IMS Grace Wahba Award and Lecture
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BERNOULLI NEWS, Vol 24 No 2 (2017)
Vol. 24 (2), November 2017 Published twice per year by the Bernoulli Society ISSN 1360–6727 CONTENTS News from the Bernoulli A VIEW FROM THE PRESIDENT Society p. 1 Awards and Prizes p. 2 New Executive Members in the Bernoulli Society p. 3 Articles and Letters On Bayesian Measures for Some Statistical Inverse Problems with Partial Differential Equations p. 5 Obituary Alastair Scott p. 10 Past Conferences, Susan A. Murphy receives the Bernoulli Book from Sara van de Geer during the General Assembly of the Bernoulli Society ISI World Congress in Marrakech, Morocco. Meetings and Workshops p. 11 Dear Members of the Bernoulli Society, Next Conferences, Meetings and Workshops It is an honor to assume the role of Bernoulli Society president, particularly be- cause this is a very exciting time to be a statistician or probabilist! As all of us have and Calendar of Events become, ever-more acutely aware, the role of data in society and in science is dramat- p. 13 ically changing. Many new challenges are due to the complex, and vast amounts of, data resulting from the development of new data collection tools such as wearable Book Reviews sensors in clothing, on eyeglasses, in toothbrushes and most commonly on our phones. Indeed there are now wearable radar sensors that provide data that might be used Editor to improve the safety of bicyclists or help visually impaired individuals gain greater MIGUEL DE CARVALHO independence. There are also wearable respiratory sensors that provide data that School of Mathematics could be used to help us investigate the impact of dietary and exercise regimens, or THE UNIVERSITY of EDINBURGH EDINBURGH, UK identify nutritional imbalances. -
Elect Your Council
Volume 41 • Issue 3 IMS Bulletin April/May 2012 Elect your Council Each year IMS holds elections so that its members can choose the next President-Elect CONTENTS of the Institute and people to represent them on IMS Council. The candidate for 1 IMS Elections President-Elect is Bin Yu, who is Chancellor’s Professor in the Department of Statistics and the Department of Electrical Engineering and Computer Science, at the University 2 Members’ News: Huixia of California at Berkeley. Wang, Ming Yuan, Allan Sly, The 12 candidates standing for election to the IMS Council are (in alphabetical Sebastien Roch, CR Rao order) Rosemary Bailey, Erwin Bolthausen, Alison Etheridge, Pablo Ferrari, Nancy 4 Author Identity and Open L. Garcia, Ed George, Haya Kaspi, Yves Le Jan, Xiao-Li Meng, Nancy Reid, Laurent Bibliography Saloff-Coste, and Richard Samworth. 6 Obituary: Franklin Graybill The elected Council members will join Arnoldo Frigessi, Steve Lalley, Ingrid Van Keilegom and Wing Wong, whose terms end in 2013; and Sandrine Dudoit, Steve 7 Statistical Issues in Assessing Hospital Evans, Sonia Petrone, Christian Robert and Qiwei Yao, whose terms end in 2014. Performance Read all about the candidates on pages 12–17, and cast your vote at http://imstat.org/elections/. Voting is open until May 29. 8 Anirban’s Angle: Learning from a Student Left: Bin Yu, candidate for IMS President-Elect. 9 Parzen Prize; Recent Below are the 12 Council candidates. papers: Probability Surveys Top row, l–r: R.A. Bailey, Erwin Bolthausen, Alison Etheridge, Pablo Ferrari Middle, l–r: Nancy L. Garcia, Ed George, Haya Kaspi, Yves Le Jan 11 COPSS Fisher Lecturer Bottom, l–r: Xiao-Li Meng, Nancy Reid, Laurent Saloff-Coste, Richard Samworth 12 Council Candidates 18 Awards nominations 19 Terence’s Stuff: Oscars for Statistics? 20 IMS meetings 24 Other meetings 27 Employment Opportunities 28 International Calendar of Statistical Events 31 Information for Advertisers IMS Bulletin 2 . -
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. -
Whoa-Psi 2016
WHOA-PSI 2016 Workshop on Higher-Order Asymptotics and Post-Selection Inference Washington University in St. Louis, St. Louis, Missouri, USA 30 September - 2 October, 2016 Schedule of Talks, Abstracts Organizers: John Kolassa, Todd Kuffner, Nancy Reid, Ryan Tibshirani, Alastair Young Workshop on Higher-Order Asymptotics and Post-Selection Inference (WHOA-PSI) 30 Sep. - 2 Oct., 2016 Friday 30th September 7:30 { 9:00 Breakfast and registration 9:00 { 9:15 Introductions 9:15 { 10:05 Tutorial on Post-Selection Inference (Todd Kuffner) 10:05 { 10:25 Coffee break 10:25 { 11:15 Tutorial on Higher-Order Asymptotics (Todd Kuffner) 11:30 { 1:00 Lunch and registration 1:00 { 1:10 Opening remarks 1:10 { 2:50 Session 1; Chair: Nan Lin, Washington University in St. Louis 1:10 { 1:35 Ryan Martin, North Carolina State University A new double empirical Bayes approach for high-dimensional problems 1:35 { 2:00 Anru Zhang, University of Wisconsin Cross: efficient low-rank tensor completion 2:00 { 2:25 Shujie Ma, UC Riverside Wild bootstrap confidence intervals in sparse high dimensional heteroscedastic linear models 2:25 { 2:50 Discussion 2:50 { 3:05 Coffee break 3:05 { 4:15 Session 2; Chair: Debraj Das, North Carolina State University 3:05 { 3:30 Xiaoying Tian, Stanford University Selective inference with a randomized response 3:30 { 3:55 Yuekai Sun, University of Michigan Fast convergence of Newton-type methods on high dimensional problems 3:55 { 4:15 Discussion 4:15 { 4:30 Coffee break 4:30 { 5:40 Session 3; Chair: Sangwon Hyun, Carnegie Mellon University 4:30 { 4:55 Hongyuan Cao, University of Missouri Columbia Change point estimation: another look at multiple testing problems 4:55 { 5:20 Jelena Bradic, UC San Diego Inference in Non-Sparse High-Dimensional Models: going beyond sparsity and de-biasing. -
Carver Award: Lynne Billard We Are Pleased to Announce That the IMS Carver Medal Committee Has Selected Lynne CONTENTS Billard to Receive the 2020 Carver Award
Volume 49 • Issue 4 IMS Bulletin June/July 2020 Carver Award: Lynne Billard We are pleased to announce that the IMS Carver Medal Committee has selected Lynne CONTENTS Billard to receive the 2020 Carver Award. Lynne was chosen for her outstanding service 1 Carver Award: Lynne Billard to IMS on a number of key committees, including publications, nominations, and fellows; for her extraordinary leadership as Program Secretary (1987–90), culminating 2 Members’ news: Gérard Ben Arous, Yoav Benjamini, Ofer in the forging of a partnership with the Bernoulli Society that includes co-hosting the Zeitouni, Sallie Ann Keller, biannual World Statistical Congress; and for her advocacy of the inclusion of women Dennis Lin, Tom Liggett, and young researchers on the scientific programs of IMS-sponsored meetings.” Kavita Ramanan, Ruth Williams, Lynne Billard is University Professor in the Department of Statistics at the Thomas Lee, Kathryn Roeder, University of Georgia, Athens, USA. Jiming Jiang, Adrian Smith Lynne Billard was born in 3 Nominate for International Toowoomba, Australia. She earned both Prize in Statistics her BSc (1966) and PhD (1969) from the 4 Recent papers: AIHP, University of New South Wales, Australia. Observational Studies She is probably best known for her ground-breaking research in the areas of 5 News from Statistical Science HIV/AIDS and Symbolic Data Analysis. 6 Radu’s Rides: A Lesson in Her research interests include epidemic Humility theory, stochastic processes, sequential 7 Who’s working on COVID-19? analysis, time series analysis and symbolic 9 Nominate an IMS Special data. She has written extensively in all Lecturer for 2022/2023 these areas, publishing over 250 papers in Lynne Billard leading international journals, plus eight 10 Obituaries: Richard (Dick) Dudley, S.S. -
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. -
Yee Whye Teh Curriculum Vitae
Yee Whye Teh Curriculum Vitae Department of Statistics Webpage: http://www.stats.ox.ac.uk/∼teh 24-29 St Giles Email: [email protected] Oxford OX1 3LB Mobile: +44-7392100886 United Kingdom Brief Biography I am a Professor of Statistical Machine Learning at the Department of Statistics, University of Oxford, a Principal Research Scientist at DeepMind, an Alan Turing Institute Faculty Fellow and an ELLIS Fellow, co-director of the ELLIS Robust Machine Learning Programme and co-director of the ELLIS@Oxford ELLIS unit. I obtained my PhD at the University of Toronto, and did postdoctoral work at the University of California at Berkeley and National University of Singapore, where I was a Lee Kuan Yew Postdoctoral Fellow. I was a Lecturer and a Reader at the Gatsby Computational Neuroscience Unit, UCL and an ERC Consolidator Fellow. My research interests are in machine learning, computational statistics and artificial intelligence, in particular probabilistic models, Bayesian nonparametrics, large scale learning and deep learning. I also have interests in using statistical and machine learning tools to solve problems in genetics, genomics, linguistics, neuroscience and artificial intelligence. I was programme co-chair of the International Conference on Artificial Intelligence and Statistics 2010, Machine Learning Summer School 2014 (Iceland), and the International Conference on Ma- chine Learning 2017, an editor for a IEEE TPAMI Special Issue on Bayesian nonparametrics, and is/was an associate/action editor for Bayesian Analysis, IEEE Transactions on Pattern Analysis and Machine Intelligence, Machine Learning Journal, Journal of the Royal Statistical Society Series B, Statistical Sciences and Journal of Machine Learning Research. -
Computational and Financial Econometrics (CFE 2017)
CFE-CMStatistics 2017 PROGRAMME AND ABSTRACTS 11th International Conference on Computational and Financial Econometrics (CFE 2017) http://www.cfenetwork.org/CFE2017 and 10th International Conference of the ERCIM (European Research Consortium for Informatics and Mathematics) Working Group on Computational and Methodological Statistics (CMStatistics 2017) http://www.cmstatistics.org/CMStatistics2017 Senate House & Birkbeck University of London, UK 16 – 18 December 2017 ⃝c ECOSTA ECONOMETRICS AND STATISTICS. All rights reserved. I CFE-CMStatistics 2017 ISBN 978-9963-2227-4-2 ⃝c 2017 - ECOSTA ECONOMETRICS AND STATISTICS Technical Editors: Gil Gonzalez-Rodriguez and Marc Hofmann. All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted, in any other form or by any means without the prior permission from the publisher. II ⃝c ECOSTA ECONOMETRICS AND STATISTICS. All rights reserved. CFE-CMStatistics 2017 International Organizing Committee: Ana Colubi, Erricos Kontoghiorghes, Marc Levene, Bernard Rachet, Herman Van Dijk. CFE 2017 Co-chairs: Veronika Czellar, Hashem Pesaran, Mike Pitt and Stefan Sperlich. CFE 2017 Programme Committee: Knut Are Aastveit, Alessandra Amendola, Josu Arteche, Monica Billio, Roberto Casarin, Gianluca Cubadda, Manfred Deistler, Jean-Marie Dufour, Ekkehard Ernst, Jean-David Fermanian, Catherine Forbes, Philip Hans Franses, Marc Hallin, Alain Hecq, David Hendry, Benjamin Holcblat, Jan Jacobs, Degui Li, Alessandra Luati, Richard Luger, J Isaac Miller, Claudio Morana, Bent -
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. -
By Alex Reinhart Yosihiko Ogata
STATISTICAL SCIENCE Volume 33, Number 3 August 2018 A Review of Self-Exciting Spatio-Temporal Point Processes and Their Applications .................................................................................Alex Reinhart 299 Comment on “A Review of Self-Exciting Spatiotemporal Point Process and Their Applications”byAlexReinhart.............................................Yosihiko Ogata 319 Comment on “A Review of Self-Exciting Spatio-Temporal Point Process and Their Applications”byAlexReinhart..........................................Jiancang Zhuang 323 Comment on “A Review of Self-Exciting Spatio-Temporal Point Processes and Their Applications”byAlexReinhart..................................Frederic Paik Schoenberg 325 Self-Exciting Point Processes: Infections and Implementations .............Sebastian Meyer 327 Rejoinder: A Review of Self-Exciting Spatio-Temporal Point Processes and Their Applications..................................................................Alex Reinhart 330 On the Relationship between the Theory of Cointegration and the Theory of Phase Synchronization..................Rainer Dahlhaus, István Z. Kiss and Jan C. Neddermeyer 334 Confidentiality and Differential Privacy in the Dissemination of Frequency Tables ......................Yosef Rinott, Christine M. O’Keefe, Natalie Shlomo and Chris Skinner 358 Piecewise Deterministic Markov Processes for Continuous-Time Monte Carlo .....................Paul Fearnhead, Joris Bierkens, Murray Pollock and Gareth O. Roberts 386 Fractionally Differenced Gegenbauer Processes -
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]).