Performance of traditional f tests in repeated measures designs under covariance heterogeneity

This study investigates the performance of two traditional F tests, one for main effects and the other for interaction in repeated measures designs under several conditions of covariance heterogeneity. Overall, the test for interaction is more vulnerable than the one for main effects. Distortion in the level of significance is less serious for the case of equal group size.     

Gay and Bisexual Men’s Experiences Using Self-Testing Kits for HIV and Rectal and Urethral Bacterial Sexually Transmitted Infections: Lessons Learned from a Study With Home-Based Testing

Abstract

Objectives: This study investigated the experiences of gay, bisexual, and other men who have sex with men (GBM) conducting HIV and sexually transmitted infection (STI) self-testing procedures. Methods: We analyzed mixed-methods data from 11 GBM who self-tested HIV-positive and 1,070 HIV-negative GBM who completed the HIV self-testing and STI self-sampling procedures. Results: Nearly all (99%) reported ease in urine-based STI self-sampling and most (90%) found rectal swab self-collection easy. Most (94%) checked their rapid-HIV self-testing results during the correct window (20-40 minutes), and nearly all (99%) trusted their HIV results. Conclusions: Recommendations for future self-testing procedures are provided based on findings from free-response data.     

A statistical measure of internal consistency

A test for homogeneity of several populations against the simple tree alternative is proposed when the observations in various groups are subject to the same pattern of random right-censorship. The test is a generalization of the one proposed by Slivka (1970) which is useful when testing time is expensive so that an early termination of an experiment is desirable. The power of the test is examined in a simulation study.     

Specification and testing of multiplicative time-varying GARCH models with applications

In this article, we develop a specification technique for building multiplicative time-varying GARCH models of Amado and Teräsvirta (2008, 2013). The variance is decomposed into an unconditional and a conditional component such that the unconditional variance component is allowed to evolve smoothly over time. This nonstationary component is defined as a linear combination of logistic transition functions with time as the transition variable. The appropriate number of transition functions is determined by a sequence of specification tests. For that purpose, a coherent modelling strategy based on statistical inference is presented. It is heavily dependent on Lagrange multiplier type misspecification tests. The tests are easily implemented as they are entirely based on auxiliary regressions. Finite-sample properties of the strategy and tests are examined by simulation. The modelling strategy is illustrated in practice with two real examples: an empirical application to daily exchange rate returns and another one to daily coffee futures returns.     

Testing Covariance Stationarity

In this paper, we show that the widely used stationarity tests such as the Kwiatkowski Phillips, Schmidt, and Shin (KPSS) test have power close to size in the presence of time-varying unconditional variance. We propose a new test as a complement of the existing tests. Monte Carlo experiments show that the proposed test possesses the following characteristics: (i) in the presence of unit root or a structural change in the mean, the proposed test is as powerful as the KPSS and other tests; (ii) in the presence of a changing variance, the traditional tests perform badly whereas the proposed test has high power comparing to the existing tests; (iii) the proposed test has the same size as traditional stationarity tests under the null hypothesis of stationarity. An application to daily observations of return on U.S. Dollar/Euro exchange rate reveals the existence of instability in the unconditional variance when the entire sample is considered, but stability is found in subsamples.     

Some small-sample results on a bounded influence rank regression method

Naranjo and HeUmansperger (1994) recently derved a bounded influence rank regression method and suggested how hypotheses about the regression coefficients might be tested. This brief note reports some simulation results on how their procedure performs when there is one predictor. Even when the error term is highly skewed, good control over the Type I error probability is obtained Power can be high relative to least squares regression when the error term has a heavy tailed distribution .and the predictor has a symmetric distribution However, if the predictor has a skewed distribution, power can be relatively low even when the distribution of the error term is heavy tailed. Despite this, it is argued that their method provides an important and useful alternative to ordinary least squares as well as other robust regression methods.     

测试技术课程改革与实践

测试技术属于边缘科学,起点高、知识面广、难度大,不仅学科跨度大,涉及机、电、液、光、声、数学及自动控制等几方面,而且该课程实践性很强,实践环节在整个教学过程中占有相当大的比重,因而教与学均存在一定的困难。文章在分析传统教学存在问题的基础上,提出新的教学方法,调整教学内容顺序,突出教学重点,运用工程实例教学法,强化学生的实验训练。     

对应分析统计检验体系探讨

对应分析因其结果的易读性，近些年得到了越来越广泛的应用。为了更好地应用对应分析，提出建立对应分析统计检验体系，包括对应分析适用性的统计检验以及对应分析效果的检验，同时还提出应用对应分析时应注意的其它问题。     

Looking for max-semistability: A new test for the extreme value condition

To test the extreme value condition, Cramér-Von Mises type tests were recently proposed by Drees et al. (2006) and Dietrich et al. (2002). Hüsler and Li (2006) presented a simulation study on the behavior of these tests and verified that they are not robust for models in the domain of attraction of a max-semistable distribution function. In this work we develop a test statistic that distinguishes quite well distribution functions which belong to a max-stable domain of attraction from those in a max-semistable one. The limit law is deduced and the results from a numerical simulation study are presented.     

Inference in Targeted Group‐Sequential Covariate‐Adjusted Randomized Clinical Trials

This article is devoted to the construction and asymptotic study of adaptive, group‐sequential, covariate‐adjusted randomized clinical trials analysed through the prism of the semiparametric methodology of targeted maximum likelihood estimation. We show how to build, as the data accrue group‐sequentially, a sampling design that targets a user‐supplied optimal covariate‐adjusted design. We also show how to carry out sound statistical inference based on such an adaptive sampling scheme (therefore extending some results known in the independent and identically distributed setting only so far), and how group‐sequential testing applies on top of it. The procedure is robust (i.e. consistent even if the working model is mis‐specified). A simulation study confirms the theoretical results and validates the conjecture that the procedure may also be efficient.