对《国际服务贸易统计制度》中服务贸易统计框架的分析与评价

 2010年8月1日新版《国际服务贸易统计制度》开始在中国正式实施,我国的服务贸易统计框架从制度上得以确定。本文首先对《制度》所构建的服务贸易统计框架进行介绍,然后通过与《国际服务贸易统计手册》的比较,找出其与《手册》相关建议的差异之处并结合我国服务贸易统计存在的问题提出相应的建议。     

太原理工大学学报（社会科学版）2006～2010年载文及被引分析

以中国知网（CNKI）中国学术文献网络出版总库为主要统计源,对太原理工大学学报（社会科学版）2006-2010年五年内发表的学术论文,从发文总量、论文被引频次、下载频次、基金项目、被引频次分布、栏目分布、作者单位等方面进行统计分析,以期客观反映本刊的论文产出情况及学术影响力,并为该刊的管理和质量的提高,以及发展提供参考和依据。     

约翰·霍普金斯大学发展探析

约翰·霍普金斯大学是美国第一所现代研究型大学。约翰·霍普金斯大学的发展经历了最初创办的成功、中期的迟滞和后期的崛起三个阶段。其最初成功的原因是重视学术自由、办学质量和学术研究;中期迟滞的原因是严重的财政危机、竞争力的丧失和社会适应性差;后期崛起的原因是坚持研究创新的办学理念、高效的组织管理结构和把握全球化带来的机遇。     

A modified two-sample t-test based on permutation method for large-scale data

In large-scale data, for example, analyzing microarray data, which includes hypothesis testing for equality of means in order to discover differentially expressed genes, often deals with a large number of features versus a few number of replicates. Furthermore, some genes are differentially expressed and some others not. Thus, a usual permutation method, which is applied facing these situations, estimates the p-value poorly. This is because two types of genes are mixed. To overcome this obstacle, the null permutation samples are suggested in the literatures. We propose a modified uniformly most powerful unbiased test for testing the null hypothesis.     

Combination tests for the equality of the covariance matrices of two dependent bivariate normals

To test the equality of the covariance matrices of two dependent bivariate normals, we derive five combination tests using the Simes method. We compare the performance of these tests using simulation to each other and to the competing tests. In particular, simulations show that one of the combination tests has the best performance in terms of controlling the type I error rate even for small samples with similar power compared to other tests. We also apply the recommended test to real data from a crossover bioavailability study.     

政府统计与统计教育

通过对改革开放30年来政府统计对大学毕业生专业、学历层次及毕业院校需求的结构分析,以及当前社会关注的统计热点问题的讨论,反思统计教育存在的问题,并提出提高统计教育质量的建议。     

稳健因子分析方法的构建及比较研究

由于传统因子分析方法对离群值较敏感,导致计算结果与实际不相符。针对这一现象,本文运用FAST-MCD方法对传统因子分析方法进行改进,构建出因子分析的稳健算法,以克服离群值的影响,并对此方法进行了模拟和实证分析。模拟和实证分析结果均表明：因子旋转前后,当数据中不存在离群值时,传统因子分析与稳健因子分析得到的结果基本保持一致;当数据中存在离群值时,运用传统因子分析得到的结果出现较大变化,而运用稳健因子分析方法得到的结果基本不变,这说明相对于传统因子分析方法,稳健因子分析方法能有效抵抗离群值的影响,具有良好的抗干扰性和高抗差性。     

Sharp upper mean-variance bounds for trimmed means from restricted families

We apply the method of projection to derive sharp upper mean-variance bounds on trimmed means of order statistics of i.i.d. samples coming from restricted families of probability measures. Two families are considered: distributions with decreasing density and decreasing failure rate. We also present some numerical results.     

Use of Dummy Variables in Testing for Equality between Sets of Coefficients in Two Linear Regressions: A Note

Elementary inductive proofs are presented for the binomial approximation to the hypergeometric distribution, the density of an order statistic, and the distribution of when X ₁, ···, X _n are a sample from N (μ, 1).     

Practical Point Estimation from Higher-Order Pivots

Point estimators for a scalar parameter of interest in the presence of nuisance parameters can be defined as zero-level confidence intervals as explained in Skovgaard (1989). A natural implementation of this approach is based on estimating equations obtained from higher-order pivots for the parameter of interest. In this paper, generalising the results in Pace and Salvan (1999) outside exponential families, we take as an estimating function the modified directed likelihood. This is a higher-order pivotal quantity that can be easily computed in practice for a wide range of models, using recent advances in higher-order asymptotics (HOA, 2000). The estimators obtained from these estimating equations are a refinement of the maximum likelihood estimators, improving their small sample properties and keeping equivariance under reparameterisation. Simple explicit approximate versions of these estimators are also derived and have the form of the maximum likelihood estimator plus a function of derivatives of the loglikelihood function. Some examples and simulation studies are discussed for widely-used model classes.