On prediction from the location-scale model with equi correlated response

The location-scale model with equi-correlated responses is discussed. The structure of the location-scale model is utilised to genera-te the prediction distribution of a future response and that of a set of future responses. The method avoids the integration procedures usually involved in derivation of prediction distributions and yields results same as those obtained by the Bayes method with the vague prior distribution* Finally the re-suits have been specialised to cover the case of the normal intra-class model.     

A property of coefficients of variation for ordered bivariate log-normal variables

This paper considers the maximum and minimum of a pair of log-normal variables with equal mean. It shows that either order statistic has a smaller coefficient of variation than the two original log-normal variables provided the latter are of equal variance. When the variances are unequal, as the variance ratio increases, the minimum (maximum), has a smaller coefficient of variation if the correlation coefficient of the log-normal variables is small (small) and the variances are large (small).     

Independence test in high-dimension using distance correlation and power enhancement technique

Abstract

This paper is concerned with independence test in high-dimension. A new test statistic is proposed with two terms: one is based on the modified distance correlation statistic, the other is constructed to enhance the power under sparse alternatives. Asymptotic properties of the test statistic are discussed under some regular conditions. The finite-sample simulations exhibit its superiority over some existing procedures. Finally, a real data example illustrates the proposed test.     

FDI 对安徽省装备制造业发展的影响分析

外商直接投资（FDI）在拓宽国内资金需求渠道的同时,具有较强的技术溢出效应,迎合了装备制造业资本和技术密集的双重特征。当前,安徽省装备制造业处在资本密集型占主导的发展阶段,对 FDI的偏好性较强。研究表明,安徽省装备制造业与FDI间具有较强的相关性,FDI能够有效促进安徽省装备制造业发展,但安徽省装备制造业对FDI的吸引力较弱,且各具体行业对 FDI依赖程度存在较大差异。据此提出相应的建议,一方面,安徽省应注重装备制造业转型升级,提升行业整体竞争力;另一方面,要优化投资环境,增强安徽省装备制造业对FDI的引致能力。     

Testing Serial Correlation in Partially Linear Additive Errors-in-variables Models

This article considers testing serial correlation in partially linear additive errors-in-variables model. Based on the empirical likelihood based approach, a test statistic was proposed, and it was shown to follow asymptotically a chi-square distribution under the null hypothesis of no serial correlation. Finally, some simulation studies are conducted to illustrate the performance of the proposed method.     

Intraday Serial Correlation,Volatility, and Jump: Evidence from China's Stock Market

In this article, we investigate the relationships among intraday serial correlation, jump-robust volatility, positive and negative jumps based on Shanghai composite index high frequency data. We implement variance ratio test to quantify intraday serial correlation. We also measure the continuous part of realized volatility using jump-robust MedRV estimator and disentangle positive and negative jumps using Realized Downside Risk Measure and Realized Upside Potential Measure proposed by Bi et al., (2013 Bi, T., Zhang, B., Wu, H. (2013). Measuring downside risk using high frequency data–realized downside risk measure. Communications in Statistics–Simulation and Computation 42(4):741–754.[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]). We find that intraday serial correlation are positively correlated with jump-robust volatility and negatively correlated with negative jumps which confirm the LeBaron effect.     

Power analysis for clustered non-continuous responses in multicenter trials

Power analysis for multi-center randomized control trials is quite difficult to perform for non-continuous responses when site differences are modeled by random effects using the generalized linear mixed-effects model (GLMM). First, it is not possible to construct power functions analytically, because of the extreme complexity of the sampling distribution of parameter estimates. Second, Monte Carlo (MC) simulation, a popular option for estimating power for complex models, does not work within the current context because of a lack of methods and software packages that would provide reliable estimates for fitting such GLMMs. For example, even statistical packages from software giants like SAS do not provide reliable estimates at the time of writing. Another major limitation of MC simulation is the lengthy running time, especially for complex models such as GLMM, especially when estimating power for multiple scenarios of interest. We present a new approach to address such limitations. The proposed approach defines a marginal model to approximate the GLMM and estimates power without relying on MC simulation. The approach is illustrated with both real and simulated data, with the simulation study demonstrating good performance of the method.     

Factor screening in nonregular two-level designs based on projection-based variable selection

In this paper, we focus on the problem of factor screening in nonregular two-level designs through gradually reducing the number of possible sets of active factors. We are particularly concerned with situations when three or four factors are active. Our proposed method works through examining fits of projection models, where variable selection techniques are used to reduce the number of terms. To examine the reliability of the methods in combination with such techniques, a panel of models consisting of three or four active factors with data generated from the 12-run and the 20-run Plackett–Burman (PB) design is used. The dependence of the procedure on the amount of noise, the number of active factors and the number of experimental factors is also investigated. For designs with few runs such as the 12-run PB design, variable selection should be done with care and default procedures in computer software may not be reliable to which we suggest improvements. A real example is included to show how we propose factor screening can be done in practice.     

中国快递业发展的区域不平衡性测度研究

选取大陆31个省份2007—2014年样本数据,分别测算三区域、八区域分组下快递业发展的不平衡性及极化趋势。研究结果表明:中国快递业发展存在较强的区域不平衡性,且这种不平衡性还呈现不断加大趋势;东中西快递业发展不平衡性明显,总体呈现东部强中西部弱的特点,基尼系数分解结果显示,东中西组间差异为不平衡性贡献了70%以上的份额,同时东部和西部地区内部也存在相当程度的不平衡性;八区域不平衡性也非常明显,总体呈现沿海强内陆弱、东强西弱、南强北弱的基本格局,组间差异贡献了88%左右的份额,就区域内而言,南部沿海和西北地区不平衡性较强,东部沿海、长江中游及东北地区则较为平衡;三区域及八区域分组下快递业不平衡发展均呈现出不断增强的极化趋势。