民国时期广州穆斯林文人的忧患意识

作者认为,民国时期广州穆斯林文人忧患于广大穆斯林宗教知识的匮乏、汉文化水平的低下等,创办学校和报刊,加强宣传。针对对伊斯兰教的侮教案和基督教的误解做出回应,呼唤民族觉醒,振兴伊斯兰文化,以期树立民族自信心。他们为广东地区伊斯兰文化的延续做出了贡献,同时他们的作品也为后人留下了珍贵史料。作者对当时回族知识分子予以一定评价。     

目标取向对员工创新行为的影响机制研究——基于隐性知识共享行为的视角

在目前中国经济创新驱动转型的时代,企业对员工个人创新行为的依赖度越来越高.文章基于11家企业184名被试的问卷调查数据,采用结构方程分析方法,探索员工不同的目标取向通过隐性知识共享行为对创新行为的影响机制.研究结果表明,学习目标取向对隐性知识共享行为产生正向影响;绩效证明取向对隐性知识共享行为产生正向影响;绩效回避取向对隐性知识共享行为的影响效果不显著;同时,隐性知识共享行为对员工创新行为有显著影响,目标取向通过隐性知识共享行为对员工创新行为起间接作用.该研究结论进一步拓展了隐性知识共享行为在目标取向和员工创新行为中间接作用的研究领域,也为创新驱动型组织建设提供理论指导依据.     

论数+形+量结构中量词的特点

现代汉语中数量词中插入形容词的结构即数+形+量结构比较常见,例如,一大张纸,一大群人,一长根铁丝。陆俭明先 生对这种语言现象进行了详细的描写,但是还是留下了一些问题给大家讨论。例如,为什么数量词中间可以插入形容词,插入 形容词对整个结构有什么影响。从量词的特点和形容词的语义指向来看,数+形+量结构中的量词具有空间性、物质性、描写 性的特点,使得形容词能够进入该结构,形容词的语义指向不同,使整个结构有不同的表达功能。     

新工业革命下我国制造业发展路径分析

探讨新工业革命下我国制造业转型升级的路径。阐述我国制造业的现状,分析新工业革命给我国制造业发展带来的挑战和机遇,提出我国制造业战略转型升级的新思路:以两化融合为契机,加大制造业人力资本投入,加强核心技术研发,完善制造业公共创新服务体系并进一步构建新型政策扶持机制,推进科技成果转化。     

可行能力与农民工的福利状况评价

基于辽宁省沈阳市农民工调查数据,应用森的可行能力理论和模糊数学方法对农民工的福利状况进行整体评价。结果发现,农民工福利水平的总模糊指数为0.365,处于较低水平。分指标看,健康状况方面的福利状况处于较高水平,居住环境和精神感受方面的福利状况处于中间水平,其它功能性指标的福利水平较低。在影响农民工福利的主要因素中,工作环境和防护性保障的改善对提升农民工福利影响最大。运用OLS回归模型对农民工的福利状况进行扩展分析,发现农民工群体内部存在较强的异质性,并且不同特征的农民工群体福利水平相差很大。     

Imposing and Testing for Shape Restrictions in Flexible Parametric Models

In many economic models, theory restricts the shape of functions, such as monotonicity or curvature conditions. This article reviews and presents a framework for constrained estimation and inference to test for shape conditions in parametric models. We show that “regional” shape-restricting estimators have important advantages in terms of model fit and flexibility (as opposed to standard “local” or “global” shape-restricting estimators). In our empirical illustration, this is the first article to impose and test for all shape restrictions required by economic theory simultaneously in the “Berndt and Wood” data. We find that this dataset is consistent with “duality theory,” whereas previous studies have found violations of economic theory. We discuss policy consequences for key parameters, such as whether energy and capital are complements or substitutes.     

On asymptotic distribution of prediction in functional linear regression

There has been substantial recent attention on problems involving a functional linear regression model with scalar response. Among them, there have been few works dealing with asymptotic distribution of prediction in functional linear regression models. In recent literature, the centeral limit theorem for prediction has been discussed, but the proof and conditions under which the random bias terms for a fixed predictor converge to zero have been ignored so that the impact of these terms on the convergence of the prediction has not been well understood. Clarifying the proof and conditions under which the bias terms converge to zero, we show that the asymptotic distribution of the prediction is normal. Furthermore, we have derived those results related to other terms that already obtained by others, under milder conditions. Finally, we conduct a simulation study to investigate performance of the asymptotic distribution under various parameter settings.     

Functional Partial Linear Single‐index Model

This paper deals with the problem of predicting the real‐valued response variable using explanatory variables containing both multivariate random variable and random curve. The proposed functional partial linear single‐index model treats the multivariate random variable as linear part and the random curve as functional single‐index part, respectively. To estimate the non‐parametric link function, the functional single‐index and the parameters in the linear part, a two‐stage estimation procedure is proposed. Compared with existing semi‐parametric methods, the proposed approach requires no initial estimation and iteration. Asymptotical properties are established for both the parameters in the linear part and the functional single‐index. The convergence rate for the non‐parametric link function is also given. In addition, asymptotical normality of the error variance is obtained that facilitates the construction of confidence region and hypothesis testing for the unknown parameter. Numerical experiments including simulation studies and a real‐data analysis are conducted to evaluate the empirical performance of the proposed method.