A Conversation with Ingram Olkin 3
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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. -
Arxiv:2104.00880V2 [Astro-Ph.HE] 6 Jul 2021
Draft version July 7, 2021 Typeset using LATEX twocolumn style in AASTeX63 Refined Mass and Geometric Measurements of the High-Mass PSR J0740+6620 E. Fonseca,1, 2, 3, 4 H. T. Cromartie,5, 6 T. T. Pennucci,7, 8 P. S. Ray,9 A. Yu. Kirichenko,10, 11 S. M. Ransom,7 P. B. Demorest,12 I. H. Stairs,13 Z. Arzoumanian,14 L. Guillemot,15, 16 A. Parthasarathy,17 M. Kerr,9 I. Cognard,15, 16 P. T. Baker,18 H. Blumer,3, 4 P. R. Brook,3, 4 M. DeCesar,19 T. Dolch,20, 21 F. A. Dong,13 E. C. Ferrara,22, 23, 24 W. Fiore,3, 4 N. Garver-Daniels,3, 4 D. C. Good,13 R. Jennings,25 M. L. Jones,26 V. M. Kaspi,1, 2 M. T. Lam,27, 28 D. R. Lorimer,3, 4 J. Luo,29 A. McEwen,26 J. W. McKee,29 M. A. McLaughlin,3, 4 N. McMann,30 B. W. Meyers,13 A. Naidu,31 C. Ng,32 D. J. Nice,33 N. Pol,30 H. A. Radovan,34 B. Shapiro-Albert,3, 4 C. M. Tan,1, 2 S. P. Tendulkar,35, 36 J. K. Swiggum,33 H. M. Wahl,3, 4 and W. W. Zhu37 1Department of Physics, McGill University, 3600 rue University, Montr´eal,QC H3A 2T8, Canada 2McGill Space Institute, McGill University, 3550 rue University, Montr´eal,QC H3A 2A7, Canada 3Department of Physics and Astronomy, West Virginia University, Morgantown, WV 26506-6315, USA 4Center for Gravitational Waves and Cosmology, Chestnut Ridge Research Building, Morgantown, WV 26505, USA 5Cornell Center for Astrophysics and Planetary Science and Department of Astronomy, Cornell University, Ithaca, NY 14853, USA 6NASA Hubble Fellowship Program Einstein Postdoctoral Fellow 7National Radio Astronomy Observatory, 520 Edgemont Road, Charlottesville, VA 22903, USA 8Institute of Physics, E¨otv¨osLor´anUniversity, P´azm´anyP. -
Beyond Traditional Assumptions in Fair Machine Learning
Beyond traditional assumptions in fair machine learning Niki Kilbertus Supervisor: Prof. Dr. Carl E. Rasmussen Advisor: Dr. Adrian Weller Department of Engineering University of Cambridge arXiv:2101.12476v1 [cs.LG] 29 Jan 2021 This thesis is submitted for the degree of Doctor of Philosophy Pembroke College October 2020 Acknowledgments The four years of my PhD have been filled with enriching and fun experiences. I owe all of them to interactions with exceptional people. Carl Rasmussen and Bernhard Schölkopf have put trust and confidence in me from day one. They enabled me to grow as a researcher and as a human being. They also taught me not to take things too seriously in moments of despair. Thank you! Adrian Weller’s contagious positive energy gave me enthusiasm and drive. He showed me how to be considerate and relentless at the same time. I thank him for his constant support and sharing his extensive network. Among others, he introduced me to Matt J. Kusner, Ricardo Silva, and Adrià Gascón who have been amazing role models and collaborators. I hope for many more joint projects with them! Moritz Hardt continues to be an inexhaustible source of inspiration and I want to thank him for his mentorship and guidance during my first paper. I was extremely fortunate to collaborate with outstanding people during my PhD beyond the ones already mentioned. I have learned a lot from every single one of them, thank you: Philip Ball, Stefan Bauer, Silvia Chiappa, Elliot Creager, Timothy Gebhard, Manuel Gomez Rodriguez, Anirudh Goyal, Krishna P. Gummadi, Ian Harry, Dominik Janzing, Francesco Locatello, Krikamol Muandet, Frederik Träuble, Isabel Valera, and Michael Veale. -
Egress Behavior from Select NYC COVID-19 Exposed Health Facilities March-May 2020 *Debra F. Laefer, C
Data Resource Profile: Egress Behavior from Select NYC COVID-19 Exposed Health Facilities March-May 2020 *Debra F. Laefer, Center for Urban Science and Progress and Department of Civil and Urban Engineering, New York University, 370 Jay St. #1303C, Brooklyn, NY 11201, USA, [email protected] Thomas Kirchner, School of Global Public Health, New York University Haoran (Frank) Jiang, Center for Data Science, New York University Darlene Cheong, Department of Biology, New York University Yunqi (Veronica) Jiang, Tandon School of Engineering, New York University Aseah Khan, Department of Biology, New York University Weiyi Qiu, Courant Institute of Mathematical Sciences, New York University Nikki Tai, Courant Institute of Mathematical Sciences, New York University Tiffany Truong, College of Arts and Science, New York University Maimunah Virk, College of Arts and Science, New York University Word count: 2845 1 Abstract Background: Vector control strategies are central to the mitigation and containment of COVID-19 and have come in the form of municipal ordinances that restrict the operational status of public and private spaces and associated services. Yet, little is known about specific population responses in terms of risk behaviors. Methods: To help understand the impact of those vector control variable strategies, a multi-week, multi- site observational study was undertaken outside of 19 New York City medical facilities during the peak of the city’s initial COVID-19 wave (03/22/20-05/19/20). The aim was to capture perishable data of the touch, destination choice, and PPE usage behavior of individuals egressing hospitals and urgent care centers. A major goal was to establish an empirical basis for future research on the way people interact with three- dimensional vector environments. -
Scientometrics1
Scientometrics1 Loet Leydesdorff a and Staša Milojević b a Amsterdam School of Communication Research (ASCoR), University of Amsterdam, Kloveniersburgwal 48, 1012 CX Amsterdam, The Netherlands; [email protected] b School of Informatics and Computing, Indiana University, Bloomington 47405-1901, United States; [email protected]. Abstract The paper provides an overview of the field of scientometrics, that is: the study of science, technology, and innovation from a quantitative perspective. We cover major historical milestones in the development of this specialism from the 1960s to today and discuss its relationship with the sociology of scientific knowledge, the library and information sciences, and science policy issues such as indicator development. The disciplinary organization of scientometrics is analyzed both conceptually and empirically. A state-of-the-art review of five major research threads is provided. Keywords: scientometrics, bibliometrics, citation, indicator, impact, library, science policy, research management, sociology of science, science studies, mapping, visualization Cross References: Communication: Electronic Networks and Publications; History of Science; Libraries; Networks, Social; Merton, Robert K.; Peer Review and Quality Control; Science and Technology, Social Study of: Computers and Information Technology; Science and Technology Studies: Experts and Expertise; Social network algorithms and software; Statistical Models for Social Networks, Overview; 1 Forthcoming in: Micheal Lynch (Editor), International -
Hadronic Light-By-Light Contribution to $(G-2) \Mu $ from Lattice QCD: A
MITP/21-019 CERN-TH-2021-047 Hadronic light-by-light contribution to (g − 2)µ from lattice QCD: a complete calculation En-Hung Chao,1 Renwick J. Hudspith,1 Antoine G´erardin,2 Jeremy R. Green,3 Harvey B. Meyer,1, 4, 5 and Konstantin Ottnad1 1PRISMA+ Cluster of Excellence & Institut f¨urKernphysik, Johannes Gutenberg-Universit¨atMainz, D-55099 Mainz, Germany 2Aix Marseille Univ, Universit´ede Toulon, CNRS, CPT, Marseille, France 3Theoretical Physics Department, CERN, 1211 Geneva 23, Switzerland 4Helmholtz Institut Mainz, Staudingerweg 18, D-55128 Mainz, Germany 5GSI Helmholtzzentrum f¨urSchwerionenforschung, Darmstadt, Germany (Dated: April 7, 2021) We compute the hadronic light-by-light scattering contribution to the muon g 2 − from the up, down, and strange-quark sector directly using lattice QCD. Our calcu- lation features evaluations of all possible Wick-contractions of the relevant hadronic four-point function and incorporates several different pion masses, volumes, and lattice-spacings. We obtain a value of aHlbl = 106:8(14:7) 10−11 (adding statistical µ × and systematic errors in quadrature), which is consistent with current phenomenolog- ical estimates and a previous lattice determination. It now appears conclusive that the hadronic light-by-light contribution cannot explain the current tension between theory and experiment for the muon g 2. − I. INTRODUCTION The anomalous magnetic moment of the muon, aµ (g 2)µ=2, is one of the most precisely measured quantities of the Standard Model (SM)≡ of− particle physics. Its value is of considerable interest to the physics community as, currently, there exists a 3:7σ tension between the experimental determination of Ref. -
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 -
AMSTATNEWS the Membership Magazine of the American Statistical Association •
January 2015 • Issue #451 AMSTATNEWS The Membership Magazine of the American Statistical Association • http://magazine.amstat.org AN UPDATE to the American Community Survey Program ALSO: Guidelines for Undergraduate Programs in Statistical Science Updated Meet Brian Moyer, Director of the Bureau of Economic Analysis AMSTATNEWS JANUARY 2015 • ISSUE #451 Executive Director Ron Wasserstein: [email protected] Associate Executive Director and Director of Operations Stephen Porzio: [email protected] features Director of Science Policy 3 President’s Corner Steve Pierson: [email protected] 5 Highlights of the November 2014 ASA Board of Directors Director of Education Meeting Rebecca Nichols: [email protected] 7 ASA Leaders Reminisce: Meet ASA Past President Managing Editor Megan Murphy: [email protected] Marie Davidian Production Coordinators/Graphic Designers 11 Benefits of the New All-Member Forum Sara Davidson: [email protected] Megan Ruyle: [email protected] 12 ASA, STATS.org Partner to Help Raise Media Statistical Literacy Publications Coordinator 13 White House Issues Policy Directive Bolstering Federal Val Nirala: [email protected] Statistical Agencies Advertising Manager 14 An Update to the American Community Survey Program Claudine Donovan: [email protected] 17 CHANCE Highlights: Special Issue Devoted to Women in Contributing Staff Members Statistics Jeff Myers • Amy Farris • Alison Smith 18 JQAS Highlights: Football, Golf, Soccer, Fly-Fishing Featured Amstat News welcomes news items and letters from readers on matters in December Issue of interest to the association and the profession. Address correspondence to Managing Editor, Amstat News, American Statistical Association, 732 North Washington Street, Alexandria VA 22314-1943 USA, or email amstat@ 19 NISS Meeting Addresses Transition amstat.org. -
13Th International Workshop on Matrices and Statistics Program And
13th International Workshop on Matrices and Statistics B¸edlewo, Poland August 18–21, 2004 Program and abstracts Organizers • Stefan Banach International Mathematical Center, Warsaw • Committee of Mathematics of the Polish Academy of Sciences, Warsaw • Faculty of Mathematics and Computer Science, Adam Mickiewicz Uni- versity, Pozna´n • Institute of Socio-Economic Geography and Spatial Management, Faculty of Geography and Geology, Adam Mickiewicz University, Pozna´n • Department of Mathematical and Statistical Methods, Agricultural Uni- versity, Pozna´n This meeting has been endorsed by the International Linear Algebra Society. II Sponsors of the 13th International Workshop on Matrices and Statistics III Mathematical Research and Conference Center in B¸edlewo IV edited by A. Markiewicz Department of Mathematical and Statistical Methods, Agricultural University, Pozna´n,Poland and W. Wo ly´nski Faculty of Mathematics and Computer Science, Adam Mickiewicz University, Pozna´n,Poland Contents Part I. Introduction Poetical Licence The New Intellectual Aristocracy ............ 3 Richard William Farebrother Part II. Local Information Part III. Program Part IV. Ingram Olkin Ingram Olkin, Statistical Statesman .......................... 21 Yadolah Dodge Why is matrix analysis part of the statistics curriculum ...... 25 Ingram Olkin A brief biography and appreciation of Ingram Olkin .......... 31 A conversation with Ingram Olkin ............................ 34 Bibliography .................................................. 58 Part V. Abstracts Asymptotic -
Jacob Wolfowitz 1910–1981
NATIONAL ACADEMY OF SCIENCES JACOB WOLFOWITZ 1910–1981 A Biographical Memoir by SHELEMYAHU ZACKS Any opinions expressed in this memoir are those of the author and do not necessarily reflect the views of the National Academy of Sciences. Biographical Memoirs, VOLUME 82 PUBLISHED 2002 BY THE NATIONAL ACADEMY PRESS WASHINGTON, D.C. JACOB WOLFOWITZ March 19, 1910–July 16, 1981 BY SHELEMYAHU ZACKS ACOB WOLFOWITZ, A GIANT among the founders of modern Jstatistics, will always be remembered for his originality, deep thinking, clear mind, excellence in teaching, and vast contributions to statistical and information sciences. I met Wolfowitz for the first time in 1957, when he spent a sab- batical year at the Technion, Israel Institute of Technology. I was at the time a graduate student and a statistician at the building research station of the Technion. I had read papers of Wald and Wolfowitz before, and for me the meeting with Wolfowitz was a great opportunity to associate with a great scholar who was very kind to me and most helpful. I took his class at the Technion on statistical decision theory. Outside the classroom we used to spend time together over a cup of coffee or in his office discussing statistical problems. He gave me a lot of his time, as though I was his student. His advice on the correct approach to the theory of statistics accompanied my development as statistician for many years to come. Later we kept in touch, mostly by correspondence and in meetings of the Institute of Mathematical Statistics. I saw him the last time in his office at the University of Southern Florida in Tampa, where he spent the last years 3 4 BIOGRAPHICAL MEMOIRS of his life. -
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.