Improving retrospective life course data by combining modularized self-reports and event history calendars: experiences from a large scale survey

Event history calendars (EHC) have proven to be a powerful tool for collecting retrospective autobiographical life course data. One problem is that they are only standardized to a limited extent. This restricts their applicability in large-scale surveys. However, in such surveys, a modularized retrospective CATI design can be combined with an EHC. This data revision module is directly integrated into the interview and used as a data revision module, allowing insights from cognitive psychology to be applied. The data revision module stimulates the respondent’s memory retrieval by detecting both temporal inconsistencies, such as gaps, and overlapping or parallel events. This approach was implemented in the IAB-ALWA study (Working and Learning in a Changing World), a large-scale representative telephone survey involving 10,000 respondents. By comparing the uncorrected data with the final data after revision, we can investigate to what extent the application of this data revision module improves data quality or, more precisely, time consistency and dating accuracy of individual reports.     

数据仓库项目进度计划编制方法研究

在阐述数据仓库结构体系和数据仓库项目特殊性的基础上,把关键路径法(CPM)和计划评审技术(PERT)相结合,提出了编制数据仓库项目进度计划网络图的综合方法。这种综合方法有利于分析处理不确定因素,能有效地保障项目成功;最后,作者按照综合方法,用具体案例的数据做了计算。     

BENFORD'S LAW AS AN INDICATOR OF SURVEY RELIABILITY—CAN WE TRUST OUR DATA?

This paper analyzes how closely different income measures conform to Benford's law, a mathematical predictor of probable first digit distribution across many sets of numbers. Because Benford's law can be used to test data set reliability, we use a Benford analysis to assess the quality of six widely used survey data sets. Our findings indicate that although income generally obeys Benford's law, almost all the data sets show substantial discrepancies from it, which we interpret as a strong indicator of reliability issues in the survey data. This result is confirmed by a simulation, which demonstrates that household level income data do not manifest the same poor performance as individual level data. This finding implies that researchers should focus on household level characteristics whenever possible to reduce observation errors.     

数据采集在统计过程控制系统SPC中的应用

本文介绍了数据采集技术在提高制造业质量方面的应用，指出数据已成为产品质量分析的关键因素，SPC成功应用的关键是质量数据的有效采集，并提出了数据采集的一般方法。     

基于分布式关联分析的CRM系统设计研究

针对企业决策环境下数据分布在不同地区和部门、随时间动态变化等特点,本文设计了一个基于分布式关联信息分析的CRM系统,该系统利用多agent处理各个节点上的信息,获得局部知识,然后融合局部知识得到全局知识。本系统适用于分布式环境下的决策分析和海量信息处理任务。     

Integrating uncertain user-generated demand data when locating facilities for disaster response commodity distribution

This paper presents a new facility location problem variant with application in disaster relief. The problem is unique in that both verified data and unverified user-generated data are available for consideration during decision making. The problem is motivated by the recent need of integrating unverified social data (e.g., Twitter posts) with data from more traditional sources, such as on-the-ground assessments and aerial flyovers, to make optimal decisions during disaster relief. Integrating social data can enable identifying larger numbers of needs in shorter amounts of time, but because the information is unverified, some of it may be inaccurate. This paper seeks to provide a “proof of concept” illustrating how the unverified social data may be exploited. To do so, a framework for incorporating uncertain user-generated data when locating Points of Distribution (PODs) for disaster relief is presented. Then, three decision strategies that differ in how the uncertain data is considered are defined. Finally, the framework and decision strategies are demonstrated via a small computational study to illustrate the benefits user-generated data may afford across a variety of disaster scenarios.     

SESAME: an Integrated Economic and Social Accounting System

This article outlines a statistical information system that serves to monitor and analyze the interactions between economic development and social change. This so-called SESAME links the monetary data in the national accounts to non-monetary social and environmental data, and yields a consistent set of core indicators on the development of national welfare.     

Using semiotics to analyze representational complexity in social media

Data from social media offer us multimedia data brimming with multiple layers of meanings. Social media enable rapid-fire digital communications. These communications are incredibly complex in content, form and meaning. This representational complexity is a stumbling block in data analysis that stands in the way of deeper explanations. These unstructured data, rich in social meanings, are as complex as the phenomena they represent. While it is possible to formulate an entire research methodology around semiotics, it is not always necessary. We can adapt semiotic analysis within existing methodologies. This paper offers and illustrates an analytical technique to address representational complexity that can be used in conjunction with other methodologies such as case study, ethnography, etc. This analytical technique espouses a critical realist philosophy to develop much needed, deeper explanations from qualitative data.     

A Cognitive Interpretation of Data Analysis

This paper proposes a scientific model to explain the analysis process. We argue that data analysis is primarily a procedure to build understanding, and as such, it dovetails with the cognitive processes of the human mind. Data analysis tasks closely resemble the cognitive process known as sensemaking. We demonstrate how data analysis is a sensemaking task adapted to use quantitative data. This identification highlights a universal structure within data analysis activities and provides a foundation for a theory of data analysis. The competing tensions of cognitive compatibility and scientific rigour create a series of problems that characterise the data analysis process. These problems form a useful organising model for the data analysis task while allowing methods to remain flexible and situation dependent. The insights of this model are especially helpful for consultants, applied statisticians and teachers of data analysis.     

数字化技术在水土保持监测中的应用

水土保持监测技术数字化,是新时代水土保持监测的趋势.在无人机倾斜摄影支持下,建立监测对象三维数据模型,获得水土保持各项监测数据,从而实时把握水土流失状况,调整水土流失防治措施.监测手段满足监测数据精度要求,同时也提高线型及大型点状项目的监测效率,为区域评估提供水土保持监测技术数字化应用提供项目依据.同时借助气象数据平台...     

Differencing versus nondifferencing in factor-based forecasting

This paper studies performance of factor-based forecasts using differenced and nondifferenced data. Approximate variances of forecasting errors from the two forecasts are derived and compared. It is reported that the forecast using nondifferenced data tends to be more accurate than that using differenced data. This paper conducts simulations to compare root mean squared forecasting errors of the two competing forecasts. Simulation results indicate that forecasting using nondifferenced data performs better. The advantage of using nondifferenced data is more pronounced when a forecasting horizon is long and the number of factors is large. This paper applies the two competing forecasting methods to 68 I(1) monthly US macroeconomic variables across a range of forecasting horizons and sampling periods. We also provide detailed forecasting analysis on US inflation and industrial production. We find that forecasts using nondifferenced data tend to outperform those using differenced data.     

A non-parametric index of corporate governance in the banking industry: An application to Indian data

This paper presents a methodological framework for constructing a non-parametric index of corporate governance for banks. The index is constructed by aggregating six distinct dimensional indices capturing different dimensions of corporate governance, namely board effectiveness, audit function, risk management, remuneration, shareholder rights and information, and disclosure and transparency. For aggregation, a tailored version of data envelopment analysis (DEA) approach which is popularly known as constrained ‘Benefit-of-the-Doubt (BoD)’ model is employed. This approach is unique and distinctive in the sense that it requires no a priori knowledge of weights, and assigns endogenous weights obtained from actual data to individual dimensions of bank governance in order to construct a composite index of corporate governance. This methodological framework has illustrated by applying it for a data set of 40 Indian banks operating in the year 2017. The data set has been compiled using 58 governance regulations as defined by relevant jurisdictions.     

Modeling data revisions: Measurement error and dynamics of “true” values

Policy makers must base their decisions on preliminary and partially revised data of varying reliability. Realistic modeling of data revisions is required to guide decision makers in their assessment of current and future conditions. This paper provides a new framework with which to model data revisions.Recent empirical work suggests that measurement errors typically have much more complex dynamics than existing models of data revisions allow. This paper describes a state-space model that allows for richer dynamics in these measurement errors, including the noise, news and spillover effects documented in this literature. We also show how to relax the common assumption that “true” values are observed after a few revisions.The result is a unified and flexible framework that allows for more realistic data revision properties, and allows the use of standard methods for optimal real-time estimation of trends and cycles. We illustrate the application of this framework with real-time data on US real output growth.     

Combining PCA With DEA to Improve the Evaluation of Project Performance Data: A Taiwanese Bureau of Energy Case Study

This article proposes a new method of project performance evaluation, by which project performance data can be better understood. This article combines principal component analysis (PCA) and data envelopment analysis (DEA) to enhance the efficiency of decision‐making units more accurately. The data used was based on energy projects promoted by the Bureau of Energy at the Ministry of Economic Affairs in Taiwan. The results of this article show that by combining PCA and DEA in evaluating the performance of energy projects, there was an improved evaluation of projects over simply using DEA data alone.     

Bed allocation techniques based on census data

This paper considers the problem of allocating a number of beds to the different medical and surgical specialties in a hospital. This task is complicated by the fact that patterns of patient arrivals differ among specialties, that the scheduling of medical procedures varies over a week, and that the demand for various medical services can show seasonality. We develop a time series model using hourly census data to make good decisions regarding the size of each unit while minimizing data collection and modeling efforts. This model is simple enough to be used widely and, we suggest, represents an improvement versus methods currently employed by hospitals. Results are presented from an application to data collected at Northside Hospital, Atlanta, GA.     

Bayesian inference for periodic regime-switching models

We present a general class of nonlinear time-series Markov regime-switching models for seasonal data which may exhibit periodic features in the hidden Markov process as well as in the laws of motion in each of the regimes. This class of models allows for non-trivial dependencies between seasonal, cyclical and long-term patterns in the data. To overcome the computational burden we adopt a Bayesian approach to estimation and inference. This paper contains two empirical examples as illustration, one uses housing starts data while the other employs US post-Second World War industrial production. © 1998 John Wiley & Sons, Ltd.     

面向Web的数据挖掘

面向Web的数据挖掘是当今数据挖掘技术研究及应用的热点之一。文章介绍了数据库挖掘技术和Web挖掘技术的相关背景,详细阐述了XML语言的卓越特性及其在Web数据挖掘中的重要应用。     

Searching for big data: How incumbents explore a possible adoption of big data technologies

Big data is often described as a new frontier of IT-enabled competitive advantage. A limited number of exemplary firms have been used recurrently in the big data debate to serve as successful illustrations of what big data technologies can offer. These firms are well-known, data-driven organizations that often, but not always, are born digital companies. Comparatively little attention has been paid to the challenges that many incumbent organizations face when they try to explore a possible adoption of such technologies. This study investigates how incumbents handle such an exploration and what challenges they face. Drawing on a four-year qualitative field study of four large Scandinavian firms, we are able to develop a typology of how incumbents handle the exploration of and resistance to adopting big data technologies. Directly affecting the incumbents’ exploration are two aspects that separate the adoption of big data technologies from that of other technologies. First, being an elusive concept, big data technologies can mean different things to different organizations. This makes the technologies difficult to explain before an investing body, while it simultaneously opens up possibilities for creative definitions. Second, big data technologies have a transformative effect on the organization of work in firms. This transformative capability will make managers wary as it might threaten their position in the firm, and it will create ripple effects, transforming other systems besides those directly connected to the technology.     

The inverse operationalisation of concepts for the secondary analysis of quantitative data: an example from the study of parental collaboration

Properly validated scalar variables are often viewed as the gold standard for the operationalisation of concepts in quantitative data. This is a sensible approach at the planning stage of the survey process. However, when working with data that has already been collected for another purpose, such variables cannot always be expected. This is particularly the case when one wishes to analyse a concept that has not previously been studied in a particular context. This paper provides an example of the construction of a binary variable for the concept of parental collaboration, using data from the Growing Up in Scotland study. It examines the decision-making process for the “inverse operationalisation” of the concept, an innovative method which starts with the assumption that all cases in the dataset demonstrate a particular property (parental collaboration), and gradually chips away at those which provide sufficient evidence to suggest otherwise, until a working variable is created.     

Forecasting data vintages

This article provides a discussion of Clements and Galvão’s paper “Forecasting with vector autoregressive models of data vintages: US output growth and inflation.” Clements and Galvão argue that a multiple-vintage VAR model can be useful for forecasting data that are subject to revisions. They draw a “distinction between forecasting future observations and revisions to past data,” which focuses forecasters’ attention on yet another real time data issue. This comment discusses the importance of taking data revisions into consideration, and compares the multiple-vintage VAR approach of Clements and Galvão to a state space approach.