普通高中教育投入绩效评估实证研究——基于江门市47所高中调查的多层数据

基于江门市47所高中调查的多层数据,运用多层统计分析模型,进行普通高中教育投入绩效评估的实证研究,结果表明:中考成绩与学校绩效之间呈负相关态势;高考成绩最重要的决定因素是学生先前的知识储备和起始能力水平;教育经费投入对高考成绩有显著正影响;学校师资质量对提高学生学业成就有正影响;在47所学校中,有18所学校教育绩效较好,有27所学校教育绩效还有待进一步提高,相应的教育资源的配置需要进一步优化等,并提出相应的政策建议.     

多水平模型及其在经济分析中的应用

针对具有层次或聚类数据的多水平模型能准确地反映变量间基于层次框架下的关系,并给出不同层次数据的差异性估计及跨级相关估计,为具有层次结构数据的统计建模提供了重要的研究工具,在社会学、心理学、生物医学及经济学领域具有广泛的应用价值。本文简要介绍常用的多水平线性模型和多水平Logistic模型的构建过程,重点介绍其在经济领域中的应用。同时对多水平模型的估计理论、应用软件以及发展展望进行了讨论。     

多水平模型及其在经济分析中的应用

针对具有层次或聚类数据的多水平模型能准确地反映变量间基于层次框架下的关系,并给出不同层次数据的差异性估计及跨级相关估计,为具有层次结构数据的统计建模提供了重要的研究工具,在社会学、心理学、生物医学及经济学领域具有广泛的应用价值。本文简要介绍常用的多水平线性模型和多水平Logistic模型的构建过程,重点介绍其在经济领域中应用。同时对多水平模型的估计理论,应用软件以和发展展望进行了讨论。     

基于动态数据驱动的改进灰色马尔科夫模型黄金价格预测

将黄金数据的尖峰厚尾、异方差性及杠杆效应等统计特征与马尔科夫概率转移矩阵所具有的动态变化规律结合,提出一种改进的灰色马尔科夫模型.模型首先对数据进行统计分析,建立相应的概率统计模型并用此模型对系统发展变化趋势进行拟合.在拟合序列的基础上利用马尔科夫链的动态转移变化建立状态转移概率矩阵,采用动态数据驱动原理对未来每一步数据进行动态预测.模型既是统计方法与数据动态驱动的结合,克服了传统的灰色马尔科夫模型中对数据内在统计规律的忽视,实证表明其预测精度较灰色马尔科夫模型预测高,具有较好的实用性.     

投入具有交互作用的数据包络分析

利用Choquet积分作为累加算子引进了一种新的数据包络分析(DEA)模型,该模型可以对投入具有交互作用的投入、产出的绩效问题给出更好的评价,而经典的DEA模型是这种新模型的特殊形式,最后通过实例验证新模型的有效性。     

基于高层次结构数据的多水平模型贝叶斯推断及应用

面对具有多层次嵌套结构的数据,构建多水平模型是统计建模的一个重要研究课题。经典的参数估计方法主要采用极大似然估计法(ML),然而当面对高层数量单位小或数据结构不平衡时,极大似然估计在估计精度上存在一定不足;而贝叶斯方法充分应用了有效的先验信息,可以弥补其不足。本文在高层次结构数据多水平模型的研究基础上,探索高层次结构数据的多水平模型贝叶斯推断理论,并以云南省红河州农户收入数据作实证分析,建立了基于县-村-户嵌套结构的农户收入影响因素多水平模型,对比分析模型参数的ML估计、经验贝叶斯(EB-ML)估计和完全贝叶斯估计,从而充分展现了高层次结构数据多水平模型的完全贝叶斯推断方法,在拟合高层数量单位小或数据不平衡时具有的特征和优势。     

碳减排政策下多周期供应链网络均衡决策

为研究碳减排政策对多周期供应链网络均衡决策的影响,分析了供应链网络结构中各层的最优条件,建立了多周期碳减排供应链网络均衡模型.首先将其转化为等价的变分不等式问题,然后利用变分不等式的投影收缩算法进行求解.并通过模型仿真分析了在不同周期下不同碳限额、单位碳排放量对供应链网络均衡的影响结果发现企业在环境绩效和经济绩效之间存在冲突,适当的控制碳税和调整产品的单位碳排放量可以缓解这种冲突.同时,政府对于碳限额的值过于宽松,对于碳减排的实施起不到明显作用.     

空间误差模型的多个异常值得分检验

本文研究了空间误差模型(SEM)中多个异常值的检验问题,基于均值漂移模型和方差加权模型这两种异常值模型给出了得分检验统计量的具体形式及其渐近分布。并应用实例分析验证了检验统计量的有效性,最后给出了修正模型的方法。     

线性回归模型多个离群点的向前逐步诊断方法

当线性回归模型中存在多个离群点时,经典的诊断方法常常因掩盖和淹没现象而失效,导致模型误用。针对此问题,本文在回顾有关文献的基础上,将稳健回归技术与经典诊断量相结合,提出一种向前逐步诊断方法。通过对模拟数据的分析,说明该法可有效地识别回归数据中潜在的离群点,并作正式的统计检验。     

基模生成集分析的矩阵算法及在人力资源管理中的应用

针对学习型组织理论的系统思考的基模分析技术,提出一个确定复杂系统的基模生成集和对新管理措施进行反馈绩效分析的矩阵算法.此算法是将系统动力学的流率基本入树模型转化为对角置零枝向量矩阵,将枝向量矩阵分解,再作矩阵乘法.然后,用一个管理案例说明了该算法的实际价值,即运用此方法,对中国企业现阶段普遍实行的绩效等级薪酬人力资源管理方法进行研究,揭示了在人力资源管理中引入绩效等级薪酬激励机制后产生的增长上限系统结构,证明了此管理对内部员工和吸引外系统员工流入具有很好的激励作用,同时证明此管理机制增加了组织成本,产生对组织绩效的制约作用,从而揭示出企业人力资源绩效管理中的增长上限系统结构.     

Methodological Issues in Studying Effects of Networks in Organizations

Three methodological issues are discussed that are important for the analysis of data on networks in organizations. The first is the two-level nature of the data: individuals are nested in organizations. This can be dealt with by using multilevel statistical methods. The second is the complicated nature of statistical methods for network analysis. The third issue is the potential of mathematical modeling for the study of network effects and network evolution in organizations. Two examples are given of mathematical models for gossip in organizations. The first example is a model for cross-sectional data, the second is a model for longitudinal data that reflect the joint development of network structure and individual behavior tendencies.     

Fast Inference for the Latent Space Network Model Using a Case-Control Approximate Likelihood

Multilevel modeling is a popular statistical technique for analyzing data in hierarchical format, and thus naturally fits within a distributed database framework. We consider the computational aspects of multilevel modeling across distributed databases. In addition, we consider these aspects under a generalization of the multilevel model where the distributed groups (or databases) are allowed to specify different models at both level-1 (individual) and level-2 (group). For a variety of scenarios, we develop the distributed computation algorithm for two-step least squares (LS) estimators and also for iterative MLE estimators of the parameters of interest; in particular, we determine the required data structure at each computing site, the necessary information (original data, cross-product matrices, coefficient vectors), and the order in which such information needs to be passed between sites. Finally, we discuss recursive updating, fault tolerance, and security issues.     

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??In this paper, the multivariate linear statistical method is applied to research the undergraduate grades of students from the school of mathematics in Hefei University of Technology, and explore the impact on the later achievement by the early stage of achievement from all undergraduate courses. First, we get the main components from the previous courses by principal component analysis, then construct a linear regression model between the later achievement and main components by the stepwise regression method. Next, a linear regression model between the later achievement and the early stage of achievement from all undergraduate courses is constructed by Adaptive-Lasso method. Finally, comparative analysis is performed for the result of the above models. The research shows that the principal component regression model based on the Adaptive-Lasso method can well fit the later achievement, and give a reasonable explanation for the later academic performance.     

基于纵向数据的半参数变系数部分线性回归模型

纵向数据是数理统计研究中的复杂数据类型之一0,在生物、医学和经济学中具有广泛的应用．在实际中经常需要对纵向数据进行统计分析和建模．文章讨论了纵向数据下的半参数变系数部分线性回归模型,这里的纵向数据的在纵向观察在时间上可以是不均等的,也可看成是按某一随机过程来发生．所研究的半参数变系数模型包括了许多半参数模型,比如部分线性模型和变系数模型等．利用计数过程理论和局部线性回归方法,对于纵向数据下半参数变系数进行了统计推断,给出了参数分量和非参数分量的profile最小二乘估计,研究了这些估计的渐近性质,获得这些估计的相合性和渐近正态性．     

Asymptotic robustness of standard errors in multilevel structural equation models

Data in social and behavioral sciences are often hierarchically organized. Multilevel statistical methodology was developed to analyze such data. Most of the procedures for analyzing multilevel data are derived from maximum likelihood based on the normal distribution assumption. Standard errors for parameter estimates in these procedures are obtained from the corresponding information matrix. Because practical data typically contain heterogeneous marginal skewnesses and kurtoses, this paper studies how nonnormally distributed data affect the standard errors of parameter estimates in a two-level structural equation model. Specifically, we study how skewness and kurtosis in one level affect standard errors of parameter estimates within its level and outside its level. We also show that, parallel to asymptotic robustness theory in conventional factor analysis, conditions exist for asymptotic robustness of standard errors in a multilevel factor analysis model.     

Relationships between emotional dispositions and mathematics achievement moderated by instructional practices: analysis of TIMSS 2015

ABSTRACT

This research is a secondary analysis with Korean students’ data collected in the TIMSS 2015 to describe the moderation effects of instructional practices on the relationships between students’ emotional dispositions toward mathematics and mathematics achievement. From the TIMSS 2015 database, we collected mathematics achievement scores, a student-level contextual scale for students’ emotional disposition, and teacher-level contextual scales representing teachers’ instructional practices. We applied hierarchical linear modelling to construct multilevel models. The findings showed that the achievement gap between emotional dispositions – like and dislike – became smaller when teachers more frequently implemented certain instructional practices like asking students to complete challenging exercises, decide their own problem-solving procedures, and express their ideas in class. Students who disliked mathematics were likely to have higher scores as their teachers implemented each of those practices more frequently. Findings provide important implications to teachers regarding: It is important to encourage students to reason through instructional practices like asking them to decide their own problem-solving procedures and to solve challenging problems.     

物质资本、人力资本、就业结构与西部民族地区农户收入增长

本文基于西部少数民族地区农户收入的微观数据,探讨了利用多水平模型分析具有分层结构数据的统计建模和估计问题,并从物质资本、人力资本、就业结构三个方面研究了影响农村农户收入及其增长的因素及其特征。通过对所得结果的分析,提出了西部少数民族地区农村经济发展的相关政策建议。     

Composite hierachical linear quantile regression

Multilevel （hierarchical） modeling is a generalization of linear and generalized linear modeling in which regression coefficients are modeled through a model, whose parameters are also estimated from data. Multilevel model fails to fit well typically by the use of the EM algorithm once one of level error variance （like Cauchy distribution） tends to infinity. This paper proposes a composite multilevel to combine the nested structure of multilevel data and the robustness of the composite quantile regression, which greatly improves the efficiency and precision of the estimation. The new approach, which is based on the Gauss-Seidel iteration and takes a full advantage of the composite quantile regression and multilevel models, still works well when the error variance tends to infinity, We show that even the error distribution is normal, the MSE of the estimation of composite multilevel quantile regression models nearly equals to mean regression. When the error distribution is not normal, our method still enjoys great advantages in terms of estimation efficiency.     

Strong model dependence in statistical analysis: goodness of fit is not enough for model choice

Most statistical methods are based on models, but most practical applications ignore the fact that the results depend on the model as well as on the data. This paper examines the size of this model dependence, and finds that there can be very considerable variation between the results of fitting different models to the same data, even if the models being considered are restricted to those which give an acceptable fit to the data. Under reasonable regularity conditions, we show that different empirically acceptable models can give rise to non-overlapping confidence intervals for the same parameter. Application papers need to recognize that the validity of conventional statistical results rests on the assumption that the underlying model is known to be correct, and that this is a much stronger requirement than merely confirming that the model gives a good fit to the data. The problem of model dependence is only partially resolved by using formal methods of model selection or model averaging.