Environmental Statistics: Current and Future

Since 1900, statistics has prospered and established for itself a central place in science and technology, as can be seen from the wide application of its methods to almost all facets of life. The primary concern of statistics is to develop a logical and efficient system for information acquisition and analysis that is geared towards broad applications. Such a system has its roots and extensions in case studies from many scientific disciplines and in the collaboration between statisticians and other scientists. During the past three decades statisticians have become actively involved in the development and use of quantitative methods to understand and solve environmental problems. These activities are commonly called Environmetrics. In this paper we review the main features of current Environmetrics research and outreach activities. The paper is structured around typical examples drawn mainly from water quality and climate change to illustrate the type of problems involved, the statistical approaches used to address them, and the new statistical methods required for their solution. In addition we shall also briefly describe efforts made at the international level to link the statisticians working in the field together and with environmental scientists.     

Statistical Thinking in Empirical Enquiry

This paper discusses the thought processes involved in statistical problem solving in the broad sense from problem formulation to conclusions. It draws on the literature and in-depth interviews with statistics students and practising statisticians aimed at uncovering their statistical reasoning processes. From these interviews, a four-dimensional framework has been identified for statistical thinking in empirical enquiry. It includes an investigative cycle, an interrogative cycle, types of thinking and dispositions. We have begun to characterise these processes through models that can be used as a basis for thinking tools or frameworks for the enhancement of problem-solving. Tools of this form would complement the mathematical models used in analysis and address areas of the process of statistical investigation that the mathematical models do not, particularly areas requiring the synthesis of problem-contextual and statistical understanding. The central element of published definitions of statistical thinking is "variation". We further discuss the role of variation in the statistical conception of real-world problems, including the search for causes.     

Applications of Statistics in the Field of General Insurance: An Overview

This is an expository paper on applications of statistics in the field of general insurance, also called non‐life insurance. Unlike life insurance where advanced statistical techniques have long been part of financial mathematics and actuarial applications, their use is only relatively recent in non‐life insurance. The business model of insurance companies, especially those active in non‐life insurance, has seen dramatic changes over the last 15 years. The aim of this paper is to convince the readers that especially today non‐life insurance is not only an exciting ground to apply existing modern statistical tools but also a fertile environment for new and challenging statistical developments. The activities of an insurance company can be viewed as an industrial process where data management and data analysis play a key role. That is why a fundamental understanding of data‐related issues (such as data quality, variability, analysis and correct interpretation) is so essential to the insurance business. These are exactly the tasks where professional statisticians excel. Also, a better understanding of the field of general insurance by statisticians will promote fruitful exchanges between actuaries and statisticians, thereby helping to bring actuarial and statistical professional societies closer to each other. Selected examples are used to cover the essential aspects of general insurance, and all of them are based on the author's experience. The paper concludes with some remarks on the role of statisticians working in general insurance.     

Multivariate statistical process control—recent results and directions for future research

The performance of a product often depends on several quality characteristics. These characteristics may have interactions. In answering the question “Is the process in control?”, multivariate statistical process control methods take these interactions into account. In this paper, we review several of these multivariate methods and point out where to fill up gaps in the theory. The review includes multivariate control charts, multivariate CUSUM charts, a multivariate MMA chart, and multivariate process capability indices. The most important open question from a practical point of view is how to detect the variables that caused an out-of-control signal. Theoretically, the statistical properties of the methods should be investigated more profoundly.     

Distributional inference

The making of statistical inferences in distributional form is conceptionally complicated because the epistemic 'probabilities' assigned are mixtures of fact and fiction. In this respect they are essentially different from 'physical' or 'frequency-theoretic' probabilities. The distributional form is so attractive and useful, however, that it should be pursued. Our approach is In line with Walds theory of statistical decision functions and with Lehmann's books about hypothesis testing and point estimation: loss functions are defined, risk functions are studied, unbiasedness and equivariance restrictions are made, etc. A central theme is that the loss function should be 'proper'. This fundamental concept has been explored by meteorologists, psychometrists, Bayesian statisticians, and others. The paper should be regarded as an attempt to reconcile various schools of statisticians. By accepting what we regard 88 good and useful in the various approaches we are trying to develop a nondogmatic approach.     

Using Statistics and Statistical Thinking to Improve Organisational Performance

A systematic approach to measuring organisational performance is fundamental to the pursuit of business excellence. As such, the area of organisational performance measurement, and its use of data and analysis to inform business decisions, affords statisticians a potentially high value-adding opportunity. To be effective in this area, statisticians need to appreciate the differing requirements for statistical information in various management zones of an enterprise.
This paper describes a strategy that seeks to link measurement to all facets of organisational performance, particularly to desired business outcomes, and also to mesh measurement with process improvement in a natural way. The use of statistics and statistical thinking is then discussed in this context, with particular focus on the opportunity for statisticians to have a key role at the top decision-making level of the organisation. We argue that the role requires skills in both advanced technical statistical modelling and analysis, and in statistical thinking. It also requires a preparedness to form an appreciation of the business and management imperatives faced by the leaders of an enterprise, and a willingness to work from this basis.     

Using On-Line Process Data to Improve Quality: Challenges for Statisticians*

In the process industries measurements on a large number of process variables are routinely collected at regular intervals by on-line computers. This paper makes a case for incorporating these process variables into Statistical Process Control (SPC) schemes. Multivariate statistical methods such as Principal Component Analysis (PCA) and Partial Least Squares (PLS) can be used to project these data down into low dimensional spaces where analysis, monitoring and diagnosis are easily performed. Strong justifications for taking this approach are presented and examples are given. The statistical process control community has been slow in adapting to the data explosion brought about by the computer era. It has continued to stick with traditional control charts on the quality variables and ignored this rich source of additional information on the process. This paper explores some of the reasons for this and argues that the SPC community must adapt rapidly or lose control of the field to scientists and engineers. The paper also tries to induce statisticians into looking more seriously at the many unsolved problems in this area of reduced rank multivariate statistics.     

Statistical process adjustment: a brief retrospective, current status, and some opportunities for further work

Industrial statisticians frequently face problems in their practice where adjustment of a manufacturing process is necessary. In this paper, a view of the origins and recent work in the area of statistical process adjustment (SPA) is provided. A discussion of some topics open for further research is also given including new problems in semiconductor manufacturing process control. The goal of the paper is to help display the SPA field as a research area with its own identity and content, and promote further interest in its development and application in the industry.     

Control Charts for One-sided Capability Indices

There are many industrial product characteristics are desired to be the bigger the best and the smaller the best. The two well-know processes capability indices C _pl and C _pu, which measure larger-the-better and smaller-the-better process capabilities. Obviously, the formulae for the two indices C _pl and C _pu are easy to understand and straightforward to apply. Thus, indices C _pl and C _pu have been utilized by a number of Japanese companies and the U.S. automotive industry by Ford Motor Company. Boyles (1991, Journal of Quality Technology. 23: 17–26) and Spring (1995, Total Quality Management 6(3): 427–438.) point out that as soon as and S control charts are in statistical control, the control charts of process capability indices can be used to monitor the quality of process. In the previous, we know that if the process is not in control, the process capability index control chart can be used to monitor the differences of process capability, and as soon as the process is in control the stable process capability can be identified. Therefore, process capability index control chart not only can be used to monitor the stability of process’s quality but also can be used to monitor the quality of process. Since Boyles (1991, Journal of Quality Technology 23: 17–26.) and Spiring (1995, Total Quality Management 6(1): 21–33.) had had research about control chart of the bilateral specification index C _pm., but there are many kinds of products, which meet unilateral quality specification. Therefore, we will construct the control chart of unilateral specification index C _pl and C _pu to monitor and evaluate the stability of process and process capability.     

Statistics in Business and Industry: Implementation

There are many statistical tools available to be utilized for general business improvement and industrial problem solving. However, there is a wide gap between the tools available and those that are actually used in business and industrial organizations. Thus it is important for statisticians to direct serious attention to bridging this gap if statistics is to be relevant in business and industry. In this paper we look at some ideas for implementing statistical methods in business and industry.     

Social Change: Measurement and Theory*

Societal change, which takes a variety of directions and forms and in no way can be assimilated or reduced to a single dimension, is often accompanied by a perception of insufficient understanding and lack of control. There is a frustrated need for mastering complexity and instability, separating the voluntary from the involuntary, the intended from the unintended, opportunities from risks, getting to the real causes and dominating the uncertain implications of social change. Social change catches us unprepared and confused. In this context statistics are generally considered a fundamental instrument of knowledge, but also part of the problem! In the public debate and in the specialized literature, the ability to measure social phenomena through current statistics and indicators is increasingly questioned. Data-it is claimed-are lacking, particularly longitudinal data; their quality (accuracy, relevance, timeliness, comparability, etc.) should be improved; indicators do not provide early warning signals, policy performance evaluation, and a precise indication of outcomes. Statistics cannot be used as a reliable and timely basis for decision making by individuals, organizations, governments, and for understanding these decisions. In some cases, statistics have been accused of giving a misleading and false picture of reality: do we measure the real extent of social exclusion and unemployment? Do we fully capture the quality of life and the degradation of the environment? Mismeasurement has been deemed by some commentators as being responsible for the wrong focus in inflation and stabilization policies, science and technology, unemployment and poverty. The productivity paradox, the informal economy, failure to measure welfare and the quality of urban life are instances where statistics do not seem to provide complete and satisfactory answers to the demand for information and knowledge. Our paper illustrates how, quite independently of measurement techniques and data production processes, the inadequacy of the conceptual framework may explain mismeasurement in relation to complex (multidimensional) and dynamic social phenomena. It is then to social theories, explanations and interpretations that statisticians need to turn, in order to come to grips with the new challenges in social measurement. We will develop this thesis looking at a few cases where measurement issues can be connected to both theoretical and empirical difficulties. The statistical gap which reveals itself in the mismeasurement or difficult measurement of social phenomena is closely interconnected with the social science gap. Only close collaboration between statisticians and social scientists can bring about continuous advancement in social science and quality improvement in social statistics.     

On the monitoring of multi-attributes high-quality production processes

Over the last decade, there have been an increasing interest in the techniques of process monitoring of high-quality processes. Based upon the cumulative counts of conforming (CCC) items, Geometric distribution is particularly useful in these cases. Nonetheless, in some processes the number of one or more types of defects on a nonconforming observation is also of great importance and must be monitored simultaneously. However, there usually exist some correlations between these two measures, which obligate the use of multi-attribute process monitoring. In the literature, by assuming independence between the two measures and for the cases in which there is only one type of defect in nonconforming items, the generalized Poisson distribution is proposed to model such a problem and the simultaneous use of two separate control charts (CCC & C chats) is recommended. In this paper, we propose a new methodology to monitor multi-attribute high-quality processes in which not only there exist more than one type of defects on the observed nonconforming item but also there is a dependence structure between the two measures. To do this, first we transform multi-attribute data in a way that their marginal probability distributions have almost zero skewnesses. Then, we estimate the transformed mean vector and covariance matrix and apply the well-known χ² control chart. In order to illustrate the proposed method and evaluate its performance, we use two numerical examples by simulation and compare the results. The results of the simulation studies are encouraging.     

Statistical Decision Problems and Bayesian Nonparametric Methods

This paper considers parametric statistical decision problems conducted within a Bayesian nonparametric context. Our work was motivated by the realisation that typical parametric model selection procedures are essentially incoherent. We argue that one solution to this problem is to use a flexible enough model in the first place, a model that will not be checked no matter what data arrive. Ideally, one would use a nonparametric model to describe all the uncertainty about the density function generating the data. However, parametric models are the preferred choice for many statisticians, despite the incoherence involved in model checking, incoherence that is quite often ignored for pragmatic reasons. In this paper we show how coherent parametric inference can be carried out via decision theory and Bayesian nonparametrics. None of the ingredients discussed here are new, but our main point only becomes evident when one sees all priors—even parametric ones—as measures on sets of densities as opposed to measures on finite-dimensional parameter spaces.     

Enhancing Data Science Ethics Through Statistical Education and Practice

As sources of data become more plentiful and massive datasets are easier to acquire, new ethical issues arise involving data quality and privacy, and the analysis, interpretation and dissemination of data‐driven decisions. There are numerous anecdotes involving abuses of complex data analyses and algorithms, and the impact they have had on society. In this paper, we discuss what statisticians can do to help enhance data science ethics in practice and what statistics educators can do to instil sound ethical behaviour in our students. We have opportunities to practice and teach ethical conduct relevant to all stages of the data life cycle. This paper discusses issues impacting ethical data science, with a focus on how statisticians can help raise awareness and encourage implementation of ethical best practices.     

The Citizens' Right to Information and the Duties of a Democratic State in Modern IT Environment in the Light of the UN Fundamental Principles of Official Statistics and the ISI Declaration on Statistical Ethics

Modern information technologies, particularly the technology of very large data bases, Internet and modern mass media, have changed the processes of production, storage and dissemination of information. These technologies have a direct impact on the practical realization of citizens' rights to information. In democratic societies, in modern IT environment, information is produced and disseminated by many social and economic entities. Many producers and disseminators of information do not know or do not obey the criteria which should be met by any information delivered to the public. In this paper the advantages and the threats for the citizens' rights to information generated by the distribution and globalization of information processes in modern IT environment are discussed.
It seems that for the protection of the citizens' rights to information, there is the need for specification and codification of quality criteria and standards for information produced and disseminated with the use of modern IT. Official statistics may and should play an important and active role in defining those criteria and standards. Professional ethics of statisticians and of official statistics seems to be the proper basis for that. Ethical principles of statistics are formulated in two basic documents: The fundamental principles of official statistics adopted by the UN Statistical Commission in 1994 and the ISI Declaration on statistical ethics adopted by the ISI in 1985.     

利用网络资源,做好医院统计

随着医院信息系统的建设给医院统计工作模式带来的变化,统计人员应更加积极主动地参与到医院管理中来。这就要求统计人员不仅需要具备扎实的统计学功底,更需要树立全新的统计工作理念,通过统计分析报告等多种形式为医院的管理服务。     

Evolution of International Statistical Standards via Life Cycle of Products and Services

This paper describes international statistical standards in the context of the product life cycle. To set the stage, the historical evolution of standardization is traced from the Xia Dynasty of China to the present. The transition from local standards geared for manufacturing to national and then international standards is highlighted with acceptance sampling standards. International statistical standards now cover a broad range of topics beyond acceptance sampling, so a scheme is needed to organize them into a coherent structure. The product Life cycle provides just such a framework. The product Life cycle (which is subsumed to include service, as well) is partitioned into four main "megaphases", namely: conception, development, delivery and maturity and death. Each megaphase is linked to relevant statistical methods in general and statistical standards in particular. A gap analysis identifies potential future directions of statistical standards developments and the attendant role that statisticians can continue to play in this arena.     

统计数据公信力探析

统计数据与人们生活息息相关,是政府决策的重要依据和政策评价标准,它在越来越受到人们注视的同时,也受到了人们的质疑,统计数据公信力受到影响。应大力宣传统计法,建立统计数据问责制,加强统计制度的完善和统计方法的更新,提高统计人员素质,以实现真实统计,提高统计数据公信力。     

What Makes Business Statistics Special?

Non-specialists might have the feeling that building statistics on businesses is a very simple task: it seems one just has to "add facts". But for survey statisticians, business statistics are extremely complex: great heterogeneity of the universe, definition of the statistical units, difficulty of classifying businesses, quality of the register, variety of accounting standards, sample co-ordination, or reduction of survey burden. This paper argues that the issues raised by business survey methodology are conceptual and not only practical. It describes different aspects of statistical processing and tries to analyse the special features of business statistics.