On Rao score and PearsonX 2 statistics in generalized linear models

The identity of the Rao score and PearsonX ² statistics is well known in the areas where the latter was first introduced: goodness-of-fit in contingency tables and binary responses. We show in this paper that the same identity holds when the two statistics are used for testing goodness-of-fit of Generalized Linear Models. We also highlight the connections that exist between the two statistics when they are used for the comparison of nested models. Finally, we discuss some merits of these unifying results. Work financially supported by cofin. MIUR grants 2000 and 2002.     

关于外语专业题库建设范式的探讨

本文通过对当前国内外语教育形式的综览 ,指出了外语专业的题库建设已成为当务之急 ,并通过对各种考试形式的分析 ,提出了外语题库建设的具体理论模式———以项目反应理论为基础的计算机自适应测试。文章就有关方面的实施提出了自己的看法。     

当前我国公务员考录管理制度弊端分析

当前我国公务员考试录用管理制度存在缺乏法律保障,报名资格随意规定,考试科目和内容设置不科学,考试录用环节不合理,考试监督体系不完善等蔽端。这些问题亟待借鉴发达国家的公务员考试录用经验,在现实中给予合理的解决,从而保证我国公务员考录制度沿着正确的方向发展,真正达到选贤任能的目的。     

VXIbus自动测试系统的研究

VXIbus(VMEbus Extcnsions for Instrumcntation)是继GPIB接口之后,在PC机广泛应用的基础上,融合了VMEbus(VERSA-bus Modular Europcan)和GPIB而产生的新一代仪器总线。本文讨论了怎样在GPIB环境下采用VXIbus标准组建新型的自动测试系统,并结合VXIB1数据采集实验系统的设计实践,探讨了选用VXIbus标准需要解决的有关技术问题。     

中国产业结构变动与就业水平的实证研究

文章运用时间序列和计量模型研究了中国产业结构与就业水平之间的发展关系.首先运用格兰杰因果检验分析了第一、二、三次产业的产值与其就业之间的因果影响关系,继而从产值与就业两个角度定量分析了第一、二、三次产业各自对总就业水平的贡献,得出结论:第一产业和第三产业是劳动力就业的决定因素,而第二产业不是就业的决定因素;从产值角度来看,第三产业对劳动力就业的贡献最大,从就业角度来看,第一产业的就业对总体就业水平的贡献最高,其次是第三产业,而第二产业的就业对总体就业水平的贡献最小.最后根据研究结论提出相应的建议措施.     

我国房地产市场财富效应的实证分析

本文应用格兰杰因果关系检验和协整分析方法,对我国房地产市场的财富效应作了实证分析。实证结果表明,我国房地产市场不具有财富效应而仅具有替代效应。     

基于VB的计算机考试系统的设计与实现

介绍了网络环境下的基于Visual Basic计算机考试系统的基本组成和功能结构,同时,就题库的建立和管理、随机成卷系统和评分系统等研制的难点进行了分析,并针对其中的关键技术,提出相应的解决方法。     

An affine‐invariant multivariate sign test for cluster correlated data

The author presents a multivariate location model for cluster correlated observations. He proposes an affine‐invariant multivariate sign statistic for testing the value of the location parameter. His statistic is an adaptation of that proposed by Randles (2000). The author shows, under very mild conditions, that his test statistic is asymptotically distributed as a chi‐squared random variable under the null hypothesis. In particular, the test can be used for skewed populations. In the context of a general multivariate normal model, the author obtains values of his test's Pitman asymptotic efficiency relative to another test based on the overall average. He shows that there is an improvement in the relative performance of the new test as soon as intra‐cluster correlation is present Even in the univariate case, the new test can be very competitive for Gaussian data. Furthermore, the statistic is easy to compute, even for large dimensional data. The author shows through simulations that his test performs well compared to the average‐based test. He illustrates its use with real data.     

A partial score test for difference among heterogeneous populations

In event time data analysis, comparisons between distributions are made by the logrank test. When the data appear to contain crossing hazards phenomena, nonparametric weighted logrank statistics are usually suggested to accommodate different-weighted functions to increase the power. However, the gain in power by imposing different weights has its limits since differences before and after the crossing point may balance each other out. In contrast to the weighted logrank tests, we propose a score-type statistic based on the semiparametric-, heteroscedastic-hazards regression model of Hsieh [2001. On heteroscedastic hazards regression models: theory and application. J. Roy. Statist. Soc. Ser. B 63, 63–79.], by which the nonproportionality is explicitly modeled. Our score test is based on estimating functions derived from partial likelihood under the heteroscedastic model considered herein. Simulation results show the benefit of modeling the heteroscedasticity and power of the proposed test to two classes of weighted logrank tests (including Fleming–Harrington's test and Moreau's locally most powerful test), a Renyi-type test, and the Breslow's test for acceleration. We also demonstrate the application of this test by analyzing actual data in clinical trials.     

An empirical likelihood goodness-of-fit test for time series

Summary. Standard goodness-of-fit tests for a parametric regression model against a series of nonparametric alternatives are based on residuals arising from a fitted model. When a parametric regression model is compared with a nonparametric model, goodness-of-fit testing can be naturally approached by evaluating the likelihood of the parametric model within a nonparametric framework. We employ the empirical likelihood for an α -mixing process to formulate a test statistic that measures the goodness of fit of a parametric regression model. The technique is based on a comparison with kernel smoothing estimators. The empirical likelihood formulation of the test has two attractive features. One is its automatic consideration of the variation that is associated with the nonparametric fit due to empirical likelihood's ability to Studentize internally. The other is that the asymptotic distribution of the test statistic is free of unknown parameters, avoiding plug-in estimation. We apply the test to a discretized diffusion model which has recently been considered in financial market analysis.