Some Statistical Heresies
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John Ashworth Nelder: 8 October 1924 – 7 August 2010
John Ashworth Nelder: 8 October 1924 – 7 August 2010. John Nelder died on Saturday 7th August 2010 in Luton & Dunstable Hospital UK, where he was recovering from a fall. John was very active even at the age of 85, and retained the strong interest in our work – and statistics generally – that we will all remember with deep affection. However, he was becoming increasingly frail and it was a shock but perhaps, in retrospect, not a surprise to hear that he had died peacefully in his sleep. John was born on 8th October 1924 in Dulverton, Somerset, UK. He was educated at Blundell's School and at Sidney Sussex College, Cambridge where he read Mathematics (interrupted by war service in the RAF) from 1942-8, and then took the Diploma in Mathematical Statistics. Most of John’s formal career was spent as a statistician in the UK Agricultural Research Service. His first job, from October 1949, was at the newly set-up Vegetable Research Station, Wellesbourne UK (NVRS). Then, in 1968, he became Head of the Statistics Department at Rothamsted, and continued there until his first retirement in 1984. The role of statistician there was very conducive for John, not only because of his strong interests in biology (and especially ornithology), but also because it allowed him to display his outstanding skill of developing new statistical theory to solve real biological problems. At NVRS, John developed the theory of general balance to provide a unifying framework for the wide range of designs that are needed in agricultural research (see Nelder, 1965, Proceedings of the Royal Society, Series A). -
Strength in Numbers: the Rising of Academic Statistics Departments In
Agresti · Meng Agresti Eds. Alan Agresti · Xiao-Li Meng Editors Strength in Numbers: The Rising of Academic Statistics DepartmentsStatistics in the U.S. Rising of Academic The in Numbers: Strength Statistics Departments in the U.S. Strength in Numbers: The Rising of Academic Statistics Departments in the U.S. Alan Agresti • Xiao-Li Meng Editors Strength in Numbers: The Rising of Academic Statistics Departments in the U.S. 123 Editors Alan Agresti Xiao-Li Meng Department of Statistics Department of Statistics University of Florida Harvard University Gainesville, FL Cambridge, MA USA USA ISBN 978-1-4614-3648-5 ISBN 978-1-4614-3649-2 (eBook) DOI 10.1007/978-1-4614-3649-2 Springer New York Heidelberg Dordrecht London Library of Congress Control Number: 2012942702 Ó Springer Science+Business Media New York 2013 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. Exempted from this legal reservation are brief excerpts in connection with reviews or scholarly analysis or material supplied specifically for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work. Duplication of this publication or parts thereof is permitted only under the provisions of the Copyright Law of the Publisher’s location, in its current version, and permission for use must always be obtained from Springer. -
Statistics Making an Impact
John Pullinger J. R. Statist. Soc. A (2013) 176, Part 4, pp. 819–839 Statistics making an impact John Pullinger House of Commons Library, London, UK [The address of the President, delivered to The Royal Statistical Society on Wednesday, June 26th, 2013] Summary. Statistics provides a special kind of understanding that enables well-informed deci- sions. As citizens and consumers we are faced with an array of choices. Statistics can help us to choose well. Our statistical brains need to be nurtured: we can all learn and practise some simple rules of statistical thinking. To understand how statistics can play a bigger part in our lives today we can draw inspiration from the founders of the Royal Statistical Society. Although in today’s world the information landscape is confused, there is an opportunity for statistics that is there to be seized.This calls for us to celebrate the discipline of statistics, to show confidence in our profession, to use statistics in the public interest and to champion statistical education. The Royal Statistical Society has a vital role to play. Keywords: Chartered Statistician; Citizenship; Economic growth; Evidence; ‘getstats’; Justice; Open data; Public good; The state; Wise choices 1. Introduction Dictionaries trace the source of the word statistics from the Latin ‘status’, the state, to the Italian ‘statista’, one skilled in statecraft, and on to the German ‘Statistik’, the science dealing with data about the condition of a state or community. The Oxford English Dictionary brings ‘statistics’ into English in 1787. Florence Nightingale held that ‘the thoughts and purpose of the Deity are only to be discovered by the statistical study of natural phenomena:::the application of the results of such study [is] the religious duty of man’ (Pearson, 1924). -
IMS Bulletin 33(5)
Volume 33 Issue 5 IMS Bulletin September/October 2004 Barcelona: Annual Meeting reports CONTENTS 2-3 Members’ News; Bulletin News; Contacting the IMS 4-7 Annual Meeting Report 8 Obituary: Leopold Schmetterer; Tweedie Travel Award 9 More News; Meeting report 10 Letter to the Editor 11 AoS News 13 Profi le: Julian Besag 15 Meet the Members 16 IMS Fellows 18 IMS Meetings 24 Other Meetings and Announcements 28 Employment Opportunities 45 International Calendar of Statistical Events 47 Information for Advertisers JOB VACANCIES IN THIS ISSUE! The 67th IMS Annual Meeting was held in Barcelona, Spain, at the end of July. Inside this issue there are reports and photos from that meeting, together with news articles, meeting announcements, and a stack of employment advertise- ments. Read on… IMS 2 . IMS Bulletin Volume 33 . Issue 5 Bulletin Volume 33, Issue 5 September/October 2004 ISSN 1544-1881 Member News In April 2004, Jeff Steif at Chalmers University of Stephen E. Technology in Sweden has been awarded Contact Fienberg, the the Goran Gustafsson Prize in mathematics Information Maurice Falk for his work in “probability theory and University Professor ergodic theory and their applications” by Bulletin Editor Bernard Silverman of Statistics at the Royal Swedish Academy of Sciences. Assistant Editor Tati Howell Carnegie Mellon The award, given out every year in each University in of mathematics, To contact the IMS Bulletin: Pittsburgh, was named the Thorsten physics, chemistry, Send by email: [email protected] Sellin Fellow of the American Academy of molecular biology or mail to: Political and Social Science. The academy and medicine to a IMS Bulletin designates a small number of fellows each Swedish university 20 Shadwell Uley, Dursley year to recognize and honor individual scientist, consists of GL11 5BW social scientists for their scholarship, efforts a personal prize and UK and activities to promote the progress of a substantial grant. -
Random Permutations and Partition Models
i i “-Random_Permutations_and_Partition_Models”—//—:—page—# Metadata of the chapter that will be visualized online Book Title International Encyclopedia of Statistical Science Book CopyRight - Year Copyright Holder Springer-Verlag Title Random Permutations and Partition Models Author Degree Given Name Peter Particle Family Name McCullagh Suffix Phone Fax Email Affiliation Role John D. MacArthur Distinguished Service Professor Division Organization University of Chicago Country USA UNCORRECTED PROOF i i i i “-Random_Permutations_and_Partition_Models”—//—:—page—# R − In matrix notation, Bσ = σBσ ,sotheactionbyconju- Random Permutations and gation permutes both the rows and columns of B in the Partition Models same way. The block sizes are preserved and are maximally Peter McCullagh invariant under conjugation. In this way, the partitions [ ] John D. MacArthur Distinguished Service Professor of may be grouped into five orbits or equivalence University of Chicago, USA classes as follows: , ∣ [],∣ [],∣∣ [],∣∣∣. Set Partitions Thus, for example, ∣ is the representative element for ≥ [ ]={ } For n , a partition B of the finite set n ,...,n is one orbit, which also includes ∣ and ∣. ● A collection B ={b,...} of disjoint non-empty sub- The symbol #B applied to a set denotes the number of sets, called blocks, whose union is [n] elements, so #B is the number of blocks, and #b is the size ∈ E ● An equivalence relation or Boolean function B∶[n]× of block b B.If n is the set of equivalence relations on [ ] [ ] E [n]→{, } that -
Probability, Analysis and Number Theory
Probability, Analysis and Number Theory. Papers in Honour of N. H. Bingham (ed. C. M. Goldie and A. Mijatovi´c), Advances in Applied Probability Special Volume 48A (2016), 1-14. PROBABILITY UNFOLDING, 1965-2015 N. H. BINGHAM Abstract. We give a personal (and we hope, not too idiosyncratic) view of how our subject of Probability Theory has developed during the last half-century, and the author in tandem with it. 1. Introduction. One of the nice things about Probability Theory is that it is still a young subject. Of course it has ancient roots in the real world, as chance is all around us, and draws on the older fields of Analysis on the one hand and Statistics on the other. We take the conventional view that the modern era begins in 1933 with Kolmogorov's path-breaking Grundbegriffe, and agree with Williams' view of probability pre-Kolmogorov as `a shambles' (Williams (2001), 23)1. The first third of the last century was an interesting transitional period, as Measure Theory, the natural machinery with which to do Proba- bility, already existed. For my thoughts on this period, see [72]2, [104]; for the origins of the Grundbegriffe, see e.g. Shafer & Vovk (2006). Regarding the history of mathematics in general, we have among a wealth of sources the two collections edited by Pier (1994, 2000), covering 1900- 50 and 1950-2000. These contain, by Doob and Meyer respectively (Doob (1994), Meyer (2000)), fine accounts of the development of probability; Meyer (2000) ends with his 12-page selection of a chronological list of key publica- tions during the century. -
Newsletter December 2012
Newsletter December 2012 2013 International Year of Statistics Richard Emsley As many readers will know, the BIR is its population, economy and education or other topics for the website at any actively participating in the Interna- system; the health of its citizens; and time by emailing the items to Richard tional Year of Statistics 2013, and similar social, economic, government Emsley and Jeff Myers of the Ameri- there are several ways that members and other relevant topics. can Statistical Association at . are encouraged to contribute to this Around the World in Statistics—A short event. Please join in where you can! The Steering Committee are also seek- 150 to 200 word article about how ing a core group of regular bloggers When the public-focused website of statistics is making life better for the to feature on the new website blog the International Year of Statistics is people of your country and are particu- unveiled later this year it will feature Statistician Job of the Week—A short larly keen for lots of exciting content informing mem- 150 to 250 word article, written by young statisti- bers of the public about the role of any members talking about his/her job cians to contrib- statistics in everyday life. So that this as a statistician and how he/she is ute to this. If content remains fresh and is frequently contributing to improving our global anyone would updated, the Steering Committee is society. like to become a seeking content contributions from par- blogger and ticipating organizations throughout the Statistics in Action—A photo with a contribute to this world in several subject areas. -
Australasian Applied Statistics Conference, 2018 and Pre-Conference Workshops
Australasian Applied Statistics Conference, 2018 and Pre-Conference Workshops 2-7 December 2018, Rotorua www.aasc.nz Welcome We are pleased to welcome you to Rotorua for the 2018 Australasian Applied Statis- tics Conference (AASC18). Our conference is devoted to providing you with the opportunity to liaise with fellow statisticians within the agricultural, biological and environmental sciences and to keep abreast of the most recent developments in statistics within this context. This conference is one of a longstanding series, originating with the initial Aus- tralasian Genstat conference in Canberra in 1979. A previous Genstat conference was held in Rotorua in 1992, so this is our second conference in Rotorua. The Gen- stat conference changed to the Australasian Applied Statistics Conference in 2011 to encompass the wider community of applied statisticians, and AASC conferences have been held in Palm Cove (Aus), Queenstown (NZ), Port Lincoln (Aus) and Bermagui (Aus). We are glad to have long time attendees of these conferences (in- cluding some who were at the first conference) and newcomers join us. One of the positive things of this conference is that it's not too large, so that you can meet most people, and we hope you will make new friends and opportunities for collaboration. The themes of AASC18 are big data analytics, reproducible research, history of ANOVA and REML, experimental design, statistical consultancy in the biosciences, and applied statistics. Our exciting group of invited speakers and workshop presen- ters (Chris Auld, Peter Baker, Salvador Gezan, Roger Payne, Alison Smith, Robin Thompson, Linley Jesson, Ruth Butler, Gabriela Borgognone and Helene Thygesen) will help explore these themes in various contexts. -
“I Didn't Want to Be a Statistician”
“I didn’t want to be a statistician” Making mathematical statisticians in the Second World War John Aldrich University of Southampton Seminar Durham January 2018 1 The individual before the event “I was interested in mathematics. I wanted to be either an analyst or possibly a mathematical physicist—I didn't want to be a statistician.” David Cox Interview 1994 A generation after the event “There was a large increase in the number of people who knew that statistics was an interesting subject. They had been given an excellent training free of charge.” George Barnard & Robin Plackett (1985) Statistics in the United Kingdom,1939-45 Cox, Barnard and Plackett were among the people who became mathematical statisticians 2 The people, born around 1920 and with a ‘name’ by the 60s : the 20/60s Robin Plackett was typical Born in 1920 Cambridge mathematics undergraduate 1940 Off the conveyor belt from Cambridge mathematics to statistics war-work at SR17 1942 Lecturer in Statistics at Liverpool in 1946 Professor of Statistics King’s College, Durham 1962 3 Some 20/60s (in 1968) 4 “It is interesting to note that a number of these men now hold statistical chairs in this country”* Egon Pearson on SR17 in 1973 In 1939 he was the UK’s only professor of statistics * Including Dennis Lindley Aberystwyth 1960 Peter Armitage School of Hygiene 1961 Robin Plackett Durham/Newcastle 1962 H. J. Godwin Royal Holloway 1968 Maurice Walker Sheffield 1972 5 SR 17 women in statistical chairs? None Few women in SR17: small skills pool—in 30s Cambridge graduated 5 times more men than women Post-war careers—not in statistics or universities Christine Stockman (1923-2015) Maths at Cambridge. -
P Values, Hypothesis Testing, and Model Selection: It’S De´Ja` Vu All Over Again1
FORUM P values, hypothesis testing, and model selection: it’s de´ja` vu all over again1 It was six men of Indostan To learning much inclined, Who went to see the Elephant (Though all of them were blind), That each by observation Might satisfy his mind. ... And so these men of Indostan Disputed loud and long, Each in his own opinion Exceeding stiff and strong, Though each was partly in the right, And all were in the wrong! So, oft in theologic wars The disputants, I ween, Rail on in utter ignorance Of what each other mean, And prate about an Elephant Not one of them has seen! —From The Blind Men and the Elephant: A Hindoo Fable, by John Godfrey Saxe (1872) Even if you didn’t immediately skip over this page (or the entire Forum in this issue of Ecology), you may still be asking yourself, ‘‘Haven’t I seen this before? Do we really need another Forum on P values, hypothesis testing, and model selection?’’ So please bear with us; this elephant is still in the room. We thank Paul Murtaugh for the reminder and the invited commentators for their varying perspectives on the current shape of statistical testing and inference in ecology. Those of us who went through graduate school in the 1970s, 1980s, and 1990s remember attempting to coax another 0.001 out of SAS’s P ¼ 0.051 output (maybe if I just rounded to two decimal places ...), raising a toast to P ¼ 0.0499 (and the invention of floating point processors), or desperately searching the back pages of Sokal and Rohlf for a different test that would cross the finish line and satisfy our dissertation committee. -
A Hierarchy of Limitations in Machine Learning
A Hierarchy of Limitations in Machine Learning Momin M. Malik Berkman Klein Center for Internet & Society at Harvard University momin [email protected] 29 February 2020∗ Abstract \All models are wrong, but some are useful," wrote George E. P. Box(1979). Machine learning has focused on the usefulness of probability models for prediction in social systems, but is only now coming to grips with the ways in which these models are wrong|and the consequences of those shortcomings. This paper attempts a comprehensive, structured overview of the specific conceptual, procedural, and statistical limitations of models in machine learning when applied to society. Machine learning modelers themselves can use the described hierarchy to identify possible failure points and think through how to address them, and consumers of machine learning models can know what to question when confronted with the decision about if, where, and how to apply machine learning. The limitations go from commitments inherent in quantification itself, through to showing how unmodeled dependencies can lead to cross-validation being overly optimistic as a way of assessing model performance. Introduction There is little argument about whether or not machine learning models are useful for applying to social systems. But if we take seriously George Box's dictum, or indeed the even older one that \the map is not the territory' (Korzybski, 1933), then there has been comparatively less systematic attention paid within the field to how machine learning models are wrong (Selbst et al., 2019) and seeing possible harms in that light. By \wrong" I do not mean in terms of making misclassifications, or even fitting over the `wrong' class of functions, but more fundamental mathematical/statistical assumptions, philosophical (in the sense used by Abbott, 1988) commitments about how we represent the world, and sociological processes of how models interact with target phenomena. -
Page 72 Page 73 5 Specification 5.1 Introduction at One Time Econometricians Tended to Assume That the Model Provided by Economi
page_72 Page 73 5 Specification 5.1 Introduction At one time econometricians tended to assume that the model provided by economic theory represented accurately the real-world mechanism generating the data, and viewed their role as one of providing "good" estimates for the key parameters of that model. If any uncertainty was expressed about the model specification, there was a tendency to think in terms of using econometrics to "find" the real-world data-generating mechanism. Both these views of econometrics are obsolete. It is now generally acknowledged that econometric models are ''false" and that there is no hope, or pretense, that through them "truth" will be found. Feldstein's (1982, p. 829) remarks are typical of this view: "in practice all econometric specifications are necessarily 'false' models. The applied econometrician, like the theorist, soon discovers from experience that a useful model is not one that is 'true' or 'realistic' but one that is parsimonious, plausible and informative." This is echoed by an oft-quoted remark attributed to George Box - "All models are wrong, but some are useful" - and another from Theil (1971, p. vi): "Models are to be used, but not to be believed." In light of this recognition, econometricians have been forced to articulate more clearly what econometric models are. There is some consensus that models are metaphors, or windows, through which researchers view the observable world, and that their acceptance and use depends not upon whether they can be deemed "true" but rather upon whether they can be said to correspond to the facts. Econometric specification analysis is a means of formalizing what is meant by "corresponding to the facts," thereby defining what is meant by a "correctly specified model." From this perspective econometric analysis becomes much more than estimation and inference in the context of a given model; in conjunction with economic theory, it plays a crucial, preliminary role of searching for and evaluating a model, leading ultimately to its acceptance or rejection.