Typologie van den statisticus Hoe men hem ziet en hoe hij

In the popular conception the statistician is a man who collects and groups figures which he publishes in the form of tables and graphs. The real statistician in the modern sense of the word, i.e. the man who does not collect the material but analyses it and draws his conclusions from it, is as yet practically unknown to the general public. It is desirable that more publicity should be given to the characteristic qualities required for this calling, so that those most fitted for i may be drawn towards it.
The aspiring statistician should be taught to think in statistical terms and not along functional or determinist lines, as the mathematician does. The statistician is only interested in individual phenomena insofar as they enable him to draw conclusions of a more general character out of a certain amount of isolated cases. A true sense of proportion is very important as it plays a major part in the framing, the working out and the conclusions to be drawn from the solution of the problems. As the sphere of his activities is so extensive, the statistician should be able to adapt himself quickly to new situations.
Those who are endowed with these varying talents can rely on finding a very useful task as a statistician.     

The changing focus of microarray analysis

In the biological sciences, the advent of microarray technology changed the way experiments were performed. Microarrays were the first mainstream high-throughput technology, generating enormous amounts of data for both the biologist and the statistician to understand. Here, I follow my own experience in microarray analysis, starting during my time at EURANDOM with experimental design and continuing today in my present position at the Netherlands Cancer Institute where the exploitation of data from many different sources is hoped will give greater insight into different aspects of cancer.     

Why Distinguish Between Statistics and Mathematical Statistics–The Case of Swedish Academia

A separation between the academic subjects statistics and mathematical statistics has existed in Sweden almost as long as there have been statistics professors. The same distinction has not been maintained in other countries. Why has it been kept for so long in Sweden, and what consequences may it have had? In May 2015, it was 100 years since Mathematical Statistics was formally established as an academic discipline at a Swedish university where Statistics had existed since the turn of the century. We give an account of the debate in Lund and elsewhere about this division during the first decades after 1900 and present two of its leading personalities. The Lund University astronomer (and mathematical statistician) C. V. L. Charlier was a leading proponent for a position in mathematical statistics at the university. Charlier's adversary in the debate was Pontus Fahlbeck, professor in political science and statistics, who reserved the word statistics for ‘statistics as a social science’. Charlier not only secured the first academic position in Sweden in mathematical statistics for his former PhD student Sven Wicksell but also demonstrated that a mathematical statistician can be influential in matters of state, finance as well as in different natural sciences. Fahlbeck saw mathematical statistics as a set of tools that sometimes could be useful in his brand of statistics. After a summary of the organisational, educational and scientific growth of the statistical sciences in Sweden that has taken place during the last 50 years, we discuss what effects the Charlier–Fahlbeck divergence might have had on this development.     

My Chancy Life as a Statistician

In this short article, I will attempt to provide some highlights of my chancy life as a statistician in chronological order spanning over 60 years, 1954 to present.     

On Becoming a Statistician—A Qualitative View

In this paper, we highlight some qualitative facets of the discipline of statistics and argue that a qualitative approach, in particular a qualitative methodology known as phenomenography, allows us to research important aspects of statistics pedagogy. We summarize several components of our recent research into students' conceptions of statistics, their learning of statistics, our teaching of statistics, and their perceptions of their future professional work. We have obtained this information on the basis of analyses of several series of interviews with students studying statistics, both as statistics majors and as service students. In each of these cases, the broadest views relate in some way to personal connection, growth, and change. In other words, they contain a strong ontological component—focusing on being or becoming a statistician—above and beyond the standard epistemological component—focusing on the knowledge required to do statistics. We discuss the importance of personal change in becoming a statistician, or an informed professional user of statistics, and investigate the pedagogical conditions under which such change is likely to occur.     

Statistics by Gender: Measures to Reduce Gender Bias in Agricultural Surveys

Statistics by gender has been the concern of policy makers in the recent past years. The demand on data disaggregated by gender has led the survey statistician to collect data and tabulate statistics by gender. In this paper, some measures will be suggested to avoid and reduce gender bias for data collection and tabulation in agricultural surveys.     

Felix Bernstein

Felix Bernstein was a mathematician and statistician who is remembered for the "Schröder-Bernstein Theorem" of set theory. He was appointed director in 1907 of what he was to develop into the Göttingen Institute for Mathematical Statistics, and in 1911 was involved in the formulation and proof of what became known as the Borel-Cantelli lemmas. Bernstein worked out in 1924 the correct hypothesis for the genetic transmission of human bloodgroups, on the basis of the available statistical material.     

The Statistical Education of Harold Jeffreys

The paper considers the statistical work of the physicist Harold Jeffreys. In 1933–4 Jeffreys had a controversy with R.A. Fisher, the leading statistician of the time. Prior to the encounter, Jeffreys had worked on probability as the basis for scientific inference and had used methods from the theory of errors in astronomy and seismology. He had also started to rework the theory of errors on the basis of his theory of probability. After the encounter Jeffreys produced a full-scale Bayesian treatment of statistics in the form of his Theory of Probability.     

Misuses of Statistics: Lessons for Statisticians, Non-statisticians, Students and Teachers

A bstract . Statisticians, students, teachers and even non statisticians can learn from a study of misuses of statistics. The practitioners can improve their use of statistical methods in research , the student can learn the correct method of analysis and the effects of incorrect analysis, the teacher can improve teaching and the non statistician can learn to evaluate statistical inputs to decision making All users of statistics, professional and nonprofessional, can benefit from the same examples. In this paper, the authors categorize misuses, by both the nature and effect of the misuse, and give examples for each category.     

The Olympic 500‐m speed skating; the inner–outer lane difference

In 1998, the International Skating Union and the International Olympic Committee decided to skate the 500‐m twice during World Single Distances Championships, Olympic Games, and World Cups. The decision was based on a study by the Norwegian statistician N. L. Hjort, who showed that in the period 1984–1994, there was a significant difference between 500‐m times skated with a start in the inner and outer lanes. Since the introduction of the clap skate in the season 1997–1998, however, there has been a general feeling that this difference is no longer significant. In this article we show that this is, in fact, the case.     

Secretary problems with inspection costs as a game

A secretary problem without recall, with given rewards and inspection costs is considered as a game of an opponent against a statistician. Probability distributions on the set of arrangements of the arrivals and randomized stopping rules are the strategies for the two players. We give a condition under which minimax strategies may be obtained in a systematic way.     

Some further aspects of ecological regression analysis

Ecological regression analysis is concerned with the inference of individual level relationships from aggregate level data. A first method for this type of inference, which here is compared to Leo Goodman's (1953) method, was presented by the German statistician F. Bernstein (1932). His hypothetical example, voting behaviour depending on age, is reanalysed, using historical data from the Reichstag elections, with special emphasis on the NSDAP vote. Furthermore it is demonstrated, why age is a bad predictor is ecological regression analysis, and how unreliable predictors can be diagnosed by means of several simple coefficients.     

Statistical aspects of interpreting DNA profiling in legal cases

DNA profiling has become one of the most powerful forensic techniques that is used in criminal investigations to identify suspects. Moreover, scientific DNA evidence has become an important component in criminal trials. The interpretation of DNA evidence involves many statistical and probabilistic aspects, which have been the subject of fierce debates over past years. Many controversies are rooted in the differences between the so-called Likelihood Ratio approach to evidence interpretation and other methods. However, though interesting for the statistician and probability theorist, these discussions are obstructive for legal proceedings. Fortunately, many issues have by now been settled, but the ever-expanding possibilities keep on raising new discussion topics. This paper provides a brief overview of the statistical discussion, and addresses some of the most interesting issues in more detail.     

De bewerking van veldproeven volgens de methode van Knut Vik

Summary  (The treatment of field experiments by Knut Vik's method).
Agriculturists and statisticians have studied the problem, how to free the yields in a field experiment from the inequality of the soil. The method of the analysis of variance, due to the English statistician R. A. Fisher has taken an important place in the study of field experiments, so that other methods fell in the background.
The Norwegian agriculturist Knut Vik has suggested a method, which leads to good results without complicated calculations and without the knowledge of a great part of the mathematical statistics.
The mathematical foundation of this method is discussed in this article, which concludes, that the method of Knut Vik is of great importance for agriculturists, who don't want to perform intricate calculations.     

On the estimation of long tailed skewed distributions with actuarial applications

Very long tailed skewed distributions often arise in practice. In particular, we find that size-of-loss distributions in casualty insurance are mainly of this type. Compounding explains why many of these losses have approximate Pareto, generalized Pareto, Burr, and log-t distributions. An adaptation of the empirical mean residual life function helps the statistician select the correct model to fit in these cases. It is then discovered that minimum distance estimates, in particular that of Cramér-von Mises, and minimum chi-square estimates are extremely valuable and easy to use in the case of grouped data. Two substantial examples are given, one involving hurricane losses and the other dealing with malpractice claims.     

Henry George and Europe

A bstract . The kingdom of Hungary still faced an unsolved land question as the 20th century dawned. More than half the land belonged to latifundia , large estates, archaically mismanaged with sweated labor. Unrest among the 6 million rural poor , a third of the population, brought forth embryonic land reform aspirations. The founder of the first farmers'party, Andras Achim, sought their expropriation and parcelling out. Publication of Henry George's writings in Hungarian by the sociologist Robert Braun and especially the activities of the physician and statistician Julius J. Pikler made the Georgist proposals known. In 1917 and 1918, Dr. Pikler's ingenious lobbying succeeded in winning over the city councils of Budapest and eight other Hungarian towns. The leaders of the 1918 republican revolution included land value taxation in their program. But the turmoil of 1919, Horthy's counter-revolution and torrential currency inflation destroyed the Georgist advance.     

Big Data,Official Statistics and Some Initiatives by the Australian Bureau of Statistics

Official statisticians have been dealing with a diversity of data sources for decades. However, new sources of data in the Big Data domain provide an opportunity to deliver a more efficient and effective statistical service. This paper outlines a number of considerations for the official statistician when deciding whether to embrace a particular new data source in the regular production of official statistics. The principal considerations are relevance, business benefit, and the validity of using the source for official statistics in finite population inferences or analytic inferences. The paper also describes the Big Data Flagship Project of the Australian Bureau of Statistics (ABS), which has been established to provide the opportunity for the ABS to gain practical experience in assessing the business, statistical, technical, computational and other issues in using Big Data. In addition, ABS participation in national and international activities in this area will help it share experience and knowledge, while collaboration with academics will enable ABS to better acquire the capability to address business problems using the new sources of data as part of the solution.     

A Conversation with Roger Koenker

Roger William Koenker was born February 21, 1947. He graduated from Grinnell College in 1969 and obtained his PhD in Economics from University of Michigan in 1974 under the direction of Saul Hymans. He was Assistant Professor of Economics at the University of Illinois at Urbana‐Champaign (UIUC) from 1974 to 1976, and a member of Technical Staff at Bell Telephone Laboratories from 1976 to 1983 and returned to UIUC as Professor in 1983. He is currently a William B. McKinley Professor of Economics and Professor of Statistics at UIUC. He is best known for his seminal work on quantile regression, which has emerged as a powerful regression analysis tool across many disciplines. He is a Fellow of the American Statistical Association, Fellow of the Institute of Mathematical Statistics, Fellow of Econometric Society, and recipient of the 2010 Emanuel and Carol Parzen Prize for Statistical Innovation. The conversation covers part of Roger Koenker's career as an econometrician and statistician, starting from his college years.     

An Interview with Chris Skinner

Chris Skinner was born in London on 12 March 1953. He completed a BA in mathematics in 1975 at the University of Cambridge. He then obtained an MSc degree in statistics from the London School of Economics and Political Science (LSE) in 1976 and worked as an assistant statistician in the Central Statistical Office for 1 year. After working as a research assistant in LSE from 1977 to 1978, he joined the University of Southampton as a lecturer in 1978, where he earned a PhD in social statistics in 1982. He remained at the University of Southampton, where he became a senior lecturer in 1989 and professor of statistics in 1994. While serving as the head of his department from 1997 to 2000, he played a crucial role in the creation of an MSc programme in official statistics in 1999. In 2011, he returned to the LSE, where he currently holds the position of professor of statistics. Chris is the author of over 80 peer‐reviewed articles in statistical journals and the co‐editor of two influential books on the analysis of survey data. He made significant research contributions covering areas that include the analysis of survey data, inference in the presence of non‐response and measurement errors and statistical disclosure control. He served on several advisory committees, including the Statistical Methods Advisory Committee at Statistics Canada (from 2000 to 2011) and the National Statistics Methodology Advisory Committee in the United Kingdom (from 2001 to 2010). He has received numerous awards and honors for his outstanding contributions to survey sampling and social statistics. He is a Fellow of the American Statistical Association, Fellow of the British Academy and a Fellow of the Academy of Social Sciences. In 2009, he received the West Medal from the Royal Statistical Society for contributions to social statistics, and in 2010, he was made a Commander of the Most Excellent Order of the British Empire. In 2019, he also received the Waksberg award to recognize his contributions to survey methodology. The following conversation took place at LSE on 21 May 2019.     

Design-based or Prediction-based Inference? Stratified Random vs Stratified Balanced Sampling

Early survey statisticians faced a puzzling choice between randomized sampling and purposive selection but, by the early 1950s, Neyman's design-based or randomization approach had become generally accepted as standard. It remained virtually unchallenged until the early 1970s, when Royall and his co-authors produced an alternative approach based on statistical modelling. This revived the old idea of purposive selection, under the new name of “balanced sampling”. Suppose that the sampling strategy to be used for a particular survey is required to involve both a stratified sampling design and the classical ratio estimator, but that, within each stratum, a choice is allowed between simple random sampling and simple balanced sampling; then which should the survey statistician choose? The balanced sampling strategy appears preferable in terms of robustness and efficiency, but the randomized design has certain countervailing advantages. These include the simplicity of the selection process and an established public acceptance that randomization is “fair”. It transpires that nearly all the advantages of both schemes can be secured if simple random samples are selected within each stratum and a generalized regression estimator is used instead of the classical ratio estimator.