota, Piotr; Szirmák, Zsofia; Zhou, Xinyue

2014-02-01

Here, two studies seek to characterize a parsimonious common-denominator personality structure with optimal cross-cultural replicability. Personality differences are observed in all human populations and cultures, but lexicons for personality attributes contain so many distinctions that parsimony is lacking. Models stipulating the most important attributes have been formulated by experts or by empirical studies drawing on experience in a very limited range of cultures. Factor analyses of personality lexicons of nine languages of diverse provenance (Chinese, Korean, Filipino, Turkish, Greek, Polish, Hungarian, Maasai, and Senoufo) were examined, and their common structure was compared to that of several prominent models in psychology. A parsimonious bivariate model showed evidence of substantial convergence and ubiquity across cultures. Analyses involving key markers of these dimensions in English indicate that they are broad dimensions involving the overlapping content of the interpersonal circumplex, models of communion and agency, and morality/warmth and competence. These "Big Two" dimensions-Social Self-Regulation and Dynamism-provide a common-denominator model involving the two most crucial axes of personality variation, ubiquitous across cultures. The Big Two might serve as an umbrella model serving to link diverse theoretical models and associated research literatures. © 2013 Wiley Periodicals, Inc.

Auto-correlation analysis of wave heights in the Bay of Bengal

Indian Academy of Sciences (India)

Time series observations of significant wave heights in the Bay of Bengal were subjected to auto- correlation analysis to determine temporal variability scale. The analysis indicates an exponen- tial fall of auto-correlation in the first few hours with a decorrelation time scale of about six hours. A similar figure was found earlier ...

Spatio-chromatic adaptation via higher-order canonical correlation analysis of natural images.

Science.gov (United States)

Gutmann, Michael U; Laparra, Valero; Hyvärinen, Aapo; Malo, Jesús

2014-01-01

Independent component and canonical correlation analysis are two general-purpose statistical methods with wide applicability. In neuroscience, independent component analysis of chromatic natural images explains the spatio-chromatic structure of primary cortical receptive fields in terms of properties of the visual environment. Canonical correlation analysis explains similarly chromatic adaptation to different illuminations. But, as we show in this paper, neither of the two methods generalizes well to explain both spatio-chromatic processing and adaptation at the same time. We propose a statistical method which combines the desirable properties of independent component and canonical correlation analysis: It finds independent components in each data set which, across the two data sets, are related to each other via linear or higher-order correlations. The new method is as widely applicable as canonical correlation analysis, and also to more than two data sets. We call it higher-order canonical correlation analysis. When applied to chromatic natural images, we found that it provides a single (unified) statistical framework which accounts for both spatio-chromatic processing and adaptation. Filters with spatio-chromatic tuning properties as in the primary visual cortex emerged and corresponding-colors psychophysics was reproduced reasonably well. We used the new method to make a theory-driven testable prediction on how the neural response to colored patterns should change when the illumination changes. We predict shifts in the responses which are comparable to the shifts reported for chromatic contrast habituation.

Optical Coherence Tomography Noise Reduction Using Anisotropic Local Bivariate Gaussian Mixture Prior in 3D Complex Wavelet Domain

OpenAIRE

Rabbani, Hossein; Sonka, Milan; Abramoff, Michael D.

2013-01-01

In this paper, MMSE estimator is employed for noise-free 3D OCT data recovery in 3D complex wavelet domain. Since the proposed distribution for noise-free data plays a key role in the performance of MMSE estimator, a priori distribution for the pdf of noise-free 3D complex wavelet coefficients is proposed which is able to model the main statistical properties of wavelets. We model the coefficients with a mixture of two bivariate Gaussian pdfs with local parameters which are able to capture th...

Aquaculture in artificially developed wetlands in urban areas: an application of the bivariate relationship between soil and surface water in landscape ecology.

Science.gov (United States)

Paul, Abhijit

2011-01-01

Wetlands show a strong bivariate relationship between soil and surface water. Artificially developed wetlands help to build landscape ecology and make built environments sustainable. The bheries, wetlands of eastern Calcutta (India), utilize the city sewage to develop urban aquaculture that supports the local fish industries and opens a new frontier in sustainable environmental planning research.

Influence of geomagnetic activity and atmospheric pressure in hypertensive adults

Science.gov (United States)

Azcárate, T.; Mendoza, B.

2017-09-01

We performed a study of the systolic and diastolic arterial blood pressure behavior under natural variables such as the atmospheric pressure and the horizontal geomagnetic field component. We worked with a group of eight adult hypertensive volunteers, four men and four women, with ages between 18 and 27 years in Mexico City during a geomagnetic storm in 2014. The data was divided by gender, age, and day/night cycle. We studied the time series using three methods: correlations, bivariate analysis, and superposed epoch (within a window of 2 days around the day of occurrence of a geomagnetic storm) analysis, between the systolic and diastolic blood pressure and the natural variables. The correlation analysis indicated a correlation between the systolic and diastolic blood pressure and the atmospheric pressure and the horizontal geomagnetic field component, being the largest during the night. Furthermore, the correlation and bivariate analyses showed that the largest correlations are between the systolic and diastolic blood pressure and the horizontal geomagnetic field component. Finally, the superposed epoch analysis showed that the largest number of significant changes in the blood pressure under the influence of geomagnetic field occurred in the systolic blood pressure for men.

Influence of geomagnetic activity and atmospheric pressure in hypertensive adults.

Science.gov (United States)

Azcárate, T; Mendoza, B

2017-09-01

We performed a study of the systolic and diastolic arterial blood pressure behavior under natural variables such as the atmospheric pressure and the horizontal geomagnetic field component. We worked with a group of eight adult hypertensive volunteers, four men and four women, with ages between 18 and 27 years in Mexico City during a geomagnetic storm in 2014. The data was divided by gender, age, and day/night cycle. We studied the time series using three methods: correlations, bivariate analysis, and superposed epoch (within a window of 2 days around the day of occurrence of a geomagnetic storm) analysis, between the systolic and diastolic blood pressure and the natural variables. The correlation analysis indicated a correlation between the systolic and diastolic blood pressure and the atmospheric pressure and the horizontal geomagnetic field component, being the largest during the night. Furthermore, the correlation and bivariate analyses showed that the largest correlations are between the systolic and diastolic blood pressure and the horizontal geomagnetic field component. Finally, the superposed epoch analysis showed that the largest number of significant changes in the blood pressure under the influence of geomagnetic field occurred in the systolic blood pressure for men.

A bivariate optimal replacement policy for a multistate repairable system

International Nuclear Information System (INIS)

Zhang Yuanlin; Yam, Richard C.M.; Zuo, Ming J.

2007-01-01

In this paper, a deteriorating simple repairable system with k+1 states, including k failure states and one working state, is studied. It is assumed that the system after repair is not 'as good as new' and the deterioration of the system is stochastic. We consider a bivariate replacement policy, denoted by (T,N), in which the system is replaced when its working age has reached T or the number of failures it has experienced has reached N, whichever occurs first. The objective is to determine the optimal replacement policy (T,N)* such that the long-run expected profit per unit time is maximized. The explicit expression of the long-run expected profit per unit time is derived and the corresponding optimal replacement policy can be determined analytically or numerically. We prove that the optimal policy (T,N)* is better than the optimal policy N* for a multistate simple repairable system. We also show that a general monotone process model for a multistate simple repairable system is equivalent to a geometric process model for a two-state simple repairable system in the sense that they have the same structure for the long-run expected profit (or cost) per unit time and the same optimal policy. Finally, a numerical example is given to illustrate the theoretical results

Assessing the genetic overlap between BMI and cognitive function

Science.gov (United States)

Marioni, R E; Yang, J; Dykiert, D; Mõttus, R; Campbell, A; Ibrahim-Verbaas, Carla A; Bressler, Jan; Debette, Stephanie; Schuur, Maaike; Smith, Albert V; Davies, Gail; Bennett, David A; Deary, Ian J; Ikram, M Arfan; Launer, Lenore J; Fitzpatrick, Annette L; Seshadri, Sudha; van Duijn, Cornelia M; Mosely Jr, Thomas H; Davies, G; Hayward, C; Porteous, D J; Visscher, P M; Deary, I J

2016-01-01

Obesity and low cognitive function are associated with multiple adverse health outcomes across the life course. They have a small phenotypic correlation (r=−0.11; high body mass index (BMI)−low cognitive function), but whether they have a shared genetic aetiology is unknown. We investigated the phenotypic and genetic correlations between the traits using data from 6815 unrelated, genotyped members of Generation Scotland, an ethnically homogeneous cohort from five sites across Scotland. Genetic correlations were estimated using the following: same-sample bivariate genome-wide complex trait analysis (GCTA)–GREML; independent samples bivariate GCTA–GREML using Generation Scotland for cognitive data and four other samples (n=20 806) for BMI; and bivariate LDSC analysis using the largest genome-wide association study (GWAS) summary data on cognitive function (n=48 462) and BMI (n=339 224) to date. The GWAS summary data were also used to create polygenic scores for the two traits, with within- and cross-trait prediction taking place in the independent Generation Scotland cohort. A large genetic correlation of −0.51 (s.e. 0.15) was observed using the same-sample GCTA–GREML approach compared with −0.10 (s.e. 0.08) from the independent-samples GCTA–GREML approach and −0.22 (s.e. 0.03) from the bivariate LDSC analysis. A genetic profile score using cognition-specific genetic variants accounts for 0.08% (P=0.020) of the variance in BMI and a genetic profile score using BMI-specific variants accounts for 0.42% (P=1.9 × 10−7) of the variance in cognitive function. Seven common genetic variants are significantly associated with both traits at Pcognitive function. PMID:26857597

Bivariate analysis of basal serum anti-Mullerian hormone measurements and human blastocyst development after IVF

LENUS (Irish Health Repository)

Sills, E Scott

2011-12-02

Abstract Background To report on relationships among baseline serum anti-Müllerian hormone (AMH) measurements, blastocyst development and other selected embryology parameters observed in non-donor oocyte IVF cycles. Methods Pre-treatment AMH was measured in patients undergoing IVF (n = 79) and retrospectively correlated to in vitro embryo development noted during culture. Results Mean (+\\/- SD) age for study patients in this study group was 36.3 ± 4.0 (range = 28-45) yrs, and mean (+\\/- SD) terminal serum estradiol during IVF was 5929 +\\/- 4056 pmol\\/l. A moderate positive correlation (0.49; 95% CI 0.31 to 0.65) was noted between basal serum AMH and number of MII oocytes retrieved. Similarly, a moderate positive correlation (0.44) was observed between serum AMH and number of early cleavage-stage embryos (95% CI 0.24 to 0.61), suggesting a relationship between serum AMH and embryo development in IVF. Of note, serum AMH levels at baseline were significantly different for patients who did and did not undergo blastocyst transfer (15.6 vs. 10.9 pmol\\/l; p = 0.029). Conclusions While serum AMH has found increasing application as a predictor of ovarian reserve for patients prior to IVF, its roles to estimate in vitro embryo morphology and potential to advance to blastocyst stage have not been extensively investigated. These data suggest that baseline serum AMH determinations can help forecast blastocyst developmental during IVF. Serum AMH measured before treatment may assist patients, clinicians and embryologists as scheduling of embryo transfer is outlined. Additional studies are needed to confirm these correlations and to better define the role of baseline serum AMH level in the prediction of blastocyst formation.

Study of relationship between MUF correlation and detection sensitivity of statistical analysis

International Nuclear Information System (INIS)

Tamura, Toshiaki; Ihara, Hitoshi; Yamamoto, Yoichi; Ikawa, Koji

1989-11-01

Various kinds of statistical analysis are proposed to NRTA (Near Real Time Materials Accountancy) which was devised to satisfy the timeliness goal of one of the detection goals of IAEA. It will be presumed that different statistical analysis results will occur between the case of considered rigorous error propagation (with MUF correlation) and the case of simplified error propagation (without MUF correlation). Therefore, measurement simulation and decision analysis were done using flow simulation of 800 MTHM/Y model reprocessing plant, and relationship between MUF correlation and detection sensitivity and false alarm of statistical analysis was studied. Specific character of material accountancy for 800 MTHM/Y model reprocessing plant was grasped by this simulation. It also became clear that MUF correlation decreases not only false alarm but also detection probability for protracted loss in case of CUMUF test and Page's test applied to NRTA. (author)

To Identify the Important Soil Properties Affecting Dinoseb Adsorption with Statistical Analysis

Directory of Open Access Journals (Sweden)

Yiqing Guan

2013-01-01

Full Text Available Investigating the influences of soil characteristic factors on dinoseb adsorption parameter with different statistical methods would be valuable to explicitly figure out the extent of these influences. The correlation coefficients and the direct, indirect effects of soil characteristic factors on dinoseb adsorption parameter were analyzed through bivariate correlation analysis, and path analysis. With stepwise regression analysis the factors which had little influence on the adsorption parameter were excluded. Results indicate that pH and CEC had moderate relationship and lower direct effect on dinoseb adsorption parameter due to the multicollinearity with other soil factors, and organic carbon and clay contents were found to be the most significant soil factors which affect the dinoseb adsorption process. A regression is thereby set up to explore the relationship between the dinoseb adsorption parameter and the two soil factors: the soil organic carbon and clay contents. A 92% of the variation of dinoseb sorption coefficient could be attributed to the variation of the soil organic carbon and clay contents.

GIS and correlation analysis of geo-environmental variables ...

African Journals Online (AJOL)

Key words: Correlation, GIS, malaria geography, malaria incidence ... problems, as it has created the possibility for geocoding, extracting and spatial analysis of health ...... Bulletin of the World Health Organization, 78(12), 1438–1444. Carter ...

Cross-correlation analysis of Ge/Li/ spectra

International Nuclear Information System (INIS)

MacDonald, R.; Robertson, A.; Kennett, T.J.; Prestwich, W.V.

1974-01-01

A sensitive technique is proposed for activation analysis using cross-correlation and improved spectral orthogonality achieved through use of a rectangular zero area digital filter. To test the accuracy and reliability of the cross-correlation procedure five spectra obtained with a Ge/Li detector were combined in different proportions. Gaussian distributed statistics were then added to the composite spectra by means of a pseudo-random number generator. The basis spectra used were 76 As, 82 Br, 72 Ga, 77 Ge, and room background. In general, when the basis spectra were combined in roughly comparable proportions the accuracy of the techique proved to be excelent (>1%). However, of primary importance was the ability of the correlation technique to identify low intensity components in the presence of high intensity components. It was found that the detection threshold for Ge, for example, was not reached until the Ge content in the unfiltered spectrum was <0.16%. (T.G.)

Co-occurrence correlations of heavy metals in sediments revealed using network analysis.

Science.gov (United States)

Liu, Lili; Wang, Zhiping; Ju, Feng; Zhang, Tong

2015-01-01

In this study, the correlation-based study was used to identify the co-occurrence correlations among metals in marine sediment of Hong Kong, based on the long-term (from 1991 to 2011) temporal and spatial monitoring data. 14 stations out of the total 45 marine sediment monitoring stations were selected from three representative areas, including Deep Bay, Victoria Harbour and Mirs Bay. Firstly, Spearman's rank correlation-based network analysis was conducted as the first step to identify the co-occurrence correlations of metals from raw metadata, and then for further analysis using the normalized metadata. The correlations patterns obtained by network were consistent with those obtained by the other statistic normalization methods, including annual ratios, R-squared coefficient and Pearson correlation coefficient. Both Deep Bay and Victoria Harbour have been polluted by heavy metals, especially for Pb and Cu, which showed strong co-occurrence with other heavy metals (e.g. Cr, Ni, Zn and etc.) and little correlations with the reference parameters (Fe or Al). For Mirs Bay, which has better marine sediment quality compared with Deep Bay and Victoria Harbour, the co-occurrence patterns revealed by network analysis indicated that the metals in sediment dominantly followed the natural geography process. Besides the wide applications in biology, sociology and informatics, it is the first time to apply network analysis in the researches of environment pollutions. This study demonstrated its powerful application for revealing the co-occurrence correlations among heavy metals in marine sediments, which could be further applied for other pollutants in various environment systems. Copyright © 2014 Elsevier Ltd. All rights reserved.

General correlation and partial correlation analysis in finding interactions: with Spearman rank correlation and proportion correlation as correlation measures

OpenAIRE

WenJun Zhang; Xin Li

2015-01-01

Between-taxon interactions can be detected by calculating the sampling data of taxon sample type. In present study, Spearman rank correlation and proportion correlation are chosen as the general correlation measures, and their partial correlations are calculated and compared. The results show that for Spearman rank correlation measure, in all predicted candidate direct interactions by partial correlation, about 16.77% (x, 0-45.4%) of them are not successfully detected by Spearman rank correla...

Tutorial on Biostatistics: Linear Regression Analysis of Continuous Correlated Eye Data.

Science.gov (United States)

Ying, Gui-Shuang; Maguire, Maureen G; Glynn, Robert; Rosner, Bernard

2017-04-01

To describe and demonstrate appropriate linear regression methods for analyzing correlated continuous eye data. We describe several approaches to regression analysis involving both eyes, including mixed effects and marginal models under various covariance structures to account for inter-eye correlation. We demonstrate, with SAS statistical software, applications in a study comparing baseline refractive error between one eye with choroidal neovascularization (CNV) and the unaffected fellow eye, and in a study determining factors associated with visual field in the elderly. When refractive error from both eyes were analyzed with standard linear regression without accounting for inter-eye correlation (adjusting for demographic and ocular covariates), the difference between eyes with CNV and fellow eyes was 0.15 diopters (D; 95% confidence interval, CI -0.03 to 0.32D, p = 0.10). Using a mixed effects model or a marginal model, the estimated difference was the same but with narrower 95% CI (0.01 to 0.28D, p = 0.03). Standard regression for visual field data from both eyes provided biased estimates of standard error (generally underestimated) and smaller p-values, while analysis of the worse eye provided larger p-values than mixed effects models and marginal models. In research involving both eyes, ignoring inter-eye correlation can lead to invalid inferences. Analysis using only right or left eyes is valid, but decreases power. Worse-eye analysis can provide less power and biased estimates of effect. Mixed effects or marginal models using the eye as the unit of analysis should be used to appropriately account for inter-eye correlation and maximize power and precision.

[Correlation analysis of major agronomic characters and the polysaccharide contents in Dendrobium officinale].

Science.gov (United States)

Zhang, Lei; Zheng, Xi-Long; Qiu, Dao-Shou; Cai, Shi-Ke; Luo, Huan-Ming; Deng, Rui-Yun; Liu, Xiao-Jin

2013-10-01

In order to provide theoretical and technological basis for the germplasm innovation and variety breeding in Dendrobium officinale, a study of the correlation between polysaccharide content and agronomic characters was conducted. Based on the polysaccharide content determination and the agronomic characters investigation of 30 copies (110 individual plants) of Dendrobium officinale germplasm resources, the correlation between polysaccharide content and agronomic characters was analyzed via path and correlation analysis. Correlation analysis results showed that there was a significant negative correlation between average spacing and polysaccharide content, the correlation coefficient was -0.695. And the blade thickness was positively correlated with the polysaccharide content, but the correlation was not significant. The path analysis results showed that the stem length was the maximum influence factor to the polysaccharide, and it was positive effect, the direct path coefficient was 1.568. According to thess results, the polysaccharide content can be easily and intuitively estimated by the agronomic characters investigating data in the germpalsm resources screening and variety breeding. Therefore, it is a visual and practical technology guidance in quality variety breeding of Dendrobium officinale.

Psychosocial correlates of suicidal ideation in rural South African adolescents.

Science.gov (United States)

Shilubane, Hilda N; Ruiter, Robert A C; Bos, Arjan E R; van den Borne, Bart; James, Shamagonam; Reddy, Priscilla S

2014-01-01

Suicide is a prevalent problem among young people in Southern Africa, but prevention programs are largely absent. This survey aimed to identify the behavioral and psychosocial correlates of suicidal ideation among adolescents in Limpopo. A two-stage cluster sample design was used to establish a representative sample of 591 adolescents. Bivariate correlations and multiple linear regression analyses were conducted. Findings show that suicidal ideation is prevalent among adolescents. The psychosocial factors perceived social support and negative feelings about the family and the behavioral factors forced sexual intercourse and physical violence by the partner were found to increase the risk of suicidal ideation. Depression mediated the relationship between these psychosocial and behavioral risk factors and suicidal ideation. This study increased our understanding of the psychosocial and behavioral predictors of adolescent suicidal ideation. The findings provide target points for future intervention programs and call for supportive structures to assist adolescents with suicidal ideation.

Probabilistic leak-before-break analysis with correlated input parameters

International Nuclear Information System (INIS)

Qian Guian; Niffenegger, Markus; Karanki, Durga Rao; Li Shuxin

2013-01-01

Highlights: ► The correlation of crack growth has the most significant impact on LBB behavior. ► The correlation impact increases with the correlation coefficients. ► The correlation impact increases with the number of cracks. ► Independent assumption may lead to nonconservative result. - Abstract: The paper presents a probabilistic methodology considering the correlations between the input variables for the analysis of leak-before-break (LBB) behavior of a pressure tube. A computer program based on Monte Carlo (MC) simulation with Nataf transformation has been developed to allow the proposed methodology to calculate both the time from the first leakage to unstable fracture and the time from leakage detection to unstable fracture. The results show that the correlation of the crack growth rates between different cracks has the most significant impact on the LBB behavior of the pressure tube. The impact of the parameters correlation on LBB behavior increases with the crack numbers. If the correlations between different parameters for an individual crack are not considered, the predicted results are nonconservative when the cumulative probability is below 50% and conservative when it is above 50%.

Estimating correlation between multivariate longitudinal data in the presence of heterogeneity.

Science.gov (United States)

Gao, Feng; Philip Miller, J; Xiong, Chengjie; Luo, Jingqin; Beiser, Julia A; Chen, Ling; Gordon, Mae O

2017-08-17

Estimating correlation coefficients among outcomes is one of the most important analytical tasks in epidemiological and clinical research. Availability of multivariate longitudinal data presents a unique opportunity to assess joint evolution of outcomes over time. Bivariate linear mixed model (BLMM) provides a versatile tool with regard to assessing correlation. However, BLMMs often assume that all individuals are drawn from a single homogenous population where the individual trajectories are distributed smoothly around population average. Using longitudinal mean deviation (MD) and visual acuity (VA) from the Ocular Hypertension Treatment Study (OHTS), we demonstrated strategies to better understand the correlation between multivariate longitudinal data in the presence of potential heterogeneity. Conditional correlation (i.e., marginal correlation given random effects) was calculated to describe how the association between longitudinal outcomes evolved over time within specific subpopulation. The impact of heterogeneity on correlation was also assessed by simulated data. There was a significant positive correlation in both random intercepts (ρ = 0.278, 95% CI: 0.121-0.420) and random slopes (ρ = 0.579, 95% CI: 0.349-0.810) between longitudinal MD and VA, and the strength of correlation constantly increased over time. However, conditional correlation and simulation studies revealed that the correlation was induced primarily by participants with rapid deteriorating MD who only accounted for a small fraction of total samples. Conditional correlation given random effects provides a robust estimate to describe the correlation between multivariate longitudinal data in the presence of unobserved heterogeneity (NCT00000125).

The PROMIS physical function correlates with the QuickDASH in patients with upper extremity illness.

Science.gov (United States)

Overbeek, Celeste L; Nota, Sjoerd P F T; Jayakumar, Prakash; Hageman, Michiel G; Ring, David

2015-01-01

To assess disability more efficiently with less burden on the patient, the National Institutes of Health has developed the Patient Reported Outcomes Measurement Information System (PROMIS) Physical Function-an instrument based on item response theory and using computer adaptive testing (CAT). Initially, upper and lower extremity disabilities were not separated and we were curious if the PROMIS Physical Function CAT could measure upper extremity disability and the Quick Disability of Arm, Shoulder and Hand (QuickDASH). We aimed to find correlation between the PROMIS Physical Function and the QuickDASH questionnaires in patients with upper extremity illness. Secondarily, we addressed whether the PROMIS Physical Function and QuickDASH correlate with the PROMIS Depression CAT and PROMIS Pain Interference CAT instruments. Finally, we assessed factors associated with QuickDASH and PROMIS Physical Function in multivariable analysis. A cohort of 93 outpatients with upper extremity illnesses completed the QuickDASH and three PROMIS CAT questionnaires: Physical Function, Pain Interference, and Depression. Pain intensity was measured with an 11-point ordinal measure (0-10 numeric rating scale). Correlation between PROMIS Physical Function and the QuickDASH was assessed. Factors that correlated with the PROMIS Physical Function and QuickDASH were assessed in multivariable regression analysis after initial bivariate analysis. There was a moderate correlation between the PROMIS Physical Function and the QuickDASH questionnaire (r=-0.55, p<0.001). Greater disability as measured with the PROMIS and QuickDASH correlated most strongly with PROMIS Depression (r=-0.35, p<0.001 and r=0.34, p<0.001 respectively) and Pain Interference (r=-0.51, p<0.001 and r=0.74, p<0.001 respectively). The factors accounting for the variability in PROMIS scores are comparable to those for the QuickDASH except that the PROMIS Physical Function is influenced by other pain conditions while the QuickDASH is

Model-independent analysis with BPM correlation matrices

International Nuclear Information System (INIS)

Irwin, J.; Wang, C.X.; Yan, Y.T.; Bane, K.; Cai, Y.; Decker, F.; Minty, M.; Stupakov, G.; Zimmermann, F.

1998-06-01

The authors discuss techniques for Model-Independent Analysis (MIA) of a beamline using correlation matrices of physical variables and Singular Value Decomposition (SVD) of a beamline BPM matrix. The beamline matrix is formed from BPM readings for a large number of pulses. The method has been applied to the Linear Accelerator of the SLAC Linear Collider (SLC)

Analyzing the Cross-Correlation Between Onshore and Offshore RMB Exchange Rates Based on Multifractal Detrended Cross-Correlation Analysis (MF-DCCA)

Science.gov (United States)

Xie, Chi; Zhou, Yingying; Wang, Gangjin; Yan, Xinguo

We use the multifractal detrended cross-correlation analysis (MF-DCCA) method to explore the multifractal behavior of the cross-correlation between exchange rates of onshore RMB (CNY) and offshore RMB (CNH) against US dollar (USD). The empirical data are daily prices of CNY/USD and CNH/USD from May 1, 2012 to February 29, 2016. The results demonstrate that: (i) the cross-correlation between CNY/USD and CNH/USD is persistent and its fluctuation is smaller when the order of fluctuation function is negative than that when the order is positive; (ii) the multifractal behavior of the cross-correlation between CNY/USD and CNH/USD is significant during the sample period; (iii) the dynamic Hurst exponents obtained by the rolling windows analysis show that the cross-correlation is stable when the global economic situation is good and volatile in bad situation; and (iv) the non-normal distribution of original data has a greater effect on the multifractality of the cross-correlation between CNY/USD and CNH/USD than the temporary correlation.

Multifractal detrended Cross Correlation Analysis of Foreign Exchange and SENSEX fluctuation in Indian perspective

Science.gov (United States)

Dutta, Srimonti; Ghosh, Dipak; Chatterjee, Sucharita

2016-12-01

The manuscript studies autocorrelation and cross correlation of SENSEX fluctuations and Forex Exchange Rate in respect to Indian scenario. Multifractal detrended fluctuation analysis (MFDFA) and multifractal detrended cross correlation analysis (MFDXA) were employed to study the correlation between the two series. It was observed that the two series are strongly cross correlated. The change of degree of cross correlation with time was studied and the results are interpreted qualitatively.

Information-Pooling Bias in Collaborative Security Incident Correlation Analysis.

Science.gov (United States)

Rajivan, Prashanth; Cooke, Nancy J

2018-03-01

Incident correlation is a vital step in the cybersecurity threat detection process. This article presents research on the effect of group-level information-pooling bias on collaborative incident correlation analysis in a synthetic task environment. Past research has shown that uneven information distribution biases people to share information that is known to most team members and prevents them from sharing any unique information available with them. The effect of such biases on security team collaborations are largely unknown. Thirty 3-person teams performed two threat detection missions involving information sharing and correlating security incidents. Incidents were predistributed to each person in the team based on the hidden profile paradigm. Participant teams, randomly assigned to three experimental groups, used different collaboration aids during Mission 2. Communication analysis revealed that participant teams were 3 times more likely to discuss security incidents commonly known to the majority. Unaided team collaboration was inefficient in finding associations between security incidents uniquely available to each member of the team. Visualizations that augment perceptual processing and recognition memory were found to mitigate the bias. The data suggest that (a) security analyst teams, when conducting collaborative correlation analysis, could be inefficient in pooling unique information from their peers; (b) employing off-the-shelf collaboration tools in cybersecurity defense environments is inadequate; and (c) collaborative security visualization tools developed considering the human cognitive limitations of security analysts is necessary. Potential applications of this research include development of team training procedures and collaboration tool development for security analysts.

L2 Reading Comprehension and Its Correlates: A Meta-Analysis

Science.gov (United States)

Jeon, Eun Hee; Yamashita, Junko

2014-01-01

The present meta-analysis examined the overall average correlation (weighted for sample size and corrected for measurement error) between passage-level second language (L2) reading comprehension and 10 key reading component variables investigated in the research domain. Four high-evidence correlates (with 18 or more accumulated effect sizes: L2…

A Vehicle for Bivariate Data Analysis

Science.gov (United States)

Roscoe, Matt B.

2016-01-01

Instead of reserving the study of probability and statistics for special fourth-year high school courses, the Common Core State Standards for Mathematics (CCSSM) takes a "statistics for all" approach. The standards recommend that students in grades 6-8 learn to summarize and describe data distributions, understand probability, draw…

Psychosocial and perceived environmental correlates of physical activity in rural and older african american and white women.

Science.gov (United States)

Wilcox, Sara; Bopp, Melissa; Oberrecht, Larissa; Kammermann, Sandra K; McElmurray, Charles T

2003-11-01

African American and rural older women are among the least active segments of the population. This study, guided by social cognitive theory, examined the correlates of physical activity (PA) in 102 rural older women (41% African American; 70.6 +/- 9.2 years). In bivariate associations, education, marital status, self-efficacy, greater pros than cons, perceived stress, social support, and perceived neighborhood safety were positively associated with PA; age, depressive symptoms, perceived sidewalks, health care provider discussion of PA, and perceived traffic were negatively associated with PA. In a hierarchical regression analysis, the sociodemographic (R(2) = 23%), psychological (IR(2) = 9%), social (IR(2) = 6%), and perceived physical environmental (IR(2) = 9%) sets of variables were significant (p motivators; falls, injuries, and heart attacks were identified most often as risks. These findings support the importance of multilevel influences on PA in older rural women and are useful for informing PA interventions.

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African Journals Online (AJOL)

USER

2015-07-22

Jul 22, 2015 ... bivariate correlation and multiple regression techniques were used to process data generated. .... multiple regression analysis were employed ... e = stochastic disturbance or error term ... parameters in a step-by-step version.

Statistical analysis of latent generalized correlation matrix estimation in transelliptical distribution

OpenAIRE

Han, Fang; Liu, Han

2016-01-01

Correlation matrices play a key role in many multivariate methods (e.g., graphical model estimation and factor analysis). The current state-of-the-art in estimating large correlation matrices focuses on the use of Pearson's sample correlation matrix. Although Pearson's sample correlation matrix enjoys various good properties under Gaussian models, it is not an effective estimator when facing heavy-tailed distributions. As a robust alternative, Han and Liu [J. Am. Stat. Assoc. 109 (2015) 275-2...

Ten Years Trend Analysis of Malaria Prevalence and its Correlation ...

African Journals Online (AJOL)

The data were analyzed using SPSS software package 16.0. Pearson's correlation analysis was conducted to see the correlation between plasmodium species and climatic variables. Within the last decade (2004–2013) a total of 30,070 blood films were examined for malaria in Sire health center and of this 6036 (20.07%) ...

Correlations between MRI and Information Processing Speed in MS: A Meta-Analysis

Directory of Open Access Journals (Sweden)

S. M. Rao

2014-01-01

Full Text Available Objectives. To examine relationships between conventional MRI measures and the paced auditory serial addition test (PASAT and symbol digit modalities test (SDMT. Methods. A systematic literature review was conducted. Included studies had ≥30 multiple sclerosis (MS patients, administered the SDMT or PASAT, and measured T2LV or brain atrophy. Meta-analysis of MRI/information processing speed (IPS correlations, analysis of MRI/IPS significance tests to account for reporting bias, and binomial testing to detect trends when comparing correlation strengths of SDMT versus PASAT and T2LV versus atrophy were conducted. Results. The 39 studies identified frequently reported only significant correlations, suggesting reporting bias. Direct meta-analysis was only feasible for correlations between SDMT and T2LV (r=-0.45, P<0.001 and atrophy in patients with mixed-MS subtypes (r=-0.54, P<0.001. Familywise Holm-Bonferroni testing found that selective reporting was not the source of at least half of significant results reported. Binomial tests (P=0.006 favored SDMT over PASAT in strength of MRI correlations. Conclusions. A moderate-to-strong correlation exists between impaired IPS and MRI in mixed MS populations. Correlations with MRI were stronger for SDMT than for PASAT. Neither heterogeneity among populations nor reporting bias appeared to be responsible for these findings.

Comparison of indocyanine green clearance with Child's-Pugh score and hepatic histology: a multivariate analysis.

Science.gov (United States)

Mukherjee, Sandeep; Rogers, Mary A M; Buniak, Borys

2006-01-01

Indocyanine green clearance, measured by percentage disappearance rate, detects alterations in liver function and may be used as a non-invasive determinant of hepatic reserve. The aims of this study were to compare liver histology and Child's-Pugh score with percentage disappearance rate and determine which variables correlated with PDR. Child's-Pugh score, liver function tests, liver biopsies and indocyanine green testing (0.5mg/kg) were performed in 102 consecutive patients with cirrhosis of diverse etiologies. Indocyanine green concentration was determined using spectrophotometric analysis (806nm) and plotted logarithmically with Michaelis-Menten kinetics to calculate the percentage disappearance rate. Liver biopsies were graded using the modified Knodell score to obtain a histological activity index. In bivariable analysis, percentage disappearance rate significantly correlated with Child's-Pugh score, albumin, bilirubin, prothrombin time and histological activity index. Albumin, prothrombin time and histological activity index were independent predictors of percentage disappearance rate in the final model (albumin ptime ptime and histological activity index.

Process correlation analysis model for process improvement identification.

Science.gov (United States)

Choi, Su-jin; Kim, Dae-Kyoo; Park, Sooyong

2014-01-01

Software process improvement aims at improving the development process of software systems. It is initiated by process assessment identifying strengths and weaknesses and based on the findings, improvement plans are developed. In general, a process reference model (e.g., CMMI) is used throughout the process of software process improvement as the base. CMMI defines a set of process areas involved in software development and what to be carried out in process areas in terms of goals and practices. Process areas and their elements (goals and practices) are often correlated due to the iterative nature of software development process. However, in the current practice, correlations of process elements are often overlooked in the development of an improvement plan, which diminishes the efficiency of the plan. This is mainly attributed to significant efforts and the lack of required expertise. In this paper, we present a process correlation analysis model that helps identify correlations of process elements from the results of process assessment. This model is defined based on CMMI and empirical data of improvement practices. We evaluate the model using industrial data.

HUBUNGAN INTELEGENSI DAN MOTIVASI DENGAN PRESTASI BELAJAR MAHASISWA S1 KEPERAWATAN STIKES YAYASAN RS ISLAM SURABAYA

Directory of Open Access Journals (Sweden)

Yanis Kartini

2015-11-01

Full Text Available There are many factors which affect the academic achievement: internal and external factors. The internal factors include the physical and psychological factors (interest, intelligence, motivation, attention, readiness, and maturity. In academic year of 2011/2012, STIKes Yarsis has added one test, the psychological test, to determine the candidates to pass the student’s admission test. So far, the data haven’t been managed as the important information for the educational management. Therefore, the purpose of this study was to find out the bivariate and multivariate correlation between intelligence and motivation toward the student’s academic achievement. The design of study was analytic-cross sectional. The population involved the students of Program of Study of S-1 in the academic year 2011/2012. 106 respondents were chosen as the samples by using simple random sampling technique. The independent variable was the level of intelligence and motivation, whereas the dependent variable was the academic achievement. The data collection was done by using secondary data, which were then analyzed by using simple and multiple linear regression tests. The result of study in bivariate correlation analysis showed that there was a correlation between intelligence and motivation toward the student’s academic achievement. The independent variable playing a role in predicting the GPA (grade-point average was motivation (p = 0.028. The value of R-Squared was 0.045 which meant that 4.5% of the academic achievement was affected by motivation, whereas the rest of them were affected by other factors. Moreover, the result of analysis described that GPA = 3.097 + 0.060 * motivation. In conclusion, in bivariate correlation analysis, intelligence and learning motivation had a correlation with academic achievement. In multivariate correlation analysis, intelligence didn’t have any correlation with academic achievement, whereas motivation had correlation with

GIS and correlation analysis of geo-environmental variables ...

African Journals Online (AJOL)

GIS and correlation analysis of geo-environmental variables influencing malaria prevalence in the Saboba district of Northern Ghana. ... The study also applied spline interpolation technique to map malaria prevalence in the district using standardised malaria incidence. The result indicates that distance to marshy areas is ...

Robustness analysis of geodetic networks in the case of correlated observations

Directory of Open Access Journals (Sweden)

Mevlut Yetkin

Full Text Available GPS (or GNSS networks are invaluable tools for monitoring natural hazards such as earthquakes. However, blunders in GPS observations may be mistakenly interpreted as deformation. Therefore, robust networks are needed in deformation monitoring using GPS networks. Robustness analysis is a natural merger of reliability and strain and defined as the ability to resist deformations caused by the maximum undetecle errors as determined from internal reliability analysis. However, to obtain rigorously correct results; the correlations among the observations must be considered while computing maximum undetectable errors. Therefore, we propose to use the normalized reliability numbers instead of redundancy numbers (Baarda's approach in robustness analysis of a GPS network. A simple mathematical relation showing the ratio between uncorrelated and correlated cases for maximum undetectable error is derived. The same ratio is also valid for the displacements. Numerical results show that if correlations among observations are ignored, dramatically different displacements can be obtained depending on the size of multiple correlation coefficients. Furthermore, when normalized reliability numbers are small, displacements get large, i.e., observations with low reliability numbers cause bigger displacements compared to observations with high reliability numbers.

Correlative SEM SERS for quantitative analysis of dimer nanoparticles.

Science.gov (United States)

Timmermans, F J; Lenferink, A T M; van Wolferen, H A G M; Otto, C

2016-11-14

A Raman microscope integrated with a scanning electron microscope was used to investigate plasmonic structures by correlative SEM-SERS analysis. The integrated Raman-SEM microscope combines high-resolution electron microscopy information with SERS signal enhancement from selected nanostructures with adsorbed Raman reporter molecules. Correlative analysis is performed for dimers of two gold nanospheres. Dimers were selected on the basis of SEM images from multi aggregate samples. The effect of the orientation of the dimer with respect to the polarization state of the laser light and the effect of the particle gap size on the Raman signal intensity is observed. Additionally, calculations are performed to simulate the electric near field enhancement. These simulations are based on the morphologies observed by electron microscopy. In this way the experiments are compared with the enhancement factor calculated with near field simulations and are subsequently used to quantify the SERS enhancement factor. Large differences between experimentally observed and calculated enhancement factors are regularly detected, a phenomenon caused by nanoscale differences between the real and 'simplified' simulated structures. Quantitative SERS experiments reveal the structure induced enhancement factor, ranging from ∼200 to ∼20 000, averaged over the full nanostructure surface. The results demonstrate correlative Raman-SEM microscopy for the quantitative analysis of plasmonic particles and structures, thus enabling a new analytical method in the field of SERS and plasmonics.

New Colors for Histology: Optimized Bivariate Color Maps Increase Perceptual Contrast in Histological Images.

Science.gov (United States)

Kather, Jakob Nikolas; Weis, Cleo-Aron; Marx, Alexander; Schuster, Alexander K; Schad, Lothar R; Zöllner, Frank Gerrit

2015-01-01

Accurate evaluation of immunostained histological images is required for reproducible research in many different areas and forms the basis of many clinical decisions. The quality and efficiency of histopathological evaluation is limited by the information content of a histological image, which is primarily encoded as perceivable contrast differences between objects in the image. However, the colors of chromogen and counterstain used for histological samples are not always optimally distinguishable, even under optimal conditions. In this study, we present a method to extract the bivariate color map inherent in a given histological image and to retrospectively optimize this color map. We use a novel, unsupervised approach based on color deconvolution and principal component analysis to show that the commonly used blue and brown color hues in Hematoxylin-3,3'-Diaminobenzidine (DAB) images are poorly suited for human observers. We then demonstrate that it is possible to construct improved color maps according to objective criteria and that these color maps can be used to digitally re-stain histological images. To validate whether this procedure improves distinguishability of objects and background in histological images, we re-stain phantom images and N = 596 large histological images of immunostained samples of human solid tumors. We show that perceptual contrast is improved by a factor of 2.56 in phantom images and up to a factor of 2.17 in sets of histological tumor images. Thus, we provide an objective and reliable approach to measure object distinguishability in a given histological image and to maximize visual information available to a human observer. This method could easily be incorporated in digital pathology image viewing systems to improve accuracy and efficiency in research and diagnostics.

Bivariate threshold models for genetic evaluation of susceptibility to and ability to recover from mastitis in Danish Holstein cows.

Science.gov (United States)

Welderufael, B G; Janss, L L G; de Koning, D J; Sørensen, L P; Løvendahl, P; Fikse, W F

2017-06-01

Mastitis in dairy cows is an unavoidable problem and genetic variation in recovery from mastitis, in addition to susceptibility, is therefore of interest. Genetic parameters for susceptibility to and recovery from mastitis were estimated for Danish Holstein-Friesian cows using data from automatic milking systems equipped with online somatic cell count measuring units. The somatic cell count measurements were converted to elevated mastitis risk, a continuous variable [on a (0-1) scale] indicating the risk of mastitis. Risk values >0.6 were assumed to indicate that a cow had mastitis. For each cow and lactation, the sequence of health states (mastitic or healthy) was converted to a weekly transition: 0 if the cow stayed within the same state and 1 if the cow changed state. The result was 2 series of transitions: one for healthy to diseased (HD, to model mastitis susceptibility) and the other for diseased to healthy (DH, to model recovery ability). The 2 series of transitions were analyzed with bivariate threshold models, including several systematic effects and a function of time. The model included effects of herd, parity, herd-test-week, permanent environment (to account for the repetitive nature of transition records from a cow) plus two time-varying effects (lactation stage and time within episode). In early lactation, there was an increased risk of getting mastitis but the risk remained stable afterwards. Mean recovery rate was 45% per lactation. Heritabilities were 0.07 [posterior mean of standard deviations (PSD) = 0.03] for HD and 0.08 (PSD = 0.03) for DH. The genetic correlation between HD and DH has a posterior mean of -0.83 (PSD = 0.13). Although susceptibility and recovery from mastitis are strongly negatively correlated, recovery can be considered as a new trait for selection. The Authors. Published by the Federation of Animal Science Societies and Elsevier Inc. on behalf of the American Dairy Science Association®. This is an open access article under

Using beta coefficients to impute missing correlations in meta-analysis research: Reasons for caution.

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Roth, Philip L; Le, Huy; Oh, In-Sue; Van Iddekinge, Chad H; Bobko, Philip

2018-06-01

Meta-analysis has become a well-accepted method for synthesizing empirical research about a given phenomenon. Many meta-analyses focus on synthesizing correlations across primary studies, but some primary studies do not report correlations. Peterson and Brown (2005) suggested that researchers could use standardized regression weights (i.e., beta coefficients) to impute missing correlations. Indeed, their beta estimation procedures (BEPs) have been used in meta-analyses in a wide variety of fields. In this study, the authors evaluated the accuracy of BEPs in meta-analysis. We first examined how use of BEPs might affect results from a published meta-analysis. We then developed a series of Monte Carlo simulations that systematically compared the use of existing correlations (that were not missing) to data sets that incorporated BEPs (that impute missing correlations from corresponding beta coefficients). These simulations estimated ρ̄ (mean population correlation) and SDρ (true standard deviation) across a variety of meta-analytic conditions. Results from both the existing meta-analysis and the Monte Carlo simulations revealed that BEPs were associated with potentially large biases when estimating ρ̄ and even larger biases when estimating SDρ. Using only existing correlations often substantially outperformed use of BEPs and virtually never performed worse than BEPs. Overall, the authors urge a return to the standard practice of using only existing correlations in meta-analysis. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

ADC histogram analysis of muscle lymphoma - Correlation with histopathology in a rare entity.

Science.gov (United States)

Meyer, Hans-Jonas; Pazaitis, Nikolaos; Surov, Alexey

2018-06-21

Diffusion weighted imaging (DWI) is able to reflect histopathology architecture. A novel imaging approach, namely histogram analysis, is used to further characterize lesion on MRI. The purpose of this study is to correlate histogram parameters derived from apparent diffusion coefficient- (ADC) maps with histopathology parameters in muscle lymphoma. Eight patients (mean age 64.8 years, range 45-72 years) with histopathologically confirmed muscle lymphoma were retrospectively identified. Cell count, total nucleic and average nucleic areas were estimated using ImageJ. Additionally, Ki67-index was calculated. DWI was obtained on a 1.5T scanner by using the b values of 0 and 1000 s/mm2. Histogram analysis was performed as a whole lesion measurement by using a custom-made Matlabbased application. The correlation analysis revealed statistically significant correlation between cell count and ADCmean (p=-0.76, P=0.03) as well with ADCp75 (p=-0.79, P=0.02). Kurtosis and entropy correlated with average nucleic area (p=-0.81, P=0.02, p=0.88, P=0.007, respectively). None of the analyzed ADC parameters correlated with total nucleic area and with Ki67-index. This study identified significant correlations between cellularity and histogram parameters derived from ADC maps in muscle lymphoma. Thus, histogram analysis parameters reflect histopathology in muscle tumors. Advances in knowledge: Whole lesion ADC histogram analysis is able to reflect histopathology parameters in muscle lymphomas.

Strong anticipation and long-range cross-correlation: Application of detrended cross-correlation analysis to human behavioral data

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Delignières, Didier; Marmelat, Vivien

2014-01-01

In this paper, we analyze empirical data, accounting for coordination processes between complex systems (bimanual coordination, interpersonal coordination, and synchronization with a fractal metronome), by using a recently proposed method: detrended cross-correlation analysis (DCCA). This work is motivated by the strong anticipation hypothesis, which supposes that coordination between complex systems is not achieved on the basis of local adaptations (i.e., correction, predictions), but results from a more global matching of complexity properties. Indeed, recent experiments have evidenced a very close correlation between the scaling properties of the series produced by two coordinated systems, despite a quite weak local synchronization. We hypothesized that strong anticipation should result in the presence of long-range cross-correlations between the series produced by the two systems. Results allow a detailed analysis of the effects of coordination on the fluctuations of the series produced by the two systems. In the long term, series tend to present similar scaling properties, with clear evidence of long-range cross-correlation. Short-term results strongly depend on the nature of the task. Simulation studies allow disentangling the respective effects of noise and short-term coupling processes on DCCA results, and suggest that the matching of long-term fluctuations could be the result of short-term coupling processes.

Burnout prevalence and correlates amongst Colombian dental students: the STRESSCODE study.

Science.gov (United States)

Mafla, A C; Villa-Torres, L; Polychronopoulou, A; Polanco, H; Moreno-Juvinao, V; Parra-Galvis, D; Durán, C; Villalobos, M J; Divaris, K

2015-11-01

Accumulating evidence amongst dental students indicates an alarming prevalence of stress, which can precipitate the development of burnout--a state of mental or physical exhaustion and disengagement. Understanding individual and educational correlates of burnout is necessary for its prevention. The aims of this study were to determine the prevalence of burnout amongst a large sample of Colombian dental undergraduates and investigate its psychosocial and educational correlates. Survey data collected from 5647 students participating in the Stress in Colombian Dental Education study were used for this analysis. Burnout was measured using the Maslach Burnout Inventory-Student Survey (MBI-SS). Covariates included participants' socio-demographic characteristics and perceived stress, as well as educational environment factors. Descriptive, bivariate and multivariate methods based on multilevel mixed-effects logistic regression modelling were used for data analysis. Seven per cent of the students surveyed met the criteria for burnout. The prevalence of burnout was higher amongst upper classes, older and married students, those who reported not having passed all required courses and not having dentistry as their first career choice, as well as students in public institutions and those with large class sizes. Moreover, students' perceived stress in the domains of workload and self-efficacy was significantly and positively associated with burnout. Both personal and educational environment characteristics were found to be associated with burnout. Irrespective of these factors, students' perceived stress with regard to workload and self-efficacy was a strong influence on burnout and its alleviation may be a promising avenue to prevent psychological morbidity. © 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Discrimination, correlation, and provenance of Bed I tephrostratigraphic markers, Olduvai Gorge, Tanzania, based on multivariate analyses of phenocryst compositions

Science.gov (United States)

Habermann, Jörg M.; McHenry, Lindsay J.; Stollhofen, Harald; Tolosana-Delgado, Raimon; Stanistreet, Ian G.; Deino, Alan L.

2016-06-01

The chronology of Pleistocene flora and fauna, including hominin remains and associated Oldowan industries in Bed I, Olduvai Gorge, Tanzania, is primarily based on 40Ar/39Ar dating of intercalated tuffs and lavas, combined with detailed tephrostratigraphic correlations within the basin. Although a high-resolution chronostratigraphic framework has been established for the eastern part of the Olduvai Basin, the western subbasin is less well known due in part to major lateral facies changes within Bed I combined with discontinuous exposure. We address these correlation difficulties using the discriminative power of the chemical composition of the major juvenile mineral phases (augite, anorthoclase, plagioclase) from tuffs, volcaniclastic sandstones, siliciclastic units, and lavas. We statistically evaluate these compositions, obtained from electron probe micro-analysis, applying principal component analysis and discriminant analysis to develop discriminant models that successfully classify most Bed I volcanic units. The correlations, resulting from integrated analyses of all target minerals, provide a basin-wide Bed I chemostratigraphic framework at high lateral and vertical resolution, consistent with the known geological context, that expands and refines the geochemical databases currently available. Correlation of proximal ignimbrites at the First Fault with medial and distal Lower Bed I successions of the western basin enables assessment of lateral facies and thickness trends that confirm Ngorongoro Volcano as the primary source for Lower Bed I, whereas Upper Bed I sediment supply is mainly from Olmoti Volcano. Compositional similarity between Tuff IA, Bed I lava, and Mafic Tuffs II and III single-grain fingerprints, together with north- and northwestward thinning of Bed I lava, suggests a common Ngorongoro source for these units. The techniques applied herein improve upon previous work by evaluating compositional affinities with statistical rigor rather than

THE BIVARIATE SIZE-LUMINOSITY RELATIONS FOR LYMAN BREAK GALAXIES AT z {approx} 4-5

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Huang, Kuang-Han; Su, Jian [Johns Hopkins University, 3400 North Charles Street, Baltimore, MD 21218 (United States); Ferguson, Henry C. [Space Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD 21218 (United States); Ravindranath, Swara, E-mail: kuanghan@pha.jhu.edu [The Inter-University Center for Astronomy and Astrophysics, Pune University Campus, Pune 411007, Maharashtra (India)

2013-03-01

We study the bivariate size-luminosity distribution of Lyman break galaxies (LBGs) selected at redshifts around 4 and 5 in GOODS and the HUDF fields. We model the size-luminosity distribution as a combination of log-normal distribution (in size) and Schechter function (in luminosity), therefore it enables a more detailed study of the selection effects. We perform extensive simulations to quantify the dropout-selection completenesses and measurement biases and uncertainties in two-dimensional size and magnitude bins, and transform the theoretical size-luminosity distribution to the expected distribution for the observed data. Using maximum-likelihood estimator, we find that the Schechter function parameters for B {sub 435}-dropouts and are consistent with the values in the literature, but the size distributions are wider than expected from the angular momentum distribution of the underlying dark matter halos. The slope of the size-luminosity (RL) relation is similar to those found for local disk galaxies, but considerably shallower than local early-type galaxies.

THE BIVARIATE SIZE-LUMINOSITY RELATIONS FOR LYMAN BREAK GALAXIES AT z ∼ 4-5

International Nuclear Information System (INIS)

Huang, Kuang-Han; Su, Jian; Ferguson, Henry C.; Ravindranath, Swara

2013-01-01

We study the bivariate size-luminosity distribution of Lyman break galaxies (LBGs) selected at redshifts around 4 and 5 in GOODS and the HUDF fields. We model the size-luminosity distribution as a combination of log-normal distribution (in size) and Schechter function (in luminosity), therefore it enables a more detailed study of the selection effects. We perform extensive simulations to quantify the dropout-selection completenesses and measurement biases and uncertainties in two-dimensional size and magnitude bins, and transform the theoretical size-luminosity distribution to the expected distribution for the observed data. Using maximum-likelihood estimator, we find that the Schechter function parameters for B 435 -dropouts and are consistent with the values in the literature, but the size distributions are wider than expected from the angular momentum distribution of the underlying dark matter halos. The slope of the size-luminosity (RL) relation is similar to those found for local disk galaxies, but considerably shallower than local early-type galaxies.

Comparison of Obesity, Overweight and Elevated Blood Pressure in ...

African Journals Online (AJOL)

Urgent measures are needed to stem this tide through education, weight .... a systematic sampling technique after randomly selecting .... The bell of the stethoscope was then applied ... Statistical analysis .... 5: Correlation of mean SBP and DBP with age, weight and height in bivariate .... MDBP in the multivariate analysis.

Bivariate analysis of basal serum anti-Müllerian hormone measurements and human blastocyst development after IVF

Directory of Open Access Journals (Sweden)

Sills E Scott

2011-12-01

Full Text Available Abstract Background To report on relationships among baseline serum anti-Müllerian hormone (AMH measurements, blastocyst development and other selected embryology parameters observed in non-donor oocyte IVF cycles. Methods Pre-treatment AMH was measured in patients undergoing IVF (n = 79 and retrospectively correlated to in vitro embryo development noted during culture. Results Mean (+/- SD age for study patients in this study group was 36.3 ± 4.0 (range = 28-45 yrs, and mean (+/- SD terminal serum estradiol during IVF was 5929 +/- 4056 pmol/l. A moderate positive correlation (0.49; 95% CI 0.31 to 0.65 was noted between basal serum AMH and number of MII oocytes retrieved. Similarly, a moderate positive correlation (0.44 was observed between serum AMH and number of early cleavage-stage embryos (95% CI 0.24 to 0.61, suggesting a relationship between serum AMH and embryo development in IVF. Of note, serum AMH levels at baseline were significantly different for patients who did and did not undergo blastocyst transfer (15.6 vs. 10.9 pmol/l; p = 0.029. Conclusions While serum AMH has found increasing application as a predictor of ovarian reserve for patients prior to IVF, its roles to estimate in vitro embryo morphology and potential to advance to blastocyst stage have not been extensively investigated. These data suggest that baseline serum AMH determinations can help forecast blastocyst developmental during IVF. Serum AMH measured before treatment may assist patients, clinicians and embryologists as scheduling of embryo transfer is outlined. Additional studies are needed to confirm these correlations and to better define the role of baseline serum AMH level in the prediction of blastocyst formation.

Correlation Factor Analysis of Retinal Microvascular Changes in Patients With Essential Hypertension

Institute of Scientific and Technical Information of China (English)

Huang Duru; Huang Zhongning

2006-01-01

Objectives To investigate correlation between retinal microvascular signs and essential hypertension classification. Methods The retinal microvascular signs in patients with essential hypertension were assessed with the indirect biomicroscopy lens, the direct and the indirect ophthalmoscopes were used to determine the hypertensive retinopathy grades and retinal arteriosclerosis grades.The rank correlation analysis was used to analysis the correlation these grades with the risk factors concerned with hypertension. Results Of 72 cases with essential hypertension, 28 cases complicated with coronary disease, 20 cases diabetes, 41 cases stroke,17 cases renal malfunction. Varying extent retinal arterioscleroses were found in 71 cases, 1 case with retinal hemorrhage, 2 cases with retina edema, 4 cases with retinal hard exudation, 5 cases with retinal hemorrhage complicated by hard exudation, 2 cases with retinal hemorrhage complicated by hard exudation and cotton wool spot, 1 case with retinal hemorrhage complicated by hard exudation and microaneurysms,1 case with retinal edema and hard exudation, 1 case with retinal microaneurysms, 1 case with branch retinal vein occlusion. The rank correlation analysis showed that either hypertensive retinopathy grades or retinal arteriosclerosis grades were correlated with risk factor lamination of hypertension (r=0.25 or 0.31, P＜0.05), other correlation factors included age and blood high density lipoprotein concerned about hypertensive retinopathy grades or retinal arteriosclerosis grades, but other parameters, namely systolic or diastolic pressure, total cholesterol, triglyceride, low density lipoprotein cholesterol, fasting blood glucose,blood urea nitrogen and blood creatinine were not confirmed in this correlation analysis (P ＞ 0.05).Conclusions Either hypertensive retinopathy grade or retinal arteriosclerosis grade is close with the hypertension risk factor lamination, suggesting that the fundus examination of patients with

Statistical analysis of correlated experimental data and neutron cross section evaluation

International Nuclear Information System (INIS)

Badikov, S.A.

1998-01-01

The technique for evaluation of neutron cross sections on the basis of statistical analysis of correlated experimental data is presented. The most important stages of evaluation beginning from compilation of correlation matrix for measurement uncertainties till representation of the analysis results in the ENDF-6 format are described in details. Special attention is paid to restrictions (positive uncertainty) on covariation matrix of approximate parameters uncertainties generated within the method of least square fit which is derived from physical reasons. The requirements for source experimental data assuring satisfaction of the restrictions mentioned above are formulated. Correlation matrices of measurement uncertainties in particular should be also positively determined. Variants of modelling the positively determined correlation matrices of measurement uncertainties in a situation when their consequent calculation on the basis of experimental information is impossible are discussed. The technique described is used for creating the new generation of estimates of dosimetric reactions cross sections for the first version of the Russian dosimetric file (including nontrivial covariation information)

Group sparse canonical correlation analysis for genomic data integration.

Science.gov (United States)

Lin, Dongdong; Zhang, Jigang; Li, Jingyao; Calhoun, Vince D; Deng, Hong-Wen; Wang, Yu-Ping

2013-08-12

The emergence of high-throughput genomic datasets from different sources and platforms (e.g., gene expression, single nucleotide polymorphisms (SNP), and copy number variation (CNV)) has greatly enhanced our understandings of the interplay of these genomic factors as well as their influences on the complex diseases. It is challenging to explore the relationship between these different types of genomic data sets. In this paper, we focus on a multivariate statistical method, canonical correlation analysis (CCA) method for this problem. Conventional CCA method does not work effectively if the number of data samples is significantly less than that of biomarkers, which is a typical case for genomic data (e.g., SNPs). Sparse CCA (sCCA) methods were introduced to overcome such difficulty, mostly using penalizations with l-1 norm (CCA-l1) or the combination of l-1and l-2 norm (CCA-elastic net). However, they overlook the structural or group effect within genomic data in the analysis, which often exist and are important (e.g., SNPs spanning a gene interact and work together as a group). We propose a new group sparse CCA method (CCA-sparse group) along with an effective numerical algorithm to study the mutual relationship between two different types of genomic data (i.e., SNP and gene expression). We then extend the model to a more general formulation that can include the existing sCCA models. We apply the model to feature/variable selection from two data sets and compare our group sparse CCA method with existing sCCA methods on both simulation and two real datasets (human gliomas data and NCI60 data). We use a graphical representation of the samples with a pair of canonical variates to demonstrate the discriminating characteristic of the selected features. Pathway analysis is further performed for biological interpretation of those features. The CCA-sparse group method incorporates group effects of features into the correlation analysis while performs individual feature

Linear and Nonlinear Multiset Canonical Correlation Analysis (invited talk)

DEFF Research Database (Denmark)

Hilger, Klaus Baggesen; Nielsen, Allan Aasbjerg; Larsen, Rasmus

2002-01-01

This paper deals with decompositioning of multiset data. Friedman's alternating conditional expectations (ACE) algorithm is extended to handle multiple sets of variables of different mixtures. The new algorithm finds estimates of the optimal transformations of the involved variables that maximize...... the sum of the pair-wise correlations over all sets. The new algorithm is termed multi-set ACE (MACE) and can find multiple orthogonal eigensolutions. MACE is a generalization of the linear multiset correlations analysis (MCCA). It handles multivariate multisets of arbitrary mixtures of both continuous...

A multimodal stress monitoring system with canonical correlation analysis.

Science.gov (United States)

Unsoo Ha; Changhyeon Kim; Yongsu Lee; Hyunki Kim; Taehwan Roh; Hoi-Jun Yoo

2015-08-01

The multimodal stress monitoring headband is proposed for mobile stress management system. It is composed of headband and earplugs. Electroencephalography (EEG), hemoencephalography (HEG) and heart-rate variability (HRV) can be achieved simultaneously in the proposed system for user status estimation. With canonical correlation analysis (CCA) and temporal-kernel CCA (tkCCA) algorithm, those different signals can be combined for maximum correlation. Thanks to the proposed combination algorithm, the accuracy of the proposed system increased up to 19 percentage points than unimodal monitoring system in n-back task.

Improving the modelling of redshift-space distortions - I. A bivariate Gaussian description for the galaxy pairwise velocity distributions

Science.gov (United States)

Bianchi, Davide; Chiesa, Matteo; Guzzo, Luigi

2015-01-01

As a step towards a more accurate modelling of redshift-space distortions (RSD) in galaxy surveys, we develop a general description of the probability distribution function of galaxy pairwise velocities within the framework of the so-called streaming model. For a given galaxy separation r, such function can be described as a superposition of virtually infinite local distributions. We characterize these in terms of their moments and then consider the specific case in which they are Gaussian functions, each with its own mean μ and dispersion σ. Based on physical considerations, we make the further crucial assumption that these two parameters are in turn distributed according to a bivariate Gaussian, with its own mean and covariance matrix. Tests using numerical simulations explicitly show that with this compact description one can correctly model redshift-space distortions on all scales, fully capturing the overall linear and non-linear dynamics of the galaxy flow at different separations. In particular, we naturally obtain Gaussian/exponential, skewed/unskewed distribution functions, depending on separation as observed in simulations and data. Also, the recently proposed single-Gaussian description of RSD is included in this model as a limiting case, when the bivariate Gaussian is collapsed to a two-dimensional Dirac delta function. We also show how this description naturally allows for the Taylor expansion of 1 + ξS(s) around 1 + ξR(r), which leads to the Kaiser linear formula when truncated to second order, explicating its connection with the moments of the velocity distribution functions. More work is needed, but these results indicate a very promising path to make definitive progress in our programme to improve RSD estimators.

Are Quantitative Measures of Academic Productivity Correlated with Academic Rank in Plastic Surgery? A National Study.

Science.gov (United States)

Susarla, Srinivas M; Lopez, Joseph; Swanson, Edward W; Miller, Devin; O'Brien-Coon, Devin; Zins, James E; Serletti, Joseph M; Yaremchuk, Michael J; Manson, Paul N; Gordon, Chad R

2015-09-01

The purpose of this study was to investigate the correlation between quantitative measures of academic productivity and academic rank among full-time academic plastic surgeons. Bibliometric indices were computed for all full-time academic plastic surgeons in the United States. The primary study variable was academic rank. Bibliometric predictors included the Hirsch index, I-10 index, number of publications, number of citations, and highest number of citations for a single publication. Descriptive, bivariate, and correlation analyses were computed. Multiple comparisons testing was used to calculate adjusted associations for subgroups. For all analyses, a value of p productivity. Although academic promotion is the result of success in multiple different areas, bibliometric measures may be useful adjuncts for assessment of research productivity.

Correlates of self-reported pelvic inflammatory disease treatment in sexually experienced reproductive-aged women in the United States, 1995 and 2006-2010.

Science.gov (United States)

Leichliter, Jami S; Chandra, Anjani; Aral, Sevgi O

2013-05-01

Few studies have examined recent temporal trends in self-reported receipt of pelvic inflammatory disease (PID) treatment. We assessed trends in receipt of PID treatment and associated correlates using national survey data. We used data from the National Survey of Family Growth, a multistage national probability survey of 15- to 44-year-old women. We examined trends in self-reported receipt of PID treatment from 1995, 2002, to 2006-2010. In addition, we examined correlates of PID treatment in 1995 and 2006-2010 in bivariate and adjusted analyses. From 1995 to 2002, receipt of PID treatment significantly declined from 8.6% to 5.7% (P < 0.0001); however, there was no difference from 2002 to 2006-2010 (5.0%, P = 0.16). In bivariate analyses, racial differences in PID treatment declined across time; in 2006-2010, there was no significant difference between racial/ethnic groups (P = 0.22). Also in bivariate analyses, similar to 1995, in 2006 to 2010, some of the highest reports of receipt of PID treatment were women who were 35 to 44 years old (5.6%), had an income less than 150% of poverty level (7.5%), had less than high school education (6.7%), douched (7.7%), had intercourse before age 15 years (10.3%), and had 10 or more lifetime partners (8.0%). In adjusted analyses, differing from 1995, women at less than 150% of the poverty level were more likely (adjusted odds ratio [AOR], 2.60; 95% confidence interval [CI], 1.79-3.76) than women at 300% or more of the poverty level to have received PID treatment in 2006-2010. Receipt of PID treatment declined from 1995 to 2006-2010, with the burden affecting women of lower socioeconomic status.

LCA profiles for building components:

DEFF Research Database (Denmark)

Marsh, Rob

2016-01-01

. A full bivariate linear regression analysis is performed, showing statistically significant correlations with strong direct relationships between environmental impact categories. A simplified LCA profile consisting of total primary energy, global warming potential and acidification potential is developed...

Bioethanol from corn stover – a review and technical assessment of alternative biotechnologies

DEFF Research Database (Denmark)

Zhao, Yan; Damgaard, Anders; Christensen, Thomas Højlund

2018-01-01

stover content. Based on the selected datasets, statistical description is provided for all parameters, including mode, median, average and deviation, within each technological configuration. Bivariate correlation analysis across and within all technological configurations indicates that some operational...

Irregular Liesegang-type patterns in gas phase revisited. II. Statistical correlation analysis

Science.gov (United States)

Torres-Guzmán, José C.; Martínez-Mekler, Gustavo; Müller, Markus F.

2016-05-01

We present a statistical analysis of Liesegang-type patterns formed in a gaseous HCl-NH3 system by ammonium chloride precipitation along glass tubes, as described in Paper I [J. C. Torres-Guzmán et al., J. Chem. Phys. 144, 174701 (2016)] of this work. We focus on the detection and characterization of short and long-range correlations within the non-stationary sequence of apparently irregular precipitation bands. To this end we applied several techniques to estimate spatial correlations stemming from different fields, namely, linear auto-correlation via the power spectral density, detrended fluctuation analysis (DFA), and methods developed in the context of random matrix theory (RMT). In particular RMT methods disclose well pronounced long-range correlations over at least 40 bands in terms of both, band positions and intensity values. By using a variant of the DFA we furnish proof of the nonlinear nature of the detected long-range correlations.

Correlation between detrended fluctuation analysis and the Lempel-Ziv complexity in nonlinear time series analysis

International Nuclear Information System (INIS)

Tang You-Fu; Liu Shu-Lin; Jiang Rui-Hong; Liu Ying-Hui

2013-01-01

We study the correlation between detrended fluctuation analysis (DFA) and the Lempel-Ziv complexity (LZC) in nonlinear time series analysis in this paper. Typical dynamic systems including a logistic map and a Duffing model are investigated. Moreover, the influence of Gaussian random noise on both the DFA and LZC are analyzed. The results show a high correlation between the DFA and LZC, which can quantify the non-stationarity and the nonlinearity of the time series, respectively. With the enhancement of the random component, the exponent a and the normalized complexity index C show increasing trends. In addition, C is found to be more sensitive to the fluctuation in the nonlinear time series than α. Finally, the correlation between the DFA and LZC is applied to the extraction of vibration signals for a reciprocating compressor gas valve, and an effective fault diagnosis result is obtained

Correlates of Persistent Smoking in Bars Subject to Smokefree Workplace Policy

Directory of Open Access Journals (Sweden)

Bong Chul Chu

2009-04-01

Full Text Available This study’s goal was to characterize physical and social environments of stand-alone bars associated with indoor smoking despite California’s smokefree workplace law. In a random sample of 121 stand-alone bars in San Francisco, trained observers collected data on patrons, staff, neighborhood, indoor settings and smoking behaviors. Using bivariate (chi-square and hierarchical linear modeling analyses, we identified four correlates of patrons’ indoor smoking: 1 bars serving predominantly Asian or Irish patrons, 2 ashtrays, 3 bartender smoking, and 4 female bartenders. Public health officials charged with enforcement of smokefree bar policies may need to attend to social practices within bars, and heighten perceptions of consistent enforcement of smokefree workplace laws.

Generalization of proposed tendon friction correlation and its application to PCCV structural analysis

International Nuclear Information System (INIS)

Kashiwase, Takako; Nagasaka, Hideo

2000-01-01

The present paper dealt with the extension of tendon friction coefficient correlation as a function of loading end load and circumferential angle, proposed in the former paper. The extended correlation further included the effects of the number of strands contacted with sheath, tendon diameter, politicization of tendon and tendon local curvature. The validity of the correlation was confirmed by several published measured data. The structural analysis of middle cylinder part of 1/4 PCCV (Prestressed Concrete Containment Vessel) model was conducted using the present friction coefficient correlation. The results were compared with the analysis using constant friction coefficient, focused on the tendon tension force distribution. (author)

Protein structure similarity from principle component correlation analysis

Directory of Open Access Journals (Sweden)

Chou James

2006-01-01

Full Text Available Abstract Background Owing to rapid expansion of protein structure databases in recent years, methods of structure comparison are becoming increasingly effective and important in revealing novel information on functional properties of proteins and their roles in the grand scheme of evolutionary biology. Currently, the structural similarity between two proteins is measured by the root-mean-square-deviation (RMSD in their best-superimposed atomic coordinates. RMSD is the golden rule of measuring structural similarity when the structures are nearly identical; it, however, fails to detect the higher order topological similarities in proteins evolved into different shapes. We propose new algorithms for extracting geometrical invariants of proteins that can be effectively used to identify homologous protein structures or topologies in order to quantify both close and remote structural similarities. Results We measure structural similarity between proteins by correlating the principle components of their secondary structure interaction matrix. In our approach, the Principle Component Correlation (PCC analysis, a symmetric interaction matrix for a protein structure is constructed with relationship parameters between secondary elements that can take the form of distance, orientation, or other relevant structural invariants. When using a distance-based construction in the presence or absence of encoded N to C terminal sense, there are strong correlations between the principle components of interaction matrices of structurally or topologically similar proteins. Conclusion The PCC method is extensively tested for protein structures that belong to the same topological class but are significantly different by RMSD measure. The PCC analysis can also differentiate proteins having similar shapes but different topological arrangements. Additionally, we demonstrate that when using two independently defined interaction matrices, comparison of their maximum

Asymptotic properties of Pearson's rank-variate correlation coefficient under contaminated Gaussian model.

Science.gov (United States)

Ma, Rubao; Xu, Weichao; Zhang, Yun; Ye, Zhongfu

2014-01-01

This paper investigates the robustness properties of Pearson's rank-variate correlation coefficient (PRVCC) in scenarios where one channel is corrupted by impulsive noise and the other is impulsive noise-free. As shown in our previous work, these scenarios that frequently encountered in radar and/or sonar, can be well emulated by a particular bivariate contaminated Gaussian model (CGM). Under this CGM, we establish the asymptotic closed forms of the expectation and variance of PRVCC by means of the well known Delta method. To gain a deeper understanding, we also compare PRVCC with two other classical correlation coefficients, i.e., Spearman's rho (SR) and Kendall's tau (KT), in terms of the root mean squared error (RMSE). Monte Carlo simulations not only verify our theoretical findings, but also reveal the advantage of PRVCC by an example of estimating the time delay in the particular impulsive noise environment.

Registration of prone and supine CT colonography scans using correlation optimized warping and canonical correlation analysis

International Nuclear Information System (INIS)

Wang Shijun; Yao Jianhua; Liu Jiamin; Petrick, Nicholas; Van Uitert, Robert L.; Periaswamy, Senthil; Summers, Ronald M.

2009-01-01

Purpose: In computed tomographic colonography (CTC), a patient will be scanned twice--Once supine and once prone--to improve the sensitivity for polyp detection. To assist radiologists in CTC reading, in this paper we propose an automated method for colon registration from supine and prone CTC scans. Methods: We propose a new colon centerline registration method for prone and supine CTC scans using correlation optimized warping (COW) and canonical correlation analysis (CCA) based on the anatomical structure of the colon. Four anatomical salient points on the colon are first automatically distinguished. Then correlation optimized warping is applied to the segments defined by the anatomical landmarks to improve the global registration based on local correlation of segments. The COW method was modified by embedding canonical correlation analysis to allow multiple features along the colon centerline to be used in our implementation. Results: We tested the COW algorithm on a CTC data set of 39 patients with 39 polyps (19 training and 20 test cases) to verify the effectiveness of the proposed COW registration method. Experimental results on the test set show that the COW method significantly reduces the average estimation error in a polyp location between supine and prone scans by 67.6%, from 46.27±52.97 to 14.98 mm±11.41 mm, compared to the normalized distance along the colon centerline algorithm (p<0.01). Conclusions: The proposed COW algorithm is more accurate for the colon centerline registration compared to the normalized distance along the colon centerline method and the dynamic time warping method. Comparison results showed that the feature combination of z-coordinate and curvature achieved lowest registration error compared to the other feature combinations used by COW. The proposed method is tolerant to centerline errors because anatomical landmarks help prevent the propagation of errors across the entire colon centerline.

How to update design floods after the construction of small ...

Indian Academy of Sciences (India)

either by statistical methods or by a hydrological model simulation, and ... Dams impact river hydrology through changes in the ... dams, and then applied canonical correlation anal- ...... Feng P and Li X 2013 Bivariate frequency analysis of non- stationary ... of multivariate statistical analysis methods to the dam hydrologic ...

Genetic Analysis of Cortical Thickness and Fractional Anisotropy of Water Diffusion in the Brain

Directory of Open Access Journals (Sweden)

Peter eKochunov

2011-10-01

Full Text Available ObjectivesThe thickness of the brain’s cortical gray matter (GM and the fractional anisotropy (FA of the cerebral white matter (WM each follow an inverted U-shape trajectory with age. Both measures are positively correlated, suggesting a common biological mechanism. We employed bivariate genetic analyses to localize quantitative trait loci (QTLs and individual genes acting pleiotropically upon these phenotypes.MethodsWhole-brain and regional GM thickness and FA values were measured from high-resolution anatomical (0.8mm isotropic and diffusion tensor MR images (1.7x1.7x3.0mm, 55 directions collected for 712 active participants (274/438 male/females, age=47.9±13.2years in the Genetics of Brain Structure study.ResultsBivariate, whole-genome quantitative trait loci (QTL analyses of the whole brain measures localized significant (LOD≥3.0 QTLs within chromosome region 15q22-23. More detailed localization was achieved using single nuclear polymorphism (SNP association and gene expression analyses. No significant association (p<510-5 was observed for 1565 SNPs located within the QTLs. However, post-hoc analysis indicated that 40% of the potentially significant (p≤10-3 polymorphisms were localized to the RORA and NARG2 genes. A potentially significant association (p=310-4 was also observed for the rs2456930 polymorphism that was previously reported as a significant GWAS finding in ADNI subjects. Lymphocyte expression measurements for two genes, located under QTL, RORA and ADAM10 were significantly (p<0.05 correlated with both FA and GM thickness values. Expression measurements for NARG2 was significantly correlated with GM thickness (p<0.05 but failed to show a significant correlation (p=0.09 with FA.Discussion This study identified a novel, significant QTL at 15q22-23. SNP association and correlation with gene-expression analyses indicated that RORA, NARG2 and ADAM10 jointly influence GM thickness and cerebral WM integrity.

Correlation analysis for forced vibration test of the Hualien large scale seismic test (LSST) program

International Nuclear Information System (INIS)

Sugawara, Y.; Sugiyama, T.; Kobayashi, T.; Yamaya, H.; Kitamura, E.

1995-01-01

The correlation analysis for a forced vibration test of a 1/4-scale containment SSI test model constructed in Hualien, Taiwan was carried out for the case of after backfilling. Prior to this correlation analysis, the structural properties were revised to adjust the calculated fundamental frequency in the fixed base condition to that derived from the test results. A correlation analysis was carried out using the Lattice Model which was able to estimate the soil-structure effects with embedment. The analysis results coincide well with test results and it is concluded that the mathematical soil-structure interaction model established by the correlation analysis is efficient in estimating the dynamic soil-structure interaction effect with embedment. This mathematical model will be applied as a basic model for simulation analysis of earthquake observation records. (author). 3 refs., 12 figs., 2 tabs

Contrast-enhanced Ultrasound for Detection of Crohn's Disease Activity: Systematic Review and Meta-analysis.

Science.gov (United States)

Serafin, Zbigniew; Białecki, Marcin; Białecka, Agnieszka; Sconfienza, Luca Maria; Kłopocka, Maria

2016-03-01

Reports on imaging of active Crohn's disease (aCD) using contrast-enhanced ultrasound (CEUS) are encouraging. However, the statistical power of most published papers is limited due to the small size of the patient groups included. This study was performed to verify the diagnostic value of CEUS in detecting aCD. A systematic literature search was performed by two independent reviewers for articles on the test characteristics of CEUS for the identification of aCD. The quality of the analysed studies was evaluated using a quality assessment tool for diagnostic accuracy studies (QUADAS-2). Pooling was performed using a diagnostic random-effect model and bivariate analysis. Eight articles were included in the final analysis, with a total of 332 patients. There was no significant publication bias. Significant heterogeneity was found regarding CEUS methodology and sonographic definitions of aCD. In a bivariate analysis, pooled sensitivity was 0.94 (95% CI 0.87-0.97) and pooled specificity was 0.79 (95% CI 0.67-0.88). Spearman correlation statistics presented no significant diagnostic threshold effect (r = 0.12, p > 0.9). Subgroup analysis showed that relative intestine wall enhancement had the highest diagnostic value (area under the curve 94%), while the presence of enhancement and analysis of the slope were less useful (area under the curve 91 and 90%, respectively). CEUS presents good sensitivity and moderate specificity in the detection of the aCD. Large-scale randomized trials with quantitative evaluation of CEUS images are necessary to promote this technique in clinical practice. Copyright © 2015 European Crohn’s and Colitis Organisation (ECCO). Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.

The effects of common risk factors on stock returns: A detrended cross-correlation analysis

Science.gov (United States)

Ruan, Qingsong; Yang, Bingchan

2017-10-01

In this paper, we investigate the cross-correlations between Fama and French three factors and the return of American industries on the basis of cross-correlation statistic test and multifractal detrended cross-correlation analysis (MF-DCCA). Qualitatively, we find that the return series of Fama and French three factors and American industries were overall significantly cross-correlated based on the analysis of a statistic. Quantitatively, we find that the cross-correlations between three factors and the return of American industries were strongly multifractal, and applying MF-DCCA we also investigate the cross-correlation of industry returns and residuals. We find that there exists multifractality of industry returns and residuals. The result of correlation coefficients we can verify that there exist other factors which influence the industry returns except Fama three factors.

Use of fuel failure correlations in accident analysis

International Nuclear Information System (INIS)

O'Dell, L.D.; Baars, R.E.; Waltar, A.E.

1975-05-01

The MELT-III code for analysis of a Transient Overpower (TOP) accident in an LMFBR is briefly described, including failure criteria currently applied in the code. Preliminary results of calculations exploring failure patterns in time and space in the reactor core are reported and compared for the two empirical fuel failure correlations employed in the code. (U.S.)

The correlates of preschoolers' compliance with screen recommendations exist across multiple domains.

Science.gov (United States)

Hinkley, Trina; Salmon, Jo; Okely, Anthony D; Crawford, David

2013-09-01

To investigate the individual, social and physical environment correlates of preschool children's compliance with Australian/Canadian and American Academy of Pediatrics (AAP) screen recommendations. An Ecological Model (EM) was used to identify constructs potentially associated with children's screen time. In 2008-2009, parents in Melbourne, Australia, reported their child's screen time and on a range of potential correlates. Children (n = 935; 54% boys, mean age 4.54 ± 0.70 years) were assessed as meeting or not meeting each of the screen recommendations. Logistic regression assessed bivariable and multivariable associations. In total, 15 explanatory variables, across the three domains of the EM were associated with boys' and/or girls' compliance with either Australian/Canadian or AAP recommendations. Correlates varied by sex and recommendation. Maternal television viewing time was the only consistent correlate for both boys' and girls' compliance with both recommendations. No demographic groups were identified as being less likely to comply with screen recommendations. Public health programs should take account of the sex-specific nature of correlates of preschool children's screen time. Preschool children across all demographic groups need support to engage in less screen use. Parents may benefit from education and parenting skills to minimize potentially harmful effects of excessive screen time for their child. Copyright © 2013 Elsevier Inc. All rights reserved.

New Colors for Histology: Optimized Bivariate Color Maps Increase Perceptual Contrast in Histological Images.

Directory of Open Access Journals (Sweden)

Jakob Nikolas Kather

Full Text Available Accurate evaluation of immunostained histological images is required for reproducible research in many different areas and forms the basis of many clinical decisions. The quality and efficiency of histopathological evaluation is limited by the information content of a histological image, which is primarily encoded as perceivable contrast differences between objects in the image. However, the colors of chromogen and counterstain used for histological samples are not always optimally distinguishable, even under optimal conditions.In this study, we present a method to extract the bivariate color map inherent in a given histological image and to retrospectively optimize this color map. We use a novel, unsupervised approach based on color deconvolution and principal component analysis to show that the commonly used blue and brown color hues in Hematoxylin-3,3'-Diaminobenzidine (DAB images are poorly suited for human observers. We then demonstrate that it is possible to construct improved color maps according to objective criteria and that these color maps can be used to digitally re-stain histological images.To validate whether this procedure improves distinguishability of objects and background in histological images, we re-stain phantom images and N = 596 large histological images of immunostained samples of human solid tumors. We show that perceptual contrast is improved by a factor of 2.56 in phantom images and up to a factor of 2.17 in sets of histological tumor images.Thus, we provide an objective and reliable approach to measure object distinguishability in a given histological image and to maximize visual information available to a human observer. This method could easily be incorporated in digital pathology image viewing systems to improve accuracy and efficiency in research and diagnostics.

Multiset Canonical Correlations Analysis and Multispectral, Truly Multitemporal Remote Sensing Data

DEFF Research Database (Denmark)

Nielsen, Allan Aasbjerg

2002-01-01

This paper describes two- and multiset canonical correlations analysis (CCA) for data fusion, multi-source, multiset or multi-temporal exploratory data analysis. These techniques transform multivariate multiset data into new orthogonal variables called canonical variates (CVs) which when applied...... in remote sensing exhibit ever decreasing similarity (as expressed by correlation measures) over sets consisting of 1) spectral variables at fixed points in time (R-mode analysis), or 2) temporal variables with fixed wavelengths (T-mode analysis). The CVs are invariant to linear and affine transformations...... of the original variables within sets which means, for example, that the R-mode CVs are insensitive to changes over time in offset and gain in a measuring device. In a case study CVs are calculated from Landsat TM data with six spectral bands over six consecutive years. Both R- and T-mode CVs clearly exhibit...

Windowed Multitaper Correlation Analysis of Multimodal Brain Monitoring Parameters

Directory of Open Access Journals (Sweden)

Rupert Faltermeier

2015-01-01

Full Text Available Although multimodal monitoring sets the standard in daily practice of neurocritical care, problem-oriented analysis tools to interpret the huge amount of data are lacking. Recently a mathematical model was presented that simulates the cerebral perfusion and oxygen supply in case of a severe head trauma, predicting the appearance of distinct correlations between arterial blood pressure and intracranial pressure. In this study we present a set of mathematical tools that reliably detect the predicted correlations in data recorded at a neurocritical care unit. The time resolved correlations will be identified by a windowing technique combined with Fourier-based coherence calculations. The phasing of the data is detected by means of Hilbert phase difference within the above mentioned windows. A statistical testing method is introduced that allows tuning the parameters of the windowing method in such a way that a predefined accuracy is reached. With this method the data of fifteen patients were examined in which we found the predicted correlation in each patient. Additionally it could be shown that the occurrence of a distinct correlation parameter, called scp, represents a predictive value of high quality for the patients outcome.

Windowed multitaper correlation analysis of multimodal brain monitoring parameters.

Science.gov (United States)

Faltermeier, Rupert; Proescholdt, Martin A; Bele, Sylvia; Brawanski, Alexander

2015-01-01

Although multimodal monitoring sets the standard in daily practice of neurocritical care, problem-oriented analysis tools to interpret the huge amount of data are lacking. Recently a mathematical model was presented that simulates the cerebral perfusion and oxygen supply in case of a severe head trauma, predicting the appearance of distinct correlations between arterial blood pressure and intracranial pressure. In this study we present a set of mathematical tools that reliably detect the predicted correlations in data recorded at a neurocritical care unit. The time resolved correlations will be identified by a windowing technique combined with Fourier-based coherence calculations. The phasing of the data is detected by means of Hilbert phase difference within the above mentioned windows. A statistical testing method is introduced that allows tuning the parameters of the windowing method in such a way that a predefined accuracy is reached. With this method the data of fifteen patients were examined in which we found the predicted correlation in each patient. Additionally it could be shown that the occurrence of a distinct correlation parameter, called scp, represents a predictive value of high quality for the patients outcome.

System Reliability Analysis Considering Correlation of Performances

Energy Technology Data Exchange (ETDEWEB)

Kim, Saekyeol; Lee, Tae Hee [Hanyang Univ., Seoul (Korea, Republic of); Lim, Woochul [Mando Corporation, Seongnam (Korea, Republic of)

2017-04-15

Reliability analysis of a mechanical system has been developed in order to consider the uncertainties in the product design that may occur from the tolerance of design variables, uncertainties of noise, environmental factors, and material properties. In most of the previous studies, the reliability was calculated independently for each performance of the system. However, the conventional methods cannot consider the correlation between the performances of the system that may lead to a difference between the reliability of the entire system and the reliability of the individual performance. In this paper, the joint probability density function (PDF) of the performances is modeled using a copula which takes into account the correlation between performances of the system. The system reliability is proposed as the integral of joint PDF of performances and is compared with the individual reliability of each performance by mathematical examples and two-bar truss example.

System Reliability Analysis Considering Correlation of Performances

International Nuclear Information System (INIS)

Kim, Saekyeol; Lee, Tae Hee; Lim, Woochul

2017-01-01

Reliability analysis of a mechanical system has been developed in order to consider the uncertainties in the product design that may occur from the tolerance of design variables, uncertainties of noise, environmental factors, and material properties. In most of the previous studies, the reliability was calculated independently for each performance of the system. However, the conventional methods cannot consider the correlation between the performances of the system that may lead to a difference between the reliability of the entire system and the reliability of the individual performance. In this paper, the joint probability density function (PDF) of the performances is modeled using a copula which takes into account the correlation between performances of the system. The system reliability is proposed as the integral of joint PDF of performances and is compared with the individual reliability of each performance by mathematical examples and two-bar truss example.

Correlation analysis between ceramic insulator pollution and acoustic emissions

Directory of Open Access Journals (Sweden)

Benjamín Álvarez-Nasrallah

2015-01-01

Full Text Available Most of the studies related to insulator pollution are normally performed based on individual analysis among leakage current, relative humidity and equivalent salt deposit density (ESDD. This paper presents a correlation analysis between the leakage current and the acoustic emissions measured in a 230 kV electrical substations in the city of Barranquilla, Colombia. Furthermore, atmospheric variables were considered to develop a characterization model of the insulator contamination process. This model was used to demonstrate that noise emission levels are a reliable indicator to detect and characterize pollution on high voltage insulators. The correlation found amount the atmospheric, electrical and sound variables allowed to determine the relations for the maintenance of ceramic insulators in high-polluted areas. In this article, the results on the behavior of the leakage current in ceramic insulators and the sound produced with different atmospheric conditions are shown, which allow evaluating the best time to clean the insulator at the substation. Furthermore, by experimentation on site and using statistical models, the correlation between ambient variables and the leakage current of insulators in an electrical substation was obtained. Some of the problems that bring the external noise were overcome using multiple microphones and specialized software that enabled properly filter the sound and better measure the variables.

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

Science.gov (United States)

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

2011-04-01

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

Clarifying associations between psychopathy facets and personality disorders among offenders

NARCIS (Netherlands)

Klipfel, Kristen M.; Garofalo, C.; Kosson, D.S.

2017-01-01

Purpose This study examined bivariate, unique, and multivariate associations between psychopathy facets and other Personality Disorders (PDs). Method 76 incarcerated males were assessed with clinical interviews measuring psychopathy and DSM-5 PDs. Canonical Correlation Analysis (CCA) was used to

Level and correlates of physical activity and sedentary behavior in patients with type 2 diabetes: A cross-sectional analysis of the Italian Diabetes and Exercise Study_2.

Directory of Open Access Journals (Sweden)

Stefano Balducci

Full Text Available Patients with type 2 diabetes usually show reduced physical activity (PA and increased sedentary (SED-time, though to a varying extent, especially for low-intensity PA (LPA, a major determinant of daily energy expenditure that is not accurately captured by questionnaires. This study assessed the level and correlates of PA and SED-time in patients from the Italian Diabetes and Exercise Study_2 (IDES_2.Three-hundred physically inactive and sedentary patients with type 2 diabetes were enrolled in the IDES_2 to be randomized to an intervention group, receiving theoretical and practical exercise counseling, and a control group, receiving standard care. At baseline, LPA, moderate-to-vigorous-intensity PA (MVPA, and SED-time were measured by accelerometer. Physical fitness and cardiovascular risk factors and scores were also assessed.LPA was 3.93±1.35 hours∙day-1, MVPA was 12.4±4.6 min∙day-1, and SED-time was 11.6±1.2 hours∙day-1, with a large range of values (0.89-7.11 hours∙day-1, 0.6-21.0 min∙day-1, and 9.14-15.28 hours∙day-1, respectively. At bivariate analysis, LPA and MVPA correlated with better cardiovascular risk profile and fitness parameters, whereas the opposite was observed for SED-time. Likewise, values of LPA, MVPA, and SED-time falling in the best tertile were associated with optimal or acceptable levels of cardiovascular risk factors and scores. At multivariate analysis, age, female gender, HbA1c, BMI or waist circumference, and high-sensitivity C reactive protein (for LPA and SED-time only were negatively associated with LPA and MPA and positively associated with SED-time in an independent manner.Physically inactive and sedentary patients with type 2 diabetes from the IDES_2 show a low level of PA, though values of LPA, MVPA, and SED-time vary largely. Furthermore, there is a strong correlation of these measures with glycemic control, adiposity and inflammation, thus suggesting that even small improvements in LPA, MVPA

Cross-Correlations between Energy and Emissions Markets: New Evidence from Fractal and Multifractal Analysis

Directory of Open Access Journals (Sweden)

Gang-Jin Wang

2014-01-01

Full Text Available We supply a new perspective to describe and understand the behavior of cross-correlations between energy and emissions markets. Namely, we investigate cross-correlations between oil and gas (Oil-Gas, oil and CO2 (Oil-CO2, and gas and CO2 (Gas-CO2 based on fractal and multifractal analysis. We focus our study on returns of the oil, gas, and CO2 during the period of April 22, 2005–April 30, 2013. In the empirical analysis, by using the detrended cross-correlation analysis (DCCA method, we find that cross-correlations for Oil-Gas, Oil-CO2, and Gas-CO2 obey a power-law and are weakly persistent. Then, we adopt the method of DCCA cross-correlation coefficient to quantify cross-correlations between energy and emissions markets. The results show that their cross-correlations are diverse at different time scales. Next, based on the multifractal DCCA method, we find that cross-correlated markets have the nonlinear and multifractal nature and that the multifractality strength for three cross-correlated markets is arranged in the order of Gas-CO2 > Oil-Gas > Oil-CO2. Finally, by employing the rolling windows method, which can be used to investigate time-varying cross-correlation scaling exponents, we analyze short-term and long-term market dynamics and find that the recent global financial crisis has a notable influence on short-term and long-term market dynamics.

Robust Correlation Analyses: False Positive and Power Validation Using a New Open Source Matlab Toolbox

Science.gov (United States)

Pernet, Cyril R.; Wilcox, Rand; Rousselet, Guillaume A.

2012-01-01

Pearson’s correlation measures the strength of the association between two variables. The technique is, however, restricted to linear associations and is overly sensitive to outliers. Indeed, a single outlier can result in a highly inaccurate summary of the data. Yet, it remains the most commonly used measure of association in psychology research. Here we describe a free Matlab(R) based toolbox (http://sourceforge.net/projects/robustcorrtool/) that computes robust measures of association between two or more random variables: the percentage-bend correlation and skipped-correlations. After illustrating how to use the toolbox, we show that robust methods, where outliers are down weighted or removed and accounted for in significance testing, provide better estimates of the true association with accurate false positive control and without loss of power. The different correlation methods were tested with normal data and normal data contaminated with marginal or bivariate outliers. We report estimates of effect size, false positive rate and power, and advise on which technique to use depending on the data at hand. PMID:23335907

Robust correlation analyses: false positive and power validation using a new open source matlab toolbox.

Science.gov (United States)

Pernet, Cyril R; Wilcox, Rand; Rousselet, Guillaume A

2012-01-01

Pearson's correlation measures the strength of the association between two variables. The technique is, however, restricted to linear associations and is overly sensitive to outliers. Indeed, a single outlier can result in a highly inaccurate summary of the data. Yet, it remains the most commonly used measure of association in psychology research. Here we describe a free Matlab((R)) based toolbox (http://sourceforge.net/projects/robustcorrtool/) that computes robust measures of association between two or more random variables: the percentage-bend correlation and skipped-correlations. After illustrating how to use the toolbox, we show that robust methods, where outliers are down weighted or removed and accounted for in significance testing, provide better estimates of the true association with accurate false positive control and without loss of power. The different correlation methods were tested with normal data and normal data contaminated with marginal or bivariate outliers. We report estimates of effect size, false positive rate and power, and advise on which technique to use depending on the data at hand.

The Correlation between Duration of Employment, Body Posture and Smoking Habit on Low Back Pain Incidence An Analytic Observational Study Among Taxi Driver in Semarang Municipality

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Syifa Dian Firmanita

2015-06-01

Full Text Available Introduction: Low back pain ((LBP ranks as number two of neurological disease’s highest prevalence after cephalgia and migraine in Indonesia. Objective: This study aim to determine the relationship between duration of employment, body posture and smoking habit on the incidence of low back pain on taxi driver. Method: This research is an observational analytic cross sectional design. Seventyfour taxi drivers in Semarang municipality was screened with Beck’s Depression Inventory Scale to meet the inclusion and exclusion criteria. Respondents were then given Risk Factor of LBP questionnaire. Data were analyzed with a bivariate correlation test contingency coefficient to see the relationship between duration of employment, body posture, smoking habit and low back pain.Result: the taxi driver with duration of employment >10 years were moderate smokers and having astenis body posture. The analysis of correlation coefficients contingency test showed a significance relationship between duration of employment (p = 0,000, body posture (p = 0,000, and moderate smokers (p=0.010 with the incidence of LBP. Conclusion: the taxi driver with duration of employment >10 years with astenis body posture, and moderate smokers were posstively correlated with LBP.

Random matrix theory analysis of cross-correlations in the US stock market: Evidence from Pearson’s correlation coefficient and detrended cross-correlation coefficient

Science.gov (United States)

Wang, Gang-Jin; Xie, Chi; Chen, Shou; Yang, Jiao-Jiao; Yang, Ming-Yan

2013-09-01

In this study, we first build two empirical cross-correlation matrices in the US stock market by two different methods, namely the Pearson’s correlation coefficient and the detrended cross-correlation coefficient (DCCA coefficient). Then, combining the two matrices with the method of random matrix theory (RMT), we mainly investigate the statistical properties of cross-correlations in the US stock market. We choose the daily closing prices of 462 constituent stocks of S&P 500 index as the research objects and select the sample data from January 3, 2005 to August 31, 2012. In the empirical analysis, we examine the statistical properties of cross-correlation coefficients, the distribution of eigenvalues, the distribution of eigenvector components, and the inverse participation ratio. From the two methods, we find some new results of the cross-correlations in the US stock market in our study, which are different from the conclusions reached by previous studies. The empirical cross-correlation matrices constructed by the DCCA coefficient show several interesting properties at different time scales in the US stock market, which are useful to the risk management and optimal portfolio selection, especially to the diversity of the asset portfolio. It will be an interesting and meaningful work to find the theoretical eigenvalue distribution of a completely random matrix R for the DCCA coefficient because it does not obey the Marčenko-Pastur distribution.

The Neural Correlates of Moral Thinking: A Meta-Analysis

OpenAIRE

Douglas J. Bryant; Wang F; Kelley Deardeuff; Emily Zoccoli; Chang S. Nam

2016-01-01

We conducted a meta-analysis to evaluate current research that aims to map the neural correlates of two typical conditions of moral judgment: right-wrong moral judgments and decision-making in moral dilemmas. Utilizing the activation likelihood estimation (ALE) method, we conducted a meta-analysis using neuroimaging data obtained from twenty-one previous studies that measured responses in one or the other of these conditions. We found that across the studies (n = 400), distinct neural circuit...

Chemical Composition, Sensory Properties, Provenance, and Bioactivity of Fruit Juices as Assessed bij Chemometrics: A Critical Review and Guideline

NARCIS (Netherlands)

Ziekinski, A.F.; Haminiuk, C.W.I.; Nunes, C.A.; Schnitzler, E.; Ruth, van S.M.; Granato, D.

2014-01-01

The use of univariate, bivariate, and multivariate statistical techniques, such as analysis of variance, multiple comparisons of means, and linear correlations, has spread widely in the area of Food Science and Technology. However, the use of supervised and unsupervised statistical techniques

Comparative analysis of heat transfer correlations for forced convection boiling

International Nuclear Information System (INIS)

Guglielmini, G.; Nannei, E.; Pisoni, C.

1978-01-01

A critical survey was conducted of the most relevant correlations of boiling heat transfer in forced convection flow. Most of the investigations carried out on partial nucleate boiling and fully developed nucleate boiling have led to the formulation of correlations that are not able to cover a wide range of operating conditions, due to the empirical approach of the problem. A comparative analysis is therefore required in order to delineate the relative accuracy of the proposed correlations, on the basis of the experimental data presently available. The survey performed allows the evaluation of the accuracy of the different calculating procedure; the results obtained, moreover, indicate the most reliable heat transfer correlations for the different operating conditions investigated. This survey was developed for five pressure range (up to 180bar) and for both saturation and subcooled boiling condition

Systematic review and meta-analysis of studies evaluating diagnostic test accuracy: A practical review for clinical researchers-Part II. general guidance and tips

International Nuclear Information System (INIS)

Kim, Kyung Won; Choi, Sang Hyun; Huh, Jimi; Park, Seong Ho; Lee, June Young

2015-01-01

Meta-analysis of diagnostic test accuracy studies differs from the usual meta-analysis of therapeutic/interventional studies in that, it is required to simultaneously analyze a pair of two outcome measures such as sensitivity and specificity, instead of a single outcome. Since sensitivity and specificity are generally inversely correlated and could be affected by a threshold effect, more sophisticated statistical methods are required for the meta-analysis of diagnostic test accuracy. Hierarchical models including the bivariate model and the hierarchical summary receiver operating characteristic model are increasingly being accepted as standard methods for meta-analysis of diagnostic test accuracy studies. We provide a conceptual review of statistical methods currently used and recommended for meta-analysis of diagnostic test accuracy studies. This article could serve as a methodological reference for those who perform systematic review and meta-analysis of diagnostic test accuracy studies

Detrended cross-correlation analysis on RMB exchange rate and Hang Seng China Enterprises Index

Science.gov (United States)

Ruan, Qingsong; Yang, Bingchan; Ma, Guofeng

2017-02-01

In this paper, we investigate the cross-correlations between the Hang Seng China Enterprises Index and RMB exchange markets on the basis of a cross-correlation statistic test and multifractal detrended cross-correlation analysis (MF-DCCA). MF-DCCA has, at best, serious limitations for most of the signals describing complex natural processes and often indicates multifractal cross-correlations when there are none. In order to prevent these false multifractal cross-correlations, we apply MFCCA to verify the cross-correlations. Qualitatively, we find that the return series of the Hang Seng China Enterprises Index and RMB exchange markets were, overall, significantly cross-correlated based on the statistical analysis. Quantitatively, we find that the cross-correlations between the stock index and RMB exchange markets were strongly multifractal, and the multifractal degree of the onshore RMB exchange markets was somewhat larger than the offshore RMB exchange markets. Moreover, we use the absolute return series to investigate and confirm the fact of multifractality. The results from the rolling windows show that the short-term cross-correlations between volatility series remain high.

Correlation dimension based nonlinear analysis of network traffics with different application protocols

International Nuclear Information System (INIS)

Wang Jun-Song; Yuan Jing; Li Qiang; Yuan Rui-Xi

2011-01-01

This paper uses a correlation dimension based nonlinear analysis approach to analyse the dynamics of network traffics with three different application protocols—HTTP, FTP and SMTP. First, the phase space is reconstructed and the embedding parameters are obtained by the mutual information method. Secondly, the correlation dimensions of three different traffics are calculated and the results of analysis have demonstrated that the dynamics of the three different application protocol traffics is different from each other in nature, i.e. HTTP and FTP traffics are chaotic, furthermore, the former is more complex than the later; on the other hand, SMTP traffic is stochastic. It is shown that correlation dimension approach is an efficient method to understand and to characterize the nonlinear dynamics of HTTP, FTP and SMTP protocol network traffics. This analysis provided insight into and a more accurate understanding of nonlinear dynamics of internet traffics which have a complex mixture of chaotic and stochastic components. (general)

Evaluation of Kinesiophobia and Its Correlations with Pain and Fatigue in Joint Hypermobility Syndrome/Ehlers-Danlos Syndrome Hypermobility Type

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Claudia Celletti

2013-01-01

Full Text Available Ehlers-Danlos syndrome hypermobility type a. k. a. joint hypermobility syndrome (JHS/EDS-HT is a hereditary musculoskeletal disorder associating generalized joint hypermobility with chronic pain. Anecdotal reports suggest a prominent role for kinesiophobia in disease manifestations, but no study has systematically addressed this point. Objective. To investigate the impact of kinesiophobia and its relationship with pain, fatigue, and quality of life in JHS/EDS-HT. Design. Cross-sectional study. Subjects/Patients. 42 patients (40 female and 2 male with JHS/EDS-HT diagnosis following standardized diagnostic criteria were selected. Methods. Disease features were analyzed by means of specific questionnaires and scales evaluating kinesiophobia, pain, fatigue, and quality of life. The relationships among variables were investigated using the Spearman bivariate analysis. Results. Kinesiophobia resulted predominantly in the patients’ sample. The values of kinesiophobia did not correlate with intensity of pain, quality of life, and (or the single component of fatigue. A strong correlation was discovered between kinesiophobia and general severity of fatigue. Conclusions. In JHS/EDS-HT, the onset of pain-avoiding strategies is related to the presence of pain but not to its intensity. The clear-cut correlation between kinesiophobia and severity of fatigue suggests a direct link between musculoskeletal pain and fatigue. In JHS/EDS-HT, the underlying mechanism is likely to be facilitated by primary disease characteristics, including hypotonia.

Psychosocial correlates of HPV vaccine acceptability in college males: A cross-sectional exploratory study

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Ovidiu Tatar

2017-12-01

Full Text Available Background: Most college males are not immunized against the human papillomavirus (HPV and are at high risk of HPV infection. Most research of correlates of HPV vaccine acceptability in college males has assessed vaccine acceptability as a binary outcome, e.g., vaccinated or not vaccinated, without considering that some students may not even be aware that the HPV vaccine can be given to males. Our objective was to evaluate the psychosocial correlates of HPV acceptability in college males, based on multiple stages of HPV decision-making. Methods: We used an online questionnaire to collect data from college men aged 18–26 enrolled at three Canadian universities between September 2013 and April 2014. Vaccine acceptability assessment was informed by the six-stage decision-making Precaution Adoption Process Model (PAPM. We sought information on socio-demographics, health behaviors, HPV vaccine benefits and barriers, worry, susceptibility, severity related to HPV infection and social norms. HPV and HPV vaccine knowledge was measured with validated scales. Psychosocial correlates of HPV vaccine acceptability were assessed with bivariate and multivariate multinomial logistic regression. Actual and perceived HPV and HPV vaccine knowledge scores were calculated. Results: The final sample size was 428. Most male college students were unaware that the HPV vaccine could be given to males, unengaged or undecided about getting the HPV vaccine. Significant correlates of higher HPV vaccine acceptability were: increased HPV knowledge, having discussed the HPV vaccine with a healthcare provider, and social norms. Being in an exclusive sexual relationship was significantly associated with lower HPV vaccine acceptability. Students' actual HPV and HPV vaccine knowledge was low and positively correlated to their perception about their HPV knowledge. Conclusions: We provided a fine-tuned analysis of psychosocial correlates of HPV vaccine acceptability in college

Correlation of Descriptive Analysis and Instrumental Puncture Testing of Watermelon Cultivars.

Science.gov (United States)

Shiu, J W; Slaughter, D C; Boyden, L E; Barrett, D M

2016-06-01

The textural properties of 5 seedless watermelon cultivars were assessed by descriptive analysis and the standard puncture test using a hollow probe with increased shearing properties. The use of descriptive analysis methodology was an effective means of quantifying watermelon sensory texture profiles for characterizing specific cultivars' characteristics. Of the 10 cultivars screened, 71% of the variation in the sensory attributes was measured using the 1st 2 principal components. Pairwise correlation of the hollow puncture probe and sensory parameters determined that initial slope, maximum force, and work after maximum force measurements all correlated well to the sensory attributes crisp and firm. These findings confirm that maximum force correlates well with not only firmness in watermelon, but crispness as well. The initial slope parameter also captures the sensory crispness of watermelon, but is not as practical to measure in the field as maximum force. The work after maximum force parameter is thought to reflect cellular arrangement and membrane integrity that in turn impact sensory firmness and crispness. Watermelon cultivar types were correctly predicted by puncture test measurements in heart tissue 87% of the time, although descriptive analysis was correct 54% of the time. © 2016 Institute of Food Technologists®

A study on the effect of the CHF correlations to the LOCA analysis

International Nuclear Information System (INIS)

Kim, Ho Kee

1998-02-01

The critical heat flux (CHF) is a major parameter which determines the cooling performance and therefore the prediction of CHF is of importance for the design and safety analysis in boiling systems; such as nuclear reactors, conventional boilers, and other various two-phase flow systems. Until now, many CHF correlations have been developed and for the actual design a correlation has been selected in consideration of its characteristics. For the analysis of Loss of Coolant Accident (LOCA) in a Nuclear Power Plant, which shows the drastic parameters change during the system transient, a correlation having a reasonable degree of accuracy over a wide range is preferred, rather than that having accuracy for a specific range. It is required to have tangible insight about the effects of the CHF correlation to the LOCA analysis for the purpose of computer code development and nuclear regulation. The related research is further recommended. The purpose of this research is to obtain an insight and/or intuition about the above effect and to evaluate the selected CHF correlations. To achieve these purposes LOCA is analysed for the UL-JIN 3 and 4 nuclear power plant, the Korea Standard Type Nuclear Power Plant and the Loss of Flow Test (LOFT) L2-5 experiment is simulated using the RELAP5/MOD3.1 computer code for each selected CHF correlation. The selected correlations are the AECL-UO Lookup Table, adapted in RELAP5 code; the K110 CHF correlation, developed by KAERI; and the original W-3 CHF correlation, developed by L.S. Tong. LOFT is also simulated using the AECL-UO Lookup Table having the CHF multiplication factors 0.5 and 1.5, and then compared with the result of the original Lookup Table and the experiment result. In the LOCA analysis, the CHF correlations affect the magnitude of peak cladding temperatures, but does not seriously affect the occurrence points of time. The effect of each CHF correlation to the fuel cladding temperature behavior becomes apparent at the end of

Correlation function analysis of the COBE differential microwave radiometer sky maps

Energy Technology Data Exchange (ETDEWEB)

Lineweaver, Charles Howe [Univ. of California, Berkeley, CA (United States). Space Sciences Lab.

1994-08-01

The Differential Microwave Radiometer (DMR) aboard the COBE satellite has detected anisotropies in the cosmic microwave background (CMB) radiation. A two-point correlation function analysis which helped lead to this discovery is presented in detail. The results of a correlation function analysis of the two year DMR data set is presented. The first and second year data sets are compared and found to be reasonably consistent. The positive correlation for separation angles less than ~20° is robust to Galactic latitude cuts and is very stable from year to year. The Galactic latitude cut independence of the correlation function is strong evidence that the signal is not Galactic in origin. The statistical significance of the structure seen in the correlation function of the first, second and two year maps is respectively > 9σ, > 10σ and > 18σ above the noise. The noise in the DMR sky maps is correlated at a low level. The structure of the pixel temperature covariance matrix is given. The noise covariance matrix of a DMR sky map is diagonal to an accuracy of better than 1%. For a given sky pixel, the dominant noise covariance occurs with the ring of pixels at an angular separation of 60° due to the 60° separation of the DMR horns. The mean covariance of 60° is 0.45%$+0.18\\atop{-0.14}$ of the mean variance. The noise properties of the DMR maps are thus well approximated by the noise properties of maps made by a single-beam experiment. Previously published DMR results are not significantly affected by correlated noise.

Time Correlations of Lightning Flash Sequences in Thunderstorms Revealed by Fractal Analysis

Science.gov (United States)

Gou, Xueqiang; Chen, Mingli; Zhang, Guangshu

2018-01-01

By using the data of lightning detection and ranging system at the Kennedy Space Center, the temporal fractal and correlation of interevent time series of lightning flash sequences in thunderstorms have been investigated with Allan factor (AF), Fano factor (FF), and detrended fluctuation analysis (DFA) methods. AF, FF, and DFA methods are powerful tools to detect the time-scaling structures and correlations in point processes. Totally 40 thunderstorms with distinguishing features of a single-cell storm and apparent increase and decrease in the total flash rate were selected for the analysis. It is found that the time-scaling exponents for AF (αAF) and FF (αFF) analyses are 1.62 and 0.95 in average, respectively, indicating a strong time correlation of the lightning flash sequences. DFA analysis shows that there is a crossover phenomenon—a crossover timescale (τc) ranging from 54 to 195 s with an average of 114 s. The occurrence of a lightning flash in a thunderstorm behaves randomly at timescales τc but shows strong time correlation at scales >τc. Physically, these may imply that the establishment of an extensive strong electric field necessary for the occurrence of a lightning flash needs a timescale >τc, which behaves strongly time correlated. But the initiation of a lightning flash within a well-established extensive strong electric field may involve the heterogeneities of the electric field at a timescale τc, which behave randomly.

Variability, correlation and path coefficient analysis of seedling traits ...

African Journals Online (AJOL)

Indirect selection is a useful means for improving yield in cotton crop. The objective of the present study was to determine the genetic variability, broad sense heritability, genetic advance and correlation among the six seedling traits and their direct and indirect effects on cotton yield by using path coefficient analysis.

Detection of Impaired Cerebral Autoregulation Using Selected Correlation Analysis: A Validation Study.

Science.gov (United States)

Proescholdt, Martin A; Faltermeier, Rupert; Bele, Sylvia; Brawanski, Alexander

2017-01-01

Multimodal brain monitoring has been utilized to optimize treatment of patients with critical neurological diseases. However, the amount of data requires an integrative tool set to unmask pathological events in a timely fashion. Recently we have introduced a mathematical model allowing the simulation of pathophysiological conditions such as reduced intracranial compliance and impaired autoregulation. Utilizing a mathematical tool set called selected correlation analysis (sca), correlation patterns, which indicate impaired autoregulation, can be detected in patient data sets (scp). In this study we compared the results of the sca with the pressure reactivity index (PRx), an established marker for impaired autoregulation. Mean PRx values were significantly higher in time segments identified as scp compared to segments showing no selected correlations (nsc). The sca based approach predicted cerebral autoregulation failure with a sensitivity of 78.8% and a specificity of 62.6%. Autoregulation failure, as detected by the results of both analysis methods, was significantly correlated with poor outcome. Sca of brain monitoring data detects impaired autoregulation with high sensitivity and sufficient specificity. Since the sca approach allows the simultaneous detection of both major pathological conditions, disturbed autoregulation and reduced compliance, it may become a useful analysis tool for brain multimodal monitoring data.

Detection of Impaired Cerebral Autoregulation Using Selected Correlation Analysis: A Validation Study

Directory of Open Access Journals (Sweden)

Martin A. Proescholdt

2017-01-01

Full Text Available Multimodal brain monitoring has been utilized to optimize treatment of patients with critical neurological diseases. However, the amount of data requires an integrative tool set to unmask pathological events in a timely fashion. Recently we have introduced a mathematical model allowing the simulation of pathophysiological conditions such as reduced intracranial compliance and impaired autoregulation. Utilizing a mathematical tool set called selected correlation analysis (sca, correlation patterns, which indicate impaired autoregulation, can be detected in patient data sets (scp. In this study we compared the results of the sca with the pressure reactivity index (PRx, an established marker for impaired autoregulation. Mean PRx values were significantly higher in time segments identified as scp compared to segments showing no selected correlations (nsc. The sca based approach predicted cerebral autoregulation failure with a sensitivity of 78.8% and a specificity of 62.6%. Autoregulation failure, as detected by the results of both analysis methods, was significantly correlated with poor outcome. Sca of brain monitoring data detects impaired autoregulation with high sensitivity and sufficient specificity. Since the sca approach allows the simultaneous detection of both major pathological conditions, disturbed autoregulation and reduced compliance, it may become a useful analysis tool for brain multimodal monitoring data.

Clinical and cognitive correlates of employment among patients with schizophrenia: a cross-sectional study in Malaysia

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Radzi Rozhan SM

2011-05-01

Full Text Available Abstract Background Gainful employment is one major area of functioning which is becoming an important goal in psychiatric rehabilitation of patients with schizophrenia. Studies in western countries are pointing to evidence that certain sociodemographic and clinical factors may contribute to employment outcomes in this group of people. However, the area is still largely unexplored in Malaysia. The aim of this study was to examine the sociodemographic, clinical and cognitive correlates of employment status among patients with Schizophrenia. Methods This was a cross-sectional study. All participants who fulfilled the requirements of the study according to the inclusion and exclusion criteria were enrolled. Study instruments included a demographic data questionnaire, Positive and Negative Symptom Scale (PANSS, Trail Making Tests, Rey's Auditory Verbal Learning Test (RAVLT and Digit Span. Bivariate analyses were done using chi-square for categorical data and t-test for continuous data and multiple logistic regression analysis was done to identify predictors of employment status. Results A total of 95 participants who fulfilled the inclusion criteria were enrolled into the study. Among the sociodemographic, clinical and cognitive variables studied marital status, educational level, mean scores of negative symptoms, Digit Span and RAVLT and Trail Making Tests were found to show significant association with employment status on bivariate analyses. However, when entered into a logistic regression model, only cognitive variables ie. Trail A and B, Digit Span and RAVLT were significant predictors of employment status. Conclusions The results from this study support the role of cognitive function, particularly, attention, working memory and executive functioning on attaining and maintaining employment in persons with schizophrenia as measured by the RAVLT, Digit Span and Trail Making Tests. These findings may act as preliminary evidence suggesting the

Analytical X-ray line profile analysis based upon correlated dislocations

International Nuclear Information System (INIS)

Rao, S.; Houska, C.R.

1988-01-01

Recent advances describing X-ray line profiles analytically, in terms of a minimum number of parameters, are related to a theory based upon correlated dislocations. It is shown that a multiple convolution approach, based upon the Warren-Averbach (W-A) analysis, leads to a form that closely approximates the strain coefficient obtained by Krivoglaz, Martynenko and Ryaboshopka. This connection enables one to determine the dislocation density and the ratio of the correlation range parameter to the mean particle size. These two results are obtained most accurately from previous analytical approaches which make use of a statistical least-squares analysis. The W-A Fourier-series approach provides redundant information and does not focus on the critical parameters that relate to dislocation theory. Results so far are limited to b.c.c. materials. Results for cold-worked W, Mo, Nb, Cr and V are compared with highly imperfect sputtered films of Mo. A major difference is relatable to higher correlation of dislocations in cold-worked metals than is found in sputtered films deposited at low temperatures. However, in each case, the dislocation density is high. (orig.)

Correlates of Unwanted Births in Bangladesh: A Study through Path Analysis.

Science.gov (United States)

Roy, Tapan Kumar; Singh, Brijesh P

2016-01-01

Unwanted birth is an important public health concern due to its negative association with adverse outcomes of mothers and children as well as socioeconomic development of a country. Although a number of studies have been investigated the determinants of unwanted births through logistic regression analysis, an extensive assessment using path model is lacking. In the current study, we applied path analysis to know the important covariates for unwanted births in Bangladesh. The study used data extracted from Bangladesh Demographic and Health Survey (BDHS) 2011. It considered sub-sample consisted of 7,972 women who had given most recent births five years preceding the date of interview or who were currently pregnant at survey time. Correlation analysis was used to find out the significant association with unwanted births. This study provided the factors affecting unwanted births in Bangladesh. The path model was used to determine the direct, indirect and total effects of socio-demographic factors on unwanted births. The result exhibited that more than one-tenth of the recent births were unwanted in Bangladesh. The differentials of unwanted births were women's age, education, age at marriage, religion, socioeconomic status, exposure of mass-media and use of family planning. In correlation analysis, it showed that unwanted births were positively correlated with women age and place of residence and these relationships were significant. On the contrary, unwanted births were inversely significantly correlated with education and social status. The total effects of endogenous variables such as women age, place of residence and use of family planning methods had favorable effect on unwanted births. Policymakers and program planners need to design programs and services carefully to reduce unwanted births in Bangladesh, especially, service should focus on helping those groups of women who were identified in the analysis as being at increased risks of unwanted births- older women

Correlates of Unwanted Births in Bangladesh: A Study through Path Analysis.

Directory of Open Access Journals (Sweden)

Tapan Kumar Roy

Full Text Available Unwanted birth is an important public health concern due to its negative association with adverse outcomes of mothers and children as well as socioeconomic development of a country. Although a number of studies have been investigated the determinants of unwanted births through logistic regression analysis, an extensive assessment using path model is lacking. In the current study, we applied path analysis to know the important covariates for unwanted births in Bangladesh.The study used data extracted from Bangladesh Demographic and Health Survey (BDHS 2011. It considered sub-sample consisted of 7,972 women who had given most recent births five years preceding the date of interview or who were currently pregnant at survey time. Correlation analysis was used to find out the significant association with unwanted births. This study provided the factors affecting unwanted births in Bangladesh. The path model was used to determine the direct, indirect and total effects of socio-demographic factors on unwanted births.The result exhibited that more than one-tenth of the recent births were unwanted in Bangladesh. The differentials of unwanted births were women's age, education, age at marriage, religion, socioeconomic status, exposure of mass-media and use of family planning. In correlation analysis, it showed that unwanted births were positively correlated with women age and place of residence and these relationships were significant. On the contrary, unwanted births were inversely significantly correlated with education and social status. The total effects of endogenous variables such as women age, place of residence and use of family planning methods had favorable effect on unwanted births.Policymakers and program planners need to design programs and services carefully to reduce unwanted births in Bangladesh, especially, service should focus on helping those groups of women who were identified in the analysis as being at increased risks of unwanted

Linearized spectrum correlation analysis for line emission measurements.

Science.gov (United States)

Nishizawa, T; Nornberg, M D; Den Hartog, D J; Sarff, J S

2017-08-01

A new spectral analysis method, Linearized Spectrum Correlation Analysis (LSCA), for charge exchange and passive ion Doppler spectroscopy is introduced to provide a means of measuring fast spectral line shape changes associated with ion-scale micro-instabilities. This analysis method is designed to resolve the fluctuations in the emission line shape from a stationary ion-scale wave. The method linearizes the fluctuations around a time-averaged line shape (e.g., Gaussian) and subdivides the spectral output channels into two sets to reduce contributions from uncorrelated fluctuations without averaging over the fast time dynamics. In principle, small fluctuations in the parameters used for a line shape model can be measured by evaluating the cross spectrum between different channel groupings to isolate a particular fluctuating quantity. High-frequency ion velocity measurements (100-200 kHz) were made by using this method. We also conducted simulations to compare LSCA with a moment analysis technique under a low photon count condition. Both experimental and synthetic measurements demonstrate the effectiveness of LSCA.

Personality disorders in substance abusers: Validation of the DIP-Q through principal components factor analysis and canonical correlation analysis

Directory of Open Access Journals (Sweden)

Hesse Morten

2005-05-01

Full Text Available Abstract Background Personality disorders are common in substance abusers. Self-report questionnaires that can aid in the assessment of personality disorders are commonly used in assessment, but are rarely validated. Methods The Danish DIP-Q as a measure of co-morbid personality disorders in substance abusers was validated through principal components factor analysis and canonical correlation analysis. A 4 components structure was constructed based on 238 protocols, representing antagonism, neuroticism, introversion and conscientiousness. The structure was compared with (a a 4-factor solution from the DIP-Q in a sample of Swedish drug and alcohol abusers (N = 133, and (b a consensus 4-components solution based on a meta-analysis of published correlation matrices of dimensional personality disorder scales. Results It was found that the 4-factor model of personality was congruent across the Danish and Swedish samples, and showed good congruence with the consensus model. A canonical correlation analysis was conducted on a subset of the Danish sample with staff ratings of pathology. Three factors that correlated highly between the two variable sets were found. These variables were highly similar to the three first factors from the principal components analysis, antagonism, neuroticism and introversion. Conclusion The findings support the validity of the DIP-Q as a measure of DSM-IV personality disorders in substance abusers.

Long-range correlation analysis of urban traffic data

International Nuclear Information System (INIS)

Peng, Sheng; Jun-Feng, Wang; Shu-Long, Zhao; Tie-Qiao, Tang

2010-01-01

This paper investigates urban traffic data by analysing the long-range correlation with detrended fluctuation analysis. Through a large number of real data collected by the travel time detection system in Beijing, the variation of flow in different time periods and intersections is studied. According to the long-range correlation in different time scales, it mainly discusses the effect of intersection location in road net, people activity customs and special traffic controls on urban traffic flow. As demonstrated by the obtained results, the urban traffic flow represents three-phase characters similar to highway traffic. Moreover, compared by the two groups of data obtained before and after the special traffic restrictions (vehicles with special numbered plates only run in a special workday) enforcement, it indicates that the rules not only reduce the flow but also avoid irregular fluctuation. (general)

Statistical analysis of latent generalized correlation matrix estimation in transelliptical distribution.

Science.gov (United States)

Han, Fang; Liu, Han

2017-02-01

Correlation matrix plays a key role in many multivariate methods (e.g., graphical model estimation and factor analysis). The current state-of-the-art in estimating large correlation matrices focuses on the use of Pearson's sample correlation matrix. Although Pearson's sample correlation matrix enjoys various good properties under Gaussian models, its not an effective estimator when facing heavy-tail distributions with possible outliers. As a robust alternative, Han and Liu (2013b) advocated the use of a transformed version of the Kendall's tau sample correlation matrix in estimating high dimensional latent generalized correlation matrix under the transelliptical distribution family (or elliptical copula). The transelliptical family assumes that after unspecified marginal monotone transformations, the data follow an elliptical distribution. In this paper, we study the theoretical properties of the Kendall's tau sample correlation matrix and its transformed version proposed in Han and Liu (2013b) for estimating the population Kendall's tau correlation matrix and the latent Pearson's correlation matrix under both spectral and restricted spectral norms. With regard to the spectral norm, we highlight the role of "effective rank" in quantifying the rate of convergence. With regard to the restricted spectral norm, we for the first time present a "sign subgaussian condition" which is sufficient to guarantee that the rank-based correlation matrix estimator attains the optimal rate of convergence. In both cases, we do not need any moment condition.

Lack of dental insurance is correlated with edentulism.

Science.gov (United States)

Simon, Lisa; Nalliah, Romesh P; Seymour, Brittany

2015-01-01

The correlation between insurance status and edentulism has not previously been reported in a population with known access to a dentist, and little is known about patient demographics in corporate dental settings. This study investigated patient demographics of a former dental franchise in Chicopee, Massachusetts, and examined a correlation between dental insurance and edentulism in this group. The correlation of edentulism with age, gender, and dental risk factors (diabetes, temporomandibular disorder, trouble with previous dental work, or oral sores and ulcers) was also examined. This was a retrospective case study. Age, gender, and presence of dental risk factors were recorded from the patient medical history intake form. Dentate status was recorded from patient odontograms. Dental insurance status was obtained from billing records. Data was aggregated and deidentified. Descriptive and bivariate statistics and logistic regression models were used to identify associations (p-value ≤ 0.05 significance). Of 1,123 records meeting inclusion criteria, 52.54 percent of patients had dental insurance, 26.27 percent had at least one dental risk factor, and 18.17 percent were edentulous. Age and insurance status were significantly correlated with edentulism. Correcting for age, individuals without insurance were 1.56 times as likely to be edentulous. This case study provides insight into patient demographics that might seek care in a corporate setting and suggests that access to a dentist alone may not be adequate in preserving the adult dentition; dental insurance may also be important to health. As the corporate dental practice model continues to grow, these topics deserve further study.

Correlates of perceived stigma for people living with epilepsy: A meta-analysis.

Science.gov (United States)

Shi, Ying; Wang, Shouqi; Ying, Jie; Zhang, Meiling; Liu, Pengcheng; Zhang, Huanhuan; Sun, Jiao

2017-05-01

Epilepsy, one of the most common, serious chronic neurological diseases, is accompanied by different levels of perceived stigma that affects people in almost all age groups. This stigma can negatively impact the physical and mental health of people living with epilepsy (PLWE). Good knowledge of perceived stigma for PLWE is important. In this study, we conducted a meta-analysis to identify the correlates of perceived stigma for PLWE. Studies on factors associated with perceived stigma for PLWE, including sociodemographic, psychosocial, and disease-related variables, were searched in PubMed, PsychINFO, EMBASE, and Web of Science. Nineteen variables (k>1) were included in the meta-analysis. For sociodemographic characteristics, findings revealed that the significant weighted mean correlation (R) for "residence" and "poor financial status" were 0.177 and 0.286, respectively. For disease-related characteristics, all variables of significance, including "seizure severity," "seizure frequency," "number of medicines," and "adverse event" (R ranging from 0.190 to 0.362), were positively correlated with perceived stigma. For psychosocial characteristics, "depression" and "anxiety" with R values of 0.414 and 0.369 were significantly associated with perceived stigma. In addition, "social support," "quality of life (QOLIE-31,89)," "knowledge," and "attitude," with R values ranging from -0.444 to -0.200 indicating negative correlation with perceived stigma. The current meta-analysis evaluated the correlates of perceived stigma for PLWE. Results can serve as a basis for policymakers and healthcare professionals for formulating health promotion and prevention strategies. Copyright © 2017 Elsevier Inc. All rights reserved.

FEM correlation and shock analysis of a VNC MEMS mirror segment

Science.gov (United States)

Aguayo, Eduardo J.; Lyon, Richard; Helmbrecht, Michael; Khomusi, Sausan

2014-08-01

Microelectromechanical systems (MEMS) are becoming more prevalent in today's advanced space technologies. The Visible Nulling Coronagraph (VNC) instrument, being developed at the NASA Goddard Space Flight Center, uses a MEMS Mirror to correct wavefront errors. This MEMS Mirror, the Multiple Mirror Array (MMA), is a key component that will enable the VNC instrument to detect Jupiter and ultimately Earth size exoplanets. Like other MEMS devices, the MMA faces several challenges associated with spaceflight. Therefore, Finite Element Analysis (FEA) is being used to predict the behavior of a single MMA segment under different spaceflight-related environments. Finite Element Analysis results are used to guide the MMA design and ensure its survival during launch and mission operations. A Finite Element Model (FEM) has been developed of the MMA using COMSOL. This model has been correlated to static loading on test specimens. The correlation was performed in several steps—simple beam models were correlated initially, followed by increasingly complex and higher fidelity models of the MMA mirror segment. Subsequently, the model has been used to predict the dynamic behavior and stresses of the MMA segment in a representative spaceflight mechanical shock environment. The results of the correlation and the stresses associated with a shock event are presented herein.

Analysis of the influences of thermal correlations on neutronic–thermohydraulic coupling calculation of SCWR

International Nuclear Information System (INIS)

Xu, Weifeng; Cai, Jiejin; Liu, Shichang; Tang, Qi

2015-01-01

Highlights: • Different thermal correlations for supercritical water are summarized. • Influences of thermal correlations on neutronic–thermohydraulic coupling calculation are analyzed. • Sensitivity analysis has been done for the thermal correlations. - Abstract: The neutronic–thermohydraulic coupling (N–T coupling) calculation is important on core design, security and stability analysis of supercritical water-coolant reactor (SCWR), and a suitable thermal correlation is also necessary for the N–T coupling calculation. In this paper, the scheme of the U.S. SCWR design and the process of the N–T coupling will be introduced as well as some of different thermal correlations firstly. Then, based on the N–T coupling system ARNT, the U.S. SCWR design is simulated to analyze the influences of thermal correlations on N–T coupling calculation of SCWR so as to find out which correlation is best. The result shows that all thermal correlations are suitable. However, using different correlations for calculation leads to a great difference in safety margin of SCWR. What's more, the Bishop and Jackson correlations are more suitable and conservative, but the Griem correlation is not very precise. And the effect of buoyancy lift makes little influence on the calculation of heat transfer of SCWR. This research is also of great significance for the further study of N–T coupling of SCWR

Analysis of Cell Phone Usage Using Correlation Techniques

OpenAIRE

T S R MURTHY; D. SIVA RAMA KRISHNA

2011-01-01

The present paper is a sample survey analysis, examined based on correlation techniques. The usage ofmobile phones is clearly almost un-avoidable these days and as such the authors have made a systematicsurvey through a well prepared questionnaire on making use of mobile phones to the maximum extent.These samples are various economical groups across a population of over one-lakh people. The resultsare scientifically categorized and interpreted to match the ground reality.

Canonical correlation analysis of synchronous neural interactions and cognitive deficits in Alzheimer's dementia

Science.gov (United States)

Karageorgiou, Elissaios; Lewis, Scott M.; Riley McCarten, J.; Leuthold, Arthur C.; Hemmy, Laura S.; McPherson, Susan E.; Rottunda, Susan J.; Rubins, David M.; Georgopoulos, Apostolos P.

2012-10-01

In previous work (Georgopoulos et al 2007 J. Neural Eng. 4 349-55) we reported on the use of magnetoencephalographic (MEG) synchronous neural interactions (SNI) as a functional biomarker in Alzheimer's dementia (AD) diagnosis. Here we report on the application of canonical correlation analysis to investigate the relations between SNI and cognitive neuropsychological (NP) domains in AD patients. First, we performed individual correlations between each SNI and each NP, which provided an initial link between SNI and specific cognitive tests. Next, we performed factor analysis on each set, followed by a canonical correlation analysis between the derived SNI and NP factors. This last analysis optimally associated the entire MEG signal with cognitive function. The results revealed that SNI as a whole were mostly associated with memory and language, and, slightly less, executive function, processing speed and visuospatial abilities, thus differentiating functions subserved by the frontoparietal and the temporal cortices. These findings provide a direct interpretation of the information carried by the SNI and set the basis for identifying specific neural disease phenotypes according to cognitive deficits.

Neural Network-Based Coronary Heart Disease Risk Prediction Using Feature Correlation Analysis

Directory of Open Access Journals (Sweden)

Jae Kwon Kim

2017-01-01

Full Text Available Background. Of the machine learning techniques used in predicting coronary heart disease (CHD, neural network (NN is popularly used to improve performance accuracy. Objective. Even though NN-based systems provide meaningful results based on clinical experiments, medical experts are not satisfied with their predictive performances because NN is trained in a “black-box” style. Method. We sought to devise an NN-based prediction of CHD risk using feature correlation analysis (NN-FCA using two stages. First, the feature selection stage, which makes features acceding to the importance in predicting CHD risk, is ranked, and second, the feature correlation analysis stage, during which one learns about the existence of correlations between feature relations and the data of each NN predictor output, is determined. Result. Of the 4146 individuals in the Korean dataset evaluated, 3031 had low CHD risk and 1115 had CHD high risk. The area under the receiver operating characteristic (ROC curve of the proposed model (0.749 ± 0.010 was larger than the Framingham risk score (FRS (0.393 ± 0.010. Conclusions. The proposed NN-FCA, which utilizes feature correlation analysis, was found to be better than FRS in terms of CHD risk prediction. Furthermore, the proposed model resulted in a larger ROC curve and more accurate predictions of CHD risk in the Korean population than the FRS.

A new method to detect transitory signatures and local time/space variability structures in the climate system: the scale-dependent correlation analysis

Science.gov (United States)

Rodó, Xavier; Rodríguez-Arias, Miquel-Àngel

2006-10-01

The study of transitory signals and local variability structures in both/either time and space and their role as sources of climatic memory, is an important but often neglected topic in climate research despite its obvious importance and extensive coverage in the literature. Transitory signals arise either from non-linearities, in the climate system, transitory atmosphere-ocean couplings, and other processes in the climate system evolving after a critical threshold is crossed. These temporary interactions that, though intense, may not last long, can be responsible for a large amount of unexplained variability but are normally considered of limited relevance and often, discarded. With most of the current techniques at hand these typology of signatures are difficult to isolate because the low signal-to-noise ratio in midlatitudes, the limited recurrence of the transitory signals during a customary interval of data considered. Also, there is often a serious problem arising from the smoothing of local or transitory processes if statistical techniques are applied, that consider all the length of data available, rather than taking into account the size of the specific variability structure under investigation. Scale-dependent correlation (SDC) analysis is a new statistical method capable of highlighting the presence of transitory processes, these former being understood as temporary significant lag-dependent autocovariance in a single series, or covariance structures between two series. This approach, therefore, complements other approaches such as those resulting from the families of wavelet analysis, singular-spectrum analysis and recurrence plots. A main feature of SDC is its high-performance for short time series, its ability to characterize phase-relationships and thresholds in the bivariate domain. Ultimately, SDC helps tracking short-lagged relationships among processes that locally or temporarily couple and uncouple. The use of SDC is illustrated in the present

Time-frequency wavelet analysis of the interrelationship between the global macro assets and the fear indexes

Science.gov (United States)

Abid, Fathi; Kaffel, Bilel

2018-01-01

Understanding the interrelationships of the global macro assets is crucial for global macro investing. This paper investigates the local variance and the interconnection between the stock, gold, oil, Forex and the implied volatility markets in the time/frequency domains using the wavelet methodology, including the wavelet power spectrum, the wavelet squared coherence and phase difference, the wavelet multiple correlation and cross-correlation. The univariate analysis reveals that, in some crisis periods, underlying asset markets present the same pattern in terms of the wavelet power spectrum indicating high volatility for the medium scale, and that for the other market stress periods, volatility behaves differently. Moreover, unlike the underlying asset markets, the implied volatility markets are characterized by high power regions across the entire period, even in the absence of economic events. Bivariate results show a bidirectional relationship between the underlying assets and their corresponding implied volatility indexes, and a steady co-movement between the stock index and its corresponding fear index. Multiple correlation analysis indicates a strong correlation between markets at high scales with evidence of a nearly perfect integration for a period longer than a year. In addition, the hedging strategies based on the volatility index lead to an increase in portfolio correlation. On the other hand, the results from multiple cross-correlations reveal that the lead-lag effect starts from the medium scale and that the VIX (stock market volatility index) index is the potential leader or follower of the other markets.

Genetic relationship and diversity analysis of Clitoria ternatea ...

African Journals Online (AJOL)

use

2011-12-12

Dec 12, 2011 ... analysis, cluster analysis of 1-0 bivariate data was carried out; 100 random primers revealed a total of ... white at the base. Its flowering is from August to October. The objectives of this study were to identify the poly- morphism amongst the C. ternatea ... shape, colour, fruit, flower shape, colour, size etc.

Modifiable Psychosocial Constructs Associated With Physical Activity Participation in People With Multiple Sclerosis: A Systematic Review and Meta-Analysis.

Science.gov (United States)

Casey, Blathin; Coote, Susan; Shirazipour, Celina; Hannigan, Ailish; Motl, Robert; Martin Ginis, Kathleen; Latimer-Cheung, Amy

2017-07-01

To synthesize current knowledge of the modifiable psychosocial constructs associated with physical activity (PA) participation in people with multiple sclerosis. A search was conducted through October 2015 in 8 electronic databases: CINAHL, PubMed, SPORTDiscus, Web of Knowledge, MEDLINE, EMBASE, Cochrane Database of Systematic Reviews, and PsycINFO. Cohort and intervention studies were included if they (1) included an objective or subjective measure of PA; (2) measured at least 1 modifiable psychosocial construct; and (3) reported bivariate correlations (or these could be extracted) between the PA and psychosocial construct measures. A total of 13,867 articles were screened for inclusion, and 26 were included in the final analysis. Meta-analyses of correlations were conducted using the Hedges-Olkin method. Where a meta-analysis was not possible, results were reported descriptively. Meta-analyses indicated a pooled correlation coefficient between (1) objective PA and self-efficacy (n=7) of r=.30 (Pgoal-setting (n=5) of r=.44 (Pgoal-setting. However, there is a need to explore the associations between other constructs outside those reported in this review. Copyright © 2016 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

Multivariate longitudinal data analysis with mixed effects hidden Markov models.

Science.gov (United States)

Raffa, Jesse D; Dubin, Joel A

2015-09-01

Multiple longitudinal responses are often collected as a means to capture relevant features of the true outcome of interest, which is often hidden and not directly measurable. We outline an approach which models these multivariate longitudinal responses as generated from a hidden disease process. We propose a class of models which uses a hidden Markov model with separate but correlated random effects between multiple longitudinal responses. This approach was motivated by a smoking cessation clinical trial, where a bivariate longitudinal response involving both a continuous and a binomial response was collected for each participant to monitor smoking behavior. A Bayesian method using Markov chain Monte Carlo is used. Comparison of separate univariate response models to the bivariate response models was undertaken. Our methods are demonstrated on the smoking cessation clinical trial dataset, and properties of our approach are examined through extensive simulation studies. © 2015, The International Biometric Society.

A new detrended semipartial cross-correlation analysis: Assessing the important meteorological factors affecting API

International Nuclear Information System (INIS)

Shen, Chen-Hua

2015-01-01

To analyze the unique contribution of meteorological factors to the air pollution index (API), a new method, the detrended semipartial cross-correlation analysis (DSPCCA), is proposed. Based on both a detrended cross-correlation analysis and a DFA-based multivariate-linear-regression (DMLR), this method is improved by including a semipartial correlation technique, which is used to indicate the unique contribution of an explanatory variable to multiple correlation coefficients. The advantages of this method in handling nonstationary time series are illustrated by numerical tests. To further demonstrate the utility of this method in environmental systems, new evidence of the primary contribution of meteorological factors to API is provided through DMLR. Results show that the most important meteorological factors affecting API are wind speed and diurnal temperature range, and the explanatory ability of meteorological factors to API gradually strengthens with increasing time scales. The results suggest that DSPCCA is a useful method for addressing environmental systems. - Highlights: • A detrended multiple linear regression is shown. • A detrended semipartial cross correlation analysis is proposed. • The important meteorological factors affecting API are assessed. • The explanatory ability of meteorological factors to API gradually strengthens with increasing time scales.

A new detrended semipartial cross-correlation analysis: Assessing the important meteorological factors affecting API

Energy Technology Data Exchange (ETDEWEB)

Shen, Chen-Hua, E-mail: shenandchen01@163.com [College of Geographical Science, Nanjing Normal University, Nanjing 210046 (China); Jiangsu Center for Collaborative Innovation in Geographical Information Resource, Nanjing 210046 (China); Key Laboratory of Virtual Geographic Environment of Ministry of Education, Nanjing 210046 (China)

2015-12-04

To analyze the unique contribution of meteorological factors to the air pollution index (API), a new method, the detrended semipartial cross-correlation analysis (DSPCCA), is proposed. Based on both a detrended cross-correlation analysis and a DFA-based multivariate-linear-regression (DMLR), this method is improved by including a semipartial correlation technique, which is used to indicate the unique contribution of an explanatory variable to multiple correlation coefficients. The advantages of this method in handling nonstationary time series are illustrated by numerical tests. To further demonstrate the utility of this method in environmental systems, new evidence of the primary contribution of meteorological factors to API is provided through DMLR. Results show that the most important meteorological factors affecting API are wind speed and diurnal temperature range, and the explanatory ability of meteorological factors to API gradually strengthens with increasing time scales. The results suggest that DSPCCA is a useful method for addressing environmental systems. - Highlights: • A detrended multiple linear regression is shown. • A detrended semipartial cross correlation analysis is proposed. • The important meteorological factors affecting API are assessed. • The explanatory ability of meteorological factors to API gradually strengthens with increasing time scales.

Dynamical Analysis of Stock Market Instability by Cross-correlation Matrix

Science.gov (United States)

Takaishi, Tetsuya

2016-08-01

We study stock market instability by using cross-correlations constructed from the return time series of 366 stocks traded on the Tokyo Stock Exchange from January 5, 1998 to December 30, 2013. To investigate the dynamical evolution of the cross-correlations, crosscorrelation matrices are calculated with a rolling window of 400 days. To quantify the volatile market stages where the potential risk is high, we apply the principal components analysis and measure the cumulative risk fraction (CRF), which is the system variance associated with the first few principal components. From the CRF, we detected three volatile market stages corresponding to the bankruptcy of Lehman Brothers, the 2011 Tohoku Region Pacific Coast Earthquake, and the FRB QE3 reduction observation in the study period. We further apply the random matrix theory for the risk analysis and find that the first eigenvector is more equally de-localized when the market is volatile.

Dynamical Analysis of Stock Market Instability by Cross-correlation Matrix

International Nuclear Information System (INIS)

Takaishi, Tetsuya

2016-01-01

We study stock market instability by using cross-correlations constructed from the return time series of 366 stocks traded on the Tokyo Stock Exchange from January 5, 1998 to December 30, 2013. To investigate the dynamical evolution of the cross-correlations, crosscorrelation matrices are calculated with a rolling window of 400 days. To quantify the volatile market stages where the potential risk is high, we apply the principal components analysis and measure the cumulative risk fraction (CRF), which is the system variance associated with the first few principal components. From the CRF, we detected three volatile market stages corresponding to the bankruptcy of Lehman Brothers, the 2011 Tohoku Region Pacific Coast Earthquake, and the FRB QE3 reduction observation in the study period. We further apply the random matrix theory for the risk analysis and find that the first eigenvector is more equally de-localized when the market is volatile. (paper)

INTER-TEMPORAL ANALYSIS OF HOUSEHOLD CAR AND MOTORCYCLE OWNERSHIP BEHAVIORS

Directory of Open Access Journals (Sweden)

Nobuhiro SANKO

2009-01-01

Full Text Available This study investigates household car and motorcycle ownership in Nagoya metropolitan area of Japan. Bivariate ordered probit models of household vehicle ownership were developed using the data from the case study area at three time points, 1981, 1991, and 2001. The accessibility that is generally known to be correlated with vehicle ownership decisions is incorporated as an input for the proposed vehicle ownership model to investigate the potential relationship between them. The mode choice models for the area were first estimated to quantify the accessibility indexes that were later integrated into the vehicle ownership models. Inter-temporal comparison and temporal transferability analysis were conducted. Some of the major findings suggest: 1 that age and gender differences have become less important in modal choices and car ownership as motorization proceeds; 2 that the accessibility seems to have a significant correlation with vehicle ownership; 3 that car and motorcycle ownership may not be independent and may have a complementary relationship; and 4 that the deep insights concerning the model selection are obtained from the viewpoints of the temporal transferability.

Denial-of-service attack detection based on multivariate correlation analysis

NARCIS (Netherlands)

Tan, Zhiyuan; Jamdagni, Aruna; He, Xiangjian; Nanda, Priyadarsi; Liu, Ren Ping; Lu, Bao-Liang; Zhang, Liqing; Kwok, James

2011-01-01

The reliability and availability of network services are being threatened by the growing number of Denial-of-Service (DoS) attacks. Effective mechanisms for DoS attack detection are demanded. Therefore, we propose a multivariate correlation analysis approach to investigate and extract second-order

Local wavelet correlation: applicationto timing analysis of multi-satellite CLUSTER data

Directory of Open Access Journals (Sweden)

J. Soucek

2004-12-01

Full Text Available Multi-spacecraft space observations, such as those of CLUSTER, can be used to infer information about local plasma structures by exploiting the timing differences between subsequent encounters of these structures by individual satellites. We introduce a novel wavelet-based technique, the Local Wavelet Correlation (LWC, which allows one to match the corresponding signatures of large-scale structures in the data from multiple spacecraft and determine the relative time shifts between the crossings. The LWC is especially suitable for analysis of strongly non-stationary time series, where it enables one to estimate the time lags in a more robust and systematic way than ordinary cross-correlation techniques. The technique, together with its properties and some examples of its application to timing analysis of bow shock and magnetopause crossing observed by CLUSTER, are presented. We also compare the performance and reliability of the technique with classical discontinuity analysis methods. Key words. Radio science (signal processing – Space plasma physics (discontinuities; instruments and techniques

Volatility-constrained multifractal detrended cross-correlation analysis: Cross-correlation among Mainland China, US, and Hong Kong stock markets

Science.gov (United States)

Cao, Guangxi; Zhang, Minjia; Li, Qingchen

2017-04-01

This study focuses on multifractal detrended cross-correlation analysis of the different volatility intervals of Mainland China, US, and Hong Kong stock markets. A volatility-constrained multifractal detrended cross-correlation analysis (VC-MF-DCCA) method is proposed to study the volatility conductivity of Mainland China, US, and Hong Kong stock markets. Empirical results indicate that fluctuation may be related to important activities in real markets. The Hang Seng Index (HSI) stock market is more influential than the Shanghai Composite Index (SCI) stock market. Furthermore, the SCI stock market is more influential than the Dow Jones Industrial Average stock market. The conductivity between the HSI and SCI stock markets is the strongest. HSI was the most influential market in the large fluctuation interval of 1991 to 2014. The autoregressive fractionally integrated moving average method is used to verify the validity of VC-MF-DCCA. Results show that VC-MF-DCCA is effective.

Analysis of the Correlation between GDP and the Final Consumption

Directory of Open Access Journals (Sweden)

Constantin ANGHELACHE

2011-09-01

Full Text Available This paper presents the results of the researches performed by the author regarding the evolution of Gross Domestic Product. One of the main aspects of GDP analysis is the correlation with the final consumption, an important macroeconomic indicator. The evolution of the Gross Domestic Product is highly influenced by the evolution of the final consumption. To analyze the correlation, the paper proposes the use of the linear regression model, as one of the most appropriate instruments for such scientific approach. The regression model described in the article uses the GDP as resultant variable and the final consumption as factorial variable.

Climate Prediction Center(CPC)Ensemble Canonical Correlation Analysis Forecast of Temperature

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The Ensemble Canonical Correlation Analysis (ECCA) temperature forecast is a 90-day (seasonal) outlook of US surface temperature anomalies. The ECCA uses Canonical...

The effects of observational correlated noises on multifractal detrended fluctuation analysis

Science.gov (United States)

Gulich, Damián; Zunino, Luciano

2012-08-01

We have numerically investigated the effects that observational correlated noises have on the generalized Hurst exponents, h(q), estimated by using the multifractal generalization of detrended fluctuation analysis (MF-DFA). More precisely, artificially generated stochastic binomial multifractals with increased amount of colored noises were analyzed via MF-DFA. It has been recently shown that for moderate additions of white noise, the generalized Hurst exponents are significantly underestimated for qeffects of additive noise, short- term memory and periodic trends, Physica A 390 (2011) 2480-2490]. In this paper, we have found that h(q) with q≥2 are also affected when correlated noises are considered. This is due to the fact that the spurious correlations influence the scaling behaviors associated to large fluctuations. The results obtained are significant for practical situations, where noises with different correlations are inherently present.

Mental Health Correlates of Cigarette Use in LGBT Individuals in the Southeastern United States.

Science.gov (United States)

Drescher, Christopher F; Lopez, Eliot J; Griffin, James A; Toomey, Thomas M; Eldridge, Elizabeth D; Stepleman, Lara M

2018-05-12

Smoking prevalence for lesbian, gay, bisexual, and transgender (LGBT) individuals is higher than for heterosexual, cisgender individuals. Elevated smoking rates have been linked to psychiatric comorbidities, substance use, poverty, low education levels, and stress. This study examined mental health (MH) correlates of cigarette use in LGBT individuals residing in a metropolitan area in the southeastern United States. Participants were 335 individuals from an LGBT health needs assessment (mean age 34.7; SD = 13.5; 63% gay/lesbian; 66% Caucasian; 81% cisgender). Demographics, current/past psychiatric diagnoses, number of poor MH days in the last 30, the Patient Health Questionnaire (PHQ) 2 depression screener, the Three-Item Loneliness Scale, and frequency of cigarette use were included. Analyses included bivariate correlations, analysis of variance (ANOVA), and regression. Multiple demographic and MH factors were associated with smoker status and frequency of smoking. A logistic regression indicated that lower education and bipolar disorder were most strongly associated with being a smoker. For smokers, a hierarchical regression model including demographic and MH variables accounted for 17.6% of the variance in frequency of cigarette use. Only education, bipolar disorder, and the number of poor MH days were significant contributors in the overall model. Conclusions/Importance: Less education, bipolar disorder, and recurrent poor MH increase LGBT vulnerability to cigarette use. Access to LGBT-competent MH providers who can address culturally specific factors in tobacco cessation is crucial to reducing this health disparities.

Groundwater travel time uncertainty analysis. Sensitivity of results to model geometry, and correlations and cross correlations among input parameters

International Nuclear Information System (INIS)

Clifton, P.M.

1985-03-01

This study examines the sensitivity of the travel time distribution predicted by a reference case model to (1) scale of representation of the model parameters, (2) size of the model domain, (3) correlation range of log-transmissivity, and (4) cross correlations between transmissivity and effective thickness. The basis for the reference model is the preliminary stochastic travel time model previously documented by the Basalt Waste Isolation Project. Results of this study show the following. The variability of the predicted travel times can be adequately represented when the ratio between the size of the zones used to represent the model parameters and the log-transmissivity correlation range is less than about one-fifth. The size of the model domain and the types of boundary conditions can have a strong impact on the distribution of travel times. Longer log-transmissivity correlation ranges cause larger variability in the predicted travel times. Positive cross correlation between transmissivity and effective thickness causes a decrease in the travel time variability. These results demonstrate the need for a sound conceptual model prior to conducting a stochastic travel time analysis

Generalized Correlation Coefficient for Non-Parametric Analysis of Microarray Time-Course Data.

Science.gov (United States)

Tan, Qihua; Thomassen, Mads; Burton, Mark; Mose, Kristian Fredløv; Andersen, Klaus Ejner; Hjelmborg, Jacob; Kruse, Torben

2017-06-06

Modeling complex time-course patterns is a challenging issue in microarray study due to complex gene expression patterns in response to the time-course experiment. We introduce the generalized correlation coefficient and propose a combinatory approach for detecting, testing and clustering the heterogeneous time-course gene expression patterns. Application of the method identified nonlinear time-course patterns in high agreement with parametric analysis. We conclude that the non-parametric nature in the generalized correlation analysis could be an useful and efficient tool for analyzing microarray time-course data and for exploring the complex relationships in the omics data for studying their association with disease and health.

Correlation between weather and incidence of selected ophthalmological diagnoses: a database analysis

Directory of Open Access Journals (Sweden)

Kern C

2016-08-01

Full Text Available Christoph Kern, Karsten Kortüm, Michael Müller, Florian Raabe, Wolfgang Johann Mayer, Siegfried Priglinger, Thomas Christian Kreutzer University Eye Hospital Munich, Faculty of Medicine, Ludwig-Maximilians-Universität München, Munich, Germany Purpose: Our aim was to correlate the overall patient volume and the incidence of several ophthalmological diseases in our emergency department with weather data. Patients and methods: For data analysis, we used our clinical data warehouse and weather data. We investigated the weekly overall patient volume and the average weekly incidence of all encoded diagnoses of “conjunctivitis”, “foreign body”, “acute iridocyclitis”, and “corneal abrasion”. A Spearman’s correlation was performed to link these data with the weekly average sunshine duration, temperature, and wind speed. Results: We noticed increased patient volume in correlation with increasing sunshine duration and higher temperature. Moreover, we found a positive correlation between the weekly incidences of conjunctivitis and of foreign body and weather data. Conclusion: The results of this data analysis reveal the possible influence of external conditions on the health of a population and can be used for weather-dependent resource allocation. Keywords: corneal injury, trauma, uveitis, conjunctivitis, weather

Serum adiponectin levels are inversely correlated with leukemia: A meta-analysis

Directory of Open Access Journals (Sweden)

Jun-Jie Ma

2016-01-01

Conclusion: Our meta-analysis suggested that serum ADPN levels may be inversely correlated with leukemia, and ADPN levels can be used as an effective biologic marker in early diagnosis and therapeutic monitoring of leukemia.

The cross-correlation analysis of multi property of stock markets based on MM-DFA

Science.gov (United States)

Yang, Yujun; Li, Jianping; Yang, Yimei

2017-09-01

In this paper, we propose a new method called DH-MXA based on distribution histograms of Hurst surface and multiscale multifractal detrended fluctuation analysis. The method allows us to investigate the cross-correlation characteristics among multiple properties of different stock time series. It may provide a new way of measuring the nonlinearity of several signals. It also can provide a more stable and faithful description of cross-correlation of multiple properties of stocks. The DH-MXA helps us to present much richer information than multifractal detrented cross-correlation analysis and allows us to assess many universal and subtle cross-correlation characteristics of stock markets. We show DH-MXA by selecting four artificial data sets and five properties of four stock time series from different countries. The results show that our proposed method can be adapted to investigate the cross-correlation of stock markets. In general, the American stock markets are more mature and less volatile than the Chinese stock markets.

Influence of geomagnetic activity and atmospheric pressure on human arterial pressure during the solar cycle 24

Science.gov (United States)

Azcárate, T.; Mendoza, B.; Levi, J. R.

2016-11-01

We performed a study of the systolic (SBP) and diastolic (DBP) arterial blood pressure behavior under natural variables such as the atmospheric pressure (AtmP) and the horizontal geomagnetic field component (H). We worked with a sample of 304 healthy normotense volunteers, 152 men and 152 women, with ages between 18 and 84 years in Mexico City during the period 2008-2014, corresponding to the minimum, ascending and maximum phases of the solar cycle 24. The data was divided by gender, age and day/night cycle. We studied the time series using three methods: Correlations, bivariate and superposed epochs (within a window of three days around the day of occurrence of a geomagnetic storm) analysis, between the SBP and DBP and the natural variables (AtmP and H). The correlation analysis indicated correlation between the SBP and DBP and AtmP and H, being the largest during the night. Furthermore, the correlation and bivariate analysis showed that the largest correlations are between the SBP and DBP and the AtmP. The superposed epoch analysis found that the largest number of significant SBP and DBP changes occurred for women. Finally, the blood pressure changes are larger during the solar minimum and ascending solar cycle phases than during the solar maximum; the storms of the minimum were more intense than those of the maximum and this could be the reason of behavior of the blood pressure changes along the solar cycle.

A Hybrid Forecasting Model Based on Bivariate Division and a Backpropagation Artificial Neural Network Optimized by Chaos Particle Swarm Optimization for Day-Ahead Electricity Price

Directory of Open Access Journals (Sweden)

Zhilong Wang

2014-01-01

Full Text Available In the electricity market, the electricity price plays an inevitable role. Nevertheless, accurate price forecasting, a vital factor affecting both government regulatory agencies and public power companies, remains a huge challenge and a critical problem. Determining how to address the accurate forecasting problem becomes an even more significant task in an era in which electricity is increasingly important. Based on the chaos particle swarm optimization (CPSO, the backpropagation artificial neural network (BPANN, and the idea of bivariate division, this paper proposes a bivariate division BPANN (BD-BPANN method and the CPSO-BD-BPANN method for forecasting electricity price. The former method creatively transforms the electricity demand and price to be a new variable, named DV, which is calculated using the division principle, to forecast the day-ahead electricity by multiplying the forecasted values of the DVs and forecasted values of the demand. Next, to improve the accuracy of BD-BPANN, chaos particle swarm optimization and BD-BPANN are synthesized to form a novel model, CPSO-BD-BPANN. In this study, CPSO is utilized to optimize the initial parameters of BD-BPANN to make its output more stable than the original model. Finally, two forecasting strategies are proposed regarding different situations.

Low Carbon-Oriented Optimal Reliability Design with Interval Product Failure Analysis and Grey Correlation Analysis

Directory of Open Access Journals (Sweden)

Yixiong Feng

2017-03-01

Full Text Available The problem of large amounts of carbon emissions causes wide concern across the world, and it has become a serious threat to the sustainable development of the manufacturing industry. The intensive research into technologies and methodologies for green product design has significant theoretical meaning and practical value in reducing the emissions of the manufacturing industry. Therefore, a low carbon-oriented product reliability optimal design model is proposed in this paper: (1 The related expert evaluation information was prepared in interval numbers; (2 An improved product failure analysis considering the uncertain carbon emissions of the subsystem was performed to obtain the subsystem weight taking the carbon emissions into consideration. The interval grey correlation analysis was conducted to obtain the subsystem weight taking the uncertain correlations inside the product into consideration. Using the above two kinds of subsystem weights and different caution indicators of the decision maker, a series of product reliability design schemes is available; (3 The interval-valued intuitionistic fuzzy sets (IVIFSs were employed to select the optimal reliability and optimal design scheme based on three attributes, namely, low carbon, correlation and functions, and economic cost. The case study of a vertical CNC lathe proves the superiority and rationality of the proposed method.

Inference for the Bivariate and Multivariate Hidden Truncated Pareto(type II) and Pareto(type IV) Distribution and Some Measures of Divergence Related to Incompatibility of Probability Distribution

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Ghosh, Indranil

2011-01-01

Consider a discrete bivariate random variable (X, Y) with possible values x[subscript 1], x[subscript 2],..., x[subscript I] for X and y[subscript 1], y[subscript 2],..., y[subscript J] for Y. Further suppose that the corresponding families of conditional distributions, for X given values of Y and of Y for given values of X are available. We…

Non-linear canonical correlation for joint analysis of MEG signals from two subjects

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Cristina eCampi

2013-06-01

Full Text Available We consider the problem of analysing magnetoencephalography (MEG data measured from two persons undergoing the same experiment, and we propose a method that searches for sources with maximally correlated energies. Our method is based on canonical correlation analysis (CCA, which provides linear transformations, one for each subject, such that the correlation between the transformed MEG signals is maximized. Here, we present a nonlinear version of CCA which measures the correlation of energies. Furthermore, we introduce a delay parameter in the modelto analyse, e.g., leader-follower changes in experiments where the two subjects are engaged in social interaction.

Prediction of East African Seasonal Rainfall Using Simplex Canonical Correlation Analysis.

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Ntale, Henry K.; Yew Gan, Thian; Mwale, Davison

2003-06-01

A linear statistical model, canonical correlation analysis (CCA), was driven by the Nelder-Mead simplex optimization algorithm (called CCA-NMS) to predict the standardized seasonal rainfall totals of East Africa at 3-month lead time using SLP and SST anomaly fields of the Indian and Atlantic Oceans combined together by 24 simplex optimized weights, and then `reduced' by the principal component analysis. Applying the optimized weights to the predictor fields produced better March-April-May (MAM) and September-October-November (SON) seasonal rain forecasts than a direct application of the same, unweighted predictor fields to CCA at both calibration and validation stages. Northeastern Tanzania and south-central Kenya had the best SON prediction results with both validation correlation and Hanssen-Kuipers skill scores exceeding +0.3. The MAM season was better predicted in the western parts of East Africa. The CCA correlation maps showed that low SON rainfall in East Africa is associated with cold SSTs off the Somali coast and the Benguela (Angola) coast, and low MAM rainfall is associated with a buildup of low SSTs in the Indian Ocean adjacent to East Africa and the Gulf of Guinea.

Prevalence and correlates of burnout among collegiate cycle students in Sri Lanka: a school-based cross-sectional study.

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Wickramasinghe, Nuwan Darshana; Dissanayake, Devani Sakunthala; Abeywardena, Gihan Sajiwa

2018-01-01

Even though the concept of burnout has been widely explored across the globe, the evidence base on burnout among high school students in the South Asian context is scanty. Against the backdrop of ever-increasing educational demands and expectations, the present study was designed to determine the prevalence and correlates of burnout among collegiate cycle students in Sri Lanka. A school-based cross-sectional study was conducted among 872 grade thirteen students in 15 government schools in an educational zone, Kegalle district, Sri Lanka selected by a stratified cluster sampling technique. The validated Sinhala version of the 15-item Maslach Burnout Inventory-Student Survey (MBI-SS) was used to assess burnout. The adjusted prevalence of burnout was computed based on the clinically validated cut-off values using the "exhaustion + 1" criterion. Multivariable logistic regression was carried out using backward elimination method to quantify the association between burnout and selected correlates identified at bivariate analysis at p value less than 0.05. The response rate was 91.3% (n = 796). The adjusted prevalence of burnout among grade thirteen students was 28.8% (95% CI = 25.0-32.7%). Multivariable analysis elicited a multitude of statistically significant associations with burnout when controlled for other factors included in the model (p burnout, whereas having to encounter disturbances while studying and being subjected to bullying at school emerged as statistically significant positive associations with burnout. The burnout prevalence among grade thirteen students in the selected educational zone, Sri Lanka is high. Most of the significant correlates of burnout are directly related to the academic endeavours. It is recommended to strengthen the counseling services at the school level to rectify the problems related to burnout among collegiate cycle students in Sri Lanka.

Error analysis of supercritical water correlations using ATHLET system code under DHT conditions

Energy Technology Data Exchange (ETDEWEB)

Samuel, J., E-mail: jeffrey.samuel@uoit.ca [Univ. of Ontario Inst. of Tech., Oshawa, ON (Canada)

2014-07-01

The thermal-hydraulic computer code ATHLET (Analysis of THermal-hydraulics of LEaks and Transients) is used for analysis of anticipated and abnormal plant transients, including safety analysis of Light Water Reactors (LWRs) and Russian Graphite-Moderated High Power Channel-type Reactors (RBMKs). The range of applicability of ATHLET has been extended to supercritical water by updating the fluid-and transport-properties packages, thus enabling the code to the used in analysis of SuperCritical Water-cooled Reactors (SCWRs). Several well-known heat-transfer correlations for supercritical fluids were added to the ATHLET code and a numerical model was created to represent an experimental test section. In this work, the error in the Heat Transfer Coefficient (HTC) calculation by the ATHLET model is studied along with the ability of the various correlations to predict different heat transfer regimes. (author)

Topic Correlation Analysis for Bearing Fault Diagnosis Under Variable Operating Conditions

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Chen, Chao; Shen, Fei; Yan, Ruqiang

2017-05-01

This paper presents a Topic Correlation Analysis (TCA) based approach for bearing fault diagnosis. In TCA, Joint Mixture Model (JMM), a model which adapts Probability Latent Semantic Analysis (PLSA), is constructed first. Then, JMM models the shared and domain-specific topics using “fault vocabulary” . After that, the correlations between two kinds of topics are computed and used to build a mapping matrix. Furthermore, a new shared space spanned by the shared and mapped domain-specific topics is set up where the distribution gap between different domains is reduced. Finally, a classifier is trained with mapped features which follow a different distribution and then the trained classifier is tested on target bearing data. Experimental results justify the superiority of the proposed approach over the stat-of-the-art baselines and it can diagnose bearing fault efficiently and effectively under variable operating conditions.

Brain Microstructural Correlates of Cognitive Dysfunction in Clinically and Biochemically Normal Hepatitis C Virus Infection.

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Kumar, Ajay; Deep, Amar; Gupta, Rakesh K; Atam, Virendra; Mohindra, Samir

2017-09-01

This study examined correlates of the brain's neurocognitive performance among clinically and biochemically normal adult patient with hepatitis C virus (HCV). We hypothesized that anti-HCV positive individuals would demonstrate structural brain abnormalities and neurocognitive dysfunction as well as the changes in cell component and extracellular space in the white matter regions of brain in asymptomatic HCV infection by using diffusion tensor tractrography (DTT) metrics. Anti-HCV positive patient ( n  = 40), and healthy controls ( n  = 31), fulfilling inclusion criteria (incidentally detected anti-HCV positive) and able to provide informed consent were screened and recruited for the study. All these subjects and controls underwent subjective assessment of their quality of life related symptoms, neuropsychometric tests (NPT) and magnetic resonance imaging. The patients were subjected to neuroimaging as well as psychological testing. There was no significant difference in basic laboratory parameters in these two groups. Independent t -test reveals significantly lower neuropsychological functioning as compared to healthy control. A significantly decreased FA values and myoinsitol were observed in HCV subjects on sensory, inferior longitudinal fascicules, and STR fiber bundles as compared to healthy control. Bivariate correlation analysis reveals that neuropsychological scores are significantly positive. Our result show that HCV positive individuals would demonstrate structural brain abnormalities and neurocognitive dysfunction as well as the changes in cell component and extracellular space in the white matter regions of brain in asymptomatic HCV infection by using DTT metrics.

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

OpenAIRE

Ole E. Barndorff-Nielsen; Neil Shephard

2002-01-01

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

Structure function analysis of long-range correlations in plasma turbulence

International Nuclear Information System (INIS)

Yu, C.X.; Gilmore, M.; Peebles, W.A.; Rhodes, T.L.

2003-01-01

Long-range correlations (temporal and spatial) have been predicted in a number of different turbulence models, both analytical and numerical. These long-range correlations are thought to significantly affect cross-field turbulent transport in magnetically confined plasmas. The Hurst exponent, H - one of a number of methods to identify the existence of long-range correlations in experimental data - can be used to quantify self-similarity scalings and correlations in the mesoscale temporal range. The Hurst exponent can be calculated by several different algorithms, each of which has particular advantages and disadvantages. One method for calculating H is via structure functions (SFs). The SF method is a robust technique for determining H with several inherent advantages that has not yet been widely used in plasma turbulence research. In this article, the SF method and its advantages are discussed in detail, using both simulated and measured fluctuation data from the DIII-D tokamak [J. L. Luxon and L. G. Davis, Fusion Technol. 8, 441 (1985)]. In addition, it is shown that SFs used in conjunction with rescaled range analysis (another method for calculating H) can be used to mitigate the effects of coherent modes in some cases

Dominating clasp of the financial sector revealed by partial correlation analysis of the stock market.

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Kenett, Dror Y; Tumminello, Michele; Madi, Asaf; Gur-Gershgoren, Gitit; Mantegna, Rosario N; Ben-Jacob, Eshel

2010-12-20

What are the dominant stocks which drive the correlations present among stocks traded in a stock market? Can a correlation analysis provide an answer to this question? In the past, correlation based networks have been proposed as a tool to uncover the underlying backbone of the market. Correlation based networks represent the stocks and their relationships, which are then investigated using different network theory methodologies. Here we introduce a new concept to tackle the above question--the partial correlation network. Partial correlation is a measure of how the correlation between two variables, e.g., stock returns, is affected by a third variable. By using it we define a proxy of stock influence, which is then used to construct partial correlation networks. The empirical part of this study is performed on a specific financial system, namely the set of 300 highly capitalized stocks traded at the New York Stock Exchange, in the time period 2001-2003. By constructing the partial correlation network, unlike the case of standard correlation based networks, we find that stocks belonging to the financial sector and, in particular, to the investment services sub-sector, are the most influential stocks affecting the correlation profile of the system. Using a moving window analysis, we find that the strong influence of the financial stocks is conserved across time for the investigated trading period. Our findings shed a new light on the underlying mechanisms and driving forces controlling the correlation profile observed in a financial market.

Correlation of Beta Angle with Antero-Posterior Dysplasia Indicators and FMA: An Institution Based Cephalometric Study.

Science.gov (United States)

Singh, Gurinder; Verma, Sanjeev; Singh, Devinder Preet; Yadav, Sumit Kumar; Yadav, Achla Bharti

2016-11-01

Beta angle utilizes three skeletal landmarks - point A, point B, and point C (the apparent axis of the condyle). It is formed between A-B line and point A perpendicular to C-B line. Further this angle indicates the severity and the type of skeletal dysplasia in the sagittal dimension and it changes with the growth pattern of the patient. Hence, it is important to study the dependence of beta angle on the growth pattern. The present study was designed to evaluate the correlation of Beta angle with point A-Nasion-point B (ANB) angle, points A and B to palatal plane (App-Bpp), Wit's appraisal and Maxillary-Mandibular plane angle Bisector (MMB) and Frankfort-Mandibular plane Angle (FMA) in Skeletal Class I, Class II and Class III malocclusion groups. Pre-treatment lateral head cephalo-grams of 120 subjects in age group of 15-25 years were obtained. Three skeletal Class I, Class II and Class III malocclusion groups (40 each) were assorted on the basis of ANB, MMB, App-Bpp, Wit's appraisal and FMA. Analysis of variance (ANOVA) and mean differences were calculated to compare the study groups. Bivariate correlations among different parameters of these groups were obtained. Normal values of beta angle in skeletal Class I group, skeletal Class II group and skeletal Class III group was 31.33±3.25, 25.28±4.28 and 40.93±4.55 respectively. Overall beta angle showed a strong correlation with all parameters of anterio-posterior dysplasia indicators except FMA. Beta angle shows weak correlation with FMA and is not affected by growth pattern/jaw rotation. The normal values are in same range irrespective of the differences in craniofacial morphology.

Estimation of Genetic Parameters for the Protein Profile in Danish Holstein Milk

DEFF Research Database (Denmark)

Buitenhuis, Albert Johannes; Poulsen, Nina Aagaard; Larsen, Lotte Bach

in the univariate and bivariate analysis of the protein traits. Hertitabilities ranged from 0.77 for κ-CN% to 0 for αs 1-CN% (SE: 0.13-0.21). The genetic correlation between β-CN and κ-CN was low (0.01 ± 0.53) whereas the genetic correlation of αs 2-CN with both β-CN and κ-CN was high (0.90). Furthermore α...

Structural Analysis of Women’s Heptathlon

Directory of Open Access Journals (Sweden)

Freya Gassmann

2016-02-01

Full Text Available The heptathlon comprises the results of seven single disciplines, assuming an equal influence from each discipline, depending on the measured performance. Data analysis was based on the data recorded for the individual performances of the 10 winning heptathletes in the World Athletics Championships from 1987 to 2013 and the Olympic Games from 1988 to 2012. In addition to descriptive analysis methods, correlations, bivariate and multivariate linear regressions, and panel data regressions were used. The transformation of the performances from seconds, centimeters, and meters into points showed that the individual disciplines do not equally affect the overall competition result. The currently valid conversion formula for the run, jump, and throw disciplines prefers the sprint and jump disciplines but penalizes the athletes performing in the 800 m run, javelin throw, and shotput disciplines. Furthermore, 21% to 48% of the variance of the sum of points can be attributed to the performances in the disciplines of long jump, 200 m sprint, 100 m hurdles, and high jump. To balance the effects of the single disciplines in the heptathlon, the formula to calculate points should be reevaluated.

Relationship between climatic variables and the variation in bulk tank milk composition using canonical correlation analysis.

Science.gov (United States)

Stürmer, Morgana; Busanello, Marcos; Velho, João Pedro; Heck, Vanessa Isabel; Haygert-Velho, Ione Maria Pereira

2018-06-04

A number of studies have addressed the relations between climatic variables and milk composition, but these works used univariate statistical approaches. In our study, we used a multivariate approach (canonical correlation) to study the impact of climatic variables on milk composition, price, and monthly milk production at a dairy farm using bulk tank milk data. Data on milk composition, price, and monthly milk production were obtained from a dairy company that purchased the milk from the farm, while climatic variable data were obtained from the National Institute of Meteorology (INMET). The data are from January 2014 to December 2016. Univariate correlation analysis and canonical correlation analysis were performed. Few correlations between the climatic variables and milk composition were found using a univariate approach. However, using canonical correlation analysis, we found a strong and significant correlation (r c  = 0.95, p value = 0.0029). Lactose, ambient temperature measures (mean, minimum, and maximum), and temperature-humidity index (THI) were found to be the most important variables for the canonical correlation. Our study indicated that 10.2% of the variation in milk composition, pricing, and monthly milk production can be explained by climatic variables. Ambient temperature variables, together with THI, seem to have the most influence on variation in milk composition.

Nanoscale protein diffusion by STED-based pair correlation analysis.

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Paolo Bianchini

Full Text Available We describe for the first time the combination between cross-pair correlation function analysis (pair correlation analysis or pCF and stimulated emission depletion (STED to obtain diffusion maps at spatial resolution below the optical diffraction limit (super-resolution. Our approach was tested in systems characterized by high and low signal to noise ratio, i.e. Capsid Like Particles (CLPs bearing several (>100 active fluorescent proteins and monomeric fluorescent proteins transiently expressed in living Chinese Hamster Ovary cells, respectively. The latter system represents the usual condition encountered in living cell studies on fluorescent protein chimeras. Spatial resolution of STED-pCF was found to be about 110 nm, with a more than twofold improvement over conventional confocal acquisition. We successfully applied our method to highlight how the proximity to nuclear envelope affects the mobility features of proteins actively imported into the nucleus in living cells. Remarkably, STED-pCF unveiled the existence of local barriers to diffusion as well as the presence of a slow component at distances up to 500-700 nm from either sides of nuclear envelope. The mobility of this component is similar to that previously described for transport complexes. Remarkably, all these features were invisible in conventional confocal mode.

Correlation analysis of the physiological factors controlling fundamental voice frequency.

Science.gov (United States)

Atkinson, J E

1978-01-01

A technique has been developed to obtain a quantitative measure of correlation between electromyographic (EMG) activity of various laryngeal muscles, subglottal air pressure, and the fundamental frequency of vibration of the vocal folds (Fo). Data were collected and analyzed on one subject, a native speaker of American English. The results show that an analysis of this type can provide a useful measure of correlation between the physiological and acoustical events in speech and, furthermore, can yield detailed insights into the organization and nature of the speech production process. In particular, based on these results, a model is suggested of Fo control involving laryngeal state functions that seems to agree with present knowledge of laryngeal control and experimental evidence.

Financial liberalization and stock market cross-correlation: MF-DCCA analysis based on Shanghai-Hong Kong Stock Connect

Science.gov (United States)

Ruan, Qingsong; Zhang, Shuhua; Lv, Dayong; Lu, Xinsheng

2018-02-01

Based on the implementation of Shanghai-Hong Kong Stock Connect in China, this paper examines the effects of financial liberalization on stock market comovement using both multifractal detrended fluctuation analysis (MF-DFA) and multifractal detrended cross-correlation analysis (MF-DCCA) methods. Results based on MF-DFA confirm the multifractality of Shanghai and Hong Kong stock markets, and the market efficiency of Shanghai stock market increased after the implementation of this connect program. Besides, analysis based on MF-DCCA has verified the existence of persistent cross-correlation between Shanghai and Hong Kong stock markets, and the cross-correlation gets stronger after the launch of this liberalization program. Finally, we find that fat-tail distribution is the main source of multifractality in the cross-correlations before the stock connect program, while long-range correlation contributes to the multifractality after this program.

Semiparametric bivariate zero-inflated Poisson models with application to studies of abundance for multiple species

Science.gov (United States)

Arab, Ali; Holan, Scott H.; Wikle, Christopher K.; Wildhaber, Mark L.

2012-01-01

Ecological studies involving counts of abundance, presence–absence or occupancy rates often produce data having a substantial proportion of zeros. Furthermore, these types of processes are typically multivariate and only adequately described by complex nonlinear relationships involving externally measured covariates. Ignoring these aspects of the data and implementing standard approaches can lead to models that fail to provide adequate scientific understanding of the underlying ecological processes, possibly resulting in a loss of inferential power. One method of dealing with data having excess zeros is to consider the class of univariate zero-inflated generalized linear models. However, this class of models fails to address the multivariate and nonlinear aspects associated with the data usually encountered in practice. Therefore, we propose a semiparametric bivariate zero-inflated Poisson model that takes into account both of these data attributes. The general modeling framework is hierarchical Bayes and is suitable for a broad range of applications. We demonstrate the effectiveness of our model through a motivating example on modeling catch per unit area for multiple species using data from the Missouri River Benthic Fishes Study, implemented by the United States Geological Survey.

Personality correlates of the Five-Factor Model for a sample of business owners/managers: associations with scores on Self-Monitoring, Type A Behavior, Locus of Control, and Subjective Well-being.

Science.gov (United States)

Morrison, K A

1997-02-01

Bivariate relationships were examined between scores on the Five-Factor Model of personality and four personality dimensions including Self-monitoring, Locus of Control, Type A Behavior, and Subjective Well-being. Data were collected from 307 franchise business owner/managers from four different industries. Scores for Self-monitoring were positively related to those on Extraversion; Self-monitoring was the only personality measure significantly correlated with scores on Openness to Experience. Scores for Type A Behavior, measured by the Jenkins Activity Survey, were negatively correlated with Agreeableness and positively correlated with those for Extraversion. Somewhat surprisingly, the score for Type A Behavior had a relatively low correlation with the score for Conscientiousness. Scores for Subjective Well-being and Locus of Control were most strongly correlated with the positive pole of Neuroticism (Emotional Stability), Conscientiousness, and Extraversion. Possible explanations for the observed relationships are discussed.

ASURV: Astronomical SURVival Statistics

Science.gov (United States)

Feigelson, E. D.; Nelson, P. I.; Isobe, T.; LaValley, M.

2014-06-01

ASURV (Astronomical SURVival Statistics) provides astronomy survival analysis for right- and left-censored data including the maximum-likelihood Kaplan-Meier estimator and several univariate two-sample tests, bivariate correlation measures, and linear regressions. ASURV is written in FORTRAN 77, and is stand-alone and does not call any specialized libraries.

METHODS OF DISTANCE MEASUREMENT’S ACCURACY INCREASING BASED ON THE CORRELATION ANALYSIS OF STEREO IMAGES

Directory of Open Access Journals (Sweden)

V. L. Kozlov

2018-01-01

Full Text Available To solve the problem of increasing the accuracy of restoring a three-dimensional picture of space using two-dimensional digital images, it is necessary to use new effective techniques and algorithms for processing and correlation analysis of digital images. Actively developed tools that allow you to reduce the time costs for processing stereo images, improve the quality of the depth maps construction and automate their construction. The aim of the work is to investigate the possibilities of using various techniques for processing digital images to improve the measurements accuracy of the rangefinder based on the correlation analysis of the stereo image. The results of studies of the influence of color channel mixing techniques on the distance measurements accuracy for various functions realizing correlation processing of images are presented. Studies on the analysis of the possibility of using integral representation of images to reduce the time cost in constructing a depth map areproposed. The results of studies of the possibility of using images prefiltration before correlation processing when distance measuring by stereo imaging areproposed.It is obtained that using of uniform mixing of channels leads to minimization of the total number of measurement errors, and using of brightness extraction according to the sRGB standard leads to an increase of errors number for all of the considered correlation processing techniques. Integral representation of the image makes it possible to accelerate the correlation processing, but this method is useful for depth map calculating in images no more than 0.5 megapixels. Using of image filtration before correlation processing can provide, depending on the filter parameters, either an increasing of the correlation function value, which is useful for analyzing noisy images, or compression of the correlation function.

Modeling both of the number of pausibacillary and multibacillary leprosy patients by using bivariate poisson regression

Science.gov (United States)

Winahju, W. S.; Mukarromah, A.; Putri, S.

2015-03-01

Leprosy is a chronic infectious disease caused by bacteria of leprosy (Mycobacterium leprae). Leprosy has become an important thing in Indonesia because its morbidity is quite high. Based on WHO data in 2014, in 2012 Indonesia has the highest number of new leprosy patients after India and Brazil with a contribution of 18.994 people (8.7% of the world). This number makes Indonesia automatically placed as the country with the highest number of leprosy morbidity of ASEAN countries. The province that most contributes to the number of leprosy patients in Indonesia is East Java. There are two kind of leprosy. They consist of pausibacillary and multibacillary. The morbidity of multibacillary leprosy is higher than pausibacillary leprosy. This paper will discuss modeling both of the number of multibacillary and pausibacillary leprosy patients as responses variables. These responses are count variables, so modeling will be conducted by using bivariate poisson regression method. Unit experiment used is in East Java, and predictors involved are: environment, demography, and poverty. The model uses data in 2012, and the result indicates that all predictors influence significantly.

Phenomenological analysis of angular correlations in 7 TeV proton-proton collisions from the CMS experiment

International Nuclear Information System (INIS)

Ray, R. L.

2011-01-01

A phenomenological analysis is presented of recent two-particle angular correlation data on relative pseudorapidity (η) and azimuth reported by the Compact Muon Solenoid (CMS) Collaboration for √(s)=7 TeV proton-proton collisions. The data are described with an empirical jetlike model developed for similar angular correlation measurements obtained from heavy-ion collisions at the Relativistic Heavy-Ion Collider (RHIC). The sameside (small relative azimuth), η-extended correlation structure, referred to as the ridge, is compared with three phenomenological correlation structures suggested by theoretical analysis. These include additional angular correlations due to soft gluon radiation in 2→3 partonic processes, a one-dimensional sameside correlation ridge on azimuth motivated, for example, by color-glass condensate models, and an azimuth quadrupole similar to that required to describe heavy-ion angular correlations. The quadrupole model provides the best overall description of the CMS data, including the ridge, based on χ 2 minimization in agreement with previous studies. Implications of these results with respect to possible mechanisms for producing the CMS sameside correlation ridge are discussed.

Pet Bottle Design, Correlation Analysis Of Pet Bottle Characteristics Subjective Judgment

Directory of Open Access Journals (Sweden)

Darko Avramović

2012-06-01

Full Text Available Ability to predict consumer’s reaction to particular design solution of the product is very important. Gathering andanalysis of subjective judgments of particular characteristics, based on which the aesthetic of the product is judged,is one of predicting the consumer’s reaction in the future. Knowledge gathered this manner can serve as a referencefor further studies of determining factors for aesthetic results and design quality. There are two opposed opinionsregarding prediction of aesthetic impression. One opinion is that taste of individual cannot be discussed because itis extremely variable and the possibility of meaningful analysis of aesthetic impression is rejected. Other opinionstates that there is a consistent preference of certain aesthetic characteristics despite individual and group differences.Main goal of this paper is to examine the correlation between subjective judgments of certain PET bottlecharacteristics. Analysis showed meaningful correlation between some of the PET bottle characteristics while othercharacteristics showed less correlation. It can be concluded that not all of the characteristics have the same influenceon the aesthetics and design quality of the PET bottle form. Emphasizing the characteristics relative to aesthetics ofthe product can produce better market results, taking in to account that consumer’s buy the product they consider tobe more attractive if other parameters of the product are similar.

The correlation between apparent diffusion coefficient and tumor cellularity in patients: a meta-analysis.

Science.gov (United States)

Chen, Lihua; Liu, Min; Bao, Jing; Xia, Yunbao; Zhang, Jiuquan; Zhang, Lin; Huang, Xuequan; Wang, Jian

2013-01-01

To perform a meta-analysis exploring the correlation between the apparent diffusion coefficient (ADC) and tumor cellularity in patients. We searched medical and scientific literature databases for studies discussing the correlation between the ADC and tumor cellularity in patients. Only studies that were published in English or Chinese prior to November 2012 were considered for inclusion. Summary correlation coefficient (r) values were extracted from each study, and 95% confidence intervals (CIs) were calculated. Sensitivity and subgroup analyses were performed to investigate potential heterogeneity. Of 189 studies, 28 were included in the meta-analysis, comprising 729 patients. The pooled r for all studies was -0.57 (95% CI: -0.62, -0.52), indicating notable heterogeneity (Pcorrelation between the ADC and cellularity for brain tumors. There was no notable evidence of publication bias. There is a strong negative correlation between the ADC and tumor cellularity in patients, particularly in the brain. However, larger, prospective studies are warranted to validate these findings in other cancer types.

Behavioral and Psychosocial Correlates of HIV Testing Among Male Clients of Female Sex Workers in Tijuana, Mexico.

Science.gov (United States)

Fleming, Paul J; Patterson, Thomas L; Chavarin, Claudia V; Semple, Shirley J; Magis-Rodriguez, Carlos; Pitpitan, Eileen V

2017-08-01

We use data collected from a sample of 400 male clients of female sex workers (FSW) to examine their HIV testing behavior. We present frequencies of HIV testing and used bivariate and multivariable analyses to assess its socio-demographic, behavioral, and psychosocial correlates. We found that the majority (55 %) of male clients of FSW in Tijuana, Mexico had never had an HIV test and the prevalence of HIV testing within the past year was low (9 %). In multivariable analyses, significant correlates of having ever tested for HIV were higher age, higher HIV knowledge score, lower sexual compulsiveness score, lower misogynistic attitudes score, having a condom break during sex with a FSW, and higher frequency of sex with a FSW while she was high. Our findings represent an important starting point for developing effective interventions to address the need to promote HIV testing among this population.

Diagrammatic analysis of correlations in polymer fluids: Cluster diagrams via Edwards' field theory

International Nuclear Information System (INIS)

Morse, David C.

2006-01-01

Edwards' functional integral approach to the statistical mechanics of polymer liquids is amenable to a diagrammatic analysis in which free energies and correlation functions are expanded as infinite sums of Feynman diagrams. This analysis is shown to lead naturally to a perturbative cluster expansion that is closely related to the Mayer cluster expansion developed for molecular liquids by Chandler and co-workers. Expansion of the functional integral representation of the grand-canonical partition function yields a perturbation theory in which all quantities of interest are expressed as functionals of a monomer-monomer pair potential, as functionals of intramolecular correlation functions of non-interacting molecules, and as functions of molecular activities. In different variants of the theory, the pair potential may be either a bare or a screened potential. A series of topological reductions yields a renormalized diagrammatic expansion in which collective correlation functions are instead expressed diagrammatically as functionals of the true single-molecule correlation functions in the interacting fluid, and as functions of molecular number density. Similar renormalized expansions are also obtained for a collective Ornstein-Zernicke direct correlation function, and for intramolecular correlation functions. A concise discussion is given of the corresponding Mayer cluster expansion, and of the relationship between the Mayer and perturbative cluster expansions for liquids of flexible molecules. The application of the perturbative cluster expansion to coarse-grained models of dense multi-component polymer liquids is discussed, and a justification is given for the use of a loop expansion. As an example, the formalism is used to derive a new expression for the wave-number dependent direct correlation function and recover known expressions for the intramolecular two-point correlation function to first-order in a renormalized loop expansion for coarse-grained models of

Reliability analysis based on a novel density estimation method for structures with correlations

Directory of Open Access Journals (Sweden)

Baoyu LI

2017-06-01

Full Text Available Estimating the Probability Density Function (PDF of the performance function is a direct way for structural reliability analysis, and the failure probability can be easily obtained by integration in the failure domain. However, efficiently estimating the PDF is still an urgent problem to be solved. The existing fractional moment based maximum entropy has provided a very advanced method for the PDF estimation, whereas the main shortcoming is that it limits the application of the reliability analysis method only to structures with independent inputs. While in fact, structures with correlated inputs always exist in engineering, thus this paper improves the maximum entropy method, and applies the Unscented Transformation (UT technique to compute the fractional moments of the performance function for structures with correlations, which is a very efficient moment estimation method for models with any inputs. The proposed method can precisely estimate the probability distributions of performance functions for structures with correlations. Besides, the number of function evaluations of the proposed method in reliability analysis, which is determined by UT, is really small. Several examples are employed to illustrate the accuracy and advantages of the proposed method.

Correlation Between Minimum Apparent Diffusion Coefficient (ADCmin) and Tumor Cellularity: A Meta-analysis.

Science.gov (United States)

Surov, Alexey; Meyer, Hans Jonas; Wienke, Andreas

2017-07-01

Diffusion-weighted imaging (DWI) is a magnetic resonance imaging (MRI) technique based on measure of water diffusion that can provide information about tissue microstructure, especially about cell count. Increase of cell density induces restriction of water diffusion and decreases apparent diffusion coefficient (ADC). ADC can be divided into three sub-parameters: ADC minimum or ADC min , mean ADC or ADC mean and ADC maximum or ADC max Some studies have suggested that ADC min shows stronger correlations with cell count in comparison to other ADC fractions and may be used as a parameter for estimation of tumor cellularity. The aim of the present meta-analysis was to summarize correlation coefficients between ADC min and cellularity in different tumors based on large patient data. For this analysis, MEDLINE database was screened for associations between ADC and cell count in different tumors up to September 2016. For this work, only data regarding ADC min were included. Overall, 12 publications with 317 patients were identified. Spearman's correlation coefficient was used to analyze associations between ADC min and cellularity. The reported Pearson correlation coefficients in some publications were converted into Spearman correlation coefficients. The pooled correlation coefficient for all included studies was ρ=-0.59 (95% confidence interval (CI)=-0.72 to -0.45), heterogeneity Tau 2 =0.04 (pcorrelated moderately with tumor cellularity. The calculated correlation coefficient is not stronger in comparison to the reported coefficient for ADC mean and, therefore, ADC min does not represent a better means to reflect cellularity. Copyright© 2017, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.

Minimizing the trend effect on detrended cross-correlation analysis with empirical mode decomposition

International Nuclear Information System (INIS)

Zhao Xiaojun; Shang Pengjian; Zhao Chuang; Wang Jing; Tao Rui

2012-01-01

Highlights: ► Investigate the effects of linear, exponential and periodic trends on DCCA. ► Apply empirical mode decomposition to extract trend term. ► Strong and monotonic trends are successfully eliminated. ► Get the cross-correlation exponent in a persistent behavior without crossover. - Abstract: Detrended cross-correlation analysis (DCCA) is a scaling method commonly used to estimate long-range power law cross-correlation in non-stationary signals. However, the susceptibility of DCCA to trends makes the scaling results difficult to analyze due to spurious crossovers. We artificially generate long-range cross-correlated signals and systematically investigate the effect of linear, exponential and periodic trends. Specifically to the crossovers raised by trends, we apply empirical mode decomposition method which decomposes underlying signals into several intrinsic mode functions (IMF) and a residual trend. After the removal of residual term, strong and monotonic trends such as linear and exponential trends are successfully eliminated. But periodic trend cannot be separated out according to the criterion of IMF, which can be eliminated by Fourier transform. As a special case of DCCA, detrended fluctuation analysis presents similar results.

From micro-correlations to macro-correlations

International Nuclear Information System (INIS)

Eliazar, Iddo

2016-01-01

Random vectors with a symmetric correlation structure share a common value of pair-wise correlation between their different components. The symmetric correlation structure appears in a multitude of settings, e.g. mixture models. In a mixture model the components of the random vector are drawn independently from a general probability distribution that is determined by an underlying parameter, and the parameter itself is randomized. In this paper we study the overall correlation of high-dimensional random vectors with a symmetric correlation structure. Considering such a random vector, and terming its pair-wise correlation “micro-correlation”, we use an asymptotic analysis to derive the random vector’s “macro-correlation” : a score that takes values in the unit interval, and that quantifies the random vector’s overall correlation. The method of obtaining macro-correlations from micro-correlations is then applied to a diverse collection of frameworks that demonstrate the method’s wide applicability.

Correlation of leptin and sex hormones with endocrine changes in healthy Saudi women of different body weights

International Nuclear Information System (INIS)

Al-Harithy, Rowyda N.; Al-Doghaither, H.; Abualnaja, K.

2006-01-01

A relationship between estrogen and leptin has been described during the follicular phase described during the follicular phase of both spontaneous menstrual cycles stimulated with exogenous follicle-stimulating hormone (FSH), which suggest that leptin has either a direct effect on or is regulated by gonadal steroids in the human ovary. To examine the changes in plasma leptin levels during the menstrual cycle, we studied the association between plasma leptin and reproductive hormones in young, healthy Saudi women. Sixty-five young women between 19 to 39 years of age, with a normal menstrual cycle, were grouped into 33 over weight and obese females of BMI>25kg/m, and 32 lean females of BMI<25 Kg/m. Anthropometrics measurements were made at the time of the collection. Samples were analyzed for leptin, progesterone, estradiol (E), FSH, luteinizing hormone (LH), cortisol, and testosterone concentrations. Overweight and obese women, compared with lean, tended to have a significantly higher plasma leptin (11.38+-4.06 vs. 6.22+-2.87ng/mL; P=0.05). In overweight and obese subjects, circulating leptin, concentrations showed a direct correlation with BMI (r=0.53; P=0.0002), hip circumference (r=0.32; P=0.005), waist-hip ratio (r=0.37; P=0.042), weight (r=0.41; P=0.021), and E, on day 3 (r=0.35; P=0.048). In all correlation analyses, leptin levels did not correlate with cortisol or testosterone. In lean subjects, a bivariate correlation analysis showed that plasma leptin concentrations were directly correlated to hip circumference (r=0.43; P=0.012). Moreover, a direct correlation was found with progesterone on day 10 (r=0.43; P=0.014) and E on day 24 (r=0.47; P=0.007). There is a link between plasma leptin and progesterone concentrations during the menstrual cycle, and the variation in circulating estrdiol concentrations may have an influence on circulating leptin in female subjects. (author)

Solar activity and terrestrial climate: an analysis of some purported correlations

DEFF Research Database (Denmark)

Laut, Peter

2003-01-01

claimed to support solar hypotheses. My analyses show that the apparent strong correlations displayed on these graphs have been obtained by an incorrect handling of the physical data. Since the graphs are still widely referred to in the literature and their misleading character has not yet been generally......The last decade has seen a revival of various hypotheses claiming a strong correlation between solar activity and a number of terrestrial climate parameters: Links between cosmic rays and cloud cover, first total cloud cover and then only low clouds, and between solar cycle lengths and Northern...... the existence of important links between solar activity and terrestrial climate. Such links have over the years been demonstrated by many authors. The sole objective of the present analysis is to draw attention to the fact that some of the widely publicized, apparent correlations do not properly reflect...

Characteristic analysis on UAV-MIMO channel based on normalized correlation matrix.

Science.gov (United States)

Gao, Xi jun; Chen, Zi li; Hu, Yong Jiang

2014-01-01

Based on the three-dimensional GBSBCM (geometrically based double bounce cylinder model) channel model of MIMO for unmanned aerial vehicle (UAV), the simple form of UAV space-time-frequency channel correlation function which includes the LOS, SPE, and DIF components is presented. By the methods of channel matrix decomposition and coefficient normalization, the analytic formula of UAV-MIMO normalized correlation matrix is deduced. This formula can be used directly to analyze the condition number of UAV-MIMO channel matrix, the channel capacity, and other characteristic parameters. The simulation results show that this channel correlation matrix can be applied to describe the changes of UAV-MIMO channel characteristics under different parameter settings comprehensively. This analysis method provides a theoretical basis for improving the transmission performance of UAV-MIMO channel. The development of MIMO technology shows practical application value in the field of UAV communication.

Prospects of Frequency-Time Correlation Analysis for Detecting Pipeline Leaks by Acoustic Emission Method

International Nuclear Information System (INIS)

Faerman, V A; Cheremnov, A G; Avramchuk, V V; Luneva, E E

2014-01-01

In the current work the relevance of nondestructive test method development applied for pipeline leak detection is considered. It was shown that acoustic emission testing is currently one of the most widely spread leak detection methods. The main disadvantage of this method is that it cannot be applied in monitoring long pipeline sections, which in its turn complicates and slows down the inspection of the line pipe sections of main pipelines. The prospects of developing alternative techniques and methods based on the use of the spectral analysis of signals were considered and their possible application in leak detection on the basis of the correlation method was outlined. As an alternative, the time-frequency correlation function calculation is proposed. This function represents the correlation between the spectral components of the analyzed signals. In this work, the technique of time-frequency correlation function calculation is described. The experimental data that demonstrate obvious advantage of the time-frequency correlation function compared to the simple correlation function are presented. The application of the time-frequency correlation function is more effective in suppressing the noise components in the frequency range of the useful signal, which makes maximum of the function more pronounced. The main drawback of application of the time- frequency correlation function analysis in solving leak detection problems is a great number of calculations that may result in a further increase in pipeline time inspection. However, this drawback can be partially reduced by the development and implementation of efficient algorithms (including parallel) of computing the fast Fourier transform using computer central processing unit and graphic processing unit

Statistical power to detect genetic (covariance of complex traits using SNP data in unrelated samples.

Directory of Open Access Journals (Sweden)

Peter M Visscher

2014-04-01

Full Text Available We have recently developed analysis methods (GREML to estimate the genetic variance of a complex trait/disease and the genetic correlation between two complex traits/diseases using genome-wide single nucleotide polymorphism (SNP data in unrelated individuals. Here we use analytical derivations and simulations to quantify the sampling variance of the estimate of the proportion of phenotypic variance captured by all SNPs for quantitative traits and case-control studies. We also derive the approximate sampling variance of the estimate of a genetic correlation in a bivariate analysis, when two complex traits are either measured on the same or different individuals. We show that the sampling variance is inversely proportional to the number of pairwise contrasts in the analysis and to the variance in SNP-derived genetic relationships. For bivariate analysis, the sampling variance of the genetic correlation additionally depends on the harmonic mean of the proportion of variance explained by the SNPs for the two traits and the genetic correlation between the traits, and depends on the phenotypic correlation when the traits are measured on the same individuals. We provide an online tool for calculating the power of detecting genetic (covariation using genome-wide SNP data. The new theory and online tool will be helpful to plan experimental designs to estimate the missing heritability that has not yet been fully revealed through genome-wide association studies, and to estimate the genetic overlap between complex traits (diseases in particular when the traits (diseases are not measured on the same samples.

Multivariate analysis of correlation between electrophysiological and hemodynamic responses during cognitive processing

Science.gov (United States)

Kujala, Jan; Sudre, Gustavo; Vartiainen, Johanna; Liljeström, Mia; Mitchell, Tom; Salmelin, Riitta

2014-01-01

Animal and human studies have frequently shown that in primary sensory and motor regions the BOLD signal correlates positively with high-frequency and negatively with low-frequency neuronal activity. However, recent evidence suggests that this relationship may also vary across cortical areas. Detailed knowledge of the possible spectral diversity between electrophysiological and hemodynamic responses across the human cortex would be essential for neural-level interpretation of fMRI data and for informative multimodal combination of electromagnetic and hemodynamic imaging data, especially in cognitive tasks. We applied multivariate partial least squares correlation analysis to MEG–fMRI data recorded in a reading paradigm to determine the correlation patterns between the data types, at once, across the cortex. Our results revealed heterogeneous patterns of high-frequency correlation between MEG and fMRI responses, with marked dissociation between lower and higher order cortical regions. The low-frequency range showed substantial variance, with negative and positive correlations manifesting at different frequencies across cortical regions. These findings demonstrate the complexity of the neurophysiological counterparts of hemodynamic fluctuations in cognitive processing. PMID:24518260

Neutron Activation Analysis and Moessbauer Correlations of Archaeological Pottery from Amazon Basin for Classification Studies

International Nuclear Information System (INIS)

Bellido, A. V. B.; Latini, R. M.; Nicoli, I.; Scorzelli, R. B.; Solorzano, P. M.

2011-01-01

The aim of the present work was to investigate the correlation between data obtained by means of two analytical methods, instrumental neutron activation analysis (INAA) and Moessbauer Spectroscopy of pottery samples combined with multivariate statistical analysis in order to optimize quantitative analysis in the classification studies. Ceramics recently discovered in archaeological earth circular structures sites in Acre state Brazil. 199 samples were analyzed by INAA, allowing simultaneous determination of twenty elements chemical concentrations, and 44 samples by using Moessbauer Spectroscopy, allowing the determination of fourteen hyperfine parameters. For the correlation study, data were treated by two multivariate statistical methods: cluster analysis for the classification and the principal component analysis for the data correlations. INAA data show that some of REE (rare earth elements) were the discriminating variables for this technique. Mossbauer parameters that exhibit the same behavior are being investigated, remarkable improve can be seem for the combined REE and the Mossbauer variables showing a good results considering the limited number of samples. This data matrix is being used for the understanding in the studies of classification and provenance of ceramics prehistory of the Amazonic basin.

Generalized Correlation Coefficient for Non-Parametric Analysis of Microarray Time-Course Data

DEFF Research Database (Denmark)

Tan, Qihua; Thomassen, Mads; Burton, Mark

2017-01-01

the heterogeneous time-course gene expression patterns. Application of the method identified nonlinear time-course patterns in high agreement with parametric analysis. We conclude that the non-parametric nature in the generalized correlation analysis could be an useful and efficient tool for analyzing microarray...... time-course data and for exploring the complex relationships in the omics data for studying their association with disease and health....

Correlation, path analysis and heritability estimation for agronomic traits contribute to yield on soybean

Science.gov (United States)

Sulistyo, A.; Purwantoro; Sari, K. P.

2018-01-01

Selection is a routine activity in plant breeding programs that must be done by plant breeders in obtaining superior plant genotypes. The use of appropriate selection criteria will determine the effectiveness of selection activities. The purpose of this study was to analysis the inheritable agronomic traits that contribute to soybean yield. A total of 91 soybean lines were planted in Muneng Experimental Station, Probolinggo District, East Java Province, Indonesia in 2016. All soybean lines were arranged in randomized complete block design with two replicates. Correlation analysis, path analysis and heritability estimation were performed on days to flowering, days to maturing, plant height, number of branches, number of fertile nodes, number of filled pods, weight of 100 seeds, and yield to determine selection criteria on soybean breeding program. The results showed that the heritability value of almost all agronomic traits observed is high except for the number of fertile nodes with low heritability. The result of correlation analysis shows that days to flowering, plant height and number of fertile nodes have positive correlation with seed yield per plot (0.056, 0.444, and 0.100, respectively). In addition, path analysis showed that plant height and number of fertile nodes have highest positive direct effect on soybean yield. Based on this result, plant height can be selected as one of selection criteria in soybean breeding program to obtain high yielding soybean variety.

Correlation between plasma homocysteine levels and craving in alcohol dependent stabilized patients.

Science.gov (United States)

Coppola, Maurizio; Mondola, Raffaella

2018-06-01

Homocysteine is a sulfur amino acid strictly related with alcohol consumption. In alcoholics, hyperhomocysteinemia can increase the risk of various alcohol-related disorders such as: brain atrophy, epileptic seizures during withdrawal, and mood disorders. To evaluate the correlation among serum homocysteine concentrations, craving, hazardous and harmful patterns of alcohol consumption in patients stabilized for withdrawal symptoms. Participants were adult outpatients accessed at the Addiction Treatment Unit. Alcoholism was assessed using the following tools: Mini-International Neuropsychiatric Interview Plus (MINI Plus), Alcohol Use Disorder Identification test (AUDIT), Visual Analogic Scale for craving (VAS). Furthermore, during the first visit a blood sample was taken from all patients to measure the plasma concentration of both homocysteine and Carboxy Deficient Transferrin (CDT). Differences between groups in socio-demographic and clinical characteristics were analyzed using the t-test and the Mann-Whitney's U test for normally and non-normally distributed data, respectively. Correlation between clinical scale scores and plasma concentration of homocysteine and CDT was evaluated using the Pearson's correlation coefficient and the Kendall's Tau-b bivariate correlation coefficient for normally and non-normally distributed data, respectively. Our study included 92 patients. No difference was found in socio-demographic characteristics between groups. The group with high homocysteine had higher prevalence of mood disorders (p correlation with both VAS score (p correlated with alcoholism in a bidirectional manner because its level appears to be related with alcohol degree, but simultaneously, hyperhomocysteinemia could enhance the alcohol consumption increasing the severity of craving in a circular self reinforcing mechanism. Copyright © 2017 Elsevier Ltd and European Society for Clinical Nutrition and Metabolism. All rights reserved.

Central depression in nucleonic densities: Trend analysis in the nuclear density functional theory approach