Regional Newsletter, June 2013
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Two Principles of Evidence and Their Implications for the Philosophy of Scientific Method
TWO PRINCIPLES OF EVIDENCE AND THEIR IMPLICATIONS FOR THE PHILOSOPHY OF SCIENTIFIC METHOD by Gregory Stephen Gandenberger BA, Philosophy, Washington University in St. Louis, 2009 MA, Statistics, University of Pittsburgh, 2014 Submitted to the Graduate Faculty of the Kenneth P. Dietrich School of Arts and Sciences in partial fulfillment of the requirements for the degree of Doctor of Philosophy University of Pittsburgh 2015 UNIVERSITY OF PITTSBURGH KENNETH P. DIETRICH SCHOOL OF ARTS AND SCIENCES This dissertation was presented by Gregory Stephen Gandenberger It was defended on April 14, 2015 and approved by Edouard Machery, Pittsburgh, Dietrich School of Arts and Sciences Satish Iyengar, Pittsburgh, Dietrich School of Arts and Sciences John Norton, Pittsburgh, Dietrich School of Arts and Sciences Teddy Seidenfeld, Carnegie Mellon University, Dietrich College of Humanities & Social Sciences James Woodward, Pittsburgh, Dietrich School of Arts and Sciences Dissertation Director: Edouard Machery, Pittsburgh, Dietrich School of Arts and Sciences ii Copyright © by Gregory Stephen Gandenberger 2015 iii TWO PRINCIPLES OF EVIDENCE AND THEIR IMPLICATIONS FOR THE PHILOSOPHY OF SCIENTIFIC METHOD Gregory Stephen Gandenberger, PhD University of Pittsburgh, 2015 The notion of evidence is of great importance, but there are substantial disagreements about how it should be understood. One major locus of disagreement is the Likelihood Principle, which says roughly that an observation supports a hypothesis to the extent that the hy- pothesis predicts it. The Likelihood Principle is supported by axiomatic arguments, but the frequentist methods that are most commonly used in science violate it. This dissertation advances debates about the Likelihood Principle, its near-corollary the Law of Likelihood, and related questions about statistical practice. -
JSM 2017 in Baltimore the 2017 Joint Statistical Meetings in Baltimore, Maryland, Which Included the CONTENTS IMS Annual Meeting, Took Place from July 29 to August 3
Volume 46 • Issue 6 IMS Bulletin September 2017 JSM 2017 in Baltimore The 2017 Joint Statistical Meetings in Baltimore, Maryland, which included the CONTENTS IMS Annual Meeting, took place from July 29 to August 3. There were over 6,000 1 JSM round-up participants from 52 countries, and more than 600 sessions. Among the IMS program highlights were the three Wald Lectures given by Emmanuel Candès, and the Blackwell 2–3 Members’ News: ASA Fellows; ICM speakers; David Allison; Lecture by Martin Wainwright—Xiao-Li Meng writes about how inspirational these Mike Cohen; David Cox lectures (among others) were, on page 10. There were also five Medallion lectures, from Edoardo Airoldi, Emery Brown, Subhashis Ghoshal, Mark Girolami and Judith 4 COPSS Awards winners and nominations Rousseau. Next year’s IMS lectures 6 JSM photos At the IMS Presidential Address and Awards session (you can read Jon Wellner’s 8 Anirban’s Angle: The State of address in the next issue), the IMS lecturers for 2018 were announced. The Wald the World, in a few lines lecturer will be Luc Devroye, the Le Cam lecturer will be Ruth Williams, the Neyman Peter Bühlmann Yuval Peres 10 Obituary: Joseph Hilbe lecture will be given by , and the Schramm lecture by . The Medallion lecturers are: Jean Bertoin, Anthony Davison, Anna De Masi, Svante Student Puzzle Corner; 11 Janson, Davar Khoshnevisan, Thomas Mikosch, Sonia Petrone, Richard Samworth Loève Prize and Ming Yuan. 12 XL-Files: The IMS Style— Next year’s JSM invited sessions Inspirational, Mathematical If you’re feeling inspired by what you heard at JSM, you can help to create the 2018 and Statistical invited program for the meeting in Vancouver (July 28–August 2, 2018). -
You May Be (Stuck) Here! and Here Are Some Potential Reasons Why
You may be (stuck) here! And here are some potential reasons why. As published in Benchmarks RSS Matters, May 2015 http://web3.unt.edu/benchmarks/issues/2015/05/rss-matters Jon Starkweather, PhD 1 Jon Starkweather, PhD [email protected] Consultant Research and Statistical Support http://www.unt.edu http://www.unt.edu/rss RSS hosts a number of “Short Courses”. A list of them is available at: http://www.unt.edu/rss/Instructional.htm Those interested in learning more about R, or how to use it, can find information here: http://www.unt.edu/rss/class/Jon/R_SC 2 You may be (stuck) here! And here are some potential reasons why. I often read R-bloggers (Galili, 2015) to see new and exciting things users are doing in the wonderful world of R. Recently I came across Norm Matloff’s (2014) blog post with the title “Why are we still teaching t-tests?” To be honest, many RSS personnel have echoed Norm’s sentiments over the years. There do seem to be some fields which are perpetually stuck in decades long past — in terms of the statistical methods they teach and use. Reading Norm’s post got me thinking it might be good to offer some explanations, or at least opinions, on why some fields tend to be stubbornly behind the analytic times. This month’s article will offer some of my own thoughts on the matter. I offer these opinions having been academically raised in one such Rip Van Winkle (Washington, 1819) field and subsequently realized how much of what I was taught has very little practical utility with real world research problems and data. -
Causal Diagrams for Interference Elizabeth L
STATISTICAL SCIENCE Volume 29, Number 4 November 2014 Special Issue on Semiparametrics and Causal Inference Causal Etiology of the Research of James M. Robins ..............................................Thomas S. Richardson and Andrea Rotnitzky 459 Doubly Robust Policy Evaluation and Optimization ..........................Miroslav Dudík, Dumitru Erhan, John Langford and Lihong Li 485 Statistics,CausalityandBell’sTheorem.....................................Richard D. Gill 512 Standardization and Control for Confounding in Observational Studies: A Historical Perspective..............................................Niels Keiding and David Clayton 529 CausalDiagramsforInterference............Elizabeth L. Ogburn and Tyler J. VanderWeele 559 External Validity: From do-calculus to Transportability Across Populations .........................................................Judea Pearl and Elias Bareinboim 579 Nonparametric Bounds and Sensitivity Analysis of Treatment Effects .................Amy Richardson, Michael G. Hudgens, Peter B. Gilbert and Jason P. Fine 596 The Bayesian Analysis of Complex, High-Dimensional Models: Can It Be CODA? .......................................Y.Ritov,P.J.Bickel,A.C.GamstandB.J.K.Kleijn 619 Q-andA-learning Methods for Estimating Optimal Dynamic Treatment Regimes .............. Phillip J. Schulte, Anastasios A. Tsiatis, Eric B. Laber and Marie Davidian 640 A Uniformly Consistent Estimator of Causal Effects under the k-Triangle-Faithfulness Assumption...................................................Peter Spirtes -
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). -
ISI99 Daily Bulletin 4
ISI99 Daily Bulletin 4 Thursday 12 August, 1999 Contents First day • ISI Service Awards 2 • Jan Tinbergen Awards 1999 2 • Changes in the programme 3 • Correction 3 • Statistical Education 3 • Making Statistics Come Alive 4 • Behind the scenes and in the frontline, part 3 5 IASS - International Association • Many questions were answered at the Information Desk. of Survey Statisticians 6 • Everyday elegance anyone can afford 6 • Golden era of Finnish art 8 • Ateneum, the ”House for the Arts” 9 • Kiasma, the museum of contemporary art 9 • The spirit of the bottle 10 • Harvest in the Market Square 11 • Kuka hän on? 12 • For people on the move 12 Webmasters Markku (left) and Samppa mastering the Web. ISI99 / Daily Bulletin / Thursday, August 12 / 4 1 ISI Service Awards From time to time, the President awards ISI Service Medals to Adolphe Quetelet (Belgium) persons whose outstanding activities were of particular Henri Willem Methorst (The Netherlands) significance for the Institute’s activities. The Service Medals are Franz von Neumann–Spallart (Austria–Hungary) named after three great statisticians who were instrumental in The ISI Executive Committee considers the following persons furthering international statistical co-operation: as deserving candidates for an ISI Service Medal, to be awarded during the August 13 ISI General Assembly meeting in Helsinki: Adolphe Quetelet Medal David Cox (United Kingdom) Ilkka Mellin (Finland) Mario Palma Rojo (Mexico) Timo Relander (Finland) Vijayan N. Nair (Malaysia) Zoltan Kenessey (in memorial -USA) Willem de Vries (Netherlands) Henri Willem Methorst Medal Constance van Eeden (The Netherlands) Agnes Herzberg (Canada) Raija Lofgren (Finland) Joop Mijnheer (The Netherlands) Jozef L.M. -
Error and Inference: an Outsider Stand on a Frequentist Philosophy
ERROR AND INFERENCE: AN OUTSIDER STAND ON A FREQUENTIST PHILOSOPHY CHRISTIAN P. ROBERT UNIVERSITE´ PARIS-DAUPHINE CEREMADE, IUF, AND CREST, 75775 PARIS CEDEX 16, FRANCE [email protected] Abstract. This note is an extended review of the book Error and Inference, edited by Deborah Mayo and Aris Spanos, about their frequentist and philosophical perspective on testing of hypothesis and on the criticisms of alternatives like the Bayesian approach. 1. Introduction. “The philosophy of science offer valuable tools for understanding and ad- vancing solutions to the problems of evidence and inference in practice”— D. Mayo and A. Spanos, p.xiv, Error and Inference, 2010 This philosophy book, Error and Inference, whose subtitle is “Recent exchanges on experimental reasoning, reliability, and the objectivity and rationality of Science”, includes contributions by P. Achinstein, A. Chalmers, D. Cox, C. Glymour, L. Laudan, A. Musgrave, and J. Worrall, and by both editors, D. Mayo and A. Spanos. It offers reflections on the nature of testing and the defence of the “Error-Statistical philosophy”, proposed by Mayo (1996). Error and Inference is the edited continuation of a conference held at Virginia Tech in 2006, ERROR 06. Given my layman interest in the philosophy of Science, I find that the book reads rather well, despite my missing references in the field. (Paradoxically, the sections I found the hardest to follow were those most centred on Statistics.) The volume is very carefully edited and thus much more than a collection of papers, thanks to the dedications of the editors. In particular, Deborah Mayo added her comments at the end of the chapters of all contributors (but her own’s and Aris Spanos’). -
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. -
Carver Award: Lynne Billard We Are Pleased to Announce That the IMS Carver Medal Committee Has Selected Lynne CONTENTS Billard to Receive the 2020 Carver Award
Volume 49 • Issue 4 IMS Bulletin June/July 2020 Carver Award: Lynne Billard We are pleased to announce that the IMS Carver Medal Committee has selected Lynne CONTENTS Billard to receive the 2020 Carver Award. Lynne was chosen for her outstanding service 1 Carver Award: Lynne Billard to IMS on a number of key committees, including publications, nominations, and fellows; for her extraordinary leadership as Program Secretary (1987–90), culminating 2 Members’ news: Gérard Ben Arous, Yoav Benjamini, Ofer in the forging of a partnership with the Bernoulli Society that includes co-hosting the Zeitouni, Sallie Ann Keller, biannual World Statistical Congress; and for her advocacy of the inclusion of women Dennis Lin, Tom Liggett, and young researchers on the scientific programs of IMS-sponsored meetings.” Kavita Ramanan, Ruth Williams, Lynne Billard is University Professor in the Department of Statistics at the Thomas Lee, Kathryn Roeder, University of Georgia, Athens, USA. Jiming Jiang, Adrian Smith Lynne Billard was born in 3 Nominate for International Toowoomba, Australia. She earned both Prize in Statistics her BSc (1966) and PhD (1969) from the 4 Recent papers: AIHP, University of New South Wales, Australia. Observational Studies She is probably best known for her ground-breaking research in the areas of 5 News from Statistical Science HIV/AIDS and Symbolic Data Analysis. 6 Radu’s Rides: A Lesson in Her research interests include epidemic Humility theory, stochastic processes, sequential 7 Who’s working on COVID-19? analysis, time series analysis and symbolic 9 Nominate an IMS Special data. She has written extensively in all Lecturer for 2022/2023 these areas, publishing over 250 papers in Lynne Billard leading international journals, plus eight 10 Obituaries: Richard (Dick) Dudley, S.S. -
Major Greenwood and Clinical Trials
From the James Lind Library Journal of the Royal Society of Medicine; 2017, Vol. 110(11) 452–457 DOI: 10.1177/0141076817736028 Major Greenwood and clinical trials Vern Farewell1 and Tony Johnson2 1MRC Biostatistics Unit, University of Cambridge, Cambridge CB2 0SR, UK 2MRC Clinical Trials Unit, Aviation House, London WC2B 6NH, UK Corresponding author: Vern Farewell. Email: [email protected] Introduction References to Greenwood’s work in clinical trials Major Greenwood (1880–1949) was the foremost medical statistician in the UK during the first Greenwood was a renowned epidemiologist who is not half of the 20th century.1 The son of a general usually associated with randomised clinical trials. At medical practitioner, he obtained a medical degree the time of his retirement, randomised clinical trials from the London Hospital in 1904 and then stu- were still under development and Peter Armitage has died statistics under Karl Pearson at University no recollection of Greenwood commenting on trials College London. Instead of general practice, he when Greenwood visited the London School of opted to pursue a career in medical research, first Hygiene and Tropical Medicine during his retirement under the physiologist Leonard Hill at the London (personal communication). Indeed, randomised clin- Hospital, where, in 1908, he set up the first depart- ical trials were a major component of the work of ment of medical statistics and gave the first lecture Austin Bradford Hill,5 who began his employment in course in the subject in 1909. He established the Greenwood’s department in 1923 and succeeded him second department of medical statistics in 1910 at as professor at the London School of Hygiene and the Lister Institute and established the second Tropical Medicine and director of the Medical course of lectures there. -
Design and Analysis Issues in Family-Based Association
Emerging Challenges in Statistical Genetics Duncan Thomas University of Southern California Human Genetics in the Big Science Era • “Big Data” – large n and large p and complexity e.g., NIH Biomedical Big Data Initiative (RFA-HG-14-020) • Large n: challenge for computation and data storage, but not conceptual • Large p: many data mining approaches, few grounded in statistical principles • Sparse penalized regression & hierarchical modeling from Bayesian and frequentist perspectives • Emerging –omics challenges Genetics: from Fisher to GWAS • Population genetics & heritability – Mendel / Fisher / Haldane / Wright • Segregation analysis – Likelihoods on complex pedigrees by peeling: Elston & Stewart • Linkage analysis (PCR / microsats / SNPs) – Multipoint: Lander & Green – MCMC: Thompson • Association – TDT, FBATs, etc: Spielman, Laird – GWAS: Risch & Merikangas – Post-GWAS: pathway mining, next-gen sequencing Association: From hypothesis-driven to agnostic research Candidate pathways Candidate Hierarchical GWAS genes models (ht-SNPs) Ontologies Pathway mining MRC BSU SGX Plans Objectives: – Integrating structural and prior information for sparse regression analysis of high dimensional data – Clustering models for exposure-disease associations – Integrating network information – Penalised regression and Bayesian variable selection – Mechanistic models of cellular processes – Statistical computing for large scale genomics data Targeted areas of impact : – gene regulation and immunological response – biomarker based signatures – targeting -
A Complete Bibliography of Applied Statistics: 1952–1959
A Complete Bibliography of Applied Statistics: 1952{1959 Nelson H. F. Beebe University of Utah Department of Mathematics, 110 LCB 155 S 1400 E RM 233 Salt Lake City, UT 84112-0090 USA Tel: +1 801 581 5254 FAX: +1 801 581 4148 E-mail: [email protected], [email protected], [email protected] (Internet) WWW URL: http://www.math.utah.edu/~beebe/ 14 March 2019 Version 4.03 Title word cross-reference [136]. Adjusting [356]. Administration [81]. Advanced [326, 75]. Age [351]. Agricultural [359, 65, 77]. Aid [104]. Aitchison [269]. Alan [326]. Allen χ2 [199]. [253, 98]. Allocation [211]. Analysed [214]. Analysis -Test [199]. [82, 229, 54, 48, 94, 140, 18, 327, 8, 247, 260, 306, 181, 328, 302, 296, 340, 262, 122, 145, 10th [49]. 11th [254]. 1951 [4]. 231, 160, 57, 285, 341, 152, 93, 324, 359, 92]. Answer [200, 142]. Answers 2 [187]. 2nd [312]. [89, 184, 10, 34, 45]. any [197]. Application [348, 49, 254, 276, 138]. Applications A. [76, 49, 254, 112, 363, 78]. Absence [12, 24, 36, 47, 61, 73, 90, 110, 125, 143, 155, [120, 105]. Absence-Proneness [120]. 173, 185, 201, 26, 312, 246, 132]. Applied Abuses [8]. Acceptance [303]. [347, 140]. Appraisal [166]. Approach Accounting [195]. Accounts [295, 350, 42, 7, 253]. Aptitude [112]. [113, 241, 364]. Accumulation [150]. Arbous [112]. Arithmetic [345]. Arthur Accuracy [31, 118, 9]. Achieve [261]. [266]. Aspects [310, 315]. Assessments Ackoff [159]. Action [120, 130]. Active 1 2 [353]. Associated [95]. Attributes [304]. [278, 320, 180]. Charts [66, 258]. Chemical Auction [277]. Auditing [350, 69].