《统计研究》之统计研究——《统计研究》创刊三十周年回眸和展望

《统计研究》作为我国统计学界的权威学术刊物,自创刊之日起,一直不遗余力地促进着统计学科的发展,为统计科学的研究和交流提供了良好的平台,记录了经济统计和数理统计研究的发展轨迹,记载了统计学科成长、发展和壮大的历史,造就了一大批统计学术精英。30年来,《统计研究》共刊载文章4640篇,涉及统计学研究的各个领域,加快了与其他学科的融合,参与成果发表的作者单位遍布国内外,提升了《统计研究》的国际影响力;《统计研究》各项期刊科学指标发展态势良好,其质量和影响力在统计类刊物中一路领先,在经济类权威刊物中,也在起步较晚的情况下稳步发展,取得了长足的进步,在权威刊物中占据一席之地。《统计研究》应乘势而上,借助统计学成为一级学科之势,将其建成国家一级期刊。     

官方统计数据修订的国际经验

随着社会各界对统计数据越来越关注,官方统计数据的修订问题越来越重要。科学修订统计数据对于确保统计数据质量,维护统计数据信誉十分重要。本文深入分析了国外官方统计数据修订的主要原因,阐述了有关国际组织和国家统计数据修订的种类、范围、条件和程序,详细介绍了美国和澳大利亚官方统计数据修订的具体做法和经验,并得出了几点有益的启示,以期对研究制定我国统计数据修订制度提供借鉴。     

改革开放以来我国三大需求走势分析

第一次经济普查之后,国家统计局利用经济普查资料,对支出法GDP历史数据进行了系统地修订。本文利用修订后的支出法GDP历史数据,以及联合国和OECD的统计资料,对改革开放以来我国投资、消费和出口“三大需求”的变动趋势进行了国内、国际比较分析,揭示出我国经济发展过程中的一些内在特征。     

当前我国统计科学的若干重要研究领域

The paper discusses the important research areas of statistical sciences from the three aspects of official statistical work, statistical theory and methods as well as other marginal overlapping statistics.     

市场调研访问员的培训与管理

市场调研访问员的培训与管理戴亦一ＡＢＳＴＲＡＣＴＴｈｅｍａｒｋｅｔｇｕｉｄａｎｃｅｉｓａｎｉｍｐｏｒｔａｎｔｃｈａｒａｃｔｅｒｉｓｔｉｃｓｏｆｅｎｔｅｒｐｒｉｓｅｓ＇ｆｕｎｃｔｉｏｎｉｎｇｍｅｃｈａｎｉｓｍｕｎｄｅｒｍａｒｋｅｔｅｃｏｎｏｍｙ．Ｗｉｔ...     

季节调整方法的历史演变及发展新趋势

本文梳理了季节调整方法的历史演变过程,深入分析了当前季节调整方法的理论、实践的最新发展趋势,找出我国在季节调整理论研究和实践应用方面存在的差距,提出加强我国季节调整理论研究和实践应用的建议。     

A review of preliminary test-based statistical methods for the benefit of Six Sigma quality practitioners

Ever since Professor Bancroft developed inference procedures using preliminary tests there has been a lot of research in this area by various authors across the world. This could be evidenced from two papers that widely reviewed the publications on preliminary test-based statistical methods. The use of preliminary tests in solving doubts arising over the model parameters has gained momentum as it has proven to be effective and powerful over to that of classical methods. Unfortunately, there has been a downward trend in research related to preliminary tests as it could be seen from only few recent publications. Obviously, the benefits of preliminary test-based statistical methods did not reach Six Sigma practitioners as the concept of Six Sigma just took off and it was in a premature state. In this paper, efforts have been made to present a review of the publications on the preliminary test-based statistical methods. Though studies on preliminary test-based methods have been done in various areas of statistics such as theory of estimation, hypothesis testing, analysis of variance, regression analysis, reliability, to mention a few, only few important methods are presented here for the benefit of readers, particularly Six Sigma quality practitioners, to understand the concept. In this regard, the define, measure, analyze, improve and control methodology of six sigma is presented with a link of analyze phase to preliminary test-based statistical methods. Examples are also given to illustrate the procedures.     

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 review of cancer screening evaluation techniques, with some particular examples in breast cancer screening

Summary.  The instigation of mass screening for breast cancer has, over the last three decades, raised various statistical issues and led to the development of new statistical approaches. Initially, the design of screening trials was the main focus of research but, as the evidence in favour of population-based screening programmes mounts, a variety of other applications have also been identified. These include administrative and quality control tasks, for monitoring routine screening services, as well as epidemiological modelling of incidence and mortality. We review the commonly used methods of cancer screening evaluation, highlight some current issues in breast screening and, using examples from randomized trials and established screening programmes, illustrate the role that statistical science has played in the development of clinical research in this field.     

Burden of bioinformatics in medical research: Statistical perspectives and controversies

The ongoing evolution of genomics and bioinformatics has an overwhelming impact on medical and clinical research, albeit this development is often marked by genuine controversies as well as lack of scientific clarities and acumen. The search for disease genes and the gene–environment interaction has drawn considerable interdisciplinary scientific attention: environmental health, clinical and medical sciences, biological as well as computational and statistical sciences are most noteworthy. Statistical reasoning (quantitative modeling and analysis perspectives) has a focal stand in this respect while data mining resolutions are far from being statistically fully understood or interpretable. The use of human subjects, though unavoidable, under various extraneous restraints, medical ethics perspectives, and human rights undercurrents, has raised concern all over the world, especially in the developing countries. In the genomics context, clinical trials may be designed on chips and yet there are greater challenges due to the curse of dimensionality perspectives. Some of these challenging statistical issues in medical and clinical research (with emphasis on clinical trials) are appraised in the light of existing statistical tools, which are available for less complex clinical research problems.