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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. -
September 2010
THE ISBA BULLETIN Vol. 17 No. 3 September 2010 The official bulletin of the International Society for Bayesian Analysis AMESSAGE FROM THE membership renewal (there will be special provi- PRESIDENT sions for members who hold multi-year member- ships). Peter Muller¨ The Bayesian Nonparametrics Section (IS- ISBA President, 2010 BA/BNP) is already up and running, and plan- [email protected] ning the 2011 BNP workshop. Please see the News from the World section in this Bulletin First some sad news. In August we lost two and our homepage (select “business” and “mee- big Bayesians. Julian Besag passed away on Au- tings”). gust 6, and Arnold Zellner passed away on Au- gust 11. Arnold was one of the founding ISBA ISBA/SBSS Educational Initiative. Jointly presidents and was instrumental to get Bayesian with ASA/SBSS (American Statistical Associa- started. Obituaries on this issue of the Analysis tion, Section on Bayesian Statistical Science) we Bulletin and on our homepage acknowledge Juli- launched a new joint educational initiative. The an and Arnold’s pathbreaking contributions and initiative formalized a long standing history of their impact on the lives of many people in our collaboration of ISBA and ASA/SBSS in related research community. They will be missed dearly! matters. Continued on page 2. ISBA Elections 2010. Please check out the elec- tion statements of the candidates for the upco- In this issue ming ISBA elections. We have an amazing slate ‰ A MESSAGE FROM THE BA EDITOR of candidates. Thanks to the nominating commit- *Page 2 tee, Mike West (chair), Renato Martins Assuncao,˜ Jennifer Hill, Beatrix Jones, Jaeyong Lee, Yasuhi- ‰ 2010 ISBA ELECTION *Page 3 ro Omori and Gareth Robert! ‰ SAVAGE AWARD AND MITCHELL PRIZE 2010 *Page 8 Sections. -
IMS Bulletin 39(4)
Volume 39 • Issue 4 IMS1935–2010 Bulletin May 2010 Meet the 2010 candidates Contents 1 IMS Elections 2–3 Members’ News: new ISI members; Adrian Raftery; It’s time for the 2010 IMS elections, and Richard Smith; we introduce this year’s nominees who are IMS Collections vol 5 standing for IMS President-Elect and for IMS Council. You can read all the candi- 4 IMS Election candidates dates’ statements, starting on page 4. 9 Amendments to This year there are also amendments Constitution and Bylaws to the Constitution and Bylaws to vote Letter to the Editor 11 on: they are listed The candidate for IMS President-Elect is Medallion Preview: Laurens on page 9. 13 Ruth Williams de Haan Voting is open until June 26, so 14 COPSS Fisher lecture: Bruce https://secure.imstat.org/secure/vote2010/vote2010.asp Lindsay please visit to cast your vote! 15 Rick’s Ramblings: March Madness 16 Terence’s Stuff: And ANOVA thing 17 IMS meetings 27 Other meetings 30 Employment Opportunities 31 International Calendar of Statistical Events The ten Council candidates, clockwise from top left, are: 35 Information for Advertisers Krzysztof Burdzy, Francisco Cribari-Neto, Arnoldo Frigessi, Peter Kim, Steve Lalley, Neal Madras, Gennady Samorodnitsky, Ingrid Van Keilegom, Yazhen Wang and Wing H Wong Abstract submission deadline extended to April 30 IMS Bulletin 2 . IMs Bulletin Volume 39 . Issue 4 Volume 39 • Issue 4 May 2010 IMS members’ news ISSN 1544-1881 International Statistical Institute elects new members Contact information Among the 54 new elected ISI members are several IMS members. We congratulate IMS IMS Bulletin Editor: Xuming He Fellow Jon Wellner, and IMS members: Subhabrata Chakraborti, USA; Liliana Forzani, Assistant Editor: Tati Howell Argentina; Ronald D. -
Orme) Wilberforce (Albert) Raymond Blackburn (Alexander Bell
Copyrights sought (Albert) Basil (Orme) Wilberforce (Albert) Raymond Blackburn (Alexander Bell) Filson Young (Alexander) Forbes Hendry (Alexander) Frederick Whyte (Alfred Hubert) Roy Fedden (Alfred) Alistair Cooke (Alfred) Guy Garrod (Alfred) James Hawkey (Archibald) Berkeley Milne (Archibald) David Stirling (Archibald) Havergal Downes-Shaw (Arthur) Berriedale Keith (Arthur) Beverley Baxter (Arthur) Cecil Tyrrell Beck (Arthur) Clive Morrison-Bell (Arthur) Hugh (Elsdale) Molson (Arthur) Mervyn Stockwood (Arthur) Paul Boissier, Harrow Heraldry Committee & Harrow School (Arthur) Trevor Dawson (Arwyn) Lynn Ungoed-Thomas (Basil Arthur) John Peto (Basil) Kingsley Martin (Basil) Kingsley Martin (Basil) Kingsley Martin & New Statesman (Borlasse Elward) Wyndham Childs (Cecil Frederick) Nevil Macready (Cecil George) Graham Hayman (Charles Edward) Howard Vincent (Charles Henry) Collins Baker (Charles) Alexander Harris (Charles) Cyril Clarke (Charles) Edgar Wood (Charles) Edward Troup (Charles) Frederick (Howard) Gough (Charles) Michael Duff (Charles) Philip Fothergill (Charles) Philip Fothergill, Liberal National Organisation, N-E Warwickshire Liberal Association & Rt Hon Charles Albert McCurdy (Charles) Vernon (Oldfield) Bartlett (Charles) Vernon (Oldfield) Bartlett & World Review of Reviews (Claude) Nigel (Byam) Davies (Claude) Nigel (Byam) Davies (Colin) Mark Patrick (Crwfurd) Wilfrid Griffin Eady (Cyril) Berkeley Ormerod (Cyril) Desmond Keeling (Cyril) George Toogood (Cyril) Kenneth Bird (David) Euan Wallace (Davies) Evan Bedford (Denis Duncan) -
CATS 10Th Anniversary Brochure
CELEBRATING 10OF INNOVATIVE YEARS RESEARCH 2000-2010 CONTENTS I. Foreword 2 II. The Centre for the Analysis of Time Series: Evolving over time… 3 III. CATS projects past and present 8 IV. CATS members past and present 11 V. Further information 24 1 1. FOREWORD We are proud of the achievements of CATS and hope that this ‘anniversary issue’ conveys something of the excitement of working in and with CATS over the last few years. Perhaps the most remarkable of these achievements has been the establishment of a strong international profile in the area of climate change which many comparable institutions would be proud of. This has been an era of policy making in addition to the science, at a national and international governmental level and in the private sector, such as insurance. It has been a period where the ‘truth’ has been at a premium. CATS is, we hope, known for its frank and honest approach and with Professor Leonard Smith at the helm has steered a careful course. CATS has come of age and this has been both acknowledged and reinforced in particular with its involvement in the Munich Re programme, the ESRC Centre for Climate Change Economics and Policy, and its seat in the Grantham Research Institute for Climate Change and the Environment. Behind and leading up to these has been strong research output with a firm mathematical basis in non-linear time series, simulation and statistical modeling, all fed by good science and a rich portfolio of research grants. Conducting high level scientific research in areas with, at times, a global decision support imperative requires the right mix of vision, pragmatism and even nerve. -
Tea: a High-Level Language and Runtime System for Automating
Tea: A High-level Language and Runtime System for Automating Statistical Analysis Eunice Jun1, Maureen Daum1, Jared Roesch1, Sarah Chasins2, Emery Berger34, Rene Just1, Katharina Reinecke1 1University of Washington, 2University of California, 3University of Seattle, WA Berkeley, CA Massachusetts Amherst, femjun, mdaum, jroesch, [email protected] 4Microsoft Research, rjust, [email protected] Redmond, WA [email protected] ABSTRACT Since the development of modern statistical methods (e.g., Though statistical analyses are centered on research questions Student’s t-test, ANOVA, etc.), statisticians have acknowl- and hypotheses, current statistical analysis tools are not. Users edged the difficulty of identifying which statistical tests people must first translate their hypotheses into specific statistical should use to answer their specific research questions. Almost tests and then perform API calls with functions and parame- a century later, choosing appropriate statistical tests for eval- ters. To do so accurately requires that users have statistical uating a hypothesis remains a challenge. As a consequence, expertise. To lower this barrier to valid, replicable statistical errors in statistical analyses are common [26], especially given analysis, we introduce Tea, a high-level declarative language that data analysis has become a common task for people with and runtime system. In Tea, users express their study de- little to no statistical expertise. sign, any parametric assumptions, and their hypotheses. Tea A wide variety of tools (such as SPSS [55], SAS [54], and compiles these high-level specifications into a constraint satis- JMP [52]), programming languages (e.g., R [53]), and libraries faction problem that determines the set of valid statistical tests (including numpy [40], scipy [23], and statsmodels [45]), en- and then executes them to test the hypothesis. -
The Scientific Rationality of Early Statistics, 1833–1877
The Scientific Rationality of Early Statistics, 1833–1877 Yasuhiro Okazawa St Catharine’s College This dissertation is submitted for the degree of Doctor of Philosophy. November 2018 Declaration Declaration This dissertation is the result of my own work and includes nothing which is the outcome of work done in collaboration except as declared in the Preface and specified in the text. It is not substantially the same as any that I have submitted, or, is being concurrently submitted for a degree or diploma or other qualification at the University of Cambridge or any other University or similar institution except as declared in the Preface and specified in the text. I further state that no substantial part of my dissertation has already been submitted, or, is being concurrently submitted for any such degree, diploma or other qualification at the University of Cambridge or any other University or similar institution except as declared in the Preface and specified in the text It does not exceed the prescribed word limit of 80,000 words for the Degree Committee of the Faculty of History. Yasuhiro Okazawa 13 November 2018 i Thesis Summary The Scientific Rationality of Early Statistics, 1833–1877 Yasuhiro Okazawa Summary This thesis examines the activities of the Statistical Society of London (SSL) and its contribution to early statistics—conceived as the science of humans in society—in Britain. The SSL as a collective entity played a crucial role in the formation of early statistics, as statisticians envisaged early statistics as a collaborative scientific project and prompted large-scale observation, which required cooperation among numerous statistical observers. -
December 2000
THE ISBA BULLETIN Vol. 7 No. 4 December 2000 The o±cial bulletin of the International Society for Bayesian Analysis A WORD FROM already lays out all the elements mere statisticians might have THE PRESIDENT of the philosophical position anything to say to them that by Philip Dawid that he was to continue to could possibly be worth ISBA President develop and promote (to a listening to. I recently acted as [email protected] largely uncomprehending an expert witness for the audience) for the rest of his life. defence in a murder appeal, Radical Probabilism He is utterly uncompromising which revolved around a Modern Bayesianism is doing in his rejection of the realist variant of the “Prosecutor’s a wonderful job in an enormous conception that Probability is Fallacy” (the confusion of range of applied activities, somehow “out there in the world”, P (innocencejevidence) with supplying modelling, data and in his pragmatist emphasis P ('evidencejinnocence)). $ analysis and inference on Subjective Probability as Contents procedures to nourish parts that something that can be measured other techniques cannot reach. and regulated by suitable ➤ ISBA Elections and Logo But Bayesianism is far more instruments (betting behaviour, ☛ Page 2 than a bag of tricks for helping or proper scoring rules). other specialists out with their What de Finetti constructed ➤ Interview with Lindley tricky problems – it is a totally was, essentially, a whole new ☛ Page 3 original way of thinking about theory of logic – in the broad ➤ New prizes the world we live in. I was sense of principles for thinking ☛ Page 5 forcibly struck by this when I and learning about how the had to deliver some brief world behaves. -
Vital and Health Statistics, Series 4, No. 29
Vital and Health Statistics Reconsidering Age Adjustment Procedures: Workshop Proceedings Series 4: Documents and Committee Reports No. 29 This report contains papers presented at the Workshop on Age-Adjustment, March 7, 1991. Presentations are made by representatives of selected Federal, State, and international institutions dealing with issues in the use of the 1940 U.S. population as a standard for the age-adjustment of official statistics. Manning Feinleib, M.D., Dr.P.H., and Alvan O. Zarate, Ph.D., Editors U.S. DEPARTMENT OF HEALTH AND HUMAN SERVICES Public Health Service Centers for Disease Control National Center for Health Statistics Hyattsville, Maryland October 1992 DHHS Publication No. (PHS) 93-1466 Copyright Information All material appearing in this report is in the public domain and may be reproduced or copied without permission; citation as to source, however, is appreciated. Suggested Citation Feinleib M, Zarate AO, eds. Reconsidering age adjustment procedures: Workshop proceedings. National Center for Health Statistics. Vital Health Stat 4(29). 1992. Library of Congress Cataloging-in-Publication Data Reconsidering age-adjustment procedures : workshop proceedings. p. cm.—(Vital and health statistics. Series 4, Documents and committee reports ; no. 29) (DHHS publication ; no. (PHS) 92-1466) ISBN 0-8406-0464-5 1. Epidemiology—United States—Statistical methods—Congresses. 2. Epidemiology—United States—Mathematical models—Congresses. 3. Age distribution (Demography)—United States—Congresses. I. National Center for Health Statistics (U.S.) II. Series. III. Series: DHHS publication ; no. (PHS) 92-1466. RA652.2.M3R43 1992 614.4’273—dc20 92-25492 CIP National Center for Health Statistics Manning Feinleib, M.D., Dr.P.H., Director Jack R. -
Historical Gregynog Statistical Conferences
Historical Gregynog Statistical Conferences 1 Speakers by date Talk Edition # Speaker Talk Title Notes 1 1st Gregynog (1965) 1 Dr. John Aitchison (Cambridge) Prediction See ‘Research notes’ 2 2 Dr. Larry Brown (Cornell) Admissable Estimators of Location Parameters 3 3 Prof. Henry Daniels (Birmingham) Stock Market Problem See ‘Research notes’ 4 4 Dr. Frank Downton (Birmingham) Order Statistics See ‘Research notes’ 5 5 Dr. Toby Lewis (UCL) Factorisation of Characteristic Function 6 6 Mr. David Wishart (St Andrews) Bacterial Colonies: Stochastic Models See ‘Research notes’ 7 2nd Gregynog (1966) 1 Prof. David Cox (Birkbeck / Imperial) Analysis of Qualitative Data See ‘Research notes’ 8 2 Dr. Marvin Zelen (National Cancer Institute, Operational Methods in the Design of Experiments See ‘Research notes’ Bethesda, MD) 9 3 Dr. Alan Durrant (Aberystwyth) Analysis of Genetic Variation See ‘Research notes’ 10 4 Prof. B.L. Welch (Leeds) Comparisons between Confidence Point Procedures See ‘Research notes’ 11 5 Dr. Peter Bickel (Berkeley) Asymptotic Theory of Bayes Solutions See ‘Research notes’ 12 6 Mr. Jeff Harrison (ICI) Short-Term Sales Forecasting See ‘Research notes’ 13 7 Dr. Murray Rosenblatt (San Diego) Spectra and their Estimates See ‘Research notes’ 14 8 Dr. John Bather (Manchester) Continuous Control of a Simple Inventory on Dam See ‘Research notes’ 15 3rd Gregynog (1967) 1 Prof. James Coleman (John Hopkins) Two Alternative Methods for Attitude Change See ‘Research notes’ 16 2 Prof. Paul Meier (Chicago) Estimation from Incomplete Observations See ‘Research notes’ Continued on next page Talk Edition # Speaker Talk Title Notes 17 3 Prof. H.D. Brunk (Oregon State) Certain Generalzed Means and Associated Families of Distributions 18 4 Prof. -
The BUGS Project: Evolution, Critique and Future Directions
STATISTICS IN MEDICINE Statist. Med. 2009; 28:3049–3067 Published online 24 July 2009 in Wiley InterScience (www.interscience.wiley.com) DOI: 10.1002/sim.3680 The BUGS project: Evolution, critique and future directions 1, , 1 2 3 David Lunn ∗ †, David Spiegelhalter ,AndrewThomas and Nicky Best 1Medical Research Council Biostatistics Unit, Institute of Public Health, University Forvie Site, Robinson Way, Cambridge CB2 0SR, U.K. 2School of Mathematics and Statistics, Mathematical Institute, North Haugh, St. Andrews, Fife KY16 9SS, U.K. 3Department of Epidemiology and Public Health, Imperial College London, St. Mary’s Campus, Norfolk Place, London W2 1PG, U.K. SUMMARY BUGS is a software package for Bayesian inference using Gibbs sampling. The software has been instru- mental in raising awareness of Bayesian modelling among both academic and commercial communities internationally, and has enjoyed considerable success over its 20-year life span. Despite this, the software has a number of shortcomings and a principal aim of this paper is to provide a balanced critical appraisal, in particular highlighting how various ideas have led to unprecedented flexibility while at the same time producing negative side effects. We also present a historical overview of the BUGS project and some future perspectives. Copyright 2009 John Wiley & Sons, Ltd. KEY WORDS: BUGS; WinBUGS; OpenBUGS; Bayesian modelling; graphical models 1. INTRODUCTION BUGS 1 is a software package for performing Bayesian inference using Gibbs sampling 2, 3 . The BUGS[ ] project began at the Medical Research Council Biostatistics Unit in Cambridge in[ 1989.] Since that time the software has become one of the most popular statistical modelling packages, with, at the time of writing, over 30000 registered users of WinBUGS (the Microsoft Windows incarnation of the software) worldwide, and an active on-line community comprising over 8000 members. -
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