Impact of Karl Pearson's Work on Statistical Developments in India

Karl Pearson's work greatly inspired P. C. Mahalanobis's interest in statistics, who was at the centre of modern statistical developments in India. Mahalanobis learned statistics on his own, reading Pearson's articles and his journal, and encouraged others to study Pearson's papers. Pearson was a Honorary Fellow of the Indian Statistical Institute, which has been a leading statistical training and research center in India. The statistical revolution that Pearson brought also facilitated the training and research of many Indian statisticians.     

Forecasting performances of three automated modelling techniques during the economic crisis 2007–2009

In this work we consider the forecasting of macroeconomic variables during an economic crisis. The focus is on a specific class of models, the so-called single hidden-layer feed-forward autoregressive neural network models. What makes these models interesting in the present context is the fact that they form a class of universal approximators and may be expected to work well during exceptional periods such as major economic crises. Neural network models are often difficult to estimate, and we follow the idea of White (2006) of transforming the specification and nonlinear estimation problem into a linear model selection and estimation problem. To this end, we employ three automatic modelling devices. One of them is White’s QuickNet, but we also consider Autometrics, which is well known to time series econometricians, and the Marginal Bridge Estimator, which is better known to statisticians. The performances of these three model selectors are compared by looking at the accuracy of the forecasts of the estimated neural network models. We apply the neural network model and the three modelling techniques to monthly industrial production and unemployment series from the G7 countries and the four Scandinavian ones, and focus on forecasting during the economic crisis 2007–2009. The forecast accuracy is measured using the root mean square forecast error. Hypothesis testing is also used to compare the performances of the different techniques.     

Industrial statistics and its recent contributions to total quality in the Netherlands

The use of statistical methods in quality management has a long history. Most of the pioneers, such as Walter A. Shewhart and W. Edwards Deming, refer to themselves as statisticians. Statistical thinking in industry means that all work is a series of interconnected processes, that all processes show variation, and that a reduction in variation is the key for continuous improvement. In the statistical literature we find several approaches to achieve this: quality by inspection, quality by process control and quality by design. In this paper we give a survey of the contributions of Dutch statisticians during the last five years.     

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.     

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。     

浅谈基层统计人员应具备的素质与能力

统计工作日益受到社会各界的广泛关注,本文从企业基层统计人员的现状分析入手,简要阐述了在当前经济形势下基层统计人员应具备的素质与能力。     

A Model for Positively Correlated Count Variables

An α-permanental random field is briefly speaking a model for a collection of non-negative integer valued random variables with positive associations. Though such models possess many appealing probabilistic properties, many statisticians seem unaware of α-permanental random fields and their potential applications. The purpose of this paper is to summarize useful probabilistic results, study stochastic constructions and simulation techniques, and discuss some examples of α-permanental random fields. This should provide a useful basis for discussing the statistical aspects in future work.     

Transaction cost economics in Scandinavian business administration

This Introduction briefly surveys transaction cost economics and explains its general relevance for business administration. A discussion of the impact of transaction cost economics on Scandinavian work in business administration is offered, after which the contributions to the present Special Issue are introduced.     

Women in the Statistics Profession: A Status Report

The mandate of the International Statistical Institute (ISI) is to foster international statistical work and interchange among statisticians and countries. However, women are under-represented in the ISI and this situation is a cause for concern in the Institute and its membership and particularly the ISI Committee on Women in Statistics (CWS). This paper analyses the representation of women in the ISI and in the statistics profession which is low in virtually every country, a pattern of generations. It looks at the diverse realities among countries and regions, and the broader context of women working in the scientific professions.     

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.     

On the Utility of E-Learning in Statistics

Students of introductory courses consider statistics as particularly difficult, as the understanding of the underlying concepts may require more time and energy than for other disciplines. For decades statisticians have tried to enhance understanding with the help of technical solutions such as animation, video or interactive tools. However, it is not clear if the added value generated by these e‐learning tools justifies the work invested. In this paper the experience with various e‐learning solutions in terms of utility and the impact on teaching is discussed.     

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.     

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.     

Adjusted Pearson Chi-Square feature screening for multi-classification with ultrahigh dimensional data

Huang et al. (J Bus Econ Stat 32:237–244, 2014) first proposed a Pearson Chi-Square based feature screening procedure tailored to multi-classification problem with ultrahigh dimensional categorical covariates, which is a common problem in practice but has seldom been discussed in the literature. However, their work establishes the sure screening property only in a limited setting. Moreover, the p value based adjustments when the number of categories involved by each covariate is different do not work well in several practical situations. In this paper, we propose an adjusted Pearson Chi-Square feature screening procedure and a modified method for tuning parameter selection. Theoretically, we establish the sure screening property of the proposed method in general settings. Empirically, the proposed method is more successful than Pearson Chi-Square feature screening in handling non-equal numbers of covariate categories in finite samples. Results of three simulation studies and one real data analysis are presented. Our work together with Huang et al. (J Bus Econ Stat 32:237–244, 2014) establishes a solid theoretical foundation and empirical evidence for the family of Pearson Chi-Square based feature screening methods.     

Karl Pearson and the Establishment of Mathematical Statistics

At the end of the nineteenth century, the content and practice of statistics underwent a series of transitions that led to its emergence as a highly specialised mathematical discipline. These intellectual and later institutional changes were, in part, brought about by a mathematical-statistical translation of Charles Darwin's redefinition of the biological species as something that could be viewed in terms of populations. Karl Pearson and W.F.R. Weldon's mathematical reconceptualisation of Darwinian biological variation and "statistical" population of species in the 1890s provided the framework within which a major paradigmatic shift occurred in statistical techniques and theory. Weldon's work on the shore crab in Naples and Plymouth from 1892 to 1895 not only brought them into the forefront of ideas of speciation and provided the impetus to Pearson's earliest statistical innovations, but it also led to Pearson shifting his professional interests from having had an established career as a mathematical physicist to developing one as a biometrician. The innovative statistical work Pearson undertook with Weldon in 1892 and later with Francis Galton in 1894 enabled him to lay the foundations of modern mathematical statistics. While Pearson's diverse publications, his establishment of four laboratories and the creation of new academic departments underscore the plurality of his work, the main focus of his life-long career was in the establishment and promulgation of his statistical methodology.     

Karl Pearson and the Origin of Kurtosis

Although the kurtosis index proposed by Karl Pearson in 1905 is introduced in statistical textbooks at all levels, the measure is not easily interpreted and has been a subject of considerable debate. In this study, the theoretical development of kurtosis is surveyed from a historical perspective of Pearson's work on evolution. It surprisingly emerges that there was no emphasis in Pearson's papers on kurtosis as measuring (in part) tail heaviness. However, it is found that Pearson used to frequently adjust the formalisation of kurtosis depending on his changing needs. This complex development partly explains the confusion that would surround kurtosis in subsequent literature. Our conclusion is that most misunderstandings arise from improper use of the kurtosis coefficient outside the Pearson system of frequency curves.     

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.     

Celebrating the Memory of Corrado Gini: a Personality Out of the Ordinary

Corrado Gini was one of the most brilliant and internationally recognized scientists of the Italian School of Statistics. This paper, written on the occasion of the 50th anniversary of his death, recalls the key aspects of his life and his extraordinary scientific contributions. In describing his impressive mind, interdisciplinary interests, his long and significant academic life, his immense scientific production, and his commitment to official statistics, we must also take into account the particular historical period that Gini lived in. But the most relevant and honourable of his acknowledgements is the impact he undeniably had and still has on the international scientific community of statisticians and non‐statisticians.     

Pearson's Statistics in the Netherlands and the Astronomer Kapteyn

In 1903 the well-known Dutch astronomer Kapteyn published a paper in which he discussed statistical methods which he thought would be relevant for biologists. His motivation was the 1895 paper of Pearson on skew frequency curves. Kapteyn had concluded that the theory was open to grave objections and was not adapted to nonmathematical readers. He was then led to an independent investigation of the subject. This publication would lead to a heated dialogue between Kapteyn and Pearson, in which they accused each other of inappropriate starting points, of plagiarism and of making serious mathematical mistakes. This article evaluates these claims. In addition, Kapteyn's struggle to make his work accessible to biologists is discussed.