美国赫蒙难民的历史与现状

上世纪中期,美国在老挝招募超过半数的赫蒙成年男性组建特别游击队,发动了秘密战争。随着美军的迅速撤退与放弃救援,赫蒙游击队的成员及其家属面临了空前的劫难,大量赫蒙人被杀,逃亡的数万名赫蒙难民被迫长期滞留于泰国的难民营,直到本世纪初才最终获得移民美国的机会,其在美国的生存与发展状况明显落后于其他族裔移民。赫蒙难民的产生及其艰难安置作为秘密战争的遗留问题,在一定程度上反映了当时美国难民政策的功利性。     

区域主导产业评价指标与数学模型

以现有研究成果为基础,建立了区域主导产业评价指标体系;基于灰色聚类的思想,提出了评价区域主导产业优度的数学模型-定权聚类评估模型,并对河南省武陟县工业主导产业选择进行了实证研究。     

艺术设计专业在线学习反馈机制探究

目前,在线学习对传统的线下学习模式产生极大冲击,因而研究在线学习为主的新型学习方式迫在眉睫.相较于文、理科专业的上课-习题的反馈机制,艺术设计专业学生面对同一知识点展开的问题思考,在设计创作中有不同的想象或联想表现形式,这意味着最佳学习活动适合并实践为一对一的互动方式.艺术设计专业的线下学习具备直观性和即时性,学习...     

网上CAI

用好现代先进技术,开展网络建设,是教育信息化的第一步。本文基于这一思想,介绍了网上CAI的设计思路,描述了该系统的功能,以及实现系统所采用的技术措施。     

用信号流图分析开关电容网络

大型开关电容（SC）网络往往是由许多SC网络级联而成的,而级联之前要对SC子网络进行优化设计,就必须得到子网络的网络函数表达式。本文介绍了一种通过观察SC网络的拓扑结构,直接得到网络的信号流图（SFG）,从而得到所需要的网络数的方法。     

远程辅助教学系统的研究

本文讨论了网上远程系统的智能系统模型和实现方法。实现教师和学生的远程交互,对学生学籍进行管理,并有测试功能便于学生自测,可记录学生的平时成绩供教师参考。     

Statistical inference for partially time-varying coefficient errors-in-variables models

This paper studies the partially time-varying coefficient models where some covariates are measured with additive errors. In order to overcome the bias of the usual profile least squares estimation when measurement errors are ignored, we propose a modified profile least squares estimator of the regression parameter and construct estimators of the nonlinear coefficient function and error variance. The proposed three estimators are proved to be asymptotically normal under mild conditions. In addition, we introduce the profile likelihood ratio test and then demonstrate that it follows an asymptotically χ²${\chi }^2$ distribution under the null hypothesis. Finite sample behavior of the estimators is investigated via simulations too.     

中国通货膨胀率的非线性动态行为研究——来自MS—ARFIMA模型的新证据

使用允许长记忆参数d服从区制转换的MS—ARFIMA模型对中国月度通货膨胀路径的动态行为进行新的实证研究,结果显示：中国通货膨胀不仅均值水平和不确定性存在着“低通胀”区制和“高通胀”区制,而且更为重要的是,通货膨胀序列的平稳性也表现出显著的区制转换动态。“低通胀”区制下,长记忆参数d1=0．361,说明通货膨胀是协方差平稳序列,“高通胀”区制下,长记忆参数d2=1．145,说明通货膨胀是非平稳序列。这一新的研究结论意味着中国通货膨胀冲击的持久性效应也存在相应的区制转移变化。这要求央行在管控通货膨胀过程中,既要考虑均值和不确定性的区制变化,又要兼顾平稳性和持久性的区制变化。     

Analyzing Binomial Data in a Split-Plot Design: Classical Approach or Modern Techniques?

Modeling data that are non-normally distributed with random effects is the major challenge in analyzing binomial data in split-plot designs. Seven methods for analyzing such data using mixed, generalized linear, or generalized linear mixed models are compared for the size and power of the tests. This study shows that analyzing random effects properly is more important than adjusting the analysis for non-normality. Methods based on mixed and generalized linear mixed models hold Type I error rates better than generalized linear models. Mixed model methods tend to have higher power than generalized linear mixed models when the sample size is small.     

Bayesian Analysis of Two-Regime Threshold Autoregressive Moving Average Model with Exogenous Inputs

We consider Bayesian analysis of threshold autoregressive moving average model with exogenous inputs (TARMAX). In order to obtain the desired marginal posterior distributions of all parameters including the threshold value of the two-regime TARMAX model, we use two different Markov chain Monte Carlo (MCMC) methods to apply Gibbs sampler with Metropolis-Hastings algorithm. The first one is used to obtain iterative least squares estimates of the parameters. The second one includes two MCMC stages for estimate the desired marginal posterior distributions and the parameters. Simulation experiments and a real data example show support to our approaches.