Correlates of experiences and perceptions of anti-Semitism among Jews in the United States

This paper investigates American Jews’ personal experiencing of anti-Semitism and perception of its extent. Analysis of NJPS-2000/2001 indicates that lower age, less education, and American nativity increase experiencing of anti-Semitism. Religious identification and attachment to Israel are positively associated with anti-Semitic experience; friendship with other Jews has the opposite effect. Contextual factors are not significant for the experiencing of anti-Semitism but living in a state that leans toward the Democratic Party has a downward effect. Contrary to experience, younger age and higher education are negatively associated with the perception of a high incidence of anti-Semitism. Being a woman, American born, and living in states with high concentrations of Jews positively affect Jews’ perception of anti-Semitism. A paramount determinant of the perception of anti-Semitism is the individual’s belief that he or she has experienced it. The results are discussed in reference to three working hypotheses of integration, group identification, and environment.     

不同文化的差异干扰第二语言学习的问题

不 同文化的差异对 第二语言学习 者有着干扰：词语 涵义不同;对道 德、价值的认 识评价不同。于是,受 汉文化的影响 ,中国学生将 汉语翻 译成英 语时, 往往 产生“中国 式英 语”的问题。为此,在英语 教学中,要采取 相应的对策：教材 应增选反映文 化差异的材料;鼓 励学生 广泛阅读;聘请外 籍英语教师;利用 电影、录像等现 代化教学手段     

微翅管几何参数对混合工质流动沸腾特性的影响

对非共沸混合工质R32／R134a在水平微翅管内的流动沸腾特性进行了实验研究，获得了微翅管的翅高和翅数对换热性能的影响，并综合比较了3根微翅管的换热性能。同时，将微翅管与光管性能进行了比较。结果表明，在相同工况下，翅高为0．2mm、翅数为60的换热性能最好；微翅管的换热性能明显优于光管。     

Managing the International Strategic Sourcing Process as a Market-Driven Organizational Learning System*

This study examines the effects of market-driven organizational learning on a select set of strategic sourcing outcomes involving the sourcing function of a Fortune 500 multinational corporation. The focus is on the dyadic activities and relationships between the domestic and international strategic business units (SBUs) and the corporate buying center. The hypothesized relationships are tested on the overall sample and based on a two-by-two matrix framework defined by international versus domestic settings and frequent versus infrequent sourcing scenarios. Based on a sample of 179 domestic and 167 international SBUs, the results suggest that market-driven organizational learning in the strategic sourcing units influences customer satisfaction, relationship commitment, and cycle time of the sourcing process. However, while the set of learning orientations appears to be strategically robust across the four quadrant scenarios (moderator settings), the sourcing situations differ somewhat in the specific tactical learning orientations preferred.     

Zero-inflated Poisson model with clustered regression coefficients: Application to heterogeneity learning of field goal attempts of professional basketball players

Although basketball is a dynamic process sport, played between two sides of five players each, learning some static information is essential for professional players, coaches, and team managers. In order to have a deep understanding of field goal attempts among different players, we propose a zero-inflated Poisson model with clustered regression coefficients to learn the shooting habits of different players over the court and the heterogeneity among them. Specifically, the zero-inflated model captures a large portion of the court with zero field goal attempts, and the mixture of finite mixtures model captures the heterogeneity among different players based on clustered regression coefficients and inflated probabilities. Both theoretical and empirical justification through simulation studies validate our proposed method. We apply our proposed model to data from the National Basketball Association (NBA), for learning players' shooting habits and heterogeneity among different players over the 2017–2018 regular season. This illustrates our model as a way of providing insights from different aspects.     

Estimating mixtures of normal distributions via empirical characteristic function

This paper uses the empirical characteristic function (ECF) procedure to estimate the parameters of mixtures of normal distributions. Since the characteristic function is uniformly bounded, the procedure gives estimates that are numerically stable. It is shown that, using Monte Carlo simulation, the finite sample properties of th ECF estimator are very good, even in the case where the popular maximum likelihood estimator fails to exist. An empirical application is illustrated using the monthl excess return of the Nyse value-weighted index.     

Hypothesis Testing of Hazard Ratio Parameters in Marginal Models for Multivariate Failure Time Data

Marginal hazard models for multivariate failure time data have been studied extensively in recent literature. However, standard hypothesis test statistics based on the likelihood method are not exactly appropriate for this kind of model. In this paper, extensions of the three commonly used likelihood hypothesis test statistics are discussed. Generalized Wald, generalized score and generalized likelihood ratio tests for hazard ratio parameters in a marginal hazard model for multivariate failure time data are proposed and their asymptotic distributions examined. The finite sample properties of these statistics are studied through simulations. The proposed method is applied to data from Busselton Population Health Surveys.     

Multilevel models for repeated binary outcomes: attitudes and voting over the electoral cycle

Models for fitting longitudinal binary responses are explored by using a panel study of voting intentions. A standard multilevel repeated measures logistic model is shown to be inadequate owing to a substantial proportion of respondents who maintain a constant response over time. A multivariate binary response model is shown to be a better fit to the data.     

Biased Bootstrap Methods for Reducing the Effects of Contamination

Contamination of a sampled distribution, for example by a heavy-tailed distribution, can degrade the performance of a statistical estimator. We suggest a general approach to alleviating this problem, using a version of the weighted bootstrap. The idea is to 'tilt' away from the contaminated distribution by a given (but arbitrary) amount, in a direction that minimizes a measure of the new distribution's dispersion. This theoretical proposal has a simple empirical version, which results in each data value being assigned a weight according to an assessment of its influence on dispersion. Importantly, distance can be measured directly in terms of the likely level of contamination, without reference to an empirical measure of scale. This makes the procedure particularly attractive for use in multivariate problems. It has several forms, depending on the definitions taken for dispersion and for distance between distributions. Examples of dispersion measures include variance and generalizations based on high order moments. Practicable measures of the distance between distributions may be based on power divergence, which includes Hellinger and Kullback–Leibler distances. The resulting location estimator has a smooth, redescending influence curve and appears to avoid computational difficulties that are typically associated with redescending estimators. Its breakdown point can be located at any desired value ε∈ (0, ½) simply by 'trimming' to a known distance (depending only on ε and the choice of distance measure) from the empirical distribution. The estimator has an affine equivariant multivariate form. Further, the general method is applicable to a range of statistical problems, including regression.