An extension of the Anderson–Darling k-sample test to arbitrary sample space partition sizes

In this paper we first show that the k-sample Anderson–Darling test is basically an average of Pearson statistics in 2?×?k contingency tables that are induced by observation-based partitions of the sample space. As an extension, we construct a family of rank test statistics, indexed by c?∈??, which is based on similarly constructed c?×?k partitions. An extensive simulation study, in which we compare the new test with others, suggests that generally very high powers are obtained with the new tests. Finally we propose a decomposition of the test statistic in interpretable components.     

Generalized variable method inference for the location parameter of the general half-normal distribution

This paper considers the development of inferential techniques based on the generalized variable method (GV-Method) for the location parameter of the general half-normal distribution. We are interested in hypothesis testing of, and interval estimation for, the location parameter. Body fat data, urinary excretion rate data, and simulated data are used to illustrate the application and to demonstrate the advantages of the proposed GV-Method over the large-sample method and the Bayesian method.     

高校艺体生开展专业化英语教学的困境与出路

通过零压力英语摸底测试和座谈得出高校艺体类专业开展专业化英语教学存在学生基础薄弱、班级分配不合理、师资不足、教学效果不明显等弊端。在充实师资力量的基础上,利用多媒体教学方式对艺体类学生开展分级教学,并加强课堂多元素的运用,活化英语专业知识和术语的学习,可实现艺体类学生英语学习专业化,从而达到将学生培养成为专业加英语的复合型人才的目的。     

Comparison of powers for the sphericity tests using both the asymptotic distribution and the bootstrap

The asymptotic distributions of two tests for sphericity:the locally most powerful invariant test and the likelihood ratio test are derived under the general alternaties ∑?σ² I. The powers of these two tests are then compared when the data are from a trivariate normal population. The bootstrap method is also used to obtain the powers and the powers obtained by this method agree with those from the asymptotic distributions.     

A survey of nonparametric tests for scale

A surrey of nonparametric tests for scale is presented. Some parametric procedures are also presented for the purpose of comparison. Suggestions on which procedures to use in various situations are given. Both 2-sample and c-sample tests are included     

Pivotal ouantities based on the conditional distribution function transform in models with accessory parameters

A general formula for porducing pivotal quantities defined free of accessory paramelers is investigated. In addition to similarity, resulting tests often have exact or asymptotic optimal properties In other cases, the sample correlation with statistics sufficient for the accescory parameters produce a simple similar test and a consistent, asymptctically normal estimator. The latter mathod should perform best when the accessories are highly dispersed.     

Power and Sample Size for Approximate Chi-Square Tests

Approximate chi-square tests for hypotheses concerning multinomial probabilities are considered in many textbooks. In this article power calculations and sample size based on power are discussed and illustrated for the three most frequently used tests of this type. Available noncentrality parameters and existing tables permit a relatively easy solution of these kinds of problems.     

C73. The diminishing significance of a p-value as the sample size increases

Barnard (1963), Birnbaum (1974), and others have proposed exact inference procedures based on simulation. This article surveys existing results in this general area and fills several important holes, addressing philosophical problems and the matter of determining a simulation size. Applications to multiple comparisons, sequential analysis, and statistical consulting are discussed.     

A Distribution Free Test for the Equality of Scales

Situations where scale parameters are not nuisance factors to be controlled but outcomes to be explained arise in many contexts such as quality control, agricultural production systems, experimental education, the pharmaceutical industry and biology. Tests for homogeneity of variances are often of interest also as a preliminary to analysis of variance, dose-response modelling or discriminant analysis. The literature on tests for the equality of scales is vast. A test which usually stands out in terms of power and robustness against non normality is the modified Levene W50 test, however in the literature no test is found to be the most powerful one for every distribution. The goal of the article is to propose an effective method for comparing scales. More precisely, we propose a test for the equality of scales that, even though was not the most powerful one for every distribution, it has good overall performance under every type of distribution. This test has the form of a combined resampling test. It is important to note that non combined tests show good performance only in particular contexts. Size and power of the proposed test are studied via simulation and compared with many other robust tests for scale. A practical application to industrial quality control is discussed.