Of what use are tests of significance and tests of hypothesis

This paper discusses the role of significance testing, in contrast to hypothesis testing. It takes the view that the two processes must be differentiated because they are in fact different even though they possess some common mathematical features. A supporting view of significance testing is presented. Difficulties and obscurities are discussed.     

A Nonparametric Test for Autoregressive Conditional Heteroscedasticity: A Markov-Chain Approach

In this article we propose a nonparametric test for autoregressive conditional heteroscedasticity based on finite-state Markov chains. A simple Monte Carlo experiment suggests that in finite samples it performs comparably to the Lagrange multiplier test under conditional normality and is superior for the t, lognormal, and exponential distributions. As an illustration, we apply both tests to Canadian/U.S. forward foreign exchange data.     

New Goodness-of-Fit Tests Based on Sample Quantiles

In this article power divergences statistics based on sample quantiles are transformed in order to introduce new goodness-of-fit tests. Quantiles of the distribution of proposed statistics are calculated under uniformity, normality, and exponentiality. Several power comparisons are performed to show that the new tests are generally more powerful than the original ones.     

Randomization tests for multiple regression

Four generic means of conducting randomization tests in the context of multiple regression are analysed. Based on their performance in traditional repeated samples, three of these are shown to be inappropriate or applicable only in special circumstances; their shortcomings are illustrated via Monte Carlo studies     

竞价排名的广告属性与法律调整

竞价排名作为一种新生事物,在其自然演进的过程中,出现了诸多问题期待法律予以回应。但是,在此领域出现了法律的真空地带,存在很多制度缺失。本文从竞价排名的法律性质谈起,分析了竞价排名实质是一种自然算法与人工干预的组合,对于人工干预部分是法律规制之重点所在。结合现实中竞价排名的乱象以及现实困境,通过将其定位为商业广告,以及借鉴美国相关制度设计,来完善我国竞价排名市场的有效法律调整。     

Estimating systems of trending variables

A survey is given of some results on inference in cointegrated systems. We discuss some regression methods, and contrast them with the analysis of the vector autoregressive model. We discuss determination of cointegrating rank and estimation of parameters, as well as asymptotic inference. The problems are treated for 1(1) and for 1(2) variables.     

Tests of symmetry based on transformed empirical processes

A probability distribution function F is said to be symmetric when 1 ‐ F(x) ‐ F(‐x) = 0 for all x∈ R. Given a sequence of alternatives contiguous to a certain symmetric F₀, the authors are concerned with testing for the null hypothesis of symmetry. The proposed tests are consistent against any nonsymmetric alternative, and their power with respect to the given sequence can easily be optimized. The tests are constructed by means of transformed empirical processes with an adequate selection of the underlying isometry, and the optimum power is obtained by suitably choosing the score functions. The test statistics are very easy to compute and their asymptotic distributions are simple.     

大学英语作文题库内容效度方面存在的问题与建议

以测试理论和《大学英语教学大纲》为依据,以教学主体和教材为研究对象,分析宁夏大学《大学英语》三级成就测试作文题库,指出其在内容效度方面存在的三方面问题:(1)考试目的不明确;(2)命题不规范;(3)写作内容与形式不具代表性。提出提高考试命题人员的测试理论知识水平、制订考试规范和组建测试小组等改进建议,进一步建立科学、合理和规范的大学英语测试体系。     

A Rank-Sum-Type Test for Paired Data with Multiple Endpoints

Clinical trials and other types of studies often examine the effects of a particular treatment or experimental condition on a number of different response variables. Although the usual approach for analysing such data is to examine each variable separately, this can increase the chance of false positive findings. Bonferroni's inequality or Hotelling's T² statistic can be employed to control the overall type I error rate, but these tests generally lack power for alternatives in which the treatment improves the outcome on most or all of the endpoints. For the comparison of independent groups, O'Brien (1984) developed a rank-sum type test that has greater power than the Bonferroni and T² procedures when one treatment is uniformly better (i.e. for all endpoints) than the other treatment(s). In this paper we adapt the rank-sum test to studies involving paired data and demonstrate that it, too, has power advantages for such alternatives. Simulation results are described, and an example from a study measuring the effects of sleep loss on glucose metabolism is presented to illustrate the methodology.