A new algorithm to estimate monotone nonparametric link functions and a comparison with parametric approach

The generalized linear model (GLM) is a class of regression models where the means of the response variables and the linear predictors are joined through a link function. Standard GLM assumes the link function is fixed, and one can form more flexible GLM by either estimating the flexible link function from a parametric family of link functions or estimating it nonparametically. In this paper, we propose a new algorithm that uses P-spline for nonparametrically estimating the link function which is guaranteed to be monotone. It is equivalent to fit the generalized single index model with monotonicity constraint. We also conduct extensive simulation studies to compare our nonparametric approach for estimating link function with various parametric approaches, including traditional logit, probit and robit link functions, and two recently developed link functions, the generalized extreme value link and the symmetric power logit link. The simulation study shows that the link function estimated nonparametrically by our proposed algorithm performs well under a wide range of different true link functions and outperforms parametric approaches when they are misspecified. A real data example is used to illustrate the results.     

Multi-neighborhood based iterated tabu search for routing and wavelength assignment problem

The routing and wavelength assignment problem (RWA) has shown to be NP-hard if the wavelength continuity constraint and the objective of minimizing the number of wavelengths are considered. This paper introduces a multi-neighborhood based iterated tabu search algorithm (MN-ITS), which consists of three neighborhoods with a unified incremental evaluation method, to solve the min-RWA problem. The proposed MN-ITS algorithm is tested on a set of widely studied real world instances as well as a set of challenging random ones in the literature. Comparison with other reference algorithms shows that the MN-ITS algorithm is able to improve five best upper bounds obtained by other competitive reference algorithms in the literature. This paper also presents an analysis to show the significance of the unified incremental evaluation technique and the combination of multiple neighborhoods.     

景气指标分类方法的理论研究与实证

景气指标分类是景气预测法中至关重要的环节.在以往的文献中,一般采用一种方法对备选指标进行景气分类,但其结果还存在优化的空间.文章采用四种典型的景气指标分类方法:峰谷对应法、时差相关分析法、K-L信息量法、灰色关联度指标分类法对同样的多个指标进行分类.按照初定方案对四种方法的结果进行综合分析,对结果进行优化调整,应用实例表明:四种方法的优化调整结果比单一方法的结果更合理、更有效、更具有科学性.     

以行为分析学为指导科学构建学生综合素质考评体系

目前,大部分高职院校对学生的考核模式不适应社会对高职院校学生的要求。应当建立科学的考评体系,对学生的行为进行科学的分析,为学生综合素质考评提供依据,促使学生能扬长避短,不断进步。     

基于改进贝叶斯网络的省级政府债务风险预警模型

利用格兰杰非因果关系检验确定贝叶斯网络节点,反映经济变量之间的影响方式。采用误判率最小原则,确定预警指标临界值。利用贝叶斯网络学习,确定贝叶斯网络节点的后验概率。利用贝叶斯网络推理,测算地方政府债务风险,计算预警指标变化对省级政府债务违约概率的影响。研究结果表明:财政收入/财政支出与GDP增速/债务增速是预警省级政府债务风险的最重要指标,保持债务依存度、GDP增速/债务增速和民间投资增速/政府债务增速在适度区间内,能够有效降低省级政府债务风险。     

刍议历史教学中史料选择三原则

史料在历史教学与研究中,作用十分巨大,须注重史料的挖掘与利用.在利用和选择史料时,尤应重视史料的典型性、鉴别性和全面性.     

幼儿快乐阅读习惯的培养路径

阅读是一种良好的习惯,它对人的一生发展起着十分重要的作用。近年来，学前阅读的重要性受到了社会的高度关注，大家纷纷开展多种形式的幼儿阅读活动,然而,如何在学前阶段培养幼儿良好的阅读意识、阅读方法，为他们进入学校后开展书面语言学习及终身学习打下良好的基础，是目前幼儿教育工作中的一个重要问题之一。因此,我们积极地开展了以培养幼儿快乐的阅读习惯为主要内容的探索,旨在通过为幼儿创设适宜的阅读环境,激发幼儿快乐开展阅读活动。     

Cramér Asymptotic Efficiency in a Semiparametric Model

This article is concerned with efficient estimation in a semiparametric model. We consider pseudo maximum likelihood estimation and prove that the proposed estimator is asymptotically efficient in the sense of Cramér; that is, the estimator has the smallest mean squared error.     

“意义”的缺在与追寻——从胡塞尔到德里达与泰勒

在《几何学的起源》中,胡塞尔以几何学为示范,采用生成现象学的方法试图寻找具有理想客观性的意义.对此,德里达指出,胡塞尔的工作仍停留在“构成性”的分析之内,并在《胡塞尔〈几何学的起源〉导引》中,从语言的差异性人手将胡塞尔关于意义的几何学式梦想无限地延异了.胡塞尔的研究主要以历史学的考察来展开并完成,这当中有着鲜明的目的论色彩.而泰勒在《差错:一种后现代非/神学》中通过对《圣经》文本进行类型学分析,深入揭示了逻各斯中心在历史构成中的作用,这一祛魅式的研究更加印证了德里达对胡塞尔构成性分析的评论.经过德里达与泰勒的研究,胡塞尔渴望的理想客观性意义的永恒在场正是以缺在的方式表明自身的存在,这种“缺在的意义”与“为获得意义而建构历史”之间的内在张力则构成了对意义不断寻找的激情,为人们在历史中对那个缺在的意义进行更加丰富而多元的阐释敞开空间.