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.     

信息化环境下企业内部审计模式思考

随着信息技术的高速发展,大数据技术呈现出爆发增长并逐渐渗入各行各业。基于信息化的大背景,大数据技术已被越来越多的企事业单位广泛运用,为企业发展带来更多可能性。大数据审计作为一种新型内部审计手段,具备独特的优势,将其运用到企业内部审计中可以较大程度提高内部审计的效率。但是它也为内部审计带来了一定不确定性,进而增加其审计风险。论文就大数据审计在企业内部审计中的应用进行了相关分析,并提出了相应的应对措施。     

基于云计算与物联网技术的数据挖掘

在科学技术快速发展的今天,信息通信行业已经与我们的日常生活完全融为一体,并且推动了整个社会的发展,所以在科学技术快速进步的过程中,信息通信相关技术也在不断发展,其中,基于云计算的物联网发展越来越受到人们的重视。论文对基于云计算的物联网数据挖掘模式进行分析及探讨,从而为互联网数据挖掘技术发展提供相应的参考。     

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。     

关于铁路单线隧道接触网弓形腕臂预配平台的相关研究

为降低铁路单线隧道接触网弓形腕臂预配施工难度,论文研究了一种接触网弓形腕臂预配平台,结合单线隧道内弓形腕臂数据精测技术及计算软件,精确测量预配,保证弓形腕臂的安装精度和质量,使弓形腕臂在现场施工中一次成优。论文结合实际,就某铁路单线隧道接触网弓形腕臂预配平台作具体分析。     

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...     

云数据中心网络虚拟化技术的实现路径

当今社会,随着科学技术的迅猛发展,信息技术实现了持续快速发展。在大环境的影响下,云计算与网络虚拟化技术应运而生。该技术的产生和发展促使云数据中心网络的变革与发展成为新一轮技术革命的核心内容之一。当前,IT行业对数据中心网络的需求越来越大。基于此,论文论述了云数据中心网络虚拟化技术的实现路径。     

发动机汽缸压力测试方法探析

发动机的汽缸压力是评价发动机动力性能的一项重要指标,在对发动机进行检查、评价和维修时需要经常测试。文章从汽车实践技术的角度阐述了发动机汽缸压力的测试仪器、测试方法与技巧,并在理论上对测试的数据和测试误差加以科学系统地分析,提出解决办法,为汽车发动机的技术维修提供科学合理的依据。