A Nonstochastic Interpretation of Reported Significance Levels

Tests of significance are often made in situations where the standard assumptions underlying the probability calculations do not hold. As a result, the reported significance levels become difficult to interpret. This article sketches an alternative interpretation of a reported significance level, valid in considerable generality. This level locates the given data set within the spectrum of other data sets derived from the given one by an appropriate class of transformations. If the null hypothesis being tested holds, the derived data sets should be equivalent to the original one. Thus, a small reported significance level indicates an unusual data set. This development parallels that of randomization tests, but there is a crucial technical difference: our approach involves permuting observed residuals; the classical randomization approach involves permuting unobservable, or perhaps nonexistent, stochastic disturbance terms.     

A Note on Overdifferencing and the Equivalence of Seasonal Time Series Models With Monthly Means and Models With (0, 1, 1)12 Seasonal Parts When ⊖ = 1

Two general models for monthly seasonal time series are considered, one in which seasonality is modeled with monthly means and another in which seasonality is modeled with a (0, 1, 1)₁₂ ARIMA structure. The models are shown to be equivalent if the seasonal moving average parameter (?) is 1 and if the same assumptions about the 12 initial observations are made for both models. The role of the assumptions about the initial observations is analyzed, and it is argued that for practical purposes the two models can be regarded as equivalent when ? = 1. It is observed that the result extends easily to more general models involving overdifferencing.     

Nonparametric product partition models for multiple change-points analysis

ABSTRACT

We propose an extension of parametric product partition models. We name our proposal nonparametric product partition models because we associate a random measure instead of a parametric kernel to each set within a random partition. Our methodology does not impose any specific form on the marginal distribution of the observations, allowing us to detect shifts of behaviour even when dealing with heavy-tailed or skewed distributions. We propose a suitable loss function and find the partition of the data having minimum expected loss. We then apply our nonparametric procedure to multiple change-point analysis and compare it with PPMs and with other methodologies that have recently appeared in the literature. Also, in the context of missing data, we exploit the product partition structure in order to estimate the distribution function of each missing value, allowing us to detect change points using the loss function mentioned above. Finally, we present applications to financial as well as genetic data.     

A monte carlo investigation of a statistic for a bivariate missing data problem

Testing the equal means hypothesis of a bivariate normal distribution with homoscedastic varlates when the data are incomplete is considered. If the correlational parameter, ρ, is known, the well-known theory of the general linear model is easily employed to construct the likelihood ratio test for the two sided alternative. A statistic, T, for the case of ρ unknown is proposed by direct analogy to the likelihood ratio statistic when ρ is known. The null and nonnull distribution of T is investigated by Monte Carlo techniques. It is concluded that T may be compared to the conventional t distribution for testing the null hypothesis and that this procedure results in a substantial increase in power-efficiency over the procedure based on the paired t test which ignores the incomplete data. A Monte Carlo comparison to two statistics proposed by Lin and Stivers (1974) suggests that the test based on T is more conservative than either of their statistics.     

Inequalities for Expected Current Record Statistics

In this article, we obtain sharp distribution-free bounds for the expected value of the gap between the current records and record values as well as upper sharp bounds for the spacings between any two upper current records. We also present two-sided bounds on the errors in approximating the means of current records by inverse hazard functions.     

数字资本主义在数字健康领域对公共利益价值内涵的重构

近年来,所有主要的大型消费科技公司都进入了健康研究领域。这种谷歌式数字健康引出一个问题,即在研究中,公共利益如何得以维系?只有将谷歌式数字健康置于数字资本主义的政治经济中,才能更好地突出公共利益和公共价值问题。以往的研究通常把谷歌式数字健康框定在一个双重的、不可通约的分析逻辑中,其中：私人利益与公共利益是对立的,经济价值与社会价值是对立的。虽然有助于揭示数字资本主义的剥削潜力,但是这种分析逻辑是有缺陷的,因为它只承认关于公共利益的一种理解。通过借鉴博尔坦斯基和泰弗诺的价值理论分析框架,可以发现：谷歌式数字健康对公共利益的价值内涵进行了多重塑造,不仅包括公民价值(为社会做好事),还包括市场价值(创造更多财富)、工业价值(提高生产效率)和项目价值(创新和实验)以及活力价值(促进生命健康)。如果把治理方案看作是一系列价值准则的组合,那么就可以设计出实现和保障公共利益的治理方案。基于此,提出研究进路：探索和构建更丰富的体现公共利益的价值准则体系,对其进行批判性评估,最终制定出能确保“公民”价值的可行治理方案。     

灾害短视频的拟像共同体传播：内聚焦叙事、价值转向与纪念仪式

为揭示灾害现场亲历者如何在媒介场域通过“影像文本”将“远处的灾难”的“感知物质空间”转化为大众切身体验的“共通意义空间”,探寻中介化过程中媒介叙事手法的基本机制及其价值转向,本文聚焦于重庆山火现场及土耳其地震亲历者发布的短视频在网络社交媒体“出圈”现象。灾害亲历者以第一人称视角拍摄的短视频通过内聚焦叙事与围观者跨越时空距离进行多维度的信息、情感、价值沟通,在中介化连接与扩散过程中增强“远处的灾难”的可感知度,并寻求社会性协助与支持,在媒介的纪念仪式中实现集体记忆认同的动态协商和反思性重构,由此有助于理解普罗大众“自传记忆”零碎的经验性文本自主传播进入社会公众视野,与专业媒体的职业创作共同构建“拟像共同体”的价值与意义。     

Strong consistency of least squares estimates with i.i.d. errors with mean values not necessarily defined

We establish strong consistency of the least squares estimates in multiple regression models discarding the usual assumption of the errors having null mean value. Thus, we required them to be i.i.d. with absolute moment of order r, 0<r<2, and null mean value when r>1. Only moderately restrictive conditions are imposed on the model matrix. In our treatment, we use an extension of the Marcinkiewicz–Zygmund strong law to overcome the errors mean value not being defined. In this way, we get a unified treatment for the case of i.i.d. errors extending the results of some previous papers.     

Risk Perception: Reflections on 40 Years of Research

Numerous studies and practical experiences with risk have demonstrated the importance of risk perceptions for people's behavior. In this narrative review, we describe and reflect upon some of the lines of research that we feel have been important in helping us understand the factors and processes that shape people's risk perceptions. In our review, we propose that much of the research on risk perceptions to date can be grouped according to three dominant perspectives and, thus, approaches to study design; they are: the characteristics of hazards, the characteristics of risk perceivers, and the application of heuristics to inform risk judgments. In making these distinctions, we also highlight what we see as outstanding challenges for researchers and practitioners. We also highlight a few new research questions that we feel warrant attention.     

Generational conflict and provisions for the elderly in Hong Kong: The relevance of position‐based interest and values

Respect for the elderly and hence the associated rights support a generational contract. Nevertheless, the case of the recent debate on the universal retirement scheme in Hong Kong has raised concerns about its strength. We identify relevant items from the sixth wave of the World Values Survey to explore the orientations of different generations toward others. The analysis shows differences between younger and older generations. Regression analyses show that values are not significant in explaining the differences, but various positions, including sex, marital status, and education, and perceptions of the elderly are relevant. Such differences across generations might weaken the generational contract and potentially engender conflicts as shown in the recent debate about the retirement scheme. We suggest short‐ and long‐term interest calculations, informed by their positions, play a more significant role in policy discussions and decisions, rather than simply appealing to values.