Multivariate Process Monitoring Using the Dynamic Biplot

In this article, we present a method for monitoring multivariate process data based on the Gabriel biplot. In contrast to existing methods that are based on some form of dimension reduction, we use reduction to two dimensions for displaying the state of the process but all the data for determining whether it is in a state of statistical control. This approach allows us to detect changes in location, variation, and correlational structure accurately yet display a large amount of information concisely. We illustrate the use of the biplot on an example of industrial data and also discuss some of the issues related to a practical implementation of the method.     

Google data in bridge equation models for German GDP

Interest in the use of “big data” when it comes to forecasting macroeconomic time series such as private consumption or unemployment has increased; however, applications to the forecasting of GDP remain rather rare. This paper incorporates Google search data into a bridge equation model, a version of which usually belongs to the suite of forecasting models at central banks. We show how such big data information can be integrated, with an emphasis on the appeal of the underlying model in this respect. As the decision as to which Google search terms should be added to which equation is crucial —- both for the forecasting performance itself and for the economic consistency of the implied relationships —- we compare different (ad-hoc, factor and shrinkage) approaches in terms of their pseudo real time out-of-sample forecast performances for GDP, various GDP components and monthly activity indicators. We find that sizeable gains can indeed be obtained by using Google search data, where the best-performing Google variable selection approach varies according to the target variable. Thus, assigning the selection methods flexibly to the targets leads to the most robust outcomes overall in all layers of the system.     

Forecasting national activity using lots of international predictors: An application to New Zealand

We assess the marginal predictive content of a large international dataset for forecasting GDP in New Zealand, an archetypal small open economy. We apply “data-rich” factor and shrinkage methods to efficiently handle hundreds of predictor series from many countries. The methods covered are principal components, targeted predictors, weighted principal components, partial least squares, elastic net and ridge regression. We find that exploiting a large international dataset can improve forecasts relative to data-rich approaches based on a large national dataset only, and also relative to more traditional approaches based on small datasets. This is in spite of New Zealand’s business and consumer confidence and expectations data capturing a substantial proportion of the predictive information in the international data. The largest forecasting accuracy gains from including international predictors are at longer forecast horizons. The forecasting performance achievable with the data-rich methods differs widely, with shrinkage methods and partial least squares performing best in handling the international data.     

工序质量控制系统研究（1）

对计算机辅助工序质量控制系统进行了研究,讨论了机加工工序质量控制系统的设计,硬件构成以及数据采集与处理等。     

Forecasting using supervised factor models

This paper examines the theoretical and empirical properties of a supervised factor model based on combining forecasts using principal components (CFPC), in comparison with two other supervised factor models (partial least squares regression, PLS, and principal covariate regression, PCovR) and with the unsupervised principal component regression, PCR. The supervision refers to training the predictors for a variable to forecast. We compare the performance of the three supervised factor models and the unsupervised factor model in forecasting of U.S. CPI inflation. The main finding is that the predictive ability of the supervised factor models is much better than the unsupervised factor model. The computation of the factors can be doubly supervised together with variable selection, which can further improve the forecasting performance of the supervised factor models. Among the three supervised factor models, the CFPC best performs and is also most stable. While PCovR also performs well and is stable, the performance of PLS is less stable over different out-of-sample forecasting periods. The effect of supervision gets even larger as forecast horizon increases. Supervision helps to reduce the number of factors and lags needed in modelling economic structure, achieving more parsimony.     

用"过程方法"寻找提高教学质量的途径

在我国职业教育改革的过程中，北京商贸学校在北京市教委的支持下，率先与英国商业技术教育会合作办学。由于中英两国社会文化的差异，在合作的初期不可避免地会有一个磨合适应阶段。本文旨在通过运用质量管理中的过程方法，找出影响BTEC证书课程的主要因素，探索尽快提高教学质量的途径。主要思路是严格落实监视和测量机制;加强对教学过程支持系统的建设；扩大教学质量管理体系的资源输入。     

提高酒精质量所引用碱法的探讨

生产中提高酒精质量的方法有多种,文章主要介绍了碱法提高酒精质量的知识,通过添加碱（NaOH）来改善化学反应,达到提高酒精质量的目的。     

Using partial least squares in operations management research: A practical guideline and summary of past research

The partial least squares (PLS) approach to structural equation modeling (SEM) has been widely adopted in business research fields such as information systems, consumer behavior, and marketing. The use of PLS in the field of operations management is also growing. However, questions still exist among some operations management researchers regarding whether and how PLS should be used. To address these questions, our study provides a practical guideline for using PLS and uses examples from the operations management literature to demonstrate how the specific points in this guideline can be applied. In addition, our study reviews and summarizes the use of PLS in the recent operations management literature according to our guideline. The main contribution of this study is to present a practical guideline for evaluating and using PLS that is tailored to the operations management field.     

提高酒精质量所引用碱法的探讨

生产中提高酒精质量的方法有多种,文章主要介绍了碱法提高酒精质量的知识,通过添加碱(NaOH)来改善化学反应,达到提高酒精质量的目的。     

应用过程质量控制提高脂松香产品质量等级的研究

文章分析了脂松香产品生产过程质量控制方法,介绍了过程质量控制的原理及应用方法,为此方法在其他生产领域中的推广应用提供参考。     

加强过程质量监控,提高高校英语专业本科毕业论文质量

当前普通高校的毕业论文质量普遍受到学者和研究者的关注,研究者对论文担忧多于积极乐观的看法和态度。本文重点针对的是高校英语专业本科论文质量问题研究,本文认为影响高校英语专业本科毕业论文质量的变量和诱因颇多,但是只要加强毕业论文过程和关键环节,高校英语专业的论文的质量是可以逐步提高和完善的。     

Analysing Your Multivariate Data as a Pictorial: A Case for Applying Biplot Methodology?

This paper illustrates the versatility of biplot methodology when analysing multivariate data from diverse disciplines. The modern approach of Gower & Hand (1996) whereby biplots are regarded as multivariate analogues of ordinary scatter plots is utilised for extending biplot methodology introducing several novel applications. Focus is on biplot applications where the merits of principal component biplots and canonical variate analysis biplots are illustrated with data sets from higher education, the manufacturing industry, the mining industry, agriculture, finance and archaeology. It is shown how to equip biplots with quality regions, classification regions and acceptance regions; how a -bags superimposed on biplots provide a quantification of the multidimensional overlap of classes as well as enable biplots to be used with large data sets; how to use biplots for exploring multi-dimensional reality and in sophisticated classification procedures.     

探索教学过程管理有效途径提升高职人才培养质量

职业教育与经济发展最为密切和直接,强化高职教育的质量意识是经济社会发展的需要,也是高等职业教育可持续发展的需要,教学过程管理有效途径是高职人才培养质量的前提和保障。     

组织提升服务质量应注意的若干问题

组织采用怎样的质量提升模式，才能从追求经济、社会、环境与资源综合指标满意的复杂系统的全局出发，快速提高整体服务质量、增强组织在国际市场上的核心竞争力、提高顾客满意度及忠诚度？文章着重探讨了服务组织要想获得持久的竞争优势，在质量决策中容易忽视但又必须着重考虑的几个问题。     

Using the Lee–Carter Method to Forecast Mortality for Populations with Limited Data*

The Lee–Carter method for modeling and forecasting mortality has been shown to work quite well given long time series of data. Here we consider how it can be used when there are few observations at uneven intervals. Assuming that the underlying model is correct and that the mortality index follows a random walk with drift, we find the method can be used with sparse data. The central forecast depends mainly on the first and last observation, and so can be generated with just two observations, preferably not too close in time. With three data points, uncertainty can also be estimated, although such estimates of uncertainty are themselves highly uncertain and improve with additional observations. We apply the methods to China and South Korea, which have 3 and 20 data points, respectively, at uneven intervals.     

Utilising Recent Advancements in Techniques for the Analysis of Incomplete Multivariate Data to Improve the Data Quality Management of Current Academic Research

This paper discusses the importance of managing data quality in academic research in its relation to satisfying the customer. This focus is on the data completeness objectivedimension of data quality in relation to recent advancements which have been made in the development of methods for analysing incomplete multivariate data. An overview and comparison of the traditional techniques with the recent advancements are provided. Multiple imputation is also discussed as a method of analysing incomplete multivariate data, which can potentially reduce some of the biases which can occur from using some of the traditional techniques. Despite these recent advancements in the analysis of incomplete multivariate data, evidence is presented which shows that researchers are not using these techniques to manage the data quality of their current research across a variety of academic disciplines. An analysis is then provided as to why these techniques have not been adopted along with suggestions to improve the frequency of their use in the future. Source-Reference. The ideas for this paper originated from research work on David J. Fogarty's Ph.D. dissertation. The subject area is the use of advanced techniques for the imputation of incomplete multivariate data on corporate data warehouses.     

Estimating the Consequences of Unintended Fertility for Child Health and Education in Romania: An Analysis Using Twins Data*

We use the natural experiment of twins at first birth to estimate the effects of unplanned fertility on the nutritional status and school enrolment of children in Romania, a country with a unique fertility history. A first‐birth twins shock has negative impacts on children's human capital investments, particularly for later‐born siblings. We infer that harsh pronatalist policies prior to the 1989 Revolution had adverse consequences for the human capital of Romanian children, and that policies to make fertility control easier will have significant positive impacts on children's health and schooling.     

An Example of Data Technology Product: A Control Chart for Weibull Processes

This work proposes a new Shewhart-type control chart of the Weibull percentile (i.e. the reliable life) as a practical example of a product attained following the Data Technology (DT) approach. DT is briefly introduced as a new discipline defined apart from Information Technology (IT). Following this approach, some specific Bayes estimators are selected from literature and then used to build the above new chart. These estimators allow to improve the control making use of any available kind of data (statistical and non-statistical). The operative steps of DT approach are fully explained. The results are illustrated by means of a real applicative example.     

Designs for Variance Components Estimation: Past and Present

This article provides (1) a comprehensive coverage of the literature on designs for estimating variance components, and (2) a review of recent applications of such designs in genetics, statistical process control, and quality improvement. In addition, recent methods of estimation of variance components and model forms, other than the linear, are discussed. The latter developments have a direct effect on the choice of design. Some suggested ideas for future research directions are also given.     

Survival Modelling for Data From Combined Cohorts: Opening the Door to Meta Survival Analyses and Survival Analysis Using Electronic Health Records

Non-parametric estimation of the survival function using observed failure time data depends on the underlying data generating mechanism, including the ways in which the data may be censored and/or truncated. For data arising from a single source or collected from a single cohort, a wide range of estimators have been proposed and compared in the literature. Often, however, it may be possible, and indeed advantageous, to combine and then analyse survival data that have been collected under different study designs. We review non-parametric survival analysis for data obtained by combining the most common types of cohort. We have two main goals: (i) to clarify the differences in the model assumptions and (ii) to provide a single lens through which some of the proposed estimators may be viewed. Our discussion is relevant to the meta-analysis of survival data obtained from different types of study, and to the modern era of electronic health records.