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.     

Investor commitment to serial entrepreneurs: A multilayer network analysis

Social networks are complex systems composed of interdependent organizations and people with diverse network structures. Understanding network dynamics, such as exchange commitment, requires a methodological toolkit that does not assume away complexity. In this study, we extend a technique for analyzing longitudinal, multilayer network data called network alignment. We introduce a novel metric – intersect proportions – for analyzing similarity between divergent graphs. We demonstrate the application of network alignment and intersect proportions to the context of investor commitment to startups and entrepreneurs. Using this technique, we are able to disentangle exchange commitment across complex networks.     

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。     

运动对大鼠骨量和力学性能影响的相关分析

探讨运动对去卵巢大鼠腰椎骨量和生物力学性能指标影响的关系.72只3月龄雌性SD大鼠去卵巢分成:正常对照组(SHAM)、骨质疏松对照组(OVX)、雌激素对照组(OVX ES)、低强度运动组(OVX EX1)、低强度运动加雌激素组(OVX EX1 ES)、中等强度运动组(OVX EX2)、中等强度运动加雌激素组(OVX EX2 ES)、大强度运动组(OVX EX3)、大强度运动加雌激素组(OVX EX3 ES),研究8周的1 h/d,5 d/w,低强度(8 m/min)、中等强度(18 m/min)和大强度(28 m/min)跑台运动,测定大鼠第4腰椎(L4)松质骨骨量、第2腰椎(L2)和第3腰椎(L3)的生物力学性能并分析指标之间的相关性.结果显示(1)腰椎力学指标与腰椎的骨小梁面积百分比(%TB.AR)和骨小梁厚度(TB.TH)成线性正相关,与骨小梁分离度(TB.SP)成线性负相关.(2)运动强度与去卵巢未补充雌激素组骨量指标显示了正或负线性相关关系,而与去卵巢后补充雌激素组的骨量指标间无相关关系.(3)去卵巢后未补充雌激素组的运动强度与两项力学指标值均有线性正相关关系;去卵巢后补充雌激素组的运动强度与L3最大压缩载荷成线性正相关关系.运动对于骨骼力学结构产生良好的作用,优于运动对于骨量产生的作用.     

阅读能力与听解能力相关性刍议

听解与阅读都是获取信息的渠道,同时也是二语学习者学习英语的主要途径。从大学英语教学的现状出发,以普通非英语专业大学生的测试成绩验证阅读能力与听解能力之间的相关关系,分析心理语言学的理论基础,进而说明如何利用两者的相关关系来提高学习者的听解能力,并提出了几点看法。