獭兔养殖综合技术研究

对獭兔的适应性、饲养管理、疾病防治、产品加工等养殖综合技术进行了研究,提出了獭兔的“三改、两杯、两早一分和两选两适”规范化饲养管理制度和疾病综合防治技术措施,探索出了优质獭兔毛皮和“富钙兔肉脯”的加工工艺以及獭兔良种推广的有效途径。     

跨国公司经营业绩评价方法的研究

跨国公司经营业绩评价较之国内经营企业的经营业绩评价有其特殊的复杂性。本文提出跨国公司经营业绩评价的一种新方法,构造跨国公司经营业绩评价标准的综合效用值模型,建立跨国公司经营业绩评价系统的整体模型。     

基于灰关联约束超效率DEA的重点实验室评估研究——以河北省省级重点实验室为例

目前国内外对重点实验室的评估主要采用专家评价等软评价方法,较少考虑科技投入和科技产出之间的关联关系。在对国内外重点实验室评估方法进行深入研究的基础上,利用灰色关联度分析指标内在的关联性,将其作为限制条件加入超效率DEA评价模型中,构建了基于灰色关联权重约束的超效率DEA评价模型,并应用于河北省省级重点实验室评估的实证分析。研究结果显示,此模型与传统的CCR模型和超效率DEA模型相比,既可以揭示科技投入和科技产出之间的内在联系,又可以保持DEA评价的客观性,与此同时,还可实现不同实验室评估结果的全排序,使得评估结果更加符合实际。     

基于发展新理念的粮食类家庭农场成长绩效测评研究

基于发展新理念对粮食类家庭农场成长绩效的评价指标进行理论遴选后,借助专家评审和鉴别力分析进行指标筛选,构建了具有较高信度和效度的评价指标体系。以华北平原五省的453户粮食类家庭农场为样本,采用AHP-模糊综合评价模型,对指标体系做了应用研究。结果表明：粮食类家庭农场成长绩效虽在总体上处于中等偏上水平,但个体间存在一定的成长绩效差异;经济绩效整体表现良好,创新绩效却较低。因此,应引导粮食类家庭农场平衡性成长、制定精准扶持方略和建立成长绩效监测体系。     

习近平“三学一体”哲学社会科学综合发展体系重要论述的理论释义

构筑学生、学术、学科一体的综合发展体系是习近平总书记在哲学社会科学工作座谈会上的重要讲话精神之一,这一重要论述准确框定了哲学社会科学育人育才的全新格局,理论内涵丰富,实践指向明确,是新时代构建中国特色哲学社会科学的行动指南.“三学一体”哲学社会科学综合发展体系的构建是一项复杂的系统工程,高校作为哲学社会科学的主力军和育人育才的主阵地,必须将构筑“三学一体”综合发展体系作为实施哲学社会科学整体发展战略的有力抓手,坚持“以学生为根本和中心”“以学术为基础”“以学科为支撑”,围绕立德树人根本任务,充分发挥哲学社会科学的育人功能,把学科建设与育人育才两个目标相统一,基于学术创新能力提高育人育才质量.从理论前提上,明确体系中各要素的地位和作用、影响与关联,在实践探索中找到构建综合发展体系的主要思路和基本原则,进而更好地实现哲学社会科学育人育才的最大化和最优化.     

长江经济带生态效率时空格局演化及影响因素研究

文章基于108个城市2004-2014年市级面板数据测度了长江经济带城市生态效率的时空演变格局,综合而言,长江经济带生态效率水平呈现出波动上升态势,长江流域城市生态效率分化特征明显,湖南、湖北和四川等长江上游城市生态效率较高,长江上、中、下游地区城市生态效率依次递减.对生态效率的影响因素研究表明,科技创新显著地提高了长江经济带的生态效率,经济快速增长降低了城市生态效率,意味着转变经济增长方式、加快科技创新是提高城市生态效率的有效途径.研究还发现,产业结构偏离度有利于提高城市生态效率,而产业升级抑制了城市生态效率的增长,意味着产业升级并不是提高城市生态效率的捷径.     

手工业社会主义改造及其效率分析(1953—1956年)——基于浙江的研究

20世纪50年代,国家对手工业进行合作化改造。在此过程中除了为手工业提供必要的技术、资金、原料等方面的支持外,发生最大变化的是手工业的生产关系,国家取代以往众多的商人、工场主,成为实际意义上的“包买商”,试图以社会化大生产的方式来提高手工业的生产力,进而壮大社会主义经济成分。但合作化后期所反映出来的种种问题揭示了只有在生产关系基本适应生产力的情况下,才能真正、有效地推动生产力的发展。     

Local composite quantile regression estimation of time-varying parameter vector for multidimensional time-inhomogeneous diffusion models

This paper is dedicated to the study of the composite quantile regression (CQR) estimations of time-varying parameter vectors for multidimensional diffusion models. Based on the local linear fitting for parameter vectors, we propose the local linear CQR estimations of the drift parameter vectors, and verify their asymptotic biases, asymptotic variances and asymptotic normality. Moreover, we discuss the asymptotic relative efficiency (ARE) of the local linear CQR estimations with respect to the local linear least-squares estimations. We obtain that the local estimations that we proposed are much more efficient than the local linear least-squares estimations. Simulation studies are constructed to show the performance of the estimations proposed.     

Asymptotic relative efficiency of the linear discriminant function under partial nonrandom classification of the training data

This paper considers the problem where the linear discriminant rule is formed from training data that are only partially classified with respect to the two groups of origin. A further complication is that the data of unknown origin do not constitute an observed random sample from a mixture of the two under- lying groups. Under the assumption of a homoscedastic normal model, the overall error rate of the sample linear discriminant rule formed by maximum likelihood from the partially classified training data is derived up to and including terms of the first order in the case of univariate feature data. This first- order expansion of the sample rule so formed is used to define its asymptotic efficiency relative to the rule formed from a completely classified random training set and also to the rule formed from a completely unclassified random set.     

Data envelopment analysis in the presence of measurement error: case study from the National Database of Nursing Quality Indicators® (NDNQI®)

Data envelopment analysis (DEA) is the most commonly used approach for evaluating healthcare efficiency [B. Hollingsworth, The measurement of efficiency and productivity of health care delivery. Health Economics 17(10) (2008), pp. 1107–1128], but a long-standing concern is that DEA assumes that data are measured without error. This is quite unlikely, and DEA and other efficiency analysis techniques may yield biased efficiency estimates if it is not realized [B.J. Gajewski, R. Lee, M. Bott, U. Piamjariyakul, and R.L. Taunton, On estimating the distribution of data envelopment analysis efficiency scores: an application to nursing homes’ care planning process. Journal of Applied Statistics 36(9) (2009), pp. 933–944; J. Ruggiero, Data envelopment analysis with stochastic data. Journal of the Operational Research Society 55 (2004), pp. 1008–1012]. We propose to address measurement error systematically using a Bayesian method (Bayesian DEA). We will apply Bayesian DEA to data from the National Database of Nursing Quality Indicators^® to estimate nursing units’ efficiency. Several external reliability studies inform the posterior distribution of the measurement error on the DEA variables. We will discuss the case of generalizing the approach to situations where an external reliability study is not feasible.