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.     

基于灰粗集属性知识简约算法的海运规则发现

海运市场运营规则能够帮助海运企业找到盈利增长点,针对海运市场营运规则难于被发现的问题,将灰色系统理论和粗糙集理论进行融合,针对知识约简中分辨函数范式转化过程中的逻辑推演难题,提出了一种基于灰色粗集融合属性的改进约简算法,选取2007年、2010年、2013年、2015年典型年份作为样本年份,应用灰色系统、粗糙集和基于灰粗集知识约简算法分别进行海运市场盈运规则发现实证分析,通过比较证明了该算法的时效性,同时发掘出了当前海运市场的船型、货品、航线、运价、运力间的营运管理规则。     

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.     

An empirical assessment of the operational performance through internal benchmarking: a case of a global logistics firm

This article solves an operational performance measurement problem of a global logistics firm through an internal benchmarking tool. The intended impact is to enable logistics firms to form a deeper understanding of their own internal processes and metrics. The methodology of this in-depth action research involves a sequential approach with a series of interviews, questionnaire-based surveys, operations data collated through observations and process mapping yielding real-world data. A series of statistical tests are conducted to analyse the collated data. Strategic priorities of the firm are integrated with the firm’s operational performance to ascertain the effective performance by considering both the tangible and intangible measures. The outcomes inform both practitioners and academics how the firm could improve its freight forwarding business’s profitability by ensuring that its operations meet the prioritised criteria. The ‘best practice’ derived from internal benchmarking forms an intermediate step towards external benchmarking. The outcomes facilitate investigating the current business strategy, the standard operating procedures and the scope of improving those.     

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.