“I Didn't Want to Be a Statistician”
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
F:\RSS\Me\Society's Mathemarica
School of Social Sciences Economics Division University of Southampton Southampton SO17 1BJ, UK Discussion Papers in Economics and Econometrics Mathematics in the Statistical Society 1883-1933 John Aldrich No. 0919 This paper is available on our website http://www.southampton.ac.uk/socsci/economics/research/papers ISSN 0966-4246 Mathematics in the Statistical Society 1883-1933* John Aldrich Economics Division School of Social Sciences University of Southampton Southampton SO17 1BJ UK e-mail: [email protected] Abstract This paper considers the place of mathematical methods based on probability in the work of the London (later Royal) Statistical Society in the half-century 1883-1933. The end-points are chosen because mathematical work started to appear regularly in 1883 and 1933 saw the formation of the Industrial and Agricultural Research Section– to promote these particular applications was to encourage mathematical methods. In the period three movements are distinguished, associated with major figures in the history of mathematical statistics–F. Y. Edgeworth, Karl Pearson and R. A. Fisher. The first two movements were based on the conviction that the use of mathematical methods could transform the way the Society did its traditional work in economic/social statistics while the third movement was associated with an enlargement in the scope of statistics. The study tries to synthesise research based on the Society’s archives with research on the wider history of statistics. Key names : Arthur Bowley, F. Y. Edgeworth, R. A. Fisher, Egon Pearson, Karl Pearson, Ernest Snow, John Wishart, G. Udny Yule. Keywords : History of Statistics, Royal Statistical Society, mathematical methods. -
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. -
National Academy Elects IMS Fellows Have You Voted Yet?
Volume 38 • Issue 5 IMS Bulletin June 2009 National Academy elects IMS Fellows CONTENTS The United States National Academy of Sciences has elected 72 new members and 1 National Academy elects 18 foreign associates from 15 countries in recognition of their distinguished and Raftery, Wong continuing achievements in original research. Among those elected are two IMS Adrian Raftery 2 Members’ News: Jianqing Fellows: , Blumstein-Jordan Professor of Statistics and Sociology, Center Fan; SIAM Fellows for Statistics and the Social Sciences, University of Washington, Seattle, and Wing Hung Wong, Professor 3 Laha Award recipients of Statistics and Professor of Health Research and Policy, 4 COPSS Fisher Lecturer: Department of Statistics, Stanford University, California. Noel Cressie The election was held April 28, during the business 5 Members’ Discoveries: session of the 146th annual meeting of the Academy. Nicolai Meinshausen Those elected bring the total number of active members 6 Medallion Lecture: Tony Cai to 2,150. Foreign associates are non-voting members of the Academy, with citizenship outside the United States. Meeting report: SSP Above: Adrian Raftery 7 This year’s election brings the total number of foreign 8 New IMS Fellows Below: Wing H. Wong associates to 404. The National Academy of Sciences is a private 10 Obituaries: Keith Worsley; I.J. Good organization of scientists and engineers dedicated to the furtherance of science and its use for general welfare. 12-3 JSM program highlights; It was established in 1863 by a congressional act of IMS sessions at JSM incorporation signed by Abraham Lincoln that calls on 14-5 JSM tours; More things to the Academy to act as an official adviser to the federal do in DC government, upon request, in any matter of science or 16 Accepting rejections technology. -
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). -
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). -
The Likelihood Principle
1 01/28/99 ãMarc Nerlove 1999 Chapter 1: The Likelihood Principle "What has now appeared is that the mathematical concept of probability is ... inadequate to express our mental confidence or diffidence in making ... inferences, and that the mathematical quantity which usually appears to be appropriate for measuring our order of preference among different possible populations does not in fact obey the laws of probability. To distinguish it from probability, I have used the term 'Likelihood' to designate this quantity; since both the words 'likelihood' and 'probability' are loosely used in common speech to cover both kinds of relationship." R. A. Fisher, Statistical Methods for Research Workers, 1925. "What we can find from a sample is the likelihood of any particular value of r [a parameter], if we define the likelihood as a quantity proportional to the probability that, from a particular population having that particular value of r, a sample having the observed value r [a statistic] should be obtained. So defined, probability and likelihood are quantities of an entirely different nature." R. A. Fisher, "On the 'Probable Error' of a Coefficient of Correlation Deduced from a Small Sample," Metron, 1:3-32, 1921. Introduction The likelihood principle as stated by Edwards (1972, p. 30) is that Within the framework of a statistical model, all the information which the data provide concerning the relative merits of two hypotheses is contained in the likelihood ratio of those hypotheses on the data. ...For a continuum of hypotheses, this principle -
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. -
Memorial to Sir Harold Jeffreys 1891-1989 JOHN A
Memorial to Sir Harold Jeffreys 1891-1989 JOHN A. HUDSON and ALAN G. SMITH University of Cambridge, Cambridge, England Harold Jeffreys was one of this century’s greatest applied mathematicians, using mathematics as a means of under standing the physical world. Principally he was a geo physicist, although statisticians may feel that his greatest contribution was to the theory of probability. However, his interest in the latter subject stemmed from his realization of the need for a clear statistical method of analysis of data— at that time, travel-time readings from seismological stations across the world. He also made contributions to astronomy, fluid dynamics, meteorology, botany, psychol ogy, and photography. Perhaps one can identify Jeffreys’s principal interests from three major books that he wrote. His mathematical skills are displayed in Methods of Mathematical Physics, which he wrote with his wife Bertha Swirles Jeffreys and which was first published in 1946 and went through three editions. His Theory o f Probability, published in 1939 and also running to three editions, espoused Bayesian statistics, which were very unfashionable at the time but which have been taken up since by others and shown to be extremely powerful for the analysis of data and, in particular, image enhancement. However, the book for which he is probably best known is The Earth, Its Origin, History and Physical Consti tution, a broad-ranging account based on observations analyzed with care, using mathematics as a tool. Jeffreys’s scientific method (now known as Inverse Theory) was a logical process, clearly stated in another of his books, Scientific Inference. -
A Parsimonious Tour of Bayesian Model Uncertainty
A Parsimonious Tour of Bayesian Model Uncertainty Pierre-Alexandre Mattei Université Côte d’Azur Inria, Maasai project-team Laboratoire J.A. Dieudonné, UMR CNRS 7351 e-mail: [email protected] Abstract: Modern statistical software and machine learning libraries are enabling semi-automated statistical inference. Within this context, it appears eas- ier and easier to try and fit many models to the data at hand, thereby reversing the Fisherian way of conducting science by collecting data after the scientific hypothesis (and hence the model) has been determined. The renewed goal of the statistician becomes to help the practitioner choose within such large and heterogeneous families of models, a task known as model selection. The Bayesian paradigm offers a systematized way of as- sessing this problem. This approach, launched by Harold Jeffreys in his 1935 book Theory of Probability, has witnessed a remarkable evolution in the last decades, that has brought about several new theoretical and methodological advances. Some of these recent developments are the focus of this survey, which tries to present a unifying perspective on work carried out by different communities. In particular, we focus on non-asymptotic out-of-sample performance of Bayesian model selection and averaging tech- niques, and draw connections with penalized maximum likelihood. We also describe recent extensions to wider classes of probabilistic frameworks in- cluding high-dimensional, unidentifiable, or likelihood-free models. Contents 1 Introduction: collecting data, fitting many models . .2 2 A brief history of Bayesian model uncertainty . .2 3 The foundations of Bayesian model uncertainty . .3 3.1 Handling model uncertainty with Bayes’s theorem . -
WILLIAM GEMMELL COCHRAN July 15, 1909-March29, 1980
NATIONAL ACADEMY OF SCIENCES WILLIAM GEMMELL C OCHRAN 1909—1980 A Biographical Memoir by MORRIS HANSEN AND FREDERICK MOSTELLER Any opinions expressed in this memoir are those of the author(s) and do not necessarily reflect the views of the National Academy of Sciences. Biographical Memoir COPYRIGHT 1987 NATIONAL ACADEMY OF SCIENCES WASHINGTON D.C. WILLIAM GEMMELL COCHRAN July 15, 1909-March29, 1980 BY MORRIS HANSEN AND FREDERICK MOSTELLER ILLIAM GEMMELL COCHRAN was born into modest Wcircumstances on July 15, 1909, in Rutherglen, Scot- land. His father, Thomas, the eldest of seven children, had begun his lifetime employment with the railroad at the age of thirteen. The family, consisting of Thomas, his wife Jean- nie, and sons Oliver and William, moved to Gourock, a hol- iday resort town on the Firth of Clyde, when William was six, and to Glasgow ten years later. Oliver has colorful recollections of their childhood. At age five, Willie (pronounced Wully), as he was known to family and friends, was hospitalized for a burst appendix, and his life hung in the balance for a day. But soon he was home, wearying his family with snatches of German taught him by a German patient in his nursing-home ward. Willie had a knack for hearing or reading something and remembering it. Oliver recalls that throughout his life, Willie would walk or sit around reciting poems, speeches, advertisements, mu- sic hall songs, and in later life oratorios and choral works he was learning. Until Willie was sixteen, the family lived in an apartment known in Scotland as a "two room and kitchen"—a parlor- cum-dining room (used on posh occasions, about twelve times a year), a bedroom used by the parents, and a kitchen. -
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.