农业生产风险管理策略的研究

农业生产经营风险管理所涉及的风险因素很多,主要有:(1)单产变化;(2)价格变化;(3)新技术或新知识变化;(4)农业投入水平变化;(5)政府政策变化;(6)法律变化;(7)消费者偏好变化,等等。通过权衡风险和收益,采用适宜的管理策略来规避风险,提高收益,形成了一系列非确定性关系下的风险决策方法。联系河北省20世纪90年代以来农业生产的实际情况,探讨了各种主要的规避或减少农业生产风险的方法和策略,如利用偏相关系数最小的方法选择稳定的生产项目;采用多种经营以分散风险的组合策略;提高农业生产经营灵活性的诸多措施;利用二次规划以建立风险最小而又能保证一定收入水平的最优规划方法等。     

河南省水资源消耗关联度与生态位适宜度研究

河南省水资源总消耗与各部门间耗水量综合关联度依次为生活及生态环境部门、工业部门和农业部门,但近年来水资源消耗尚未在部门间配置达到最优生态位,河南省水资源消耗在部门间配置有待进一步优化。因此,为提高水资源利用效率,优化水资源消耗在部门间的配置,既要加强产业结构升级和技术升级,提高水资源的利用效率,又要完善用水权制度,利用价格杠杆,做好水资源信息化建设。     

陕西省文化创意产业的产业聚集能力培育

为从理论探索文化创意产业的聚集能力培养的途径,运用产业聚集理论分析制约陕西文化创意产业发展的瓶颈根源。分析认为：陕西省文化资源的产业化开发环节薄弱、产业间及区域内发展不平衡导致了陕西文化创意产业的产业链条较短、产业关联度不高的现状,从而难以实现跨越式发展。为此,提出推动陕西省文化资源的产业化、建立高效的政府管理体制模式、进一步提高产业关联度等建议。     

绿色供应链管理动力/压力影响模型实证研究

文章借鉴国外文献及国内环保专家的意见设计问卷进行调研,通过因子分析识别出中国制造企业绿色供应链管理的压力/动力和实践的主要因子.对因子进行相关分析和回归分析,根据数据结合国内外相关情况探讨动力/压力是如何影响绿色供应链管理实践,提出中国制造企业绿色供应链管理的动力/压力影响模型.     

战略管理视角下的供应链绩效评价研究

传统的供应链绩效评价指标体系一般着重评价整个供应链效率或者评价节点企业而忽视了各个节点企业之间的联系、影响和制约。本文从建立学习型供应链企业战略的视角出发,构建以客户绩效、社会绩效、财务绩效和学习绩效为一级评价指标的供应链绩效评价指标体系。在评价方法上采用了层次分析法和灰色关联分析相结合的模型,对四个模拟供应链节点企业的实际数据进行分析,证实该指标评价体系和数学方法在实际操作中是可行的,比较准确地反映了供应链节点企业运营的实际效果。     

输入模态对认知负荷及听力成绩的影响

探讨了输入模态对学生认知负荷和听力理解的影响。90名来自两个平行班的大学英语学生被平均分为三个小组参加实验:文本输入组,声音输入组,文本和声音输入组。文本组的听力题目用文本呈现,声音组的听力题目仅用声音呈现,文本和声音组的题目则是采用文本和声音双重模态呈现。认知负荷量表结果和听力测试结果显示不同模态的听力输入形式对听力成绩有显著影响:文本组学生的成绩最高,其次是文本加音频组,音频输入组的成绩最低。认知负荷量表结果显示文本组的认知负荷最低,然后是文本加音频组,音频输入组的认知负荷最高。认知负荷与听力成绩的相关分析显示负荷程度与听力成绩呈负相关。研究结果对听力教学有较好的借鉴作用。     

Asymptotic distribution of regression correlation coefficient for Poisson regression model

This study treats an asymptotic distribution for measures of predictive power for generalized linear models (GLMs). We focus on the regression correlation coefficient (RCC) that is one of the measures of predictive power. The RCC, proposed by Zheng and Agresti is a population value and a generalization of the population value for the coefficient of determination. Therefore, the RCC is easy to interpret and familiar. Recently, Takahashi and Kurosawa provided an explicit form of the RCC and proposed a new RCC estimator for a Poisson regression model. They also showed the validity of the new estimator compared with other estimators. This study discusses the new statistical properties of the RCC for the Poisson regression model. Furthermore, we show an asymptotic normality of the RCC estimator.     

Testing for heteroscedasticity of exponential correlation mixed-effects linear models based on M-estimation

Homogeneity of variance is a basic assumption in longitudinal data analysis. However, the assumption is not necessarily appropriate. In this paper, Fisher scoring method is applied to get M-estimator in the exponential correlation mixed-effects linear model. The score tests for heteroscedasticity and correlation coefficient based on M-estimator are then studied. Monte Carlo method is applied to investigate the properties of test statistics. At last, the methods and properties are illustrated by an actual data example.     

Geometric Anisotropic Spatial Point Pattern Analysis and Cox Processes

We consider spatial point processes with a pair correlation function, which depends only on the lag vector between a pair of points. Our interest is in statistical models with a special kind of ‘structured’ anisotropy: the pair correlation function is geometric anisotropic if it is elliptical but not spherical. In particular, we study Cox process models with an elliptical pair correlation function, including shot noise Cox processes and log Gaussian Cox processes, and we develop estimation procedures using summary statistics and Bayesian methods. Our methodology is illustrated on real and synthetic datasets of spatial point patterns.     

Causality and interdependence analysis in linear econometric models with an application to fertility

This paper is an applied analysis of the causal structure of linear multi-equational econometric models. Its aim is to identify the kind of relationships linking the endogenous variables of the model, distinguishing between causal links and feedback loops. The investigation is first carried out within a deterministic framework and then moves on to show how the results may change inside a more realistic stochastic context. The causal analysis is then specifically applied to a linear simultaneous equation model explaining fertility rates. The analysis is carried out by means of a specific RATS programming code designed to show the specific nature of the relationships within the model.