Quantile dispersion graphs to compare the efficiencies of cluster randomized designs

The purpose of this article is to compare efficiencies of several cluster randomized designs using the method of quantile dispersion graphs (QDGs). A cluster randomized design is considered whenever subjects are randomized at a group level but analyzed at the individual level. A prior knowledge of the correlation existing between subjects within the same cluster is necessary to design these cluster randomized trials. Using the QDG approach, we are able to compare several cluster randomized designs without requiring any information on the intracluster correlation. For a given design, several quantiles of the power function, which are directly related to the effect size, are obtained for several effect sizes. The quantiles depend on the intracluster correlation present in the model. The dispersion of these quantiles over the space of the unknown intracluster correlation is determined, and then depicted by the QDGs. Two applications of the proposed methodology are presented.     

A new alternative to the standard F test for clustered data

The data collection process and the inherent population structure are the main causes for clustered data. The observations in a given cluster are correlated, and the magnitude of such correlation is often measured by the intra-cluster correlation coefficient. The intra-cluster correlation can lead to an inflated size of the standard F test in a linear model. In this paper, we propose a solution to this problem. Unlike previous adjustments, our method does not require estimation of the intra-class correlation, which is problematic especially when the number of clusters is small. Our simulation results show that the new method outperforms the existing methods.     

Fast robust feature screening for ultrahigh-dimensional varying coefficient models

In this paper, we propose a new partial correlation, the so-called composite quantile partial correlation, to measure the relationship of two variables given other variables. We further use this correlation to screen variables in ultrahigh-dimensional varying coefficient models. Our proposed method is fast and robust against outliers and can be efficiently employed in both single index variable and multiple index variable varying coefficient models. Numerical results indicate the preference of our proposed method.     

Joint GEEs for multivariate correlated data with incomplete binary outcomes

This study considers a fully-parametric but uncongenial multiple imputation (MI) inference to jointly analyze incomplete binary response variables observed in a correlated data settings. Multiple imputation model is specified as a fully-parametric model based on a multivariate extension of mixed-effects models. Dichotomized imputed datasets are then analyzed using joint GEE models where covariates are associated with the marginal mean of responses with response-specific regression coefficients and a Kronecker product is accommodated for cluster-specific correlation structure for a given response variable and correlation structure between multiple response variables. The validity of the proposed MI-based JGEE (MI-JGEE) approach is assessed through a Monte Carlo simulation study under different scenarios. The simulation results, which are evaluated in terms of bias, mean-squared error, and coverage rate, show that MI-JGEE has promising inferential properties even when the underlying multiple imputation is misspecified. Finally, Adolescent Alcohol Prevention Trial data are used for illustration.     

Generalized correlation and kernel causality with applications in development economics

New generalized correlation measures of 2012, GMC(Y|X), use Kernel regressions to overcome the linearity of Pearson's correlation coefficients. A new matrix of generalized correlation coefficients is such that when |r*_ij| > |r*_ji|, it is more likely that the column variable X_j is what Granger called the “instantaneous cause” or what we call “kernel cause” of the row variable X_i. New partial correlations ameliorate confounding. Various examples and simulations support robustness of new causality. We include bootstrap inference, robustness checks based on the dependence between regressor and error, and on the out-of-sample forecasts. Data for 198 countries on nine development variables support growth policy over redistribution and Deaton's criticism of foreign aid. Potential applications include Big Data, since our R code is available in the online supplementary material.     

Monitoring means and covariances of multivariate non linear time series with heavy tails

In this paper, the focus is on sequential analysis of multivariate financial time series with heavy tails. The mean vector and the covariance matrix of multivariate non linear models are simultaneously monitored by modifying conventional control charts to identify structural changes in the data. The considered target process is a constant conditional correlation model (cf. Bollerslev, 1990 Bollerslev, T. (1990). Modeling the coherence in short-run nominal exchange rates: A multivariate generalized ARCH model. Rev. Econ. Stat. 72:498–505.[Crossref], [Web of Science ®] , [Google Scholar]), an extended constant conditional correlation model (cf. He and Teräsvirta, 2004 He, C., Teräsvirta, T. (2004). An extended constant conditional correlation GARCH model and its fourth-moment structure. Economet. Theory 20:904–926.[Crossref], [Web of Science ®] , [Google Scholar]), a dynamic conditional correlation model (cf. Engle, 2002 Engle, R.F. (2002). Dynamic conditional correlation: A simple class of multivariate GARCH models. J. Bus. Econ. Stat. 20(3):339–350.[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]), or a generalized dynamic conditional correlation model (cf. Capiello et al., 2006 Capiello, L., Engle, R., Sheppard, K. (2006). Asymmetric correlations in the dynamics of global equity and bond returns. J. Financial Economet. 4(4):537–572.[Crossref] , [Google Scholar]). For statistical surveillance we use control charts based on residuals. Further, the procedures are constructed for t-distribution. The detection speed of these charts is compared via Monte Carlo simulation. In the empirical study, the procedure with the best performance is applied to log-returns of the stock market indices FTSE and CAC.     

Robust test for independence in high dimensions

This article develops a new test based on Spearman’s rank correlation coefficients for total independence in high dimensions. The test is robust to the non normality and heavy tails of the data, which is a merit that is not shared by the existing tests in literature. Simulation results suggest that the new test performs well under several typical null and alternative hypotheses. Besides, we employ a real data set to illustrate the use of the new test.     

初中生学业自我概念与学业成就的相关研究

自我概念是心理学中一个非常重要的研究领域 ,具有浓厚的民族化和本土化色彩。而学业自我概念是青少年自我概念发展中的一个重要内容 ,对其学习、生活等有很大影响。本研究采用自编学业自我概念调查问卷对初中生学业自我概念与学业成就进行相关分析 ,考查学业自我概念的性别、年级特点以及不同学业成就组学生学业自我概念的特点。结果表明 :初中生学业自我概念与学业成就相关显著 ,不同学业成就组学生的学业自我概念差异显著 ;学业自我概念中的言语自我、一般自我性别差异显著 ;学业自我概念各方面年级差异显著     

棉籽壳价格变动轨迹及其对食用菌生产的影响——基于2010-2012年我国棉籽壳价格分析

分析了2010年1月至2012年2月间我国棉籽壳的价格变动轨迹以及近10年来的棉籽壳供求特征,运用灰色关联法分析棉籽壳价格变动对食用菌市场价格的影响.结果表明：我国棉籽壳价格变动幅度较大;棉籽壳处于供不应求的状态,且这种供求关系在不同地区表现出较大差异;食用菌生产对棉籽壳具有较强的依赖;棉籽壳价格波动对食用菌市场价格具有重要影响.提出了促进我国食用菌产业发展的对策建议：加大科研力度,寻找食用菌基料新配方,降低食用菌生产对棉籽壳依赖程度;建立棉籽壳交易中心,完善棉籽壳交易机制,平抑棉籽壳价格波动,保证棉籽壳供应,提高菇农收入;增加棉籽壳进口,从海外市场寻求棉籽壳供给.     

语篇衔接与英语习得程度调查分析

通过对大学非英语专业学生的英语议论文写作中使用的语篇连接词进行统计分析,探讨学习者语篇衔接意识和能力与英语习得程度的关系。研究结果发现:1)学习者使用连接词的意识和能力与他们的英语习得程度有关系;2)英语习得程度好的学习者与程度差的学习者所使用的连接词在总数和类别数量上有显著差异;3)在议论文体中所使用的连接词存在单一的特点。