National Academy Members
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Reminiscences of a Statistician Reminiscences of a Statistician the Company I Kept
Reminiscences of a Statistician Reminiscences of a Statistician The Company I Kept E.L. Lehmann E.L. Lehmann University of California, Berkeley, CA 94704-2864 USA ISBN-13: 978-0-387-71596-4 e-ISBN-13: 978-0-387-71597-1 Library of Congress Control Number: 2007924716 © 2008 Springer Science+Business Media, LLC All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Springer Science+Business Media, LLC, 233 Spring Street, New York, NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. Printed on acid-free paper. 987654321 springer.com To our grandchildren Joanna, Emily, Paul Jacob and Celia Gabe and Tavi and great-granddaughter Audrey Preface It has been my good fortune to meet and get to know many remarkable people, mostly statisticians and mathematicians, and to derive much pleasure and benefit from these contacts. They were teachers, colleagues and students, and the following pages sketch their careers and our interactions. Also included are a few persons with whom I had little or no direct contact but whose ideas had a decisive influence on my work. -
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. -
April 2021 HAROLD VINCENT POOR CONTACT INFORMATION
April 2021 HAROLD VINCENT POOR CONTACT INFORMATION: Electrical and Computer Engineering Phone: +1 (609) 258-1816 Princeton University Email: [email protected] Princeton, NJ 08544 USA Web: www.princeton.edu/poor EDUCATION: Princeton University, Princeton, New Jersey Ph.D. in Electrical Engineering and Computer Science, 1977 M.A. in Electrical Engineering, 1976 Auburn University, Auburn, Alabama M.S. in Electrical Engineering, 1974 B.E.E. with Highest Honor, 1972 EMPLOYMENT AND EXPERIENCE: Principal Employment Princeton University Michael Henry Strater University Professor, 2005 - present Professor of Electrical and Computer Engineering, 1990 - present Associated Faculty, Center for Statistics and Machine Learning, 2014 - present Associated Faculty, Andlinger Center for Energy and the Environment, 2012 - present Associated Faculty, High Meadows Environmental Institute, 2006 - present Associated Faculty, Department of Operations Research & Financial Eng'g, 2001 - present Associated Faculty, Program in Applied and Computational Mathematics, 1996 - present Interim Dean, School of Engineering and Applied Science, 2019-20 Dean, School of Engineering and Applied Science, 2006-16 Founding Director, (Keller) Center for Innovation in Engineering Education, 2005-06 George Van Ness Lothrop Professor in Engineering, 2003-05 University of Illinois at Urbana-Champaign Beckman Associate, Center for Advanced Study, 1989-90 Professor, Beckman Institute for Advanced Science and Technology, 1988-90 Professor of Electrical and Computer Engineering, 1984-90 -
Elect New Council Members
Volume 43 • Issue 3 IMS Bulletin April/May 2014 Elect new Council members CONTENTS The annual IMS elections are announced, with one candidate for President-Elect— 1 IMS Elections 2014 Richard Davis—and 12 candidates standing for six places on Council. The Council nominees, in alphabetical order, are: Marek Biskup, Peter Bühlmann, Florentina Bunea, Members’ News: Ying Hung; 2–3 Sourav Chatterjee, Frank Den Hollander, Holger Dette, Geoffrey Grimmett, Davy Philip Protter, Raymond Paindaveine, Kavita Ramanan, Jonathan Taylor, Aad van der Vaart and Naisyin Wang. J. Carroll, Keith Crank, You can read their statements starting on page 8, or online at http://www.imstat.org/ Bani K. Mallick, Robert T. elections/candidates.htm. Smythe and Michael Stein; Electronic voting for the 2014 IMS Elections has opened. You can vote online using Stephen Fienberg; Alexandre the personalized link in the email sent by Aurore Delaigle, IMS Executive Secretary, Tsybakov; Gang Zheng which also contains your member ID. 3 Statistics in Action: A If you would prefer a paper ballot please contact IMS Canadian Outlook Executive Director, Elyse Gustafson (for contact details see the 4 Stéphane Boucheron panel on page 2). on Big Data Elections close on May 30, 2014. If you have any questions or concerns please feel free to 5 NSF funding opportunity e [email protected] Richard Davis contact Elyse Gustafson . 6 Hand Writing: Solving the Right Problem 7 Student Puzzle Corner 8 Meet the Candidates 13 Recent Papers: Probability Surveys; Stochastic Systems 15 COPSS publishes 50th Marek Biskup Peter Bühlmann Florentina Bunea Sourav Chatterjee anniversary volume 16 Rao Prize Conference 17 Calls for nominations 19 XL-Files: My Valentine’s Escape 20 IMS meetings Frank Den Hollander Holger Dette Geoffrey Grimmett Davy Paindaveine 25 Other meetings 30 Employment Opportunities 31 International Calendar 35 Information for Advertisers Read it online at Kavita Ramanan Jonathan Taylor Aad van der Vaart Naisyin Wang http://bulletin.imstat.org IMSBulletin 2 . -
This Thesis Has Been Submitted in Fulfilment of the Requirements for a Postgraduate Degree (E.G. Phd, Mphil, Dclinpsychol) at the University of Edinburgh
This thesis has been submitted in fulfilment of the requirements for a postgraduate degree (e.g. PhD, MPhil, DClinPsychol) at the University of Edinburgh. Please note the following terms and conditions of use: This work is protected by copyright and other intellectual property rights, which are retained by the thesis author, unless otherwise stated. A copy can be downloaded for personal non-commercial research or study, without prior permission or charge. This thesis cannot be reproduced or quoted extensively from without first obtaining permission in writing from the author. The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the author. When referring to this work, full bibliographic details including the author, title, awarding institution and date of the thesis must be given. Sociology and Statistics in Britain, 1830-1990 Plamena Yankova Panayotova A thesis submitted to the Department of Sociology in conformity with the requirements for the degree of Doctor of Philosophy The University of Edinburgh 2018 2 Declaration I hereby confirm that this doctoral thesis has been written entirely by myself, is solely the product of my own work unless otherwise specified, and has not been submitted for any other degree or professional qualification. Signed: Plamena Yankova Panayotova Dated: 17th July 2018 3 4 Acknowledgements My interest in the history of sociology and statistics in Britain began when I was still an undergraduate student; at a time when I had more curiosity to find out about these subjects than actual experience in studying them. Back then, I had been studying sociology for three years but knew little about its development in this country. -
Statistical Inference: Paradigms and Controversies in Historic Perspective
Jostein Lillestøl, NHH 2014 Statistical inference: Paradigms and controversies in historic perspective 1. Five paradigms We will cover the following five lines of thought: 1. Early Bayesian inference and its revival Inverse probability – Non-informative priors – “Objective” Bayes (1763), Laplace (1774), Jeffreys (1931), Bernardo (1975) 2. Fisherian inference Evidence oriented – Likelihood – Fisher information - Necessity Fisher (1921 and later) 3. Neyman- Pearson inference Action oriented – Frequentist/Sample space – Objective Neyman (1933, 1937), Pearson (1933), Wald (1939), Lehmann (1950 and later) 4. Neo - Bayesian inference Coherent decisions - Subjective/personal De Finetti (1937), Savage (1951), Lindley (1953) 5. Likelihood inference Evidence based – likelihood profiles – likelihood ratios Barnard (1949), Birnbaum (1962), Edwards (1972) Classical inference as it has been practiced since the 1950’s is really none of these in its pure form. It is more like a pragmatic mix of 2 and 3, in particular with respect to testing of significance, pretending to be both action and evidence oriented, which is hard to fulfill in a consistent manner. To keep our minds on track we do not single out this as a separate paradigm, but will discuss this at the end. A main concern through the history of statistical inference has been to establish a sound scientific framework for the analysis of sampled data. Concepts were initially often vague and disputed, but even after their clarification, various schools of thought have at times been in strong opposition to each other. When we try to describe the approaches here, we will use the notions of today. All five paradigms of statistical inference are based on modeling the observed data x given some parameter or “state of the world” , which essentially corresponds to stating the conditional distribution f(x|(or making some assumptions about it). -
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. -
Letter from the RSS Covid-19 Task Force To
Letter from the Royal Statistical Society Covid-19 Task Force to the Guardian regarding data transparency 7 August 2020 The Covid-19 public health crisis has placed a sharp emphasis on the role of data in government decision-making. During the current phase – where the emphasis is on Test and Trace and local lockdowns – data is playing an increasingly central role in informing policy. We recognise that the stakes are high and that decisions need to be made quickly. However, this makes it even more important that data is used in a responsible and effective manner. Transparency around the data that are being used to inform decisions is central to this. Over the past week, there have been two major data-led government announcements where the supporting data were not made available at the time. First, the announcement that home and garden visits would be made illegal in parts of northern England. The Prime Minister cited unpublished data which suggested that these visits were the main setting for transmission. Second, the purchase of two new tests for the virus that claim to deliver results within 90 minutes, without data regarding the tests’ effectiveness being published. We are concerned about the lack of transparency in these two cases – these are important decisions and the data upon which they are based should be publicly available for scrutiny, as Paul Nurse pointed out in this paper at the weekend. Government rhetoric often treats data as a managerial tool for informing decisions. But beyond this, transparency and well sign-posted data builds public trust and encourages compliance: the daily provision of statistical information was an integral part of full lockdown and was both expected and valued by the public. -
Do Algorithms Homogenize Students' Achievements in Secondary School Better Than Teachers' Tracking Decisions?
education policy analysis archives A peer-reviewed, independent, open access, multilingual journal epaa aape Arizona State University Volume 23 Number 62 July 5th, 2015 ISSN 1068-2341 Do Algorithms Homogenize Students’ Achievements in Secondary School Better Than Teachers’ Tracking Decisions? Florian Klapproth University of Luxembourg Luxembourg Citation: Klapproth, F. (2015). Do algorithms homogenize students’ achievements in secondary school better than teachers’ tracking decisions? Education Policy Analysis Archives, 23(62). http://dx.doi.org/10.14507/epaa.v23.2007 Abstract: Two objectives guided this research. First, this study examined how well teachers’ tracking decisions contribute to the homogenization of their students’ achievements. Second, the study explored whether teachers’ tracking decisions would be outperformed in homogenizing the students’ achievements by statistical models of tracking decisions. These models were akin to teachers’ decisions in that they were based on the same information teachers are supposed to use when making tracking decisions. It was found that the assignments of students to the different tracks made either by teachers or by the models allowed for the homogenization of the students’ achievements for both test scores and school marks. Moreover, the models’ simulations of tracking decisions were more effective in the homogenization of achievement than were the tracking decisions, if the students assigned to the different tracks were at the center of the achievement distribution. For the remaining students, there was no significant difference found between teachers’ tracking decisions and the models’ simulations thereof. The reason why algorithms produced more homogeneous groups was assumed to be due to the higher consistency of model decisions compared to teacher decisions. -
Longitudinal and Life Course Studies: International Journal
VOLUME 3 | ISSUE 2 | MAY 2012 ISSN 1757-9597 Longitudinal and Life Course Studies: International Journal Published by Society for Longitudinal and Life Course Studies Inside this issue G Special Section: transition to adulthood in the UK, the US and Finland G Father involvement, family poverty and adversity G Study profile: Panel Study of Income Dynamics G Social class returns to higher education debate Co-sponsors: Promoting Longitudinal and Life Course Studies EDITORIAL BOARD EDITORIAL COMMITTEE Executive Editor John Bynner, Institute of Education, UK Health Sciences Section Editor - Michael Wadsworth, MRC Unit for Lifelong Health and Ageing London, UK Associate Editors - David Blane, Imperial College Medical School, UK - Scott Montgomery, Karolinska University Hospital, Sweden Social and Economic Sciences Section Editor - Robert Erikson, University of Stockholm, Sweden Associate Editors - Paul Gregg, University of Bath, UK - John Hobcraft, University of York, UK - Jan Smit, VU Medical Centre, Netherlands Statistical Sciences and Methodology Section Editor - Harvey Goldstein, University of Bristol, UK Associate Editor - Bianca de Stavola, London School of Hygiene and Tropical Medicine, UK Development and Behavioural Sciences Section Editor - Barbara Maughan, Institute of Psychiatry, Kings College, UK Associate Editors - Lars Bergman, University of Stockholm, Sweden - Amanda Sacker, University of Essex, UK BOARD MEMBERS Mel Bartley, University College London, UK; Nick Buck, University of Essex, UK; Richard Burkhauser, Cornell University, -
“It Took a Global Conflict”— the Second World War and Probability in British
Keynames: M. S. Bartlett, D.G. Kendall, stochastic processes, World War II Wordcount: 17,843 words “It took a global conflict”— the Second World War and Probability in British Mathematics John Aldrich Economics Department University of Southampton Southampton SO17 1BJ UK e-mail: [email protected] Abstract In the twentieth century probability became a “respectable” branch of mathematics. This paper describes how in Britain the transformation came after the Second World War and was due largely to David Kendall and Maurice Bartlett who met and worked together in the war and afterwards worked on stochastic processes. Their interests later diverged and, while Bartlett stayed in applied probability, Kendall took an increasingly pure line. March 2020 Probability played no part in a respectable mathematics course, and it took a global conflict to change both British mathematics and D. G. Kendall. Kingman “Obituary: David George Kendall” Introduction In the twentieth century probability is said to have become a “respectable” or “bona fide” branch of mathematics, the transformation occurring at different times in different countries.1 In Britain it came after the Second World War with research on stochastic processes by Maurice Stevenson Bartlett (1910-2002; FRS 1961) and David George Kendall (1918-2007; FRS 1964).2 They also contributed as teachers, especially Kendall who was the “effective beginning of the probability tradition in this country”—his pupils and his pupils’ pupils are “everywhere” reported Bingham (1996: 185). Bartlett and Kendall had full careers—extending beyond retirement in 1975 and ‘85— but I concentrate on the years of setting-up, 1940-55. -
The Royal Statistical Society Getstats Campaign Ten Years to Statistical Literacy? Neville Davies Royal Statistical Society Cent
The Royal Statistical Society getstats Campaign Ten Years to Statistical Literacy? Neville Davies Royal Statistical Society Centre for Statistical Education University of Plymouth, UK www.rsscse.org.uk www.censusatschool.org.uk [email protected] twitter.com/CensusAtSchool RSS Centre for Statistical Education • What do we do? • Who are we? • How do we do it? • Where are we? What we do: promote improvement in statistical education For people of all ages – in primary and secondary schools, colleges, higher education and the workplace Cradle to grave statistical education! Dominic Mark John Neville Martignetti Treagust Marriott Paul Hewson Davies Kate Richards Lauren Adams Royal Statistical Society Centre for Statistical Education – who we are HowWhat do we we do: do it? Promote improvement in statistical education For people of all ages – in primary and secondary schools, colleges, higher education and theFunders workplace for the RSSCSE Cradle to grave statistical education! MTB support for RSSCSE How do we do it? Funders for the RSSCSE MTB support for RSSCSE How do we do it? Funders for the RSSCSE MTB support for RSSCSE How do we do it? Funders for the RSSCSE MTB support for RSSCSE How do we do it? Funders for the RSSCSE MTB support for RSSCSE How do we do it? Funders for the RSSCSE MTB support for RSSCSE Where are we? Plymouth Plymouth - on the border between Devon and Cornwall University of Plymouth University of Plymouth Local attractions for visitors to RSSCSE - Plymouth harbour area The Royal Statistical Society (RSS) 10-year