中国资本外逃的测算与防范

资本外逃是金融危机产生的催化剂与蔓延的助动器 ,对一国经济安全构成极大威胁。通过介绍资本外逃及其测算模型 ,运用间接法对我国资本外逃进行估算 ,结果显示中国存在严重的资本外逃现象。在详细分析中国资本外逃的产生原因之后 ,具体提出了一些相关防范对策     

普通话对英语语音学习的正迁移作用辨析

语音迁移是第二语言习得过程中较突出的迁移现象.以语言迁移理论为理论基础,结合实证研究分析普通话对英语语音学习的正迁移作用,提出尽快提高学习者普通话水平、充分发挥正迁移效应、客观看待负迁移作用是提高英语语音教学效果的有效途径.     

根数错位致斗转星移——薛凤祚《历学会通》的历元换算

薛凤祚在《历学会通》中所给出的中西历元纪年出现了整体错位和局部混乱,由根数错位而致斗转星移。实难想像其天文和历算结果能够被观测所证实或验证,而况薛凤祚从来不作天文和天象观测。他的随意和著文之粗糙、混乱,使他耗费半生精力所作的西学引进、译介的成果黯然失色。这也许正是他被同时代人忽略,继而又被历史遗忘的根本原因之一。     

有错检验情况下标准型抽样方案的设计

本文给出了在有错检验情况下标准型抽样方案的设计方法，讨论了误检概率ｐ，ｐ′对抽样方案的影响，并给出抽样方案的实际应用。     

再论毛泽东在社会主义建设史上失误原因

在社会主义建设的探索中,没有搞清什么是社会主义和怎样建设社会主义是毛泽东发生失误的根本原因;民主集中制的破坏是毛泽东发生失误的重要原因;对国际、国内政治局势判断失误是其探索发生失误的直接原因.     

喀麦隆汉法语音对比在对外汉语教学中的应用

本文通过笔者在喀麦隆汉语教学中体验较深的教学实例，主要从语音教学方面对汉语和法语进行了对比分析，提出了相应的语音教学策略，并从教师素质及教学方法方面对对外汉语教学工作提出了两条建议。     

Prediction in Some Poisson Errors in Variables Models

Predictive distributions are developed and illustrated for prediction in some Poisson errors in variables models. Two different situations in which multiplicative treatment effects are appropriate are considered within the context of predicting counts of road accidents. Hierarchical prior structures are investigated, and numerical integration and Gibbs sampling routines are used to derive the predictive and posterior probabilities. Examples of analyses are provided with data from road accidents in Sweden.     

On testing random effects in the nested-error regression model

We derive an exact F-test for random effects in the nested-error regression model. The derivation utilizes a matrix decomposition that offers a transformation of the response vector into two independent subvectors. When the random effects are absent, the test statistic reduces to a ratio of two independent residual sums of squares that are computed by fitting a regression model using each subvector. A small simulation study compares the power of the F-test with various recent tests and shows that the proposed test has a competitive performance under small as well as large number of clusters.     

Estimating nonlinear additive models with nonstationarities and correlated errors

In this paper, we study a nonparametric additive regression model suitable for a wide range of time series applications. Our model includes a periodic component, a deterministic time trend, various component functions of stochastic explanatory variables, and an AR(p) error process that accounts for serial correlation in the regression error. We propose an estimation procedure for the nonparametric component functions and the parameters of the error process based on smooth backfitting and quasimaximum likelihood methods. Our theory establishes convergence rates and the asymptotic normality of our estimators. Moreover, we are able to derive an oracle‐type result for the estimators of the AR parameters: Under fairly mild conditions, the limiting distribution of our parameter estimators is the same as when the nonparametric component functions are known. Finally, we illustrate our estimation procedure by applying it to a sample of climate and ozone data collected on the Antarctic Peninsula.     

The effects of data input errors on subsequent statistical inference

Data input errors can potentially affect statistical inferences, but little research has been published to date on this topic. In the present paper, we report the effect of data input errors on the statistical inferences drawn about population parameters in an empirical study involving 280 students from two Polish universities, namely the Warsaw University of Life Sciences – SGGW and the University of Information Technology and Management in Rzeszow. We found that 28% of the students committed at least one data error. While some of these errors were small and did not have any real effect, a few of them had substantial effects on the statistical inferences drawn about the population parameters.