JERZY NEYMAN April 16, 1894-August 5, 1981
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. -
Memorial Resolution George Bernard Dantzig (1914–2005)
MEMORIAL RESOLUTION GEORGE BERNARD DANTZIG (1914–2005) George Bernard Dantzig, the C. A. Criley Professor of Transportation Sciences and Professor of Operations Research and of Computer Science, Emeritus, died at his campus home on May 13, 2005 at age 90. Born on November 8, 1914 in Portland, Oregon, George Dantzig was given the middle name “Bernard” as an expression of his parents’ hope that he would become a writer. This was not to be, even though late in his life George was engaged in writing a novel. Instead, George became a mathematician. He graduated from the University of Maryland with an A.B. in mathematics and physics (1936) and took his M.A. in mathematics from the University of Michigan (1938). After a two-year period at the Bureau of Labor Statistics, he enrolled in the doctoral program in mathematics at the University of California, Berkeley, with the intention of writing his dissertation on mathematical statistics under the supervision of Jerzy Neyman. Arriving late to one of Neyman’s lectures, George copied down two problem statements from the blackboard thinking they were a homework assignment. George found these problems challenging. After a while though, he was able to solve them both and turned them in to the professor. As it happened, the problems were not just exercises but open questions in the field. The solutions to these problems became the two independent parts of George’s doctoral dissertation. With the outbreak of World War II, George took a leave of absence from the doctoral program at Berkeley to join the U.S. -
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 -
Proceedings of the Fourth Berkeley Symposium
PROCEEDINGS OF THE FOURTH BERKELEY SYMPOSIUM VOLUME III PROCEEDINGS of the FOURTH BERKELEY SYMPOSIUM ON MATHEMATICAL STATISTICS AND PROBABILITY Held at the Statistical Laboratory University of California June 20-jtuly 30, 1960, with the support of University of California National Science Foundation Office of Naval Research Office of Ordnance Research Air Force Office of Research National Institutes of Health VOLUME III CONTRIBUTIONS TO ASTRONOMY, METEOROLOGY, AND PHYSICS EDITED BY JERZY NEYMAN UNIVERSITY OF CALIFORNIA PRESS BERKELEY AND LOS ANGELES 1961 UNIVERSITY OF CALIFORNIA PRESS BERKELEY AND LOS ANGELES CALIFORNIA CAMBRIDGE UNIVERSITY PRESS LONDON, ENGLAND © 1961, BY THE REGENTS OF THE UNIVERSITY OF CALIFORNIA The United States Government and its offices, agents, an I em- ployees, acting within the scope of their duties, may reproduce, publish, and use this material in whole or in part for governmental purposes without payment of royalties thereon or therefor. The publication or republication by the government either separately or in a public document of any material in which copyright subsists shall not be taken to cause any abridgment or annulment of the copyright or to authorize any use or appropriation of such copy- right material without the consent of the copyright proprietor. LIBRARY OF CONGRESS CATALOG CARD NUMBER: 49-8189 PRINTED IN THE UNITED STATES OF AMERICA CONTENTS OF PROCEEDINGS, VOLUMES I, II, AND IV Volume I-Theory of Statistics F. J. ANSCOMBE, Examination of residuals. RICHARD BELLMAN, A mathematical formulation of variational processes of adaptive type. Z. W. BIRNBAUM, On the probabil- istic theory of complex structuires. DAVID BLACKWELL, Exponential error bounds for finite state channels. -
Academic Genealogy of the Oakland University Department Of
Basilios Bessarion Mystras 1436 Guarino da Verona Johannes Argyropoulos 1408 Università di Padova 1444 Academic Genealogy of the Oakland University Vittorino da Feltre Marsilio Ficino Cristoforo Landino Università di Padova 1416 Università di Firenze 1462 Theodoros Gazes Ognibene (Omnibonus Leonicenus) Bonisoli da Lonigo Angelo Poliziano Florens Florentius Radwyn Radewyns Geert Gerardus Magnus Groote Università di Mantova 1433 Università di Mantova Università di Firenze 1477 Constantinople 1433 DepartmentThe Mathematics Genealogy Project of is a serviceMathematics of North Dakota State University and and the American Statistics Mathematical Society. Demetrios Chalcocondyles http://www.mathgenealogy.org/ Heinrich von Langenstein Gaetano da Thiene Sigismondo Polcastro Leo Outers Moses Perez Scipione Fortiguerra Rudolf Agricola Thomas von Kempen à Kempis Jacob ben Jehiel Loans Accademia Romana 1452 Université de Paris 1363, 1375 Université Catholique de Louvain 1485 Università di Firenze 1493 Università degli Studi di Ferrara 1478 Mystras 1452 Jan Standonck Johann (Johannes Kapnion) Reuchlin Johannes von Gmunden Nicoletto Vernia Pietro Roccabonella Pelope Maarten (Martinus Dorpius) van Dorp Jean Tagault François Dubois Janus Lascaris Girolamo (Hieronymus Aleander) Aleandro Matthaeus Adrianus Alexander Hegius Johannes Stöffler Collège Sainte-Barbe 1474 Universität Basel 1477 Universität Wien 1406 Università di Padova Università di Padova Université Catholique de Louvain 1504, 1515 Université de Paris 1516 Università di Padova 1472 Università -
School of Social Sciences Economics Division University of Southampton Southampton SO17 1BJ, UK
School of Social Sciences Economics Division University of Southampton Southampton SO17 1BJ, UK Discussion Papers in Economics and Econometrics Professor A L Bowley’s Theory of the Representative Method John Aldrich No. 0801 This paper is available on our website http://www.socsci.soton.ac.uk/economics/Research/Discussion_Papers ISSN 0966-4246 Key names: Arthur L. Bowley, F. Y. Edgeworth, , R. A. Fisher, Adolph Jensen, J. M. Keynes, Jerzy Neyman, Karl Pearson, G. U. Yule. Keywords: History of Statistics, Sampling theory, Bayesian inference. Professor A. L. Bowley’s Theory of the Representative Method * John Aldrich Economics Division School of Social Sciences University of Southampton Southampton SO17 1BJ UK e-mail: [email protected] Abstract Arthur. L. Bowley (1869-1957) first advocated the use of surveys–the “representative method”–in 1906 and started to conduct surveys of economic and social conditions in 1912. Bowley’s 1926 memorandum for the International Statistical Institute on the “Measurement of the precision attained in sampling” was the first large-scale theoretical treatment of sample surveys as he conducted them. This paper examines Bowley’s arguments in the context of the statistical inference theory of the time. The great influence on Bowley’s conception of statistical inference was F. Y. Edgeworth but by 1926 R. A. Fisher was on the scene and was attacking Bayesian methods and promoting a replacement of his own. Bowley defended his Bayesian method against Fisher and against Jerzy Neyman when the latter put forward his concept of a confidence interval and applied it to the representative method. * Based on a talk given at the Sample Surveys and Bayesian Statistics Conference, Southampton, August 2008. -
Frequentist and Bayesian Statistics
Faculty of Life Sciences Frequentist and Bayesian statistics Claus Ekstrøm E-mail: [email protected] Outline 1 Frequentists and Bayesians • What is a probability? • Interpretation of results / inference 2 Comparisons 3 Markov chain Monte Carlo Slide 2— PhD (Aug 23rd 2011) — Frequentist and Bayesian statistics What is a probability? Two schools in statistics: frequentists and Bayesians. Slide 3— PhD (Aug 23rd 2011) — Frequentist and Bayesian statistics Frequentist school School of Jerzy Neyman, Egon Pearson and Ronald Fischer. Slide 4— PhD (Aug 23rd 2011) — Frequentist and Bayesian statistics Bayesian school “School” of Thomas Bayes P(D|H) · P(H) P(H|D)= P(D|H) · P(H)dH Slide 5— PhD (Aug 23rd 2011) — Frequentist and Bayesian statistics Frequentists Frequentists talk about probabilities in relation to experiments with a random component. Relative frequency of an event, A,isdefinedas number of outcomes consistent with A P(A)= number of experiments The probability of event A is the limiting relative frequency. Relative frequency 0.0 0.2 0.4 0.6 0.8 1.0 020406080100 n Slide 6— PhD (Aug 23rd 2011) — Frequentist and Bayesian statistics Frequentists — 2 The definition restricts the things we can add probabilities to: What is the probability of there being life on Mars 100 billion years ago? We assume that there is an unknown but fixed underlying parameter, θ, for a population (i.e., the mean height on Danish men). Random variation (environmental factors, measurement errors, ...) means that each observation does not result in the true value. Slide 7— PhD (Aug 23rd 2011) — Frequentist and Bayesian statistics The meta-experiment idea Frequentists think of meta-experiments and consider the current dataset as a single realization from all possible datasets. -
Statistical Inference a Work in Progress
Ronald Christensen Department of Mathematics and Statistics University of New Mexico Copyright c 2019 Statistical Inference A Work in Progress Springer v Seymour and Wes. Preface “But to us, probability is the very guide of life.” Joseph Butler (1736). The Analogy of Religion, Natural and Revealed, to the Constitution and Course of Nature, Introduction. https://www.loc.gov/resource/dcmsiabooks. analogyofreligio00butl_1/?sp=41 Normally, I wouldn’t put anything this incomplete on the internet but I wanted to make parts of it available to my Advanced Inference Class, and once it is up, you have lost control. Seymour Geisser was a mentor to Wes Johnson and me. He was Wes’s Ph.D. advisor. Near the end of his life, Seymour was finishing his 2005 book Modes of Parametric Statistical Inference and needed some help. Seymour asked Wes and Wes asked me. I had quite a few ideas for the book but then I discovered that Sey- mour hated anyone changing his prose. That was the end of my direct involvement. The first idea for this book was to revisit Seymour’s. (So far, that seems only to occur in Chapter 1.) Thinking about what Seymour was doing was the inspiration for me to think about what I had to say about statistical inference. And much of what I have to say is inspired by Seymour’s work as well as the work of my other professors at Min- nesota, notably Christopher Bingham, R. Dennis Cook, Somesh Das Gupta, Mor- ris L. Eaton, Stephen E. Fienberg, Narish Jain, F. Kinley Larntz, Frank B. -
FOCUS August/September 2005
FOCUS August/September 2005 FOCUS is published by the Mathematical Association of America in January, February, March, April, May/June, FOCUS August/September, October, November, and Volume 25 Issue 6 December. Editor: Fernando Gouvêa, Colby College; [email protected] Inside Managing Editor: Carol Baxter, MAA 4 Saunders Mac Lane, 1909-2005 [email protected] By John MacDonald Senior Writer: Harry Waldman, MAA [email protected] 5 Encountering Saunders Mac Lane By David Eisenbud Please address advertising inquiries to: Rebecca Hall [email protected] 8George B. Dantzig 1914–2005 President: Carl C. Cowen By Don Albers First Vice-President: Barbara T. Faires, 11 Convergence: Mathematics, History, and Teaching Second Vice-President: Jean Bee Chan, An Invitation and Call for Papers Secretary: Martha J. Siegel, Associate By Victor Katz Secretary: James J. Tattersall, Treasurer: John W. Kenelly 12 What I Learned From…Project NExT By Dave Perkins Executive Director: Tina H. Straley 14 The Preparation of Mathematics Teachers: A British View Part II Associate Executive Director and Director By Peter Ruane of Publications: Donald J. Albers FOCUS Editorial Board: Rob Bradley; J. 18 So You Want to be a Teacher Kevin Colligan; Sharon Cutler Ross; Joe By Jacqueline Brennon Giles Gallian; Jackie Giles; Maeve McCarthy; Colm 19 U.S.A. Mathematical Olympiad Winners Honored Mulcahy; Peter Renz; Annie Selden; Hortensia Soto-Johnson; Ravi Vakil. 20 Math Youth Days at the Ballpark Letters to the editor should be addressed to By Gene Abrams Fernando Gouvêa, Colby College, Dept. of 22 The Fundamental Theorem of ________________ Mathematics, Waterville, ME 04901, or by email to [email protected]. -
“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. -
34 6 ISSUE.Indd
Volume 34 Issue 6 IMS Bulletin July 2005 Iain Johnstone elected to NAS Iain M Johnstone was elected ce airs Offi UC Berkeley Aff Photo: Public foray to Berkeley, has been CONTENTS to the US National Academy his scientifi c base ever since. 1 Iain Johnstone of Sciences on May 3 2005. Initially appointed in the Th e NAS elects 72 members Statistics Department, since 2 Members’ News & contacts each year over every branch 1989 his joint appointment 4 Obituary: William Kruskal of science. Of these, typically in Statistics and Biostatistics 5 New UK Statistics Centre fi ve or fewer work in the refl ects the duality of his mathematical sciences, so Iain research. His work in medical 6 Terence’s Stuff : A Toast to should be proud of this recognition. statistics is wide-ranging: he is the model Posters Iain was born in Melbourne, Australia versatile statistician, able to contribute 7 Donate/request IMS and took his BSc and MSc degrees at the right across theory, methodology and journals Australian National University in the late applications, showing how the diff erent 8 Abel Prize for Mathematics 1970s. His Master’s thesis led to his fi rst aspects of our fi eld should support one published paper, joint with his advisor another seamlessly. 9 Mu Sigma Rho Chris Heyde; more unusually his under- Iain’s wider contributions to the 11 Medallion Lecture preview graduate dissertation was itself published profession are prodigious. His term as 13 Minneapolis Events in a monograph series. He then moved to President of IMS (2001–2) was the cul- the USA for his PhD at Cornell, where mination of a remarkable and prolonged 14 IMS Meetings his advisor was Larry Brown.