Diagnostics in elliptical regression models with stochastic restrictions applied to econometrics

We propose an influence diagnostic methodology for linear regression models with stochastic restrictions and errors following elliptically contoured distributions. We study how a perturbation may impact on the mixed estimation procedure of parameters in the model. Normal curvatures and slopes for assessing influence under usual schemes are derived, including perturbations of case-weight, response variable, and explanatory variable. Simulations are conducted to evaluate the performance of the proposed methodology. An example with real-world economy data is presented as an illustration.     

儒学与学问——关于儒学思想发展的学问化/人格化路径的省思

儒学,尤其是以孔子为代表的原始儒学,究竟是学问,还是哲学、宗教、科学?这是儒学传承历史中的重大问题,也是儒学思想现代化特别是儒学思想当下生存和未来发展所直接面对的根本问题。从儒学思想自身发生和发展的历史看,儒学思想的学科性质以及关于这种学科性质的理解,构成了儒学思想自身传承系统的演化路径和存在状态;在全球化语境中,儒学思想的学科性质以及关于这种学科性质的理解,决定了儒学思想当下生存和未来发展的前途和命运。虽然,在儒学思想自身发展的历史上,儒学曾经被作为哲学（哲理化）、宗教（信仰化）和科学（技术化）而获得传承和发展并取得巨大成就,但由于遮蔽了儒学教化个人如何做人如何做事的学问根本,从而也就与现实的个人的生活世界渐行渐远。在市场经济生活方式渐次主导社会生活的今天,激活和启动儒学教化个人如何做人如何做事的学问根本,有利于克制道德冷漠、伦理疏松和人性功利化的趋向,也是儒学思想在全球化条件下生存和发展的可能路径和生存策略。     

湖南沅江话“持续/进行体”的表达形式

对沅江话中五种"持续/进行体"的表达形式进行描写和分析,发现沅江话中主要通过在动词后加助词"着/哒/起"来表示各种持续或进行的意义,缺少普通话中于动词前加副词"正在/在"表示进行的方式。这五种形式及其具体意义分别是:表示暂且/短暂持续的"V下着、V哒(O)着";表示事物存在状态或行为伴随状态的持续的"一V起、V1起(O)V2";表示动作行为或存在状态或行为伴随状态的持续的"V哒O、V1哒(O)V2";表示动态行为的持续的"V餐V餐…、V餐(O)";表示动态行为的持续与进行的"在咯里/哦里+V"。     

汉语方言田野调查与音韵学

从高本汉最早的著作开始就把中国音韵学和方言田野调查紧密地结合起来。在中古音的构拟和间接的上古音研究方面,方言资料是相当重要的角色。早期的方言田野调查以字音为主,后来认识到俗传词才是探索方言历史的关键。     

笛卡儿"怀疑"的方法论研究

笛卡儿以理性为指导,以扬弃的方法和敢于怀疑一切的勇气寻求新知识的方法论原则,这种从"怀疑"开始的思维开辟了创新思维的先河,提出了研究事物的方法的重要性.在这个追求创新的时代,对我们现代人来说,转换思维方式以提高创新思维能力和勇气,进行理论和实践创新,在现实经济体制改革中选择适当的经济行为模式,正确认识处理社会出现的种种问题,具有一定的理论意义和现实意义.     

英汉“上／下”方位隐喻的认知文化对比分析

许多抽象概念的认知需借助于方位隐喻.由"上/下(up / down)"域产生的隐喻是应用最普遍的一种方位隐喻,它以"上/下(up / down)"的空间意义为核心拓展出一系列丰富的隐喻意义.本文对英汉方位隐喻中的"上/下"源域进行初步探讨,分析其认知基础,并深入剖析英汉语中"上/下"方位隐喻差异及其认知、文化根源     

Robust signed-rank estimation and variable selection for semi-parametric additive partial linear models

A fully nonparametric model may not perform well or when the researcher wants to use a parametric model but the functional form with respect to a subset of the regressors or the density of the errors is not known. This becomes even more challenging when the data contain gross outliers or unusual observations. However, in practice the true covariates are not known in advance, nor is the smoothness of the functional form. A robust model selection approach through which we can choose the relevant covariates components and estimate the smoothing function may represent an appealing tool to the solution. A weighted signed-rank estimation and variable selection under the adaptive lasso for semi-parametric partial additive models is considered in this paper. B-spline is used to estimate the unknown additive nonparametric function. It is shown that despite using B-spline to estimate the unknown additive nonparametric function, the proposed estimator has an oracle property. The robustness of the weighted signed-rank approach for data with heavy-tail, contaminated errors, and data containing high-leverage points are validated via finite sample simulations. A practical application to an economic study is provided using an updated Canadian household gasoline consumption data.     

Variable selection in elliptical linear mixed model

Variable selection in elliptical Linear Mixed Models (LMMs) with a shrinkage penalty function (SPF) is the main scope of this study. SPFs are applied for parameter estimation and variable selection simultaneously. The smoothly clipped absolute deviation penalty (SCAD) is one of the SPFs and it is adapted into the elliptical LMM in this study. The proposed idea is highly applicable to a variety of models which are set up with different distributions such as normal, student-t, Pearson VII, power exponential and so on. Simulation studies and real data example with one of the elliptical distributions show that if the variable selection is also a concern, it is worthwhile to carry on the variable selection and the parameter estimation simultaneously in the elliptical LMM.     

A COMPARISON OF SEQUENCING RULES FOR A TWO-STATE HYBRID FLOW SHOP

A common manufacturing environment in many industries (such as the glass, steel, paper, costume jewelry, and textile industries) is a hybrid flow shop. This system has continuous-process machinery in the fist state of manufacturing and repetitive-batch equipment in the second. Little research has investigated this type system. Scheduling managers of hybrid flow shops tend either to use existing job-shop rules or to devise their own rules. These approaches often are less than adequate for efficient scheduling. In this paper we extend the rule presented by Narasimhan and Panwalker [4] to include a general class of hybrid flow shops. This extenstion, called the generalized cumulative minimum-deviation (GCMD) rule, is compared under various operation conditions to three other sequencing rules: shortest processing time, longest processing time, and minimum deviation. The operating conditions are determined by the number of machines at both stages. The results of 7200 simulation runs demonstrate that the GCMD rule is better than the other rules in minimizing each of five chosen criteria. Thus, the GCMD rule can help managers to schedule hybrid flow shops efficiently to achieve various corporate objectives.