Measuring non-linear dependence for two random variables distributed along a curve

We propose new dependence measures for two real random variables not necessarily linearly related. Covariance and linear correlation are expressed in terms of principal components and are generalized for variables distributed along a curve. Properties of these measures are discussed. The new measures are estimated using principal curves and are computed for simulated and real data sets. Finally, we present several statistical applications for the new dependence measures.     

“返乡潮”下农民工就业问题的研究

自2008年9月金融危机全面爆发后，我国东南沿海大量外向型的中小企业倒闭或大量裁员，农民工首当其冲，出现了“返乡潮”。在我国就业形势已经比较严峻的前提下，农民工“返乡潮”加剧了我国目前的就业压力。“返乡潮”的形成有多方面的原因，这就需要通过开展培训加强就业技能；创新失业保险金使用来稳定就业岗位；采取以工代赈拓宽就业渠道；建立失业预警机制等措施来解决农民工就业问题。     

Rank-based analysis of repeated measures block designs

A rank-based inference is developed for repeated measures balanced incomplete block and randomized complete block designs using a suitable dispersion function. Asymptotic distributions of rank estimators are developed after establishing approximate linearity of the gradient vector of the dispersion function. Unlike available nonparametric procedures for those designs, estimation and testing are tied together. Three different test statistics are developed for testing the linear hypotheses. Friedman's (1937) statistic and Durbin's (1951) statistic are particular cases of one of the three proposed statistics. An estimate of a scale parameter which appears in the ARE expression as well as as in the variences and covariances of the rank estimators is discussed.     

The behavior of estimated measures of association in small and moderate sample sizes for 2×3 tables

We consider the distributions of Goodman and Kruskal's G, Kendall's tau-b, and correlation coefficients rho and rho-s for sample sizes 10‘10’40 from 2×3 tables. The results are compared with asymptotic theory. It is found that the convergence of G to its asymptotic normal distribution is much slower than the convergence of the other measures to theirs, and that G is more likely to be significantly biased. However, the variances and biases of all four measures come close to their asymptotic values for quite moderate sample sizes.     

Influence analysis in multivariate linear general models

In this paper we obtain several influence measures for the multivariate linear general model through the approach proposed by Muñoz-Pichardo et al. (1995), which is based on the concept of conditional bias. An interesting charasteristic of this approach is that it does not require any distributional hypothesis. Appling the obtained results to the multivariate regression model, we obtain some measures proposed by other authors. Nevertheless, on the results obtained in this paper, we emphasize two aspects. First, they provide a theoretical foundation for measures proposed by other authors for the mul¬tivariate regression model. Second, they can be applied to any linear model that can be formulated as a particular case of the multivariate linear general model. In particular, we carry out an application to the multivariate analysis of covariance.     

New Goodness-of-Fit Tests Based on Sample Quantiles

In this article power divergences statistics based on sample quantiles are transformed in order to introduce new goodness-of-fit tests. Quantiles of the distribution of proposed statistics are calculated under uniformity, normality, and exponentiality. Several power comparisons are performed to show that the new tests are generally more powerful than the original ones.     

Closeness centrality via the Condorcet principle

We provide a characterization of closeness centrality in the class of distance-based centralities. To this end, we introduce a natural property, called majority comparison, that states that out of two adjacent nodes the one closer to more nodes is more central. We prove that any distance-based centrality that satisfies this property gives the same ranking in every graph as closeness centrality. The axiom is inspired by the interpretation of the graph as an election in which nodes are both voters and candidates and their preferences are determined by the distances to the other nodes.     

Variable order ante-dependence models

Ante-dependence models can be used to model the covariance structure in problems involving repeated measures through time. They are conditional regression models which generalize Gabriel’s constant-order ante-dependence model. Likelihood-based procedures are presented, together with simple expressions for likelihood ratio test statistics in terms of sum of squares from appropriate analysis of covariance. The estimation of the orders is approached as a model selection problem, and penalized likelihood criteria are suggested. Extensions of all procedures discussed here to situations with a monotone pattern of missing data are presented.     

我国中小型家具企业的问题及对策分析

我国中小型家具企业在人才结构、营销服务、市场管理、产品系列化、分配机制等方面存在很多问题,应该根据企业自身优势,明确企业定位,更新管理理念,重视人才引进和改善人才结构,打破家族管理模式,建立科学合理的分配机制,加强销售人员的终端培训,建立市场信息网络,谋求中小型家具企业的持久发展,始终保持较强的市场竞争力。     

女篮运动训练中常见的运动损伤及其预防措施探讨

篮球运动是一项集跑、跳、投等多种能力的综合性运动项目,快节奏、高速度、强对抗的特点,使其在训练、比赛中极容易造成不同身体部位的损伤,主要集合在膝、踝、腰背、小腿、手腕、手指等部位。常见损伤类型以关节韧带扭伤、肌肉拉伤居多,带伤训练比赛、旧伤病未痊愈和身体过度疲劳三个因素是致伤的主要原因,这些都可以通过加强医务监督和自我保护,合理、科学地安排训练内容等方法得到有效预防。