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。     

实证分析二氧化碳排放量主要影响因素

能源是经济与社会发展的基本动力, 但能源燃烧排放二氧化碳所引起的地球温暖化和气候变化更危及人类的生存。采用1990—2009年煤炭、石油、天然气消费结构数据, 电力生产系数, 道琼斯工业指数, 城镇人口数, 国内生产总值及二氧化碳排放量等统计数据进行灰色关联分析和协整检验;反映二氧化碳排放量与城镇人口数, 国内生产总值, 煤炭、石油、天然气消费结构, 电力生产系数之间的规律性变化, 从而突出协整检验的优越性。结果表明:城镇人口数, 国内生产总值, 煤炭、天然气消费结构, 电力生产系数是二氧化碳排放量的主要影响因素;道琼斯工业指数、石油消费结构是次要影响因素。     

区域一体化背景下城市土地利用效率的空间关联及其解释——以长江中游城市群为例

从理论层面揭示区域一体化影响城市土地利用效率（ULUE）的内在机理,构建区域一体化背景下“规模+结构+集聚”的ULUE测度体系,并综合利用小波神经网络、空间分异模型和空间回归分析探索2003-2015年长江中游城市群ULUE的空间关联特征及其影响因素。结果发现：①区域一体化背景下长江中游城市群ULUE从2003的0.561 4增长至2015年的0.811 1,且呈现出明显的区域差异。②ULUE并非随机分布,而是具有空间上的关联特征和集群趋势。且由于不同集聚区内扩散效应在空间上渗透不均衡,其关联性主要表现为空间依赖性和空间异质性。③区域一体化背景下长江中游城市群ULUE在空间关联上具有“近水楼台先得月”和“门当户对”的特征,地理位置的空间相邻、投资消费结构和产业结构的相似是其空间关联的主要成因。     

浙江省高职大学生体育价值观和体育活动体验的相关性研究

通过文献资料、问卷调查、数理统计等方法对浙江省高职大学生体育价值观与体育活动体验的相关性进行了调查研究。结果显示,浙江省高职大学生的体育价值观学生认为体育能愉悦心情、促进健康、丰富生活。根据调查数据,对不同类别的高职大学生的体育价值观和体育活动的体验进行了分析与比较。     

Averaging Lorenz curves

A large number of functional forms has been suggested in the literature for estimating Lorenz curves that describe the relationship between income and population shares. The traditional way of overcoming functional-form uncertainty when estimating a Lorenz curve is to choose the function that best fits the data in some sense. In this paper we describe an alternative approach for accommodating functional-form uncertainty, namely, how to use Bayesian model averaging to average the alternative functional forms. In this averaging process, the different Lorenz curves are weighted by their posterior probabilities of being correct. Unlike a strategy of picking the best-fitting function, Bayesian model averaging gives posterior standard deviations that reflect the functional-form uncertainty. Building on our earlier work (Chotikapanich and Griffiths, 2002), we construct likelihood functions using the Dirichlet distribution and estimate a number of Lorenz functions for Australian income units. Prior information is formulated in terms of the Gini coefficient and the income shares of the poorest 10% and poorest 90% of the population. Posterior density functions for these quantities are derived for each Lorenz function and are averaged over all the Lorenz functions.     

医学教育中隐性课程的相关性研究

近年来,隐性课程的研究成为一个热点问题,特别是在高等教育领域备受关注。从教育界对隐性课程的研究角度（隐性课程的概念、特征、功能、建设与管理几个方面）切入,对实践中存在的一些问题进行分析,以期对以后的研究有所帮助。     

A copula-based Markov chain model for the analysis of binary longitudinal data

A fully parametric first-order autoregressive (AR(1)) model is proposed to analyse binary longitudinal data. By using a discretized version of a copula, the modelling approach allows one to construct separate models for the marginal response and for the dependence between adjacent responses. In particular, the transition model that is focused on discretizes the Gaussian copula in such a way that the marginal is a Bernoulli distribution. A probit link is used to take into account concomitant information in the behaviour of the underlying marginal distribution. Fixed and time-varying covariates can be included in the model. The method is simple and is a natural extension of the AR(1) model for Gaussian series. Since the approach put forward is likelihood-based, it allows interpretations and inferences to be made that are not possible with semi-parametric approaches such as those based on generalized estimating equations. Data from a study designed to reduce the exposure of children to the sun are used to illustrate the methods.     

Quantile dispersion graphs to compare the efficiencies of cluster randomized designs

The purpose of this article is to compare efficiencies of several cluster randomized designs using the method of quantile dispersion graphs (QDGs). A cluster randomized design is considered whenever subjects are randomized at a group level but analyzed at the individual level. A prior knowledge of the correlation existing between subjects within the same cluster is necessary to design these cluster randomized trials. Using the QDG approach, we are able to compare several cluster randomized designs without requiring any information on the intracluster correlation. For a given design, several quantiles of the power function, which are directly related to the effect size, are obtained for several effect sizes. The quantiles depend on the intracluster correlation present in the model. The dispersion of these quantiles over the space of the unknown intracluster correlation is determined, and then depicted by the QDGs. Two applications of the proposed methodology are presented.     

Exact average coverage probabilities and confidence coefficients of confidence intervals for discrete distributions

For a confidence interval (L(X),U(X)) of a parameter θ in one-parameter discrete distributions, the coverage probability is a variable function of θ. The confidence coefficient is the infimum of the coverage probabilities, inf  _θ P _θ (θ∈(L(X),U(X))). Since we do not know which point in the parameter space the infimum coverage probability occurs at, the exact confidence coefficients are unknown. Beside confidence coefficients, evaluation of a confidence intervals can be based on the average coverage probability. Usually, the exact average probability is also unknown and it was approximated by taking the mean of the coverage probabilities at some randomly chosen points in the parameter space. In this article, methodologies for computing the exact average coverage probabilities as well as the exact confidence coefficients of confidence intervals for one-parameter discrete distributions are proposed. With these methodologies, both exact values can be derived.