On some significance tests in cluster analysis

We investigate the properties of several significance tests for distinguishing between the hypothesisH of a homogeneous population and an alternativeA involving clustering or heterogeneity, with emphasis on the case of multidimensional observationsx ₁, ...,x _n ^p . Four types of test statistics are considered: the (s-th) largest gap between observations, their mean distance (or similarity), the minimum within-cluster sum of squares resulting from a k-means algorithm, and the resulting maximum F statistic. The asymptotic distributions underH are given forn and the asymptotic power of the tests is derived for neighboring alternatives.     

DBL(1,0,1)误差非线性回归模型相关性和方差齐性的检验

讨论了具有双线性DBL(1，0，1)误差的非线性回归模型的相关性和方差齐性的检验问题，用Score检验方法给出了双线性项检验以及相关性和方差齐性同时检验的检验统计量，并用随机模拟验证了检验方法的正确性；推广和发展了具有线性序列误差回归模型的结果；最后将结果应用于DBL(0，1，1)误差的非线性回归模型。     

畲汉陈十四信仰的比较研究

陈十四信仰在畲汉两族的生活中扮演了重要角色,畲汉两族在陈十四传说的内容与陈十四信仰的形式上体现了同质性,但在陈十四传说的口述形式、记录文本以及民众对陈十四信仰的艺术展演上则表现出异质性.共同的心理诉求是畲汉两族对陈十四信仰趋同的重要因素;畲族对汉族强势文化的认同是畲汉两族陈十四信仰同质性的中坚力量;畲族民族意识的延续是畲汉两族陈十四信仰异质性的核心原因.     

全息法检测光学玻璃的均匀性

叙述了用小功率氦—氖激光器和小实验台对光学玻璃的均匀性进行全息检测的一种方法.与以往采用的方法相比,本实验在光路设计上做了一定的改进,从而获得更清楚的全息合干涉图。     

浅议如何利用统计学技术进行能力验证样品均匀性、稳定性评价

能力验证样品的均匀性和稳定性评价是能力验证活动中最基础的一个环节,用单因素方差分析法判定均匀性和t检验法判定稳定性是两种最常见的方法。本文从统计学的角度对两种判定方法进行了解释,希望对能力验证组织实施有一定的借鉴指导作用。     

各总体方差单调时一种新的齐一性检验法

Pearson—χ^2检验法是一种常见的齐一性检验法，但是所需要的样本量很大．在多个总体期望相等而方差单调的假设下，提出用F-检验法进行齐一性检验，所需样本量比Pearson—χ^2检验法大为减少．通过Monte—Carlo模拟还可以看到，在节省样本的同时，F-检验法还具有比Pearson—χ^2检验法更高的功效．     

分布式系统典型体系结构及实现范例

本文着重介绍了分布式系统的基本概念,典型体系结构及要解决的相关设计问题,并结合实例进行了分布式系统的一般实现方法。同时,也探讨了分布式系统研究的新热点及面临的问题。     

黑龙江省高考体育术科考试现行办法的同质性研究

运用数理统计方法,结合黑龙江省2008年高校体育专业招生术科考试的实际情况,研究高考体育专业招生考试办法的同质性.研究结果:现行的术科专项考试标准较容易,且各项目间存在不同质问题;结合存在的问题拟订调整方案对现行考试办法进行调整;提出运用标准分的方法彻底解决体育术科考试存在的项目间不同质问题.     

Optimal Production Functions and Their Homogeneity Superadditive，and Concavity

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Fitting a distance model to homogeneous subsets of variables: Points of view analysis of categorical data

An approach is presented for analyzing a heterogeneous set of categorical variables assumed to form a limited number of homogeneous subsets. The variables generate a particular set of proximities between the objects in the data matrix, and the objective of the analysis is to represent the objects in lowdimensional Euclidean spaces, where the distances approximate these proximities. A least squares loss function is minimized that involves three major components: a) the partitioning of the heterogeneous variables into homogeneous subsets; b) the optimal quantification of the categories of the variables, and c) the representation of the objects through multiple multidimensional scaling tasks performed simultaneously. An important aspect from an algorithmic point of view is in the use of majorization. The use of the procedure is demonstrated by a typical example of possible application, i.e., the analysis of categorical data obtained in a free-sort task. The results of points of view analysis are contrasted with a standard homogeneity analysis, and the stability is studied through a Jackknife analysis.