非统计专业统计学教育刍议

针对非统计专业统计学教育目前存在的问题,阐述了提高对统计学重要性的认识是保证统计教学效果的前提;统计学教学环节的改革是提高教学效果的重要保证;统计教师的业务素质是提高统计教学效果的关键。     

Divergence-based estimation and testing with misclassified data

The well-known chi-squared goodness-of-fit test for a multinomial distribution is generally biased when the observations are subject to misclassification. In Pardo and Zografos (2000) the problem was considered using a double sampling scheme and ø-divergence test statistics. A new problem appears if the null hypothesis is not simple because it is necessary to give estimators for the unknown parameters. In this paper the minimum ø-divergence estimators are considered and some of their properties are established. The proposed ø-divergence test statistics are obtained by calculating ø-divergences between probability density functions and by replacing parameters by their minimum ø-divergence estimators in the derived expressions. Asymptotic distributions of the new test statistics are also obtained. The testing procedure is illustrated with an example.     

A Semi-Parametric Quantile Function Estimator for Use in Bootstrap Estimation Procedures

In this note we develop a new quantile function estimator called the tail extrapolation quantile function estimator. The estimator behaves asymptotically exactly the same as the standard linear interpolation estimator. For finite samples there is small correction towards estimating the extreme quantiles. We illustrate that by employing this new estimator we can greatly improve the coverage probabilities of the standard bootstrap percentile confidence intervals. The method does not reqiure complicated calculations and hence it should appeal to the statistical practitioner.     

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.     

Expected sample moments of concomitants of selected order statistics

In this paper, the task of determining expected values of sample moments, where the sample members have been selected based on noisy information, is considered. This task is a recurring problem in the theory of evolution strategies. Exact expressions for expected values of sums of products of concomitants of selected order statistics are derived. Then, using Edgeworth and Cornish-Fisher approximations, explicit results that depend on coefficients that can be determined numerically are obtained. While the results are exact only for normal populations, it is shown experimentally that including skewness and kurtosis in the calculations can yield greatly improved results for other distributions.     

A relation for the covariances of order statistics from n independent and non-identically distributed random variables

We derive a simple relation satisfied by the covariances of order statistics in the i.i.d. case and then generalize it to the case when the variables are independent and non-identically distributed. This relation could be employed successfully either to check the calculations or to reduce the amount of direct computations involved in evaluating the covariances of order statistics from an outlier model.     

Uniform asymptotic linearity of a process based on a signed rank statistic

Consider the process with, cf. (1.2) on page 265 in B1, X₁, …, X_N a sample from a distribution F and, for i = 1, …, N, R${}_{\text{|x}{}_{\text{1}}\text{, - q}{}_{\text{1}}\text{ø|}}$ , the rank of |X₁ - q₁ø| among |X₁ - q₁ø|, …, |X_N - q_Nø|. It is shown that, under certain regularity conditions on F and on the constants p_i and q_i, T^ø_N(t) is asymptotically approximately a linear function of ø uniformly in t and in ø for |ø| ≤ C. The special case where the p_i and the q_i, are independent of i is considered.     

一种基于区间数判断矩阵的群决策新方法

针对基于区间数判断矩阵的群决策问题,提出了一种群决策新方法.借助于集值统计原理,构造了与区间数判断矩阵群信息等价的具有一定可靠度的确定数判断矩阵,建立了计算这类群信息集结值可靠度的模型,运用和积法对所得新判断矩阵进行处理,得到了一种简便的方案排序方法.最后,通过一个算例验证了该方法的有效性和实用性.     

1998-2005年间青岛科技大学人文社会科学论文统计分析

利用清华同方《中国期刊全文数据库》及重庆维普《中文科技期刊数据库》,对青岛科技大学1998-2005年发表的人文社会科学论文进行了检索和统计,统计的项目包括论文总量与学科分布、作者、期刊源、基金与资助产出论文情况等,在此基础上对其进行了综合评价,并提出相应建议。     

统计资料应用探讨

在分析了统计资料的特征与结构的基础上,重点就统计资料的应用要诀进行了探讨,指出了统计资料的应用要诀是在掌握统计资料的基本结构基础上,掌握审核、系统、综合、特征、动静五个方面的要诀。同时,简要说明了统计分析技术及写作方法在统计资料应用中的意义。