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。     

不同生产组织模式下农户技术效率研究——基于江苏省桃农的调研数据

基于2016年在江苏省桃主要产区调研所获得的微观数据,运用超越对数随机前沿生产函数模型,测算了不同生产组织模式下桃种植户的技术效率,并深入分析了影响其效率差异的主要因素。结果表明：不同组织模式下桃农生产技术效率存在明显差异,“农户+合作社（协会）”模式下桃农生产技术效率最高,“农户+企业”模式次之,“农户+市场”模式最低,组织化的农业生产方式能够在一定程度上改善要素投入配置,从而带来桃农技术效率的提高。在影响桃农技术效率的各主要因素中,种植规模、种植年限和受教育程度对不同模式下桃农技术效率有显著性影响,但影响方向和程度有所不同;家庭劳动力人口数和参加技术培训次数在“农户+合作社（协会）”模式下表现出显著的负效应。     

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。     

The efficiency of mutual fund companies: Evidence from an innovative network SBM approach

This paper is the first DEA (Data Envelopment Analysis) evaluation of the efficiency of a sample of mutual fund companies in a large Euro fund industry, i.e. Spain. Our novel network model links the efficiency of the core activities (portfolio management and marketing/distribution) of a mutual fund company to the efficiency of its operational management function. Our results highlight the important networking effects at the marketing stage and question the prime role of portfolio management skills in the overall efficiency of a mutual fund company. The evaluation of robust efficiency clusters indicates that the efficiency rankings obtained with our network model persist significantly along our sample period. Finally, the application of the SBM (Slacks-Based Measure) Variation III to our network model allows us to identify a large number of globally inefficient but locally efficient companies with reference to competitors with similar management resources.     

Estimation of variance in bivariate normal distribution after the preliminary test of homogeneity

A preliminary test estimator of variance in the bivariate normal distribution is proposed after the Pitman–Morgan test of homogeneity of two variances. The bias and mean square error of the estimator are derived. The relative efficiency (RE) of the preliminary test estimator is studied. Computations and 3D graphs of RE for different parameters are analyzed. In order to get the maximum RE, recommendations of the significance level for the preliminary test are given for various sample sizes by using the max–min criterion.     

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.     

On an asymptotic relative efficiency concept based on expected volumes of confidence regions

ABSTRACT

The paper deals with an asymptotic relative efficiency concept for confidence regions of multidimensional parameters that is based on the expected volumes of the confidence regions. Under standard conditions the asymptotic relative efficiencies of confidence regions are seen to be certain powers of the ratio of the limits of the expected volumes. These limits are explicitly derived for confidence regions associated with certain plugin estimators, likelihood ratio tests and Wald tests. Under regularity conditions, the asymptotic relative efficiency of each of these procedures with respect to each one of its competitors is equal to 1. The results are applied to multivariate normal distributions and multinomial distributions in a fairly general setting.