中科院院士陈希孺教授作学术报告

20 0 0年 7月 17日 ,山西省统计局、山西省统计学会邀请中国科学院院士陈希孺教授来山西省统计局机关为全局干部作了题为“统计学的发展与展望”的学术报告。陈教授首先简要介绍了数理统计、社会经济统计及现场统计三者的主要区别 ,指出数理统计是一种工具而不是一种流派。之后 ,陈教授着重从数理统计的基本特点、社会经济统计的发展受益于数理统计学科的发展和应用、数理统计的发展历史和趋势等三个层面讲述了数理统计与社会经济统计、部门统计的密切联系 ,指出了“大型、复杂数据”、“复杂现实的统计模型”、“统计与其它学科的交叉”、…     

拓广数理统计方法的应用——“数理统计在社会经济领域中的应用”科学讨论会在沪举行

中国统计学会和上海市统计学会于1982年8月23日至28日在上海联合召开了“数理统计在社会经济领域中的应用”科学讨论会,23个省市自治区的社会经济统计与数理统计理论工作者和实际工作者共62人参加了会议.     

略谈社会经济统计学的发展

19世纪中叶以后的100多年,是社会经济统计学成熟和发展的阶段。首先,涌现了一大批政府统计学家兼社会经济统计学家,促进了社会调查和社会研究的发展。社会调查和社会研究的紧密结合,促进了社会经济统计学的发展。他们以研究社会现象的数量方面为主,把大量观察法作为统计的基本方法,此外,研究目的从探索社会现象中的统计规律,向提供研究社会现象统计方法的转移,并逐渐主张采用抽样法,把某些数理统计方法引入社会统计学     

峨眉会议开创了统计学界各学科共同发展的新时期

在１９７８年冬峨眉会议解放思想、酝酿准备的基础上，１９７９年１１月成立了中国统计学会。我们二人作为数理统计学界的代表被选入学会领导机构。从此，开始了新中国社会经济统计学界与数理统计学界携手合作、共同发展的新时期。改革开放之前，我国统计学界存在一个严重...     

努力实现两种方法、两支队伍的融合

数理统计在社会经济领域中是一种有用的方法,但因长期以来的闭塞排外,把数理统计视之为西方资产阶级的数字游戏,否认它在社会经济统计领域的作用,对提高我国统计工作     

征集提交国际统计学会第四十三届会议的论文的启事

国际统计学会第四十二届会议,曾初步决定一九八一年冬季在阿根廷召开第四十三届会议.我们准备在这次会议上提出若干篇理论联系实际、在某一方面有新的研究成果、具有国际水平的论文(包括社会经济统计的和数理统计的).欢迎统计理论工作者和实际工作者踊跃撰写,以便择优选用.论文篇幅一般不超过六千字.请于一九八一年二月底以前一式二份,分别函寄本省、     

关于社会经济统计学三个基本理论问题的再认识

关于社会经济统计学三个基本理论问题的再认识杨立勋目前，理论界又一次展开了关于统计学科体系建设的广泛讨论，其中，对社会经济统计学的讨论成为近期焦点之一。以理论界看，绝大部分学者认为社会经济统计学的体系不完善，部分内容与数理统计相重复，而且有部分内容过于...     

杂谈统计与普查

统计杂议 关于对统计的定义和理解每个人都有不同的解读.按照通常的分类,统计一般被分为数理统计、生物统计(或称生化统计)和社会经济统计.通常我们一般意义上所说的统计是指社会经济统计.     

第五次全国中青年统计科学研讨会征文通知

第五次全国中青年统计科学研讨会征文通知中国统计学会、国家统计局统计科学研究所和厦门大学拟于１９９５年联合举办第五次全国中青年统计科学研讨会。这次会议的主要目的是：适应统计学上升为独立的一级学科后提出的建立＂大统计＂的要求，寻求社会经济统计学与数理统计...     

统计教育改革刍议

一、社会发展对统计人才的新要求 技术进步使得统计这种研究各种经济、社会、自然现象总体性质的科学方法应用更加广泛,它在数据分析、信息处理、情况判断、决策支持中的重大作用在知识经济中愈益凸现.发达国家的政府部门、工商企业、科研机构均十分重视统计工作,其统计从业人员素质很高,先进的抽样调查、信息处理,统计电算技术和数理统计理论的结合使得他们具有强大的数据检索、统计分析和科学判断能力,从而大大促进了西方国家政府和企业的决策科学化,加强了其竞争优势地位.     

治学报国:民国时期的统计留学生

民国时期统计留学生主要求学地分布在英美等国,其中既有专门研究数理统计方法的许宝騄等人,也有利用统计学方法研究生物学、经济学、教育学、社会学、心理学等问题的朱君毅、陈达、吴定良等人。这批统计留学生求学于当时的世界统计中心或顶尖大学,其中不乏令世界统计学界为之瞩目的杰出人才,以相对较小的规模取得了比肩国际一流水平的学术成就,为世界统计学发展贡献了中国智慧。他们的教育背景和学术水平,奠定了我国近代统计教育的较高起点和坚实基础,值得学术界深入挖掘和研究。其学术报国的家国情怀,至今仍是激励后辈学人的精神力量。     

Tests of serial independence based on Kendall's process

The authors propose new rank statistics for testing the white noise hypothesis in a time series. These statistics are Cramér‐von Mises and Kolmogorov‐Smirnov functionals of an empirical distribution function whose mean is related to a serial version of Kendall's tau through a linear transform. The authors determine the asymptotic behaviour of the underlying serial process and the large‐sample distribution of the proposed statistics under the null hypothesis of white noise. They also present simulation results showing the power of their tests.     

Optimization in statistics: an overview

This note presents a brief overview of the applications of optimization techniques in statistics. Optimizing techniques are broadly classified as classical, variational, numerical, and mathematical programming. A brief discussion of their applications to statistics is given, and a few references are provided for illustrative purposes.     

In Memoriam: Emeritus Professor Henry Oliver Lancaster, AO FAA 1 February 1913 – 2 December 2001

The death, in Sydney, of Oliver Lancaster marks the end of an era in the histories of the Statistical Society of Australia, which (in its previous existence as the Statistical Society of New South Wales) he helped found in 1947, and of the Australian Journal of Statistics of which he was founding editor (1959–1971). Oliver Lancaster was Foundation Professor of Mathematical Statistics at the University of Sydney (1959–1978), where he spent his life as student and academic. During his academic career, he achieved scholarly distinction in at least four fields: mathematical statistics, medical and public health statistics, the history of medicine and of statistics, and statistical bibliography. With E.J.G. Pitman (1897–1993), M.H. Belz (1897–1975), E.A. Cornish (1909–1973) and P.A.P. Moran (1917–1988) he was part of a cohort of renowned Australian mathematical statisticians who laid the foundation of the glory days of Australian mathematical statistics. This obituary and tribute focuses on some of these aspects, within a broader historical picture.     

Elementary statistics laboratory

Students in elementary statistics traditionally see experiments and data as words and numbers in a text. They receive little exposure to the important statistical activities of sample selection, data collection, experimental design, development of statistical models, the need for randomization, selection of factors, etc. They often leave the first course without a firm understanding of the role of applied statistics or of the statistician in scientific investigations. In an attempt to improve elementary statistics education, we have developed a statistics laboratory similar to those of other elementary science courses. We will discuss our experiences in teaching a laboratory component with the traditional elementary statistics course. In each lab session, students, working in teams, discuss the design of an experiment, carry out the experiment, and analyze their data using Minitab on a Macintosh or MS-DOS based computer. The students then individually either answer a series of short answer questions or write a formal scientific report. The labs are designed to be relatively inexpensive and portable. They do not require a prior background in science, statistics or computing.     

Martingales without tears

This pedagogical paper presents a casual introduction to martingales, or fair gambling processes. Our objective is to describe the concept of a martingale and its application to common statistical tests used in the analysis of survival data, but without the mathematical rigor required for formal proofs.We use heuristic arguements to demonstrate that the logrank statistic evaluated over followup time is a fair gambling process, and introduce some mathematical notation and terminology along the way. We then employ the counting process approach to show that the logrank statistic computed over followup time can be expressed as the difference of two martingale transforms, and thus is a martingale. These ideas are first introduced in the context of a discrete time process, and are then generalized to a continuous time process. With slight modifications, the same idea extends from the logrank to other weighted Mantel-Haenszel statistics computed over time.     

社会经济统计学与数理统计学的比较

多年来,在统计学界关于社会经济统计学与数理统计学的“是非”问题一直争论不休,本文通过对这两种统计学在研究对象、研究范围和内容,以及二者的发展与创新机理和研究理论几个方面的比较,阐述了保持两门统计学并存的必要性。     

Kendall's tau for serial dependence

The authors show how Kendall's tau can be adapted to test against serial dependence in a univariate time series context. They provide formulas for the mean and variance of circular and noncircular versions of this statistic, and they prove its asymptotic normality under the hypothesis of independence. They present also a Monte Carlo study comparing the power and size of a test based on Kendall's tau with the power and size of competing procedures based on alternative parametric and nonparametric measures of serial dependence. In particular, their simulations indicate that Kendall's tau outperforms Spearman's rho in detecting first‐order autoregressive dependence, despite the fact that these two statistics are asymptotically equivalent under the null hypothesis, as well as under local alternatives.     

Testing the homogeneity of several two‐parameter populations

The authors extend Fisher's method of combining two independent test statistics to test homogeneity of several two‐parameter populations. They explore two procedures combining asymptotically independent test statistics: the first pools two likelihood ratio statistics and the other, score test statistics. They then give specific results to test homogeneity of several normal, negative binomial or beta‐binomial populations. Their simulations provide evidence that in this context, Fisher's method performs generally well, even when the statistics to be combined are only asymptotically independent. They are led to recommend Fisher's test based on score statistics, since the latter have simple forms, are easy to calculate, and have uniformly good level properties.     

Current challenges in statistics: large-lecture courses

Each semester approximately 80% of the students taught by the Florida State University Department of Statistics are enrolled in STA 3014: Fundamental Business Statistics. During the academic year this course is taught in large lecture sections of 250 students each. It is either the only statistics course or one of two statistics courses taken in their undergraduate career for probably 90% of these students. A similar situation exists in many statistics departments around the nation.

These large introductory courses offer us the opportunity to introduce the power of statistics to a large fraction of our future business leaders. In the past it appears that this opportunity has often been missed. In fact, some suggest that these courses help contribute to the general public's negative attitude toward statistics courses, the discipline of statistics, and statisticians.

Hence, I propose that one of the current challenges in statistics is the challenge of improving the quality of these courses so that statistics may contribute to the improvement of quality and productivity in the United States, a vital national issue. In this paper I report on my experiences in grappling with this challenge in STA 3014.