A useful approach to identify the multicollinearity in the presence of outliers

The presence of outliers in the data sets affects the structure of multicollinearity which arises from a high degree of correlation between explanatory variables in a linear regression analysis. This affect could be seen as an increase or decrease in the diagnostics used to determine multicollinearity. Thus, the cases of outliers reduce the reliability of diagnostics such as variance inflation factors, condition numbers and variance decomposition proportions. In this study, we propose to use a robust estimation of the correlation matrix obtained by the minimum covariance determinant method to determine the diagnostics of multicollinearity in the presence of outliers. As a result, the present paper demonstrates that the diagnostics of multicollinearity obtained by the robust estimation of the correlation matrix are more reliable in the presence of outliers.     

Model Selection Information Criteria in Latent Class Models with Missing Data and Contingency Question

Latent class analysis (LCA) has been found to have important applications in social and behavioral sciences for modeling categorical response variables, and nonresponse is typical when collecting data. In this study, the nonresponse mainly included “contingency questions” and real “missing data.” The primary objective of this research was to evaluate the effects of some potential factors on model selection indices in LCA with nonresponse data.

We simulated missing data with contingency questions and evaluated the accuracy rates of eight information criteria for selecting the correct models. The results showed that the main factors are latent class proportions, conditional probabilities, sample size, the number of items, the missing data rate, and the contingency data rate. Interactions of the conditional probabilities with class proportions, sample size, and the number of items are also significant. From our simulation results, the impact of missing data and contingency questions can be amended by increasing the sample size or the number of items.     

Data Dependent Cells Chi‐Square Test With Recurrent Events

We consider a recurrent event wherein the inter‐event times are independent and identically distributed with a common absolutely continuous distribution function F. In this article, interest is in the problem of testing the null hypothesis that F belongs to some parametric family where the q‐dimensional parameter is unknown. We propose a general Chi‐squared test in which cell boundaries are data dependent. An estimator of the parameter obtained by minimizing a quadratic form resulting from a properly scaled vector of differences between Observed and Expected frequencies is used to construct the test. This estimator is known as the minimum chi‐square estimator. Large sample properties of the proposed test statistic are established using empirical processes tools. A simulation study is conducted to assess the performance of the test under parameter misspecification, and our procedures are applied to a fleet of Boeing 720 jet planes' air conditioning system failures.     

宅基地退出对农户收入的影响——基于倾向得分匹配(PSM)的反事实估计

基于宅基地退出试点区安徽金寨县626户农户的微观调研数据,在考虑样本选择偏差和农户异质性基础上,运用倾向得分匹配法(PSM)分析宅基地退出对农户收入的影响。结果发现:宅基地退出对农户经营性收入有负向影响,对农户工资性收入和家庭总收入有正向影响;农户宅基地退出,一方面通过缩小耕地经营面积、提高农业生产成本和减少亩均收入降低其经营性收入,另一方面通过增加外出务工人数、提高外出务工时长提升其工资性收入;由于农户工资性收入增加强度大于其经营性收入降低强度,故对其家庭总收入有正向影响;此外,不同教育背景、年龄段农户宅基地退出后收入变化存在差异,因此,应增强宅基地退出就业帮扶政策针对性,宣传宅基地退出政策的增收效应,实施差别化的宅基地退出政策安排。     

关于最低生活保障城乡一体化研究——以安徽省为例

目前,我国社会保障制度改革进入一个新的重要时期。围绕如何实现经济社会的全面协调可持续发展,安徽省已经开始在部分地区进行了社会救助管理城乡一体化实践。在取得了一定的积极成果的同时,也发现了漏洞和不足,尤其是城乡低保制度的失衡问题突出。进一步完善社会救助体系,推进城乡低保一体化建设,要结合区域特点,因地制宜,分阶段分步骤,逐步实现。要在探索建立城乡低保一体化架构、完善运行机制、加强过程控制、加强财政保障机制建设、城乡低保队伍建设等方面,进行改进和完善,进行全面系统的顶层设计和思考。     

儿童文学翻译标准的构建

儿童文学翻译标准是文学翻译理论的一个重要部分,也是衡量儿童文学翻译作品质量的主要因素.但是较之于市场上儿童文学作品的数量和种类,对儿童文学作品的衡量标准的研究少之又少.本文以佐哈尔·沙维特提出的翻译儿童文学作品的两条原则为指导,在此基础上阐发、细化、构建出更加具体的具有广泛适应性的儿童文学翻译标准.     

Did We Overestimate the Value of Health?

Adam Smith's idea that wage differences reveal preferences for risk rests on strong theoretical foundations. This paper argues, however, that the dominant approach to identify compensating wage differentials—regressing individual wages on aggregate measures of risk—may lead to arbitrary estimates of these risk differentials. In a dataset with information on both, the incidence of illnesses or injuries across firms and industries, I calculate an implicit value of one injury or illness of about (1990) USD 18,800 pursuing the dominant approach. In contrast, regressing wages on the incidence of risk across firms produces a value of one injury or illness of about USD 11,300.     

论翻译文学批评———特殊性、批评标准与批评方法

翻译文学批评对于翻译文学来说,非常必要,但做起来非常困难。它与一般的文学批评有共通性,更有特殊性。它是跨越不同语言的文学批评,必须建立在语言学批评的基础上。一般的文学批评可以“得意而忘言”,翻译文学批评却应该是“言意兼顾”,应该将语言学批评与关学批评结合起来;细节批评与总体批评结合起来;特殊的批评角度与全面公正的评价结合起来。为此,翻译文学批评应该有一定的标准。对我国翻译文学而言,“信达雅”是基本标准,“神似”、“化境”可以作为最高标准。在批评方式方法上,对同一原作的不同译本的比较批评,即译本比较批评,在翻译文学批评中是一种重要的、行之有效的批评方式。     

The elasticity of labor demand and the minimum wage

We show that, contrary to widespread belief, low-pay workers do not generally prefer that the minimum wage rate be increased until the labor demand is unitary elastic. Rather, there exists a critical value of elasticity of labor demand so that increases in the minimum wage rate make low-pay workers better off for higher elasticities, but worse off for lower elasticities. This critical value decreases with unemployment benefits and increases with workers’ risk aversion. We also show that in some countries the benefits for long-term unemployed are so low that workers would probably prefer that the minimum wage rate be decreased. I thank the anonymous referees for helpful suggestions.     

Confidence regions for Pareto parameters from a single and independent samples

[Abstract] Based on a single and on two independent samples, joint confidence regions for parameters of Pareto distributions are proposed with minimum volume properties and without assigning the confidence level to dimensions. In the one-sample case, comparisons are made to former simultaneous confidence sets for Pareto parameters by means of simulation and a real data set. The two-sample case is studied in various set-ups and comprises simultaneous confidence regions for the shape parameters, the scale parameters, and higher-dimensional vectors of these parameters, where common shape and common scale models are also considered.