A linear model-based test for the heterogeneity of conditional correlations

Current methods of testing the equality of conditional correlations of bivariate data on a third variable of interest (covariate) are limited due to discretizing of the covariate when it is continuous. In this study, we propose a linear model approach for estimation and hypothesis testing of the Pearson correlation coefficient, where the correlation itself can be modeled as a function of continuous covariates. The restricted maximum likelihood method is applied for parameter estimation, and the corrected likelihood ratio test is performed for hypothesis testing. This approach allows for flexible and robust inference and prediction of the conditional correlations based on the linear model. Simulation studies show that the proposed method is statistically more powerful and more flexible in accommodating complex covariate patterns than the existing methods. In addition, we illustrate the approach by analyzing the correlation between the physical component summary and the mental component summary of the MOS SF-36 form across a fair number of covariates in the national survey data.     

Baker- Lin-Huang Type Bivariate Distributions Based on Order Statistics

Baker (2008 Baker, R. (2008). An order-statistics-based method for constructing multivariate distributions with fixed marginals. J. Multivariate Anal. 99: 23122327.[Crossref], [Web of Science ®] , [Google Scholar]) introduced a new class of bivariate distributions based on distributions of order statistics from two independent samples of size n. Lin and Huang (2010 Lin, G.D., Huang, J.S. (2010). A note on the maximum correlation for Baker’s bivariate distributions with fixed marginals. J. Multivariate Anal. 101: 22272233.[Crossref], [Web of Science ®] , [Google Scholar]) discovered an important property of Baker’s distribution and showed that the Pearson’s correlation coefficient for this distribution converges to maximum attainable value, i.e., the correlation coefficient of the Fréchet upper bound, as n increases to infinity. Bairamov and Bayramoglu (2013 Bairamov, I., Bayramoglu, K. (2013). From Huang-Kotz distribution to Baker’s distribution. J. Multivariate Anal. 113: 106115.[Crossref], [Web of Science ®] , [Google Scholar]) investigated a new class of bivariate distributions constructed by using Baker’s model and distributions of order statistics from dependent random variables, allowing higher correlation than that of Baker’s distribution. In this article, a new class of Baker’s type bivariate distributions with high correlation are constructed based on distributions of order statistics by using an arbitrary continuous copula instead of the product copula.     

Correlation estimation in Downton's bivariate exponential distribution using incomplete samples

Suppose (X, Y) has a Downton's bivariate exponential distribution with correlation ρ. For a random sample of size n from (X, Y), let X _r:n be the rth X-order statistic and Y _[r:n] be its concomitant. We investigate estimators of ρ when all the parameters are unknown and the available data is an incomplete bivariate sample made up of (i) all the Y-values and the ranks of associated X-values, i.e. (i, Y _[i:n]), 1≤i≤n, and (ii) a Type II right-censored bivariate sample consisting of (X _i:n , Y _[i:n]), 1≤i≤r<n. In both setups, we use simulation to examine the bias and mean square errors of several estimators of ρ and obtain their estimated relative efficiencies. The preferred estimator under (i) is a function of the sample correlation of (Y _i:n , Y _[i:n]) values, and under (ii), a method of moments estimator involving the regression function is preferred.     

Computing and approximating multivariate chi-square probabilities

We consider computational methods for evaluating and approximating multivariate chi-square probabilities in cases where the pertaining correlation matrix or blocks thereof have a low factorial representation. To this end, techniques from matrix factorization and probability theory are applied. We outline a variety of statistical applications of multivariate chi-square distributions and provide a system of MATLAB programs implementing the proposed algorithms. Computer simulations demonstrate the accuracy and the computational efficiency of our methods in comparison with Monte Carlo approximations, and a real data example from statistical genetics illustrates their usage in practice.     

Variance reduction through negative correlation,a simulation study

A new method for reducing the variance in simalation experiments is presented and evaluated. The method can be regarded as an extension of the antithetic variates principle. We create negative correlation within all pairs of random number while the original mcihod creates correlation only within some pairs. Our method is compared to ihe antithetic variates and crude Monte-Carlo techniques by repeated simulations. The results obtained are ail in favour of our method.     

中国与金砖国家产业内贸易水平测度及影响因素

通过运用改进后的G-L指数测度中国与金砖国家产业内贸易水平,并与不同发展水平国家间的产业内贸易进行对比分析,表明金砖国家产业内贸易水平显著低于发达国家,同时发展水平不同不是限制两国产业内贸易的绝对因素;测度SITC(Rev.4)分类下的中国与金砖国家0-8类产品产业内贸易水平,显示金砖国家内部不同国家、不同种类产品的产业内贸易水平具有较明显的差异性;运用灰色关联分析法计算出影响因素与产业内贸易水平的关联度,得出市场规模差异、人均收入差异、市场开放度差异、外商直接投资对产业内贸易影响程度较大。因此,应开展文化交流、发挥比较优势、提升创新能力以及有侧重地根据影响因素的作用强度进行针对性调整,以提升中国与其他金砖四国产业内贸易水平。     

基于灰色关联分析法的农业信息化发展研究——以河南省为例

通过构建农业信息化指标评价体系,运用灰色关联分析法建立指标分析模型进行实证分析,由关联度排序来衡量各指标对农业信息化发展的影响和作用,最终根据分析结果提出相应的对策和建议来加快河南省农业信息化的发展.     

中国当前生态文明建设关键影响因素及建设策略

生态文明建设是一项整体性事业,它不仅要求提高生态系统活力、环境质量水平和资源可持续利用水平,还要求转变经济发展方式和生产生活方式。因此,生态文明建设的影响因素也非常多。基于全国各省面板数据的最新相关性分析显示,各省的协调发展能力和社会发展水平对生态文明建设起到巨大的促进作用,生态系统活力也是促进生态文明建设的积极因素,而环境质量仍然不符合生态文明建设的发展要求。各省应准确掌握中国当前生态文明建设关键影响因素,并制定恰当的生态文明建设策略。     

基于h指数的科研人员学术评价及其相关性分析

利用CNKI引文数据库为数据源,收集h指数≥10共985位科研人员的相关信息,阐述h指数的特征及其在科研人员学术评价中的适用性,从发文量、被引频次、年龄、科研单位、学科类别、地区等方面对h指数进行实证分析和对比研究,揭示h指数产生差异的影响因素,在此基础上说明h指数的利弊并提出可行性建议,以期为h指数的正确应用提供参考。研究发现:(1)h指数综合考虑论文产出数量和影响力,能够比较准确地评价科研人员的学术成就,但自身也存在局限性;(2)h指数与被引频次有较强的相关性,与发文量关系较弱,与科研人员的年龄无关;(3)h指数在不同科研单位、不同学科间的差异很大,不适合用于跨学科之间的比较;(4)不同省份科研人员h指数平均水平有显著的统计差异。