Tests for homogeneity of risk differences in stratified design with correlated bilateral data

ABSTRACT

Correlated bilateral data arise from stratified studies involving paired body organs in a subject. When it is desirable to conduct inference on the scale of risk difference, one needs first to assess the assumption of homogeneity in risk differences across strata. For testing homogeneity of risk differences, we herein propose eight methods derived respectively from weighted-least-squares (WLS), the Mantel-Haenszel (MH) estimator, the WLS method in combination with inverse hyperbolic tangent transformation, and the test statistics based on their log-transformation, the modified Score test statistic and Likelihood ratio test statistic. Simulation results showed that four of the tests perform well in general, with the tests based on the WLS method and inverse hyperbolic tangent transformation always performing satisfactorily even under small sample size designs. The methods are illustrated with a dataset.     

Three Recommendations for Improving the Use of p-Values

ABSTRACT

Researchers commonly use p-values to answer the question: How strongly does the evidence favor the alternative hypothesis relative to the null hypothesis? p-Values themselves do not directly answer this question and are often misinterpreted in ways that lead to overstating the evidence against the null hypothesis. Even in the “post p?<?0.05 era,” however, it is quite possible that p-values will continue to be widely reported and used to assess the strength of evidence (if for no other reason than the widespread availability and use of statistical software that routinely produces p-values and thereby implicitly advocates for their use). If so, the potential for misinterpretation will persist. In this article, we recommend three practices that would help researchers more accurately interpret p-values. Each of the three recommended practices involves interpreting p-values in light of their corresponding “Bayes factor bound,” which is the largest odds in favor of the alternative hypothesis relative to the null hypothesis that is consistent with the observed data. The Bayes factor bound generally indicates that a given p-value provides weaker evidence against the null hypothesis than typically assumed. We therefore believe that our recommendations can guard against some of the most harmful p-value misinterpretations. In research communities that are deeply attached to reliance on “p?<?0.05,” our recommendations will serve as initial steps away from this attachment. We emphasize that our recommendations are intended merely as initial, temporary steps and that many further steps will need to be taken to reach the ultimate destination: a holistic interpretation of statistical evidence that fully conforms to the principles laid out in the ASA statement on statistical significance and p-values.     

On the power of goodness-of-fit tests for the exponential distribution under progressive Type-II censoring

The aim of this article is to review existing goodness-of-fit tests for the exponential distribution under progressive Type-II censoring and to provide some new ideas and adjustments. In particular, we consider two-parameter exponentially distributed random variables and adapt the proposed test procedures to our scenario if necessary. Then, we compare their power by an extensive simulation study. Furthermore, we propose five new test procedures that provide reasonable alternatives to those already known.     

Some count-based nonparametric tests for circular symmetry of a bivariate distribution

In this paper, we revisit the problem of testing of the hypothesis of circular symmetry of a bivariate distribution. We propose some nonparametric tests based on sector counts. These include tests based on chi-square goodness-of-fit test, the classical likelihood ratio, mean deviation, and the range. The proposed tests are easy to implement and the exact null distributions for small sample sizes of the test statistics are obtained. Two examples with small and large data sets are given to illustrate the application of the tests proposed. For small and moderate sample sizes, the performances of the proposed tests are evaluated using empirical powers (empirical sizes are also reported). Also, we evaluate the performance of these count-based tests with adaptations of several well-known tests such as the Kolmogorov–Smirnov-type tests, tests based on kernel density estimator, and the Wilcoxon-type tests. It is observed that among the count-based tests the likelihood ratio test performs better.     

“四史”教育融入高校思政课教学的五维逻辑进路

历史照亮过去，历史启迪未来。以党史、新中国史、改革开放史、社会主义发展史为核心内容的“四史”教育，是以历史为基础的思想政治教育。从党史、新中国史、改革开放史、社会主义发展史中挖掘思想政治理论课教育资源，对高校传承红色基因、落实立德树人根本任务具有重要的指导意义。以庆祝中国共产党成立100周年为契机，将“四史”教育融入高校思想政治理论课教学，必须遵循相应的逻辑理路：树立正确党史观、坚定理想信念的价值追求是融入的逻辑立场；强化大历史观、理解党的伟大贡献是融入的逻辑方法；坚持内在统一性、把握“四史”教育的整体性逻辑是融入的基本前提；抓住侧重点、精准对接各门大学生思想政治理论课程是融入的逻辑核心；坚持实践逻辑、引导大学生从“四史”中汲取智慧和力量是融入的根本遵循。     

Improved functional portmanteau tests

Functional time series is a popular method of forecasting in functional data analysis. The Box-Jenkins methodology for model building, with the aim of forecasting, includes three iterative steps of model identification, parameter estimation and diagnostic checking. Portmanteau tests are one of the most popular diagnostic checking tools. In particular, they are applied to find if the residuals of the fitted model are white noise. Gabrys and Kokoszka [Portmanteau test of independence for functional observations. J Am Stat Assoc. 2007;102(480):1338–1348.] proposed a portmanteau test of independence for functional observation based on Box and Pierce's statistic. Their statistic is too sensitive to the lag value, specially when the sample size is small. Here, two modifications of Gabrys and Kokoszka statistic are presented, which have superior properties in small samples. The efficiency of the modified statistics is demonstrated through a simulation study.     

数据生产要素的结合机制——互补性资产视角

数据作为生产要素创造的价值备受关注,但目前对数据与其他生产要素的结合机制缺乏明确的解释。本文从互补性资产理论出发,分别探讨数据与技术、资本、劳动生产要素的结合机制。研究表明:在与技术要素的结合上,数据要素的加入能够帮助创新企业更有效地使用互补性资产,从而产生技术创新;在与资本要素的结合上,数据要素的加入降低了企业获取作为生产资源和营销资源的互补性资产的成本,从而产生成本创新;在与劳动要素的结合上,数据要素能够从产品设计和生产方面提升互补性资产的生成效率,从而产生流程创新。最终,数据要素在与核心生产要素的有机结合下,极大促进了数字经济的创新与发展。     

基于Hawkes因子模型的股价共同跳跃研究

本文首先比较了三种目前主流的共跳检验方法：基于LM检验的共跳检验、BLT共跳检验和FHLL共跳检验，结果表明，三种方法在识别共跳数量上差距明显，但三者结果的重合部分基本属于市场暴涨暴跌行情，说明共跳识别对市场剧烈波动的聚集性较为敏感。基于跳跃、共跳存在的聚集性问题，本文将Hawkes过程引入跳跃和共跳的研究，构建了基于Hawkes过程的因子模型，结果显示，基于Hawkes因子模型的MJ统计量、CJ统计量和实证数据的拟合程度较好，表明因子模型能够更好地描述跳跃和共跳的聚集性。