Small area estimation strategies for large population surveys: a comparison of design and model-based methods

Small area estimation (SAE) concerns with how to reliably estimate population quantities of interest when some areas or domains have very limited samples. This is an important issue in large population surveys, because the geographical areas or groups with only small samples or even no samples are often of interest to researchers and policy-makers. For example, large population health surveys, such as Behavioural Risk Factor Surveillance System and Ohio Mecaid Assessment Survey (OMAS), are regularly conducted for monitoring insurance coverage and healthcare utilization. Classic approaches usually provide accurate estimators at the state level or large geographical region level, but they fail to provide reliable estimators for many rural counties where the samples are sparse. Moreover, a systematic evaluation of the performances of the SAE methods in real-world setting is lacking in the literature. In this paper, we propose a Bayesian hierarchical model with constraints on the parameter space and show that it provides superior estimators for county-level adult uninsured rates in Ohio based on the 2012 OMAS data. Furthermore, we perform extensive simulation studies to compare our methods with a collection of common SAE strategies, including direct estimators, synthetic estimators, composite estimators, and Datta GS, Ghosh M, Steorts R, Maples J.'s [Bayesian benchmarking with applications to small area estimation. Test 2011;20(3):574–588] Bayesian hierarchical model-based estimators. To set a fair basis for comparison, we generate our simulation data with characteristics mimicking the real OMAS data, so that neither model-based nor design-based strategies use the true model specification. The estimators based on our proposed model are shown to outperform other estimators for small areas in both simulation study and real data analysis.     

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.     

风险投资公司业务流程评价及标杆管理的应用

考虑到风险投资公司知识型组织的特性及其业务流程的独特之处 ,对风险投资公司的业务流程进行评价时 ,主要针对的是知识管理流程 ,在设计具体指标时 ,除了考虑业务流程的一般度量指标外 ,还要从知识管理的角度来设置     

Mutual-Fund Benchmarking and Market Bubbles: A Behavioral Approach

Dynamics of equity risk premium is not directly measurable on the market. Numerous studies and empirical research analyse its volatility also considering the time span, concluding that the dynamics of equity risk premium over time is inversely proportional to the economic cycle. This study analyses the passive role that, implicitly, would place institutional investors in such a context. In reality, savings management is delegated to a small number of professional operators (institutional investors), as opposed to pure theoretical models in which every person can act directly on the market thus ensuring unlimited price elasticity. Institutional investors should be rational and completely informed so that they can assume an anticyclical position on the market. Thus, supply and demand should quickly smooth over emerging price pressures and avoid price bubbles. We analyse one possible explanation for this situation not to occur, namely, that professionals suffer from operational limits that prevent them from doing their job in the best possible way. Using empirical evidence from the Italian Stock Exchange (Comit Index), we conclude that three factors reduce the freedom of institutional investors to manage their portfolios – the market target size, the fund structure, and the benchmarking – and discuss some implications for each of them.     

Simulation and quality of a synthetic close-to-reality employer–employee population

It is of essential importance that researchers have access to linked employer–employee data, but such data sets are rarely available for researchers or the public. Even in case that survey data have been made available, the evaluation of estimation methods is usually done by complex design-based simulation studies. For this aim, data on population level are needed to know the true parameters that are compared with the estimations derived from complex samples. These samples are usually drawn from the population under various sampling designs, missing values and outlier scenarios. The structural earnings statistics sample survey proposes accurate and harmonized data on the level and structure of remuneration of employees, their individual characteristics and the enterprise or place of employment to which they belong in EU member states and candidate countries. At the basis of this data set, we show how to simulate a synthetic close-to-reality population representing the employer and employee structure of Austria. The proposed simulation is based on work of A. Alfons, S. Kraft, M. Templ, and P. Filzmoser [{\em On the simulation of complex universes in the case of applying the German microcensus}, DACSEIS research paper series No. 4, University of Tübingen, 2003] and R. Münnich and J. Schürle [{\em Simulation of close-to-reality population data for household surveys with application to EU-SILC}, Statistical Methods & Applications 20(3) (2011c), pp. 383–407]. However, new challenges are related to consider the special structure of employer–employee data and the complexity induced with the underlying two-stage design of the survey. By using quality measures in form of simple summary statistics, benchmarking indicators and visualizations, the simulated population is analysed and evaluated. An accompanying study on literature has been made to select the most important benchmarking indicators.     

基于聚类分析和DEA的中国机场标杆管理研究

本文运用聚类分析和数据包络分析（DEA）方法,对2001—2005年80家中国大陆机场的效率现状进行了实证研究。基于机场的运营效率,使用系统聚类的方法将样本机场分为了三类,比较了各类机场的效率。同时,运用交叉效率DEA模型对每类样本机场进行分析,确立各类机场的效率标杆,并对无效率机场实施差额变量分析,以确定各类机场效率不高的原因以及效率改进的路径。     

An Introduction to ‘Benefit of the Doubt’ Composite Indicators

Despite their increasing use, composite indicators remain controversial. The undesirable dependence of countries’ rankings on the preliminary normalization stage, and the disagreement among experts/stakeholders on the specific weighting scheme used to aggregate sub-indicators, are often invoked to undermine the credibility of composite indicators. Data envelopment analysis may be instrumental in overcoming these limitations. One part of its appeal in the composite indicator context stems from its invariance to measurement units, which entails that a normalization stage can be skipped. Secondly, it fills the informational gap in the ‘right’ set of weights by generating flexible ‘benefit of the doubt’-weights for each evaluated country. The ease of interpretation is a third advantage of the specific model that is the main focus of this paper. In sum, the method may help to neutralize some recurring sources of criticism on composite indicators, allowing one to shift the focus to other, and perhaps more essential stages of their construction. An abridged version of this paper was presented at the Workshop on European Indicators and Scoreboards, organised by DG Education and the Joint Research Centre within the auspices of CRELL, in Brussels, October 24–25, 2005.     

法国公司创新的高标定位

本文对法国企业创新的成功实践进行了高标定位分析,并描述了这些公司的战略、组织机构、技术资源与人力资源的特征.     

荣辱观：个体道德建构的基础

荣辱观以良心对荣誉、耻辱的体验,促使个体道德构建发生机制过程,成为道德认知价值构成、道德行为情感体验、道德性格社会认同的基础;以牵引荣誉的张力和排斥耻辱的驱力合成个体道德构建的动力;以高标要求和底线原则成为个体道德构建的鲜明标杆。     

Applications of neural networks in manufacturing management systems

Abstract. The inherent complexities of modern manufacturing require more sophisticated systems for their management. Neural networks, which are massively parallel interconnected networks, have been shown to have extensive applications in various systems that can be used in manufacturing management. An extensive amount of literature has been published on the applications of neural networks in manufacturing, yet no comprehensive review of this literature to date has been offered. In this paper, we review the concepts and applications of neural networks as they relate to various aspects of manufacturing management.