约束非线性l1问题的极大熵方法

本文给出求解约束非线性l1问题的极大熵方法并证明了其收敛性。给出了极大熵与增广La grange乘子法相结合的算法 ,最后给出一个算例。     

基于2PLM项目参数估计的病态矩阵控制

病态矩阵是IRT理论中项目参数估计必然面对的问题．本文以2PLM参数估计为例．推导出参数估计迭代公式，系统阐述了三种病态控制方法的原理及在编程中数据处理的技巧。     

多出口串行生产线系统矩阵的行列单调性

研究了极大代数意义下矩阵的行列单调性质，给出了带缓冲器的多出口串行生产线系统矩阵的行列单调性．     

经济生活中的数学问题

用数学的知识与方法分析生活中的经济问题,如小本经营者的初等代数问题,股民必须具备的数学常识,彩票中的概率统计思想,打折销售中的数学运用,投资决策中微积分应用,有助于我们理解这些经济活动,找出其中的规律,并作出决策。     

具有Hardy项和Hardy-Sobolev临界指数的半线性椭圆方程正解的存在性及估计

本文用Ekeland变分原理证明了一类具Hardy项和Hardy-Sobolev临界指数的半线性椭圆方程正解的存在性,并用最大值原理对正解进行估计.     

民主问题简论

民主的原则是承认多数人的权利并按多数人的意志管理国家和社会事务,民主的目标是追求社会公正和实现社会整体利益的最大化。在现代社会,由于各种因素的制约,民主的政治实践正遭遇着诸多“尴尬”。民主目标实现的前提只能是公有制,虽然社会主义国家在民主建设过程中存在许多问题,但只要是按社会主义本质运行,按党的宗旨要求云做,我们一定会无限地接近民主的真正目标。     

Revisiting the Model Size Effect in Structural Equation Modeling

Fitting a large structural equation modeling (SEM) model with moderate to small sample sizes results in an inflated Type I error rate for the likelihood ratio test statistic under the chi-square reference distribution, known as the model size effect. In this article, we show that the number of observed variables (p) and the number of free parameters (q) have unique effects on the Type I error rate of the likelihood ratio test statistic. In addition, the effects of p and q cannot be fully explained using degrees of freedom (df). We also evaluated the performance of 4 correctional methods for the model size effect, including Bartlett’s (1950), Swain’s (1975), and Yuan’s (2005) corrected statistics, and Yuan, Tian, and Yanagihara’s (2015) empirically corrected statistic. We found that Yuan et al.’s (2015) empirically corrected statistic generally yields the best performance in controlling the Type I error rate when fitting large SEM models.     

半极大条件环的探讨

主要讨论对左(右)理想满足半极大条件的环与对零因子满足极大条件的环的性质。     

在《有机化学实验》教学中引入边缘性观念的探讨

本文探讨了在有机化学实验教学中引入边缘性条件的做法与体会.     

Brief Research Report: Bayesian Versus REML Estimations With Noninformative Priors in Multilevel Single-Case Data

Abstract

Recently, researchers have used multilevel models for estimating intervention effects in single-case experiments that include replications across participants (e.g., multiple baseline designs) or for combining results across multiple single-case studies. Researchers estimating these multilevel models have primarily relied on restricted maximum likelihood (REML) techniques, but Bayesian approaches have also been suggested. The purpose of this Monte Carlo simulation study was to examine the impact of estimation method (REML versus Bayesian with noninformative priors) on the estimation of treatment effects (relative bias, root mean square error) and on the inferences about those effects (interval coverage) for autocorrelated multiple-baseline data. Simulated conditions varied with regard to the number of participants, series length, and distribution of the variance within and across participants. REML and Bayesian estimation led to estimates of the fixed effects that showed little to no bias but that differentially impacted the inferences about the fixed effects and the estimates of the variances. Implications for applied researchers and methodologists are discussed.