Catanova for Two-Way Contingency Tables with Ordinal Variables Using Orthogonal Polynomials

ABSTRACT

The analysis of variance of cross-classified (categorical) data (CATANOVA) is a technique designed to identify the variation between treatments of interest to the researcher. There are well-established links between CATANOVA and the Goodman and Kruskal tau statistic as well as the Light and Margolin R ² for the purposes of the graphical identification of this variation.

The aim of this article is to present a partition of the numerator of the tau statistic, or equivalently, the BSS measure in the CATANOVA framework, into location, dispersion, and higher order components. Even if a CATANOVA identifies an overall lack of variation, by considering this partition and calculations derived from them, it is possible to identify hidden, but statistically significant, sources of variation.     

SOJOURN TIMES IN NON-HOMOGENEOUS QBD PROCESSES WITH PROCESSOR SHARING

We study sojourn times of customers in a processor sharing queue with a service rate that varies over time, depending on the number of customers and on the state of a random environment. An explicit expression is derived for the Laplace–Stieltjes transform of the sojourn time conditional on the state upon arrival and the amount of work brought into the system. Particular attention is paid to the conditional mean sojourn time of a customer as a function of his required amount of work, and we establish the existence of an asymptote as the amount of work tends to infinity. The method of random time change is then extended to include the possibility of a varying service rate. By means of this method, we explain the well-established proportionality between the conditional mean sojourn time and required amount of work in processor sharing queues without random environment. Based on numerical experiments, we propose an approximation for the conditional mean sojourn time. Although first presented for exponentially distributed service requirements, the analysis is shown to extend to phase-type services. The service discipline of discriminatory processor sharing is also shown to fall within the framework.     

Rank-based analysis of repeated measures block designs

A rank-based inference is developed for repeated measures balanced incomplete block and randomized complete block designs using a suitable dispersion function. Asymptotic distributions of rank estimators are developed after establishing approximate linearity of the gradient vector of the dispersion function. Unlike available nonparametric procedures for those designs, estimation and testing are tied together. Three different test statistics are developed for testing the linear hypotheses. Friedman's (1937) statistic and Durbin's (1951) statistic are particular cases of one of the three proposed statistics. An estimate of a scale parameter which appears in the ARE expression as well as as in the variences and covariances of the rank estimators is discussed.     

Some further aspects of the löwner-ordering antitonicity of the moore-penrose inverse

A survey is given of known proofs of the antitonicity of the inverse matrix function for positive definite matrices w.r.t. the Lowner partial ordering, and of the corresponding result for the Moore-Penrose inverse of nonnegative definite matrices [the theorem of Milliken and Akdeniz (1977)]. A short new proof of the latter result is obtained by employing an extremal representation of a nonnegative definite quadratic form. Another proof of this result involving Schur complements is also given, and is seen to be extendable to the case of symmetric (not necessarily nonnegative definite) matrices. A geometrical interpretation of Milliken and Akdeniz's theorem is presented. As an application, the relationship between the concepts of greater (maximum) concentration and smaller (minimum) dispersion is considered for a pair (class) of vector-valued statistics with possibly degenerate distributions.     

Quantile dispersion graphs to compare the efficiencies of cluster randomized designs

The purpose of this article is to compare efficiencies of several cluster randomized designs using the method of quantile dispersion graphs (QDGs). A cluster randomized design is considered whenever subjects are randomized at a group level but analyzed at the individual level. A prior knowledge of the correlation existing between subjects within the same cluster is necessary to design these cluster randomized trials. Using the QDG approach, we are able to compare several cluster randomized designs without requiring any information on the intracluster correlation. For a given design, several quantiles of the power function, which are directly related to the effect size, are obtained for several effect sizes. The quantiles depend on the intracluster correlation present in the model. The dispersion of these quantiles over the space of the unknown intracluster correlation is determined, and then depicted by the QDGs. Two applications of the proposed methodology are presented.     

Recent and classical goodness-of-fit tests for the Poisson distribution

We give a critical synopsis of classical and recent tests for Poissonity, our emphasis being on procedures which are consistent against general alternatives. Two classes of weighted Cramér–von Mises type test statistics, based on the empirical probability generating function process, are studied in more detail. Both of them generalize already known test statistics by introducing a weighting parameter, thus providing more flexibility with regard to power against specific alternatives. In both cases, we prove convergence in distribution of the statistics under the null hypothesis in the setting of a triangular array of rowwise independent and identically distributed random variables as well as consistency of the corresponding test against general alternatives. Therefore, a sound theoretical basis is provided for the parametric bootstrap procedure, which is applied to obtain critical values in a large-scale simulation study. Each of the tests considered in this study, when implemented via the parametric bootstrap method, maintains a nominal level of significance very closely, even for small sample sizes. The procedures are applied to four well-known data sets.     

关于分布函数的散布函数

研究了一种散布函数Ｄｘ（ｔ）的性质，得到了如下结论：设Ｘ～Ｎ（μ１，σ１２），Ｙ～Ｎ（μ２，σ２２），则Ｙ≤ＤＸσ２≤σ     

湍流模型和施密特数对街谷内污染扩散模拟的影响

选取不同的湍动施密特数,分别采用标准κ-ε模型和realizable κ-ε模型对孤立街道峡谷内的气流运动和污染物扩散进行了数值模拟.计算得到的气流速度场和污染物浓度分布表明,标准κ-ε模型和realizable κ-ε模型预测出极为相似的气流旋涡结构,即在峡谷内生成一个中心大致位于峡谷中央的顺时针大旋涡,并在此大旋涡的作用下污染物往峡谷的背风侧积聚.通过对比分析峡谷迎风面和背风面上的无量纲污染物浓度计算结果与风洞试验数据表明,湍动施密特数的改变并不影响计算空气流场,但改变污染物扩散方程中的湍动扩散率,增大湍动施密特数将减弱污染物的湍动扩散作用,使得峡谷顶部污染物外溢减少,从而导致峡谷内的污染物浓度计算值增大;只要给定合适的计算参数,标准κ-ε模型和realizable κ-ε模型均能预测出峡谷内的污染物扩散分布,标准κ-ε模型合适的湍动施密特数为0.4～0.6,而realizable κ-ε模型则为0.3～0.5;当取湍动施密特数为0.7(Fluent软件系统中的默认值)时,标准κ-ε模型的计算精度高于realizable κ-ε模型的计算精度.     

当前传播语境下传受主体的消解与重建

以web2．0和3G手机为代表的新媒体传播中,非专业化的私人传播日益扩大,很多方面已经超越了职业传播者的影响,传统意义上的传播者和受众的概念正在模糊化和彼此消解。这对传播的社会影响肯定会带来一系列新的问题。传统的新闻传播者要充分认识网络传播环境的特点,把传统优势和网络特征结合起来,在网络传播时代重建自己的话语主导权。     

工会发展与工资不平等： 美英工会的标准化工资率策略

从历史分析的角度看,工会和集体谈判对降低工资不平等有积极作用。美英两国的工会力量在过去一百年的发展中呈现出先增后降的倒U形趋势,与此同时,工资差距和收入不平等程度则呈同步反向运动。工会出于组织考虑、客观标准和工人团结的原因,在集体谈判中采用标准化工资率策略,提高了低工资者收入,压缩了工资分布范围,从而缩小了不平等程度。而我国存在集体合同覆盖率低、协商不充分等问题,为了改善我国收入不平等问题,需要进一步发挥工会在初次分配中的作用。