Robust multivariate analysis for mixed-type data: Novel algorithm and its practical application in socio-economic research

We propose a novel method and algorithm for the analysis and clustering of mixed-type data using a hierarchical approach based on Forward Search. In our procedure, the identification of groups is based on the identification of similar trajectories and then linked to very intuitive two-dimensional maps. The proposed algorithm can use different measures for the calculation of distance in the case of mixed-type data, such as Gower’s metric and Related metric scaling. A key feature of our algorithm is its ability to discard redundant information from a given set of variables. The practical usefulness of the algorithm is illustrated through two applications of high relevance for empirical economic research. The first one focuses on comparing different indicators of environmental policy stringency in different countries. The second one applies our procedure to identify clusters of countries based on information regarding their institutional characteristics.     

Metafrontier efficiency analysis for hospital pharmacy services using dynamic network DEA framework

Medication management is a complex but critical process within the healthcare delivery system. The pharmacy department in healthcare facilities is responsible for acquisition, distribution and dispensing of medication to patients that involves a series of network process. This process can be categorized into two main subprocesses: i) pharmacy store and ii) patient services (i.e., ambulatory and inpatient pharmacy). The objective of this study was to investigate the relative efficiency level of the two subprocesses of pharmacy services in Malaysia's public hospitals. We used a nonconvex metafrontier technology to estimate technical efficiency of hospital pharmacies between specialist and nonspecialist hospital categories under the dynamic network data envelopment analysis (DNDEA) framework. For overall efficiency, both specialist and nonspecialist categories had similar mean efficiency scores. However, when measuring the efficiency level according to pharmacy store and patient services separately, the nonspecialist category had a higher mean efficiency score in pharmacy store, whereas for patient service efficiency, both categories had similar results. The patient service efficiency level was lower consistently over time, suggesting that the overall inefficiency of hospital pharmacy is greatly influenced by the inefficiency of the patient service. This suggests that systematic improvements in patient service efficiency should be a higher priority than pharmacy store efficiency to further increase the overall efficiency level. We believe that our findings will provide valuable information to policymakers towards achieving an efficient public healthcare service.     

Optimal model averaging forecasting in high-dimensional survival analysis

This article considers ultrahigh-dimensional forecasting problems with survival response variables. We propose a two-step model averaging procedure for improving the forecasting accuracy of the true conditional mean of a survival response variable. The first step is to construct a class of candidate models, each with low-dimensional covariates. For this, a feature screening procedure is developed to separate the active and inactive predictors through a marginal Buckley–James index, and to group covariates with a similar index size together to form regression models with survival response variables. The proposed screening method can select active predictors under covariate-dependent censoring, and enjoys sure screening consistency under mild regularity conditions. The second step is to find the optimal model weights for averaging by adapting a delete-one cross-validation criterion, without the standard constraint that the weights sum to one. The theoretical results show that the delete-one cross-validation criterion achieves the lowest possible forecasting loss asymptotically. Numerical studies demonstrate the superior performance of the proposed variable screening and model averaging procedures over existing methods.     

试论大数据技术在生态环境保护领域的应用架构及相关技术

促进大数据在生态环境保护领域的深化应用,可以提高生态环境监管能力,并实现环境管理工作精细化。实践中需要把握生态环境信息化的大数据需求,对其应用架构及相关技术进行深入探究,以从多维度出发挖掘有价值的信息,更好地服务生态环境保护工作。     

移动网络数据中大数据处理的关键技术探索

最近几年以来,我国科技水平持续提升,计算机网络技术也逐渐在各行各业中获得了广泛运用,这也使得信息技术的深度融合发展受到一定的促进,数据规模愈发庞大,在此背景下,开始演变成大数据供不应求的状况.为此,国内陆续推出了一系列相关的管理法规与条例,同时扩大了对大数据的探究创新力度,有效提升了移动通信网络处理数据的速度,这同时促...     

Selecting Sites for New Facilities Using Data Envelopment Analysis

This paper develops a mathematical programming model for obtaining a best set of sites for planned facilities. The model is concerned with those situations where resource constraints are present. The specific setting for the paper involves the selection of sites for a set of retail outlets, wherein the ratio of aggregate outputs to inputs for the selected set is maximal among all possible sets that could be chosen. At the same time, the model guarantees that the only sets of stores allowable are those for which the available resources are used to the maximum extent possible.     

供应链信息共享实现机制研究

牛鞭效应使得供应链管理变得更加复杂和困难,而在供应链中高效地共享信息能够极大减弱牛鞭效应。结合供应链信息共享的特点,给出供应链信息共享需要考虑的内容,并根据这些内容提出供应链信息共享的信息查询机制、数据交换机制和安全机制。这三种机制保证了在供应链中安全高效地共享信息。     

论物流客户关系网络管理

从物流客户关系网络管理的产生入手,对基于网络的物流客户细分和物流客户关系网络管理的关键技术网络数据挖掘在物流客户关系网络管理中作用及功能进行分析,在此基础上建构物流客户关系网络管理系统。     

构建基于数据挖掘技术的军事物流信息平台

着眼于军事物流发展趋势,针对来．采战争对军事物流提出的新要求,在剖析当前军事物流信息平台弊端的基础上,提出了基于DM技术的军事物流信息平台,并对该平台的优势进行了详细论述。     

Characteristics and implications of Chinese macroeconomic data revisions

The research examining macroeconomic data for developed economies suggests that an understanding of the nature of data revisions is important both for the production of accurate macroeconomic forecasts and for forecast evaluation. This paper focuses on Chinese data, for which there has been substantial debate about data quality for some time. The key finding in this paper is that, while it is true that the Chinese macroeconomic data revisions are not well-behaved, they are not very different from similarly-timed U.S. macroeconomic data revisions. The positive bias in Chinese real GDP revisions is a result of the fast-growing service sector, which is notably hard to measure in real time. A better understanding of the revisions process is particularly helpful for studies of the forecast errors from surveys of forecasters, where the choice of the vintage for outcomes may have an impact on the estimated forecast errors.