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。     

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.     

Abandon Statistical Significance

ABSTRACT

We discuss problems the null hypothesis significance testing (NHST) paradigm poses for replication and more broadly in the biomedical and social sciences as well as how these problems remain unresolved by proposals involving modified p-value thresholds, confidence intervals, and Bayes factors. We then discuss our own proposal, which is to abandon statistical significance. We recommend dropping the NHST paradigm—and the p-value thresholds intrinsic to it—as the default statistical paradigm for research, publication, and discovery in the biomedical and social sciences. Specifically, we propose that the p-value be demoted from its threshold screening role and instead, treated continuously, be considered along with currently subordinate factors (e.g., related prior evidence, plausibility of mechanism, study design and data quality, real world costs and benefits, novelty of finding, and other factors that vary by research domain) as just one among many pieces of evidence. We have no desire to “ban” p-values or other purely statistical measures. Rather, we believe that such measures should not be thresholded and that, thresholded or not, they should not take priority over the currently subordinate factors. We also argue that it seldom makes sense to calibrate evidence as a function of p-values or other purely statistical measures. We offer recommendations for how our proposal can be implemented in the scientific publication process as well as in statistical decision making more broadly.     

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.     

Effect magnitude: A different focus

The role of measures of effect magnitude in the research enterprise is examined. Measures of effect magnitude are used for four purposes: (a) to estimate the sample size required to achieve an acceptable power, (b) to integrate the results of empirical research studies in meta-analyses, (c) to supplement the information provided by null hypothesis significance tests, and (d) to determine whether research results are practically significant. The advantage of focusing on effect magnitude and practical significance instead of statistical significance and p values is illustrated with an example.     

The World of Research Has Gone Berserk: Modeling the Consequences of Requiring “Greater Statistical Stringency” for Scientific Publication

ABSTRACT

In response to growing concern about the reliability and reproducibility of published science, researchers have proposed adopting measures of “greater statistical stringency,” including suggestions to require larger sample sizes and to lower the highly criticized “p?<?0.05” significance threshold. While pros and cons are vigorously debated, there has been little to no modeling of how adopting these measures might affect what type of science is published. In this article, we develop a novel optimality model that, given current incentives to publish, predicts a researcher’s most rational use of resources in terms of the number of studies to undertake, the statistical power to devote to each study, and the desirable prestudy odds to pursue. We then develop a methodology that allows one to estimate the reliability of published research by considering a distribution of preferred research strategies. Using this approach, we investigate the merits of adopting measures of “greater statistical stringency” with the goal of informing the ongoing debate.     

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.