Model selection criteria in beta regression with varying dispersion

We address the issue of model selection in beta regressions with varying dispersion. The model consists of two submodels, namely: for the mean and for the dispersion. Our focus is on the selection of the covariates for each submodel. Our Monte Carlo evidence reveals that the joint selection of covariates for the two submodels is not accurate in finite samples. We introduce two new model selection criteria that explicitly account for varying dispersion and propose a fast two step model selection scheme which is considerably more accurate and is computationally less costly than usual joint model selection. Monte Carlo evidence is presented and discussed. We also present the results of an empirical application.     

Evaluating modified generalized information criterion in presence of multicollinearity

When there are many explanatory variables in the regression model, there is a chance that some of these are intercorrelated. This is where the problem of multicollinearity creeps in due to which precision and accuracy of the coefficients is marred, and the quest to find the best model becomes tedious. To tackle such a situation, Model selection criteria are applied for selecting the best model that fits the data. Current study focuses on the evaluation of the four unmodified and four modified versions of generalized information criteria—Akaike Information Criterion, Schwarz's Bayes Information Criteria, Hannan-Quinn Information Criterion, and Akaike Information Criterion corrected for small samples. A simulation study using SAS software was carried out in order to compare the unmodified and modified versions of the generalized information criteria and to discover the best version amongst the four modified model selection criteria, for identifying the best model, when the collinearity assumption is violated. For the proposed simulation, two samples of size 50 and 100, for three explanatory variables X₁, X₂, and X₃, are drawn from Normal distribution. Two situations of collinearity violations between X₁ and X₂ are looked into, first when ρ = 0.6 and second when ρ = 0.8. The outcomes of the simulations are displayed in the tables along with visual representations. The results revealed that modified versions of the generalized information criteria are more sensitive in identifying models marred with high multicollinearity as compared to the unmodified generalized information criteria.     

新型订单农业合作模式的优化模型分析

根据当前的中央脱贫政策,本文考虑到脱贫方式,提出了基于新型合作模式的订单农业方式,构建了新型订单农业优化模型。首先根据区域经济发展需要成立一个农业合作社,农户以土地面积比例入股,合作社通过与农产品收购公司谈判并签订收购合同来获得收益,并把收益的一部分用于农户分红。合作社聘用职业经理负责经营,除此之外,合作社还会按所入股份给农户提供一份固定费用以保证农户的收益。根据上述内容构建了一个新的"农户+合作社+收购公司"型三级新型订单农业供应链优化模型,并在条件风险（CVaR）度量准则下得出合作社在不同风险规避度下的具体收益,在保证农户和合作社收益的前提下,建立相应的约束优化模型,利用拉格朗日函数及其相应的KKT条件得出农户加入合作社可获得更多利益的最小土地面积和合作社应提供的最小固定费用。     

论法与法之间抵触的判断规则——在放权地方立法背景下的观察

现行法律并未明确规定法与法之间抵触的判断标准,理论和实务也大多倾向于对抵触情形进行列举式描述。为了使抵触的判断标准更具操作性,从规范角度进行精细化建构尤为必要。法与法之间的抵触微观上是法的要素之间的抵触,法的要素包括法律概念、法律规则、法律原则,通过逻辑的整合,可以将法与法的抵触分为下位法规则与上位法原则的抵触、下位法规则与上位法规则的抵触。下位法应当遵循上位法的基本原则,但法律原则不应包括主观的立法者本意。对于不同位阶法律规则之间的抵触,法律规则逻辑结构仅可以作为抵触的抽象分类标准,至于具体的判断规则,仍需依据法律规范的内容种类进行类型化分析。     

基于序化机理的稳健型股票价值投资决策——一个研究框架

面对资本市场风险加剧的现实背景,以"公司经营业绩与股票市场业绩一致趋优"为稳健型投资的核心要素,立足于区间数据表示、会计信息度量两个关键要素,开展稳健型股票价值投资的多准则决策建模研究。面向稳健型投资决策目标,提出满足"稳健性""局部性""全局性"3个特性的序化机理,围绕关键特征选择、特征评价、全序化建模的主体脉络建立系统性多准则决策方法,进而构建"稳健型股票价值投资决策"的研究框架。     

马克思主义的价值评判标准为什么是正确的

马克思主义的价值评判标准是马克思主义理论的重要组成部分,它来源于无产阶级革命斗争的实践,突破了现代伦理学的意识内在性的困境,代表广大人民群众的需求和利益,具有鲜明的人民性。马克思主义的价值评判标准批判地继承了西方价值理论,对金钱本位的价值观进行了批判,体现了科学性与价值性的统一,并且处于不断的创新与发展之中。     

论服装设计大赛与高校服装设计教育的关系

近年来,中国各类服装设计大赛迅猛发展,在为服装行业挖掘设计人才的同时,也为中国高校服装设计教育提供了一个良好的行业氛围。本文通过分析服装大赛与高校服装设计教育之间的关系,总结了各类服装设计大赛的规则设置、评判标准及组织形式,并针对大赛参与各方如何共同进入一个良性的发展空间提出了若干建议。     

Lag Length Selection for Unit Root Tests in the Presence of Nonstationary Volatility

A number of recent papers have focused on the problem of testing for a unit root in the case where the driving shocks may be unconditionally heteroskedastic. These papers have, however, taken the lag length in the unit root test regression to be a deterministic function of the sample size, rather than data-determined, the latter being standard empirical practice. We investigate the finite sample impact of unconditional heteroskedasticity on conventional data-dependent lag selection methods in augmented Dickey–Fuller type regressions and propose new lag selection criteria which allow for unconditional heteroskedasticity. Standard lag selection methods are shown to have a tendency to over-fit the lag order under heteroskedasticity, resulting in significant power losses in the (wild bootstrap implementation of the) augmented Dickey–Fuller tests under the alternative. The proposed new lag selection criteria are shown to avoid this problem yet deliver unit root tests with almost identical finite sample properties as the corresponding tests based on conventional lag selection when the shocks are homoskedastic.     

我国近现代译论的演进与发展

本文针对我国近现代以来译学界在译论上的不同思潮进行了梳理，指出现已演进成各具特色，相互促进的四大译论流派。     

对现行数理统计方法质量评定判别式若干问题的商榷

通过对《公路工程质量检验评定标准》（ＪＴＪ０７１－９４）各种的质量评定判别式的可靠性的分析和探讨，提 出对现行采用的某些质量评定判别式的几点修正建议．