汉语词汇系统:网络性、层级性、平面性

词汇成分在词汇系统内部所形成的是以语言单位的共同特征为基础的聚合关系.聚合关系表现在语音、语义、语法、词汇、文字五个方面.它们以辐射状、连环状、并联状三种聚合方式聚合词汇成分,并由此形成了网络、序列、层面这样的聚合结果.这正是词汇系统的网络性、层级性、平面性的具体体现.     

论团队管理理论的有效性

团队管理理论是西方管理界极力主张的一种管理思想,该理论强调组织内部成员之间建立良好的人际关系对组织目标的实现具有重要作用.本文从团队管理理论的由来与发展问题入手,探讨了团队管理理论的主要内容,以及在当今社会组织中的重要效能,设置了创建高效业绩团队的对策.     

浅谈基于形义关系的英语动词教学

从英语动词的构词形式、词法结构以及动词之间的词法结构与其各自的意义之间的关系入手,探寻指导动词教学的一些具有普遍意义的规律,以期能更有效地进行教学,以便取得事半功倍的效果。     

Réglementation et avenir du travail: la relation de travail facilite l'innovation

La transformation numérique et la réorganisation des entreprises font apparaître de nouvelles modalités de travail fort éloignées de la relation de travail typique. Selon les partisans de la rupture numérique, le cadre juridique actuel n'est pas adapté aux formes de travail et modèles d'entreprises «innovants». Pourtant, la réglementation du travail peut faciliter l'innovation et la flexibilité, comme l'emploi typique peut être un moyen d'améliorer l'efficacité et de réduire les coûts. En effet, la relation de travail permet le plein exercice des prérogatives de l'employeur et le déploiement interne de la main-d'œuvre; c'est aussi un bon moyen d'assurer formation et montée des compétences.     

Sample size planning for testing significance of curves

Smoothing methods for curve estimation have received considerable attention in statistics with a wide range of applications. However, to our knowledge, sample size planning for testing significance of curves has not been discussed in the literature. This paper focuses on sample size calculations for nonparametric regression and partially linear models based on local linear estimators. We describe explicit procedures for sample size calculations based on non- and semi-parametric F-tests. Data examples are provided to demonstrate the use of the procedures.     

A Comparative Study of Semiparametric Estimation in Partially Linear Single-index Models

We consider a semiparametric method based on partial splines for estimating the unknown function and partially linear regression parameters in partially linear single-index models. Three methods—project pursuit regression (PPR), average derivative estimation (ADE), and a boosting method—are considered for estimating the single-index parameters. Simulations revealed that PPR with partial splines was superior in estimating single-index parameters, while the boosting method with partial splines performed no better than PPR and ADE. All three methods performed similarly in estimating the partially linear regression parameters. The relative performances of the methods are also illustrated using a real-world data example.     

Subset Selection in Linear Regression using Sequentially Normalized Least Squares: Asymptotic Theory

This article examines the recently proposed sequentially normalized least squares criterion for the linear regression subset selection problem. A simplified formula for computation of the criterion is presented, and an expression for its asymptotic form is derived without the assumption of normally distributed errors. Asymptotic consistency is proved in two senses: (i) in the usual sense, where the sample size tends to infinity, and (ii) in a non‐standard sense, where the sample size is fixed and the noise variance tends to zero.     

Two-sample high-dimensional empirical likelihood

In this paper, we apply empirical likelihood for two-sample problems with growing high dimensionality. Our results are demonstrated for constructing confidence regions for the difference of the means of two p-dimensional samples and the difference in value between coefficients of two p-dimensional sample linear model. We show that empirical likelihood based estimator has the efficient property. That is, as p → ∞ for high-dimensional data, the limit distribution of the EL ratio statistic for the difference of the means of two samples and the difference in value between coefficients of two-sample linear model is asymptotic normal distribution. Furthermore, empirical likelihood (EL) gives efficient estimator for regression coefficients in linear models, and can be as efficient as a parametric approach. The performance of the proposed method is illustrated via numerical simulations.     

Monitoring of autocorrelated general linear profiles

A collection of quality data represented by a functional relationship between response and explanatory variables is called a profile. In the literature, the errors of profiles are often assumed to be independent. However, quality data often exhibits time correlations in real applications. Therefore, in this paper, we investigate a general linear regression model with a between-profile autocorrelation. We propose a multivariate exponentially weighted moving average chart for monitoring shifts in the regression parameters, and an exponentially weighted moving average chart for monitoring shifts in the standard deviation. A simulation study reveals that our proposed schemes outperform competing existing schemes based on the average run length criterion. An example is used to illustrate the applicability of the proposed scheme.     

Simultaneous estimation of Cronbach’s alpha coefficients

The simultaneous estimation of Cronbachs alpha coefficients from q populations under the compound symmetry assumption is considered. In a multi-sample scenario, it is suspected that all the Cronbachs alpha coefficients are identical. Consequently, the inclusion of non-sample information (NSI) on the homogeneity of Cronbachs alpha coefficients in the estimation process may improve precision. We propose improved estimators based on the linear shrinkage, preliminary test, and the Steins type shrinkage strategies, to incorporate available NSI into the estimation. Their asymptotic properties are derived and discussed using the concepts of bias and risk. Extensive Monte-Carlo simulations were conducted to investigate the performance of the estimators.