IMS Bulletin 39(5)
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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. -
Press Release
Japan Statistical Society July 10, 2020 The Institute of Statistical Mathematics (ISM) The Japan Statistical Society (JSS) Announcement of the Awardee of the Third Akaike Memorial Lecture Award Awardee/Speaker: Professor John Brian Copas (University of Warwick, UK) Lecture Title: “Some of the Challenges of Statistical Applications” Debater: Professor Masataka Taguri(Yokohama City University) Dr. Masayuki Henmi (The Institute of Statistical Mathematics) Date/Time: 16:00-18:00 September 9, 2020 1. Welcome Speech and Explanation of the Akaike Memorial Lecture Award 2. Akaike Memorial Lecture 3. Discussion with Young Scientists and Professor Copas’s rejoinder Venue: Toyama International Conference Center (https://www.ticc.co.jp/english/) 1-2 Ote-machi Toyama-city Toyama 930-0084MAP,Japan • Detailed information will be uploaded on the website of ISM (http://www.ism.ac.jp/index_e.html) and other media. Professor John B. Copas (current age, 76) Emeritus Professor, University of Warwick, UK Professor Copas, born in 1943, is currently Professor Emeritus at the University of Warwick. With a focus on both theory and application, his works provide a deep insight and wide perspective on various sources of data bias. His contributions span over a wide range of academic disciplines, including statistical medicine, ecometrics, and pscyhometrics. He has developed the Copas selection model, a well-known method for sensitivity analysis, and the Copas rate, a re-conviction risk estimator used in the criminal justice system. Dr. Hirotugu Akaike The late Dr. Akaike was a statistician who proposed the Akaike Information Criterion (AIC). He established a novel paradigm of statistical modeling, characterized by a predictive point of view, that was completely distinct from traditional statistical theory. -
Akaike Receives 2006 Kyoto Prize
Akaike Receives 2006 Kyoto Prize In June 2006 the Inamori global network of closely linked systems. Modern Foundation announced statistical sciences are expected to help our un- the laureates who will re- derstanding of the study of such complicated, un- ceive its 22nd annual certain, and dynamic systems, or the study of sit- Kyoto Prize, Japan’s high- uations where only incomplete information is est private award for life- available. The main role of statistical sciences must time achievement, pre- be to give useful scientific methodologies for the sented to individuals and development of new technologies and for the fur- groups worldwide who ther development of society, which is characterized have contributed signifi- by increased uncertainty. One of the characteris- cantly to humankind’s tics of the statistical sciences is their interdisci- betterment. Receiving the plinary nature, with applicability in various fields. prize in the Basic Sciences For example, the role of the statistical sciences has Hirotugu Akaike category is the Japanese recently become increasingly important in the un- statistical mathematician derstanding and forecasting of phenomena in eco- HIROTUGU AKAIKE, professor emeritus at the Institute nomic-related fields, such as finance and insur- of Statistical Mathematics. The prize consists of a ance; safety-related fields, including pharma- diploma, a Kyoto Prize Medal of 20-karat gold, and ceuticals, food, and transportation; natural phe- a cash gift of 50 million yen (approximately nomena, such as weather, natural disasters, and the US$446,000). environment; and in the management of huge sys- tems. The Work of Hirotugu Akaike Starting in the early 1970s Akaike explained the Rapid advances in science and technology in the importance of modeling in analysis and in fore- twentieth century brought numerous benefits to so- casting. -
Preface: Special Issue in Honor of Dr. Hirotugu Akaike
Ann Inst Stat Math (2010) 62:1–2 DOI 10.1007/s10463-009-0269-6 Preface: Special issue in honor of Dr. Hirotugu Akaike Published online: 10 December 2009 © The Institute of Statistical Mathematics, Tokyo 2009 We deeply regret to announce that Dr. Hirotugu Akaike passed away on August 4, 2009. In 2007, because of his excellent contribution to the field of statistical sci- ence, we planned to publish “Special Issue of the Annals of the Institute of Statistical Mathematics in Honor of Dr. Hirotugu Akaike”. We received the sad news of his death, just after all editorial process has completed. This issue begins with an article by Dr. Akaike’s last manuscript. Dr. Hirotugu Akaike was awarded the 2006 Kyoto Prize for his major contribution to statistical science and modeling with the Akaike Information Criterion (AIC) with the praise that “In the early 1970’s, Dr. Hirotugu Akaike formulated the Akaike Infor- mation Criterion (AIC), a new practical, yet versatile criterion for the selection of statistical models, based on basic concepts of information mathematics. This criterion established a new paradigm that bridged the world of data and the world of mod- eling, thus contributing greatly to the information and statistical sciences” (Inamori Foundation). In 1973, he proposed the AIC as a natural extension of the log-likelihood. The most natural way of applying the AIC was to use it as the model selection or order selection criterion. In the minimum AIC procedure, the model with the minimum value of the AIC is selected as the best one among many possible models. -
Model Selection and Inference
INFORMATION THEORY AND LOG-LIKELIHOOD MODELS: A BASIS FOR MODEL SELECTION AND INFERENCE Full reality cannot be included in a model; thus we seek a good model to approximate the effects or factors supported by the empirical data. The selection of an appropriate approximating model is critical to statistical inference from many types of empirical data. Information theory (see Guiasu 1977 and Cover and Thomas 1991) and, in particular the Kullback-Leibler (Kullback and Liebler 1951) “distance," or “information" forms the deep theoretical basis for data-based model selection. Akaike (1973) found a simple relationship between expected Kullback-Leibler information and Fisher's maximized log-likelihood function (see deLeeuw 1992 for a brief review). This relationship leads to a simple, effective, and very general methodology for selecting a parsimonious model for the analysis of empirical data. Some Background Well over a century ago measures were derived for assessing the “distance" between two models or probability distributions. Most relevant here is Boltzmann's (1877) concept of generalized entropy in physics and thermodynamics (see Akaike 1985 for a brief review). Shannon (1948) employed entropy in his famous treatise on communication theory. Kullback and Leibler (1951) derived an information measure that happened to be the negative of Boltzmann's entropy: now referred to as the Kullback-Leibler (K-L) distance. The motivation for Kullback and Leibler's work was to provide a rigorous definition of “information" in relation to Fisher's “sufficient statistics." The K-L distance has also been called the K-L discrepancy, divergence, information and number – these terms are synonyms, we tend to use distance or information in the material to follow. -
Emanuel Parzen and a Tale of Two Kernels
DEPARTMENT OF STATISTICS University of Wisconsin 1300 University Ave. Madison, WI 53706 TECHNICAL REPORT NO. 1183 February 28, 2017 Emanuel Parzen and a Tale of Two Kernels An Appreciation of Manny Parzen. Grace Wahba1 Department of Statistics, Department of Computer Sciences and Department of Biostatistics and Medical Informatics University of Wisconsin, Madison 1Research supported in part by CPCP and NSF Grant DMS-1308877 Emanuel Parzen and a Tale of Two Kernels Grace Wahba1 Department of Statistics, Department of Computer Sciences and Department of Biostatistics and Medical Informatics University of Wisconsin, Madison February 27, 2017 Abstract I was Manny Parzen's fifth student, according to the Mathemat- ical Genealogy Project, receiving my PhD under his supervision in 1966, and remaining at Stanford with him for a postdoc, leaving for the University of Wisconsin-Madison in 1967, where I remain as I write. To be Manny's PhD student at Stanford in the 60's was noth- ing short of bliss. Many was full of ideas, the weather was warm and sunny, sometimes classes were held on the grass in front of the old Sequoia Hall with Reproducing Kernel Hilbert Spaces on the impro- vised blackboard. A lovely, elegant dinner at their Stanford home that Manny and Carol threw for a group of graduate students bring back fond memories. Manny and Carol remained lifelong friends. Manny launched me on a 50 year academic year, buttressed by what I learned about RKHS from him in those days. In this article I first describe many fond memories over the years, and then present some techni- cal results and thoughts relating to RKHS, Parzen density estimates, Statistical Machine learning and related topics. -
Towards Group-Based Configuration
18th International Configuration Workshop Proceedings of the 18th International Configuration Workshop within CP 2016 Conference Edited by Élise Vareilles, Lars Hvam, Cipriano Forza and Caroline Becker September 5-6, 2016 Toulouse, France Organized by Centre Génie Industriel, Mines Albi, France 2 ISBN: 979-10-91526-04-3 École des Mines d’Albi-Carmaux Campus Jarlard Route de Teillet Albi 81013 Cedex 09 France Proceedings of the 18th International Configuration Workshop September 5-6, 2016, Toulouse, France 3 18th International Configuration Workshop Chairs Élise Vareilles, Mines Albi, France Lars Hvam, Technical University of Denmark, Denmark Cipriano Forza, University of Padova, Italy Caroline Becker, PROS Toulouse, France Program Committee Michel Aldanondo, Mines Albi, France Andres Barco, Mines Albi, France Caroline Becker, PROS, France Jean-Guillaume Fages, COSLING S.A.S., France Andreas Falkner, Siemens AG, Austria Hélène Fargier, IRIT, Université de Toulouse, France Alexander Felfernig, Graz University of Technology, Austria Cipriano Forza, Universita di Padova, Italy Gerhard Friedrich, Alpen-Adria-Universitaet Klagenfurt, Austria Paul Gaborit, Mines Albi, France Luis Garcés, Mines Albi, France Chiara Grosso, Universita di Padova, Italy Albert Haag, SAP SE, Germany Alois Haselboeck, Siemens AG, Austria Lothar Hotz, HITeC e.V. / University of Hamburg, Germany Lars Hvam, Technical University of Denmark, Denmark Dietmar Jannach, TU Dortmund, Germany Manuel Korell, Finja AB / Achoice, Denmark Thorsten Krebs, encoway GmbH, Germany Katrin Kristjansdottir, -
Calendar of AMS Meetings and Conferences
Calendar of AMS Meetings and Conferences This calendar lists all meetings an.d conferences approved prior to the date this issue be submitted on special forms which are available in many departments of mathematics went to press. The summer and annual meetings are joint meetings of the ·Mathe and from the headquarters office of the Society. Abstracts of papers to be presented matical Association of America and the American Mathematical Society. The meeting at the meeting must be received at the headquarters of the Society in Providence, dates which fall rather far in the future are subject to change; this is particularly true Rhode Island, on or before the deadline given below for the meeting. The abstract of meetings to which no numbers have been assigned. Programs of the meetings will deadlines listed below should be carefully reviewed &ince an abstract deadline may appear in the issues indicated below. First and supplementary announcements of the expire before publication of a first announcement. Note that the deadline for abstracts meetings will have appeared in earlier issues. Abstracts of papers presented at a for consideration for presentation at special sessions is usually three weeks earlier than meeting of the Society are published in the journal Abstracts of papers presented to that specified below. For additional information, consult the meeting announcements the American Mathematical Society in ihe issue corresponding to that of the Notices and the list of special sessions. which contains the program of the meeting, insofar as is possible. Abstracts should Meetings Abstract Program Meeting# Date Place Deadline Issue 879 • March 26-27, 1993 Knoxville, Tennessee Expired March 880 • April9-10, 1993 Salt Lake City, Utah Expired April 881 • April17-18, 1993 Washington, D.C. -
IMS Bulletin 39(6)
Volume 39 • Issue 6 IMS1935–2010 Bulletin July 2010 IMS at 75: A Little History Contents Peter, Hall IMS President-Elect, writes about 75 years of the Institute of Mathematical 1 IMS at 75 Statistics: The founding of the IMS in 1935 actually followed, by some five years, that of the 2–3 Members’ News: Stephen Stigler; Sally Morton; Jean journal that established its reputation, The Annals of Mathematical Statistics. That Opsomer; Sastry Pantula; Annals was established by Harry Carver, with financial assistance from the American Robert Rodriguez Sta tistical Association. Willford King, an economist at New York University’s School of Commerce and an ASA member, wrote in the first issue in 1930 that: 5 2010 IMS Fellows 6 Wald lectures: Jean-François For some time past ... it has been evident that the membership of our organi zation Le Gall [the ASA] is tending to become divided into two groups—those familiar with 7 Medallion Preview: Edward advanced mathematics, and those who have not devoted themselves to this field. The George mathematicians are, of course, interested in articles of a type which are not intelligible 8 Nominations for awards; to the non-mathematical readers of our Journal. The Editor of our Journal has, then, IMS Treasurer found it a puzzling problem to satisfy both classes of readers. (King, 1930) 9 Obituary: Samuel Kotz I’ve heard avid, contemporary JASA readers, who have turned to the Annals for 10 Tweedie lecture: Harrison enlightenment, make similar remarks today. Clearly King, and those whose needs the Zhou; NISS grants new journal was intended to fill, faced a dichotomy that is still familiar to us. -
Larry Shepp: from Putnam to CAT Scan Mathematical Conversations
ISSUE 16 Newsletter of Institute for Mathematical Sciences, NUS 2010 Mathematical Conversations Larry Shepp: From Putnam to CAT Scan has a great influence on biomedical imaging which has important applications in medical X-ray and nuclear magnetic resonance (NMR) technology. He was a winner of the William Lowell Putnam Intercollegiate Mathematics Competition in 1958 and obtained his PhD from Princeton University in 1961. From 1962 to 1996, he was a Distinguished Member of Technical Staff at Bell Laboratories, and concurrently held joint appointments at Columbia Presbyterian Hospital, Columbia University and Stanford University. From 1996 onwards, he returned to academia full-time, first at Columbia University and then at Rutgers University. He is Board of Governor’s Professor Larry Shepp at the Statistics Department, Rutgers University since 2004. From June 2010, he will be emeritus professor at Rutgers University and professor of statistics at Wharton School, Interview of Larry Shepp by Y.K. Leong University of Pennsylvania. Lawrence Shepp is world-renown for pioneering and His work in stochastic processes and computer tomography fundamental contributions to computed tomography and has earned him numerous honors and awards, among them for extensive work on applications of probability, statistics membership of the National Academy of Science, Institute and mathematics to physics, engineering, communications, of Medicine, Academy of Arts and Science, fellowship of the mathematical finance and genetics. His work in tomography American Institute for Medical and Biological Engineering, Paul Lévy Prize and the IEEE Distinguished Scientist Award. 11 He has been invited for visiting positions by many countries throughout the world. After returning to academia, he continues to offer his services to the medical and engineering Continued from page 10 industries. -
Notices of the American Mathematical Society Is Publishing Process and Make Accessible the Available Literature
Providence, Rhode Island, USA ISSN 0002-9920 Calendar of AMS Meetings and Conferences This calendar lists all meetingS and conferences approved prior to the date this issue should be submitted on special forms which are available in many departments of went to press The summer and annual meetings are joint meetings of the Mathematical mathematics and from the headquarters office of the Society. Abstracts of papers to Association cl America and the American Mathematical Society. Abstracts of papers be presented at the meeting must be recalved at the headquarters of the Society in presented at a meeting of the Society are published in the joumal Abstracts of papers Providence, Rhode Island, on or before the deadline given below for the meeting. Note presented to the American Mathematical Society I~ th~ issue co~respon_dlng to that of that the deadline for abstracts for consideration for presentation at special sessions is the Notices which contains the program of the maeting, msofar as IS possible. Abstracts usually three weeks earlier than that specified below. Meetings -----·----- Abstract Program Meeting# Date Place Deadline Issue 880 * April9-10, 1993 Salt Lake City, Utah Expired April 881 * April17-18, 1993 Washington, D.C. Expired April 882 * May 2Q-23, 1993 DeKalb, Illinois Expired May-June 883 * August 15-19, 1993 (96th Summer Meeting) Vancouver, British Columbia May18 July-August (Joint Meeting with the Canadian Mathematical Society) 884 * September 18-19, 1993 Syracuse, New York May18 September 885 * October 1-3, 1993 Heidelberg, -
Factor Analysis and Aic Hirotugu Akaike
PSYCHOMETRIKA--VOL.52, NO. 3, 317-332 SEPTEMBER 1987 SPECIAL SECTION FACTOR ANALYSIS AND AIC HIROTUGU AKAIKE THE INSTITU~ OF STATISTICAL MATHEMA'F/CS The information criterion AIC was introduced to extend the method of maximum likelihood to the multimodel situation. It was obtained by relating the successful experience of the order determination of an autoregressive model to the determination of the number of factors in the maximum likelihood factor analysis. The use of the AIC criterion in the factor analysis is particu- larly interesting when it is viewed as the choice of a Bayesian model. This observation shows that the area of application of AIC can be much wider than the conventional i.i.d, type models on which the original derivation of the criterion was based. The observation of the Bayesian structure of the factor analysis model leads us to the handling of the problem of improper solution by introducing a natural prior distribution of factor loadings. Key words: factor analysis, maximum likelihood, information criterion AIC, improper solution, Bayesian modeling. 1. Introduction The factor analysis model has been producing thought provoking statistical prob- lems. The model is typically represented by y(n) = Ax(n) + u(n), n = 1, 2, ..., N where y(n) denotes a p-dimensional vector of observations, x(n) a k-dimensional vector of factor scores, u(n) a p-dimensional vector of specific variations. It is assumed that the variables with different n's are mutually independent and that x(n) and u(n) are mutually independently distributed as Gaussian random variables with variance covariance matrices I k x ~ and W, respectively, where W is a diagonal matrix.