Within groups analysis of covariance: multiple comparisons at specified design points using a robust measure location when there is curvature

Consider the situation where measurements are taken at two different times and let M_j(x) be some conditional robust measure of location associated with the random variable Y at time j, given that some covariate X=x. The goal is to test H₀: M₁(x)=M₂(x) for each x∈ x₁,?…?, x_K such that the probability of one or more Type I errors is less than α, where x₁,?…?, x_K are K specified values of the covariate. The paper reports simulation results comparing two methods aimed at accomplishing this goal without specifying some parametric form for the regression line. The first method is based on a simple modification of the method in Wilcox [Introduction to robust estimation and hypothesis testing. 3rd ed. San Diego, CA: Academic Press; 2012, Section 11.11.1]. The main result here is that the second method, which has never been studied, can have higher power, sometimes substantially so. Data from the Well Elderly 2 study, which motivated this paper, are used to illustrate that the alternative approach can make a practical difference. Here, the estimate of M_j(x) is based in part on either a 20% trimmed mean or the Harrell–Davis quantile estimator, but in principle the more successful method can be used with any robust location estimator.     

Predicted outcomes in a national sample: A source of comparison for program evaluation

The lack of comparison groups is a common problem for researchers conducting evaluations of small, specialized programs. When conventional comparison groups are not available, the authors suggest a possible alternative approach which involves: (a) locating a sub-group in a national study for whom relevant variables have been measured; (b) generating a statistical model which predicts the outcome of interest in the national data; and (c) applying the predictive model to program data, in order to estimate outcomes in the absence of progam intervention. The authors illustrate this new application by presenting the results of an analysis of data from Project Talent, a national manpower study, and discuss its application to the evaluation of a medical training program in New York City.     

Comparing two dependent groups via quantiles

This paper considers two general ways dependent groups might be compared based on quantiles. The first compares the quantiles of the marginal distributions. The second focuses on the lower and upper quantiles of the usual difference scores. Methods for comparing quantiles have been derived that typically assume that sampling is from a continuous distribution. There are exceptions, but generally, when sampling from a discrete distribution where tied values are likely, extant methods can perform poorly, even with a large sample size. One reason is that extant methods for estimating the standard error can perform poorly. Another is that quantile estimators based on a single-order statistic, or a weighted average of two-order statistics, are not necessarily asymptotically normal. Our main result is that when using the Harrell–Davis estimator, good control over the Type I error probability can be achieved in simulations via a standard percentile bootstrap method, even when there are tied values, provided the sample sizes are not too small. In addition, the two methods considered here can have substantially higher power than alternative procedures. Using real data, we illustrate how quantile comparisons can be used to gain a deeper understanding of how groups differ.     

Small-Sample Quantile Estimators in a Large Nonparametric Model

The large nonparametric model in this note is a statistical model with the family ? of all continuous and strictly increasing distribution functions. In the abundant literature of the subject, there are many proposals for nonparametric estimators that are applicable in the model. Typically the kth order statistic X _k:n is taken as a simplest estimator, with k = [nq], or k = [(n + 1)q], or k = [nq] + 1, etc. Often a linear combination of two consecutive order statistics is considered. In more sophisticated constructions, different L-statistics (e.g., Harrel–Davis, Kaigh–Lachenbruch, Bernstein, kernel estimators) are proposed. Asymptotically the estimators do not differ substantially, but if the sample size n is fixed, which is the case of our concern, differences may be serious. A unified treatment of quantile estimators in the large, nonparametric statistical model is developed.     

Global comparisons of medians and other quantiles in a one-way design when there are tied values

For J ? 2 independent groups, the article deals with testing the global hypothesis that all J groups have a common population median or identical quantiles, with an emphasis on the quartiles. Classic rank-based methods are sometimes suggested for comparing medians, but it is well known that under general conditions they do not adequately address this goal. Extant methods based on the usual sample median are unsatisfactory when there are tied values except for the special case J = 2. A variation of the percentile bootstrap used in conjunction with the Harrell–Davis quantile estimator performs well in simulations. The method is illustrated with data from the Well Elderly 2 study.     

美国高校教师聘任与管理的特点及启示——以加州大学戴维斯分校为例

高校教师聘任及晋升是高校发展的一项重要管理工作,事关学校的核心竞争力和人才培养质量。加州大学戴维斯分校作为美国一所典型的公立大学,其教师聘任管理理念及制度可以代表美国的主流做法。本文详细分析加州大学戴维斯分校的教师招聘流程、聘任制度、教师管理、考核及晋升体系,并对其中的理念及特点进行总结,即以提升教师素质为出发点,健全的人事制度为保障,明晰的管理和流程为支撑,责任负责制为监督。我国应该在立足国情的基础上,认真研究和总结国外教师聘任制的先进经验和特点,从中寻求借鉴和启示,建立和健全我国高校教师聘任新体制,使我国高等教育不断走向国际化。     

论中国古典文学作品翻译中的受众意识——以德庇时英译《好逑传》为例

翻译中国古典文学作品能更好地弘扬中华民族优秀文化,增强中国文化软实力,促进东西方文化的交流与理解。但因缺乏受众意识,中国文学典籍“走出去”步履维艰。从本质上看,文学典籍翻译是一种跨文化、跨语际的信息传播,受众是影响传播过程的要素之一。译者只有具备受众意识,仔细分析受众类型与受众接受动机,才能真正有效地推动中国文学“走出去”。       

Limit Laws for Maxima of Contracted Stationary Gaussian Sequences

The principal results of this contribution are the weak and strong limits of maxima of contracted stationary Gaussian random sequences. Due to the random contraction we introduce a modified Berman condition which is sufficient for the weak convergence of the maxima of the scaled sample. Under a stronger assumption the weak convergence is strengthened to almost convergence.