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IMS Grace Wahba Award and Lecture
Volume 50 • Issue 4 IMS Bulletin June/July 2021 IMS Grace Wahba Award and Lecture The IMS is pleased to announce the creation of a new award and lecture, to honor CONTENTS Grace Wahba’s monumental contributions to statistics and science. These include her 1 New IMS Grace Wahba Award pioneering and influential work in mathematical statistics, machine learning and opti- and Lecture mization; broad and career-long interdisciplinary collaborations that have had a sig- 2 Members’ news : Kenneth nificant impact in epidemiology, bioinformatics and climate sciences; and outstanding Lange, Kerrie Mengersen, Nan mentoring during her 51-year career as an educator. The inaugural Wahba award and Laird, Daniel Remenik, Richard lecture is planned for the 2022 IMS annual meeting in London, then annually at JSM. Samworth Grace Wahba is one of the outstanding statisticians of our age. She has transformed 4 Grace Wahba: profile the fields of mathematical statistics and applied statistics. Wahba’s RKHS theory plays a central role in nonparametric smoothing and splines, and its importance is widely 7 Caucus for Women in recognized. In addition, Wahba’s contributions Statistics 50th anniversary straddle the boundary between statistics and 8 IMS Travel Award winners optimization, and have led to fundamental break- 9 Radu’s Rides: Notes to my Past throughs in machine learning for solving problems Self arising in prediction, classification and cross-vali- dation. She has paved a foundation for connecting 10 Previews: Nancy Zhang, Ilmun Kim theory and practice of function estimation, and has developed, along with her students, unified 11 Nominate IMS Lecturers estimation methods, scalable algorithms and 12 IMS Fellows 2021 standard software toolboxes that have made regu- 16 Recent papers: AIHP and larization approaches widely applicable to solving Observational Studies complex problems in modern science discovery Grace Wahba and technology innovation. -
Coauthorship and Citation Networks for Statisticians
Submitted to the Annals of Applied Statistics arXiv: arXiv:0000.0000 COAUTHORSHIP AND CITATION NETWORKS FOR STATISTICIANS By Pengsheng Jiy and Jiashun Jinz University of Georgiay and Carnegie Mellon Universityz We have collected and cleaned two network data sets: Coauthor- ship and Citation networks for statisticians. The data sets are based on all research papers published in four of the top journals in statis- tics from 2003 to the first half of 2012. We analyze the data sets from many different perspectives, focusing on (a) centrality, (b) commu- nity structures, and (c) productivity, patterns and trends. For (a), we have identified the most prolific/collaborative/highly cited authors. We have also identified a handful of \hot" papers, suggesting \Variable Selection" as one of the \hot" areas. For (b), we have identified about 15 meaningful communities or research groups, including large-size ones such as \Spatial Statis- tics", \Large-Scale Multiple Testing", \Variable Selection" as well as small-size ones such as \Dimensional Reduction", \Objective Bayes", \Quantile Regression", and \Theoretical Machine Learning". For (c), we find that over the 10-year period, both the average number of papers per author and the fraction of self citations have been decreasing, but the proportion of distant citations has been increasing. These suggest that the statistics community has become increasingly more collaborative, competitive, and globalized. Our findings shed light on research habits, trends, and topological patterns of statisticians. The data sets provide a fertile ground for future researches on or related to social networks of statisticians. 1. Introduction. It is frequently of interest to identify \hot" areas and key authors in a scientific community, and to understand the research habits, trends, and topological patterns of the researchers. -
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. -
BCASA Newsletter Page 1 of 29
BostonBCASA Chapter of theNEWSLETTER American Statistical Association Serving Maine, Massachusetts, New Hampshire, Rhode Island, and Vermont Volume 35, No. 3, January 2017 Homepage: http://www.amstat.org/chapters/boston E-Mail: [email protected] SCHEDULED EVENTS & MEETINGS January 28, 2017, BCASA Annual Winter Potluck Dinner and Party Carlisle, MA Statistical Practice: Innovations and Best Practices for the Applied February 23-25, 2017 Jacksonville, FL Statisticians March 8, 2017 Annual Mosteller Statistician of the Year Award Banquet Simmons College April 8, 2017 Short Course: Introduction to Statistics for Spatio-Temporal Data Cambridge, MA April 21-22, 2017 The New England Statistics Symposium (NESS) Storrs, CT May 5, 2017 Adaptive Designs in Clinical Trials Cambridge, MA May 22-25, 2017 Modern Modeling Methods (M3) Conference Storrs, CT May 2017 Annual Meeting and Spring Social TBD July 29 – August 3, 2017 Joint Statistical Meetings Baltimore, MD September 23, 2017 2017 New England Symposium on Statistics in Sports Cambridge, MA Event schedule at the chapter website: http://www.amstat.org/chapters/boston Detailed announcements appear later in this newsletter. All events are announced in advance to members on our email list. We are currently planning events for the coming year. If you have suggestions please contact Program Chair Fotios Kokkotos, [email protected] _________________________________________________________________________________________________________________________________________ BCASA Newsletter Page 1 of 29 A NOTE FROM THE PLANNING COMMITTEE Welcome back. And Happy New Year to All! This edition of the newsletter begins with Presidents’ Reports from outgoing president James McDougall and incoming president Greta Ljung. We thank Jim for his excellent service as president during the past two years and we welcome Greta as our new president. -
Robust High Dimensional Factor Models with Applications to Statistical Machine Learning∗
Robust high dimensional factor models with applications to statistical machine learning∗ Jianqing Fanyz, Kaizheng Wangy, Yiqiao Zhongy, Ziwei Zhuy August 14, 2018 Abstract Factor models are a class of powerful statistical models that have been widely used to deal with dependent measurements that arise frequently from various applications from genomics and neuroscience to economics and finance. As data are collected at an ever-growing scale, statistical machine learning faces some new challenges: high dimensionality, strong dependence among observed variables, heavy-tailed variables and heterogeneity. High-dimensional robust factor analysis serves as a powerful toolkit to conquer these challenges. This paper gives a selective overview on recent advance on high-dimensional factor models and their applications to statistics including Factor-Adjusted Robust Model selection (FarmSe- lect) and Factor-Adjusted Robust Multiple testing (FarmTest). We show that classical methods, especially principal component analysis (PCA), can be tailored to many new problems and pro- vide powerful tools for statistical estimation and inference. We highlight PCA and its connec- tions to matrix perturbation theory, robust statistics, random projection, false discovery rate, etc., and illustrate through several applications how insights from these fields yield solutions to modern challenges. We also present far-reaching connections between factor models and popular statistical learning problems, including network analysis and low-rank matrix recovery. Key Words: Factor model, PCA, covariance estimation, perturbation bounds, robustness, random sketch, FarmSelect, FarmTest 1 Introduction In modern data analytics, dependence across high-dimensional outcomes or measurements is ubiq- uitous. For example, stocks within the same industry exhibit significantly correlated returns, hous- ing prices of a country depend on various economic factors, gene expressions can be stimulated arXiv:1808.03889v1 [stat.ME] 12 Aug 2018 by cytokines. -
Biographical Sketch for Jianqing Fan
Biographical Sketch for Jianqing Fan Jianqing Fan is Frederick L. Moore'18 Professor of Finance and Director of Commit- tee of Statistical Studies at Princeton University, and the past president of the Institute of Mathematical Statistics (2006-2009), and president of International Chinese Statistical Association. He is the Co-editor of Econometrical Journal published by Royal Economics Society and an associate editor of The Journal of American Statistical Association, and was the co-editor(-in-chief) of The Annals of Statistics (2004-2006) and an editor of Prob- ability Theory and Related Fields (2003-2005) and on the editorial boards of a number of other journals. After receiving his Ph.D. in Statistics from the University of California at Berkeley, he has been appointed as assistant, associate, and full professor at the Univer- sity of North Carolina at Chapel Hill (1989-2003), and as professor at the University of California at Los Angeles (1997-2000), Professor of Statistics and Chairman at the Chinese University of Hong Kong (2000-2003), and as professor at the Princeton University(2003{). He has coauthored two highly-regarded books on \Local Polynomial Modeling" (1996) and \Nonlinear time series: Parametric and Nonparametric Methods" (2003) and authored or coauthored over 150 articles on computational biology, ¯nancial econometrics, semipara- metric and non-parametric modeling, statistical learning, nonlinear time series, survival analysis, longitudinal data analysis, and other aspects of theoretical and methodological statistics. -
Alan Agresti
Historical Highlights in the Development of Categorical Data Analysis Alan Agresti Department of Statistics, University of Florida UF Winter Workshop 2010 CDA History – p. 1/39 Karl Pearson (1857-1936) CDA History – p. 2/39 Karl Pearson (1900) Philos. Mag. Introduces chi-squared statistic (observed − expected)2 X2 = X expected df = no. categories − 1 • testing values for multinomial probabilities (Monte Carlo roulette runs) • testing fit of Pearson curves • testing statistical independence in r × c contingency table (df = rc − 1) CDA History – p. 3/39 Karl Pearson (1904) Advocates measuring association in contingency tables by approximating the correlation for an assumed underlying continuous distribution • tetrachoric correlation (2 × 2, assuming bivariate normality) X2 2 • contingency coefficient 2 based on for testing q X +n X independence in r × c contingency table • introduces term “contingency” as a “measure of the total deviation of the classification from independent probability.” CDA History – p. 4/39 George Udny Yule (1871-1951) (1900) Philos. Trans. Royal Soc. London (1912) JRSS Advocates measuring association using odds ratio n11 n12 n21 n22 n n n n − n n θˆ = 11 22 Q = 11 22 12 21 = (θˆ − 1)/(θˆ + 1) n12n21 n11n22 + n12n21 “At best the normal coefficient can only be said to give us. a hypothetical correlation between supposititious variables. The introduction of needless and unverifiable hypotheses does not appear to me a desirable proceeding in scientific work.” (1911) An Introduction to the Theory of Statistics (14 editions) CDA History – p. 5/39 K. Pearson, with D. Heron (1913) Biometrika “Unthinking praise has been bestowed on a textbook which can only lead statistical students hopelessly astray.” . -
October 2011 • Issue #412 AMSTATNEWS the Membership Magazine of the American Statistical Association •
October 2011 • Issue #412 AMSTATNEWS The Membership Magazine of the American Statistical Association • http://magazine.amstat.org JSM 2011 Highlights & Trends Many Honored at Presidential Address, Awards Ceremony ALSO: ‘Reverse’ Time Capsule Kicks Off Preparations for 175th Anniversary Celebration The ASA Fellow Award—Revisited Publications Agreement No. 41544521 AMSTATNews OCTOBER 2011 • IssuE #412 Executive Director Ron Wasserstein: [email protected] Associate Executive Director and Director of Operations Stephen Porzio: [email protected] Director of Education Martha Aliaga: [email protected] Director of Science Policy features Steve Pierson: [email protected] 3 President’s Corner Managing Editor Megan Murphy: [email protected] 5 Highlights of the July 2011 ASA Board of Directors Meeting Production Coordinators/Graphic Designers Melissa Muko: [email protected] Kathryn Wright: [email protected] 7 ‘Reverse’ Time Capsule Kicks Off Preparations for 175th Anniversary Celebration Publications Coordinator Val Nirala: [email protected] 7 Papers Sought for Journal of Statistical Research Advertising Manager 9 ASA Accreditation Program Update Claudine Donovan: [email protected] 9 Call for Nominations: ASA President-elect and Contributing Staff Members Vice President Candidates Pam Craven • Amy Farris • Eric Sampson 10 Salary Survey of Business, Industry, and Amstat News welcomes news items and letters from readers on matters of interest to the association and the profession. Address correspondence to Government Statisticians Managing Editor, Amstat News, American Statistical Association, 732 North Washington Street, Alexandria VA 22314-1943 USA, or email amstat@ 14 JSM 2011 Highlights & Trends amstat.org. Items must be received by the first day of the preceding month to ensure appearance in the next issue (for example, June 1 for the July issue). -
Embracing the Blessing of Dimensionality in Factor Models
Embracing the Blessing of Dimensionality in Factor Models Quefeng Li, Guang Cheng, Jianqing Fan and Yuyan Wang ∗ October 18, 2016 Abstract Factor modeling is an essential tool for exploring intrinsic dependence structures among high-dimensional random variables. Much progress has been made for esti- mating the covariance matrix from a high-dimensional factor model. However, the blessing of dimensionality has not yet been fully embraced in the literature: much of the available data is often ignored in constructing covariance matrix estimates. If our goal is to accurately estimate a covariance matrix of a set of targeted variables, shall we employ additional data, which are beyond the variables of interest, in the estimation? In this paper, we provide sufficient conditions for an affirmative answer, and further quantify its gain in terms of Fisher information and convergence rate. In fact, even an oracle-like result (as if all the factors were known) can be achieved when a sufficiently large number of variables is used. The idea of utilizing data as much as possible brings computational challenges. A divide-and-conquer algorithm is thus proposed to alleviate the computational burden, and also shown not to sacrifice any statistical accuracy in comparison with a pooled analysis. Simulation studies further confirm our advocacy for the use of full data, and demonstrate the effectiveness of the above algorithm. Our proposal is applied to a microarray data example that shows empirical benefits of using more data. Keywords: Asymptotic normality, auxiliary data, divide-and-conquer, factor model, Fisher information, high-dimensionality. ∗Quefeng Li is Assistant Professor, Department of Biostatistics, University of North Carolina at Chapel Hill, NC 27599 (Email:[email protected]). -
Singapore Meeting: Registration Open
Volume 36 • Issue 10 IMS Bulletin December 2007 Singapore meeting: registration open Registration and abstract submission are now open for the next IMS Annual Meeting, CONTENTS the Seventh World Congress in Probability and Statistics, held jointly with the 1 Singapore meeting news Bernoulli Society, from July 14–19, 2008, in Singapore. Details about the meeting, including accommodation registration forms, are at www.ims.nus.edu.sg/Programs/ 2 Members’ News: H. Christian Gromoll, Amber Puha, Ruth wc2008/index.htm Williams, Scott Zeger, David Chair of the Local Organizing Committee, Louis Chen, urges participants to make Kendall hotel reservations early, as the Congress dates coincide with the peak travel season in Singapore as well as several other large conventions. Most hotels in Singapore will be 3 Journal news: PMS fully booked in advance during this period. 4 Evaluating research: a This quadrennial joint meeting is a major worldwide event featuring the latest response scientific developments in the fields of probability and statistics and their applications. 5 Science vs Justice The program will cover a wide range of topics and will include invited lectures by Rick Durrett Peter 6 Statistics in Germany the following leading specialists: Wald Lectures: , Neyman Lecture: McCullagh, and IMS Medallion Lectures from Martin Barlow, Mark Low, and Zhi- IMS awards 8 Ming Ma. Also special Bernoulli Society lectures from Jianqing Fan (Laplace Lecture), 9 Awards and nominations Alice Guionnet (Lévy Lecture), Douglas Nychka (Public Lecture), David Spiegelhalter (Bernoulli Lecture), Alain-Sol Sznitman (Kolmogorov Lecture) and Elizabeth 10 Obituary: Yao-Ting Zhang Thompson (Tukey Lecture). IMS and the Bernoulli Society are also sponsoring two 11 Meeting reports: High- BS–IMS Special Lectures from Oded Schramm and Wendelin Werner. -
History of the Department of Statistics at Columbia University
Columbia University Statistics Tian Zheng and Zhiliang Ying Statistical Activities at Columbia Before 1946 Statistical activities at Columbia predate the formation of the department. Faculty members from other disciplines had carried out research and instruction of sta- tistics, especially in the Faculty of Political Sciences (Anderson 1955). Harold Hotelling’s arrival in 1931, a major turning point, propelled Columbia to a position of world leadership in Statistics. Hotelling’s primary research activities were in Mathematical, Economics, and Statistics but his interests had increasingly shifted toward Statistics. According to Paul Samuelson (Samuelson 1960), ‘‘It was at Columbia, in the decade before World War II, that Hotelling became the Mecca towards whom the best young students of economics and mathematical statistics turned. Hotelling’s increasing preoccupation with mathematical statistics was that discipline’s gain but a loss to the literature of economics.’’ His many contributions include canonical correlation, principal components and Hotelling’s T2. In 1938, after attending a conference of the Cowles Commission for Research in Economics, Abraham Wald, concerned by the situation in Europe, decided to stay in the United States. With Hotelling’s assistance, Wald came to Columbia on A substantial part of this chapter was developed based on Professor T. W. Anderson’s speech during our department’s sixtieth year anniversary reunion. We thank Professor Anderson for his help (as a founding member of our department) during the preparation of this chapter, our current department faculty members, Professors Ingram Olkin, and and Tze Leung Lai for their comments and suggestions on earlier versions of this chapter. Columbia University, Room 1005 SSW, MC 4690 1255 Amsterdam Avenue, New York, NY 10027, USA T. -
A Complete Bibliography of the Journal of Business & Economic
A Complete Bibliography of the Journal of Business & Economic Statistics Nelson H. F. Beebe University of Utah Department of Mathematics, 110 LCB 155 S 1400 E RM 233 Salt Lake City, UT 84112-0090 USA Tel: +1 801 581 5254 FAX: +1 801 581 4148 E-mail: [email protected], [email protected], [email protected] (Internet) WWW URL: http://www.math.utah.edu/~beebe/ 20 May 2021 Version 1.01 Title word cross-reference (0; 1; 1)12 [Bel87]. 1=2 [GI11]. 4 [Fre85]. α [McC97]. F(d) [CdM19]. L [BC11]. M [TZ19]. N [AW07, CSZZ17, GK14]. = 1 [Bel87]. P [Han97, Ray90]. R [CW96]. R2 [MRW19]. T [AW07, GK14, RSW18, FSC03, IM10, JS10, MV91, NS96, TM19]. U [TZ19]. -Estimators [TZ19]. -Processes [TZ19]. -R [Fre85]. -Squared [CW96]. -Statistic [IM10, TM19]. -Statistics [BC11]. 100 [ACW97]. 11 [McK84, dBM90]. 225 [ACW97]. 2SLS [Lee18]. 500 [ACW97]. 1 2 a.m [HRV20]. Ability [CM09, Han05, Son12]. Abnormal [MlM87]. Absorbing [KK15]. Abuse [Die15a]. Accelerated [KLS15]. Access [AKO12]. Accessibility [FM97]. Accident [GFdH87]. Accommodating [KDM20]. Accountability [BDJ12]. Accounting [HPSW88, HW84, MPTK91]. Accounts [Che12]. Accumulated [DM01]. Accumulation [Cag03, DM01]. Accuracies [Fom86]. Accuracy [DM95a, DM02, Die15a, McN86a, NP90, PG93, Wec89]. Accurate [McK18]. Acidic [PR87]. Acknowledgment [Ano93a]. Acreages [HF87]. Across [PG93, PP11]. Act [Fre85]. Action [GW85]. Activities [AC03, But85]. Activity [FS93, JLR11, Li11, RS94b]. Actually [CO19]. Adaptive [CZ14, CHL18, CL17, HHK11, HF19, Tsa93b, ZS15]. Addiction [BG01]. Additional [LZ95]. Additive [Abe99, FZ18, FH94, Lia12, OHS14]. Addressing [Mel18]. Adequacy [HM89]. Adjust [Nev03]. Adjusted [AW84, LD19, MS03, NTP94, Rad83, Rub86, Son10]. Adjusting [vdHH86]. Adjustment [BH84a, BH02, BW84, CW86b, DM84, DL87, EG98, ES01a, FMB+98a, Ghy90, GGS96a, Ghy97, HR91, HKR97, Hil85, Jai89, Jor99, Lin86, LHM20, McE17, ML90, McK84, Pat86, Pfe91a, Pie83, Pie84, PGC84, Sim85, Tay02, dBM90].