International Prize in Statistics the 2021 International Prize in Statistics Has Been Awarded to US Biostatistician and CONTENTS IMS Fellow Nan Laird, Harvey V
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National Academy Elects IMS Fellows Have You Voted Yet?
Volume 38 • Issue 5 IMS Bulletin June 2009 National Academy elects IMS Fellows CONTENTS The United States National Academy of Sciences has elected 72 new members and 1 National Academy elects 18 foreign associates from 15 countries in recognition of their distinguished and Raftery, Wong continuing achievements in original research. Among those elected are two IMS Adrian Raftery 2 Members’ News: Jianqing Fellows: , Blumstein-Jordan Professor of Statistics and Sociology, Center Fan; SIAM Fellows for Statistics and the Social Sciences, University of Washington, Seattle, and Wing Hung Wong, Professor 3 Laha Award recipients of Statistics and Professor of Health Research and Policy, 4 COPSS Fisher Lecturer: Department of Statistics, Stanford University, California. Noel Cressie The election was held April 28, during the business 5 Members’ Discoveries: session of the 146th annual meeting of the Academy. Nicolai Meinshausen Those elected bring the total number of active members 6 Medallion Lecture: Tony Cai to 2,150. Foreign associates are non-voting members of the Academy, with citizenship outside the United States. Meeting report: SSP Above: Adrian Raftery 7 This year’s election brings the total number of foreign 8 New IMS Fellows Below: Wing H. Wong associates to 404. The National Academy of Sciences is a private 10 Obituaries: Keith Worsley; I.J. Good organization of scientists and engineers dedicated to the furtherance of science and its use for general welfare. 12-3 JSM program highlights; It was established in 1863 by a congressional act of IMS sessions at JSM incorporation signed by Abraham Lincoln that calls on 14-5 JSM tours; More things to the Academy to act as an official adviser to the federal do in DC government, upon request, in any matter of science or 16 Accepting rejections technology. -
Felix Elwert
July 2021 FELIX ELWERT University of Wisconsin–Madison Department of Sociology and Center for Demography and Ecology 4426 Sewell Social Sciences Building 1180 Observatory Drive Madison, WI 53706 [email protected] ACADEMIC POSITIONS University of Wisconsin–Madison 2017- Professor, Department of Sociology Department of Biostatistics and Medical Informatics (affiliated) Department of Population Health Sciences (affiliated) 2013–2017 Associate Professor (Affiliated), Department of Population Health Sciences 2012–2017 Associate Professor, Department of Sociology 2007–2012 Assistant Professor, Department of Sociology WZB Berlin Social Science Center, Germany 2015–2016 Acting Director, Research Unit Social Inequality and Social Policy 2014–2015 Karl W. Deutsch Visiting Professor Harvard Medical School 2006–2007 Postdoctoral Fellow, Department of Health Care Policy EDUCATION 2007 Ph.D., Sociology, Harvard University 2006 A.M., Statistics, Harvard University 1999 M.A., Sociology, New School for Social Research 1997 Vordiplom, Sociology, Free University of Berlin Felix Elwert / May 2020 AWARDS AND FELLOWSHIPS 2018 Leo Goodman Award, Methodology Section, American Sociological Association 2018 Elected member, Sociological Research Association 2018 Vilas Midcareer Faculty Award, University of Wisconsin-Madison 2017- H. I. Romnes Faculty Fellowship, University of Wisconsin-Madison 2016-2018 Fellow, WZB Berlin Social Science Center, Berlin, Germany 2013 Causality in Statistics Education Award, American Statistical Association 2013 Vilas Associate Award, University of Wisconsin–Madison 2012 Jane Addams Award (Best Paper), Community and Urban Sociology Section, American Sociological Association 2010 Gunther Beyer Award (Best Paper by a Young Scholar), European Association for Population Studies 2009, 2010, 2017 University Housing Honored Instructor Award, University of Wisconsin- Madison 2009 & 2010 Best Poster Awards, Annual Meeting of the Population Association of America 2005–2006 Eliot Fellowship, Harvard University 2004 Aage B. -
Frontiers of Statistics and Forecasting in Celebration of the 80Th Birthday of George C
Frontiers of Statistics and Forecasting in Celebration of the 80th Birthday of George C. Tiao Humanities and Social Sciences Building Academia Sinica, Taipei, Taiwan December 17~18, 2013 The Chinese Institute of Probability and Statistics Institute of Economics, Academia Sinica Institute of Statistical Science, Academia Sinica DGBAS, Executive Yuan, R.O.C. Welcome Dear Friends: On behalf of the organizing committee, it is my great privilege and honor to welcome you to the conference “Frontiers of Statistics and Forecasting” to celebrate the 80th birthday of Professor George C. Tiao. A leading figure in statistics, George has had a great impact on many econometricians and statisticians around the world, especially in the Chinese community. It is particularly fitting to have the celebration hosted by the Academia Sinica and sponsored jointed by The Chinese Institute of Probability and Statistics, Institute of Economics, Institute of Statistical Science, and the Directorate General of Budget, Accounting and Statistics. Many of you took a long journey, including from Sao Paulo, London, Madrid, Illinois, New Jersey, Washington DC, Beijing, Hong Kong, Shanghai, and Tokyo, to join the celebration. To you, we say “thank you”. In the next two days, you will hear many of George’s innovative contributions in statistics and economics. However, the conference cannot cover all of George’s contributions. He is instrumental in the developments of statistical education and research in Taiwan, Beijing, and Hong Kong, and in the establishment of the International Chinese Statistical Association. He is also the founding chair-editor of Statistica Sinica. He has co-established both the annual NBER/NSF Time Series Conference and the Conference for Making Statistics More Effective in Schools of Business. -
JEROME P. REITER Department of Statistical Science, Duke University Box 90251, Durham, NC 27708 Phone: 919 668 5227
JEROME P. REITER Department of Statistical Science, Duke University Box 90251, Durham, NC 27708 phone: 919 668 5227. email: [email protected]. September 26, 2021 EDUCATION Ph.D. in Statistics, Harvard University, 1999. A.M. in Statistics, Harvard University, 1996. B.S. in Mathematics, Duke University, 1992. DISSERTATION \Estimation in the Presence of Constraints that Prohibit Explicit Data Pooling." Advisor: Donald B. Rubin. POSITIONS Academic Appointments Professor of Statistical Science and Bass Fellow, Duke University, 2015 - present. Mrs. Alexander Hehmeyer Professor of Statistical Science, Duke University, 2013 - 2015. Mrs. Alexander Hehmeyer Associate Professor of Statistical Science, Duke University, 2010 - 2013. Associate Professor of Statistical Science, Duke University, 2009 - 2010. Assistant Professor of Statistical Science, Duke University, 2006 - 2008. Assistant Professor of the Practice of Statistics and Decision Sciences, Duke University, 2002 - 2006. Lecturer in Statistics, University of California at Santa Barbara, 2001 - 2002. Assistant Professor of Statistics, Williams College, 1999 - 2001. Other Appointments Chair, Department of Statistical Science, Duke University, 2019 - present. Associate Chair, Department of Statistical Science, Duke University, 2016 - 2019. Mathematical Statistician (part-time), U. S. Bureau of the Census, 2015 - present. Associate/Deputy Director of Information Initiative at Duke, Duke University, 2013 - 2019. Social Sciences Research Institute Data Services Core Director, Duke University, 2010 - 2013. Interim Director, Triangle Research Data Center, 2006. Senior Fellow, National Institute of Statistical Sciences, 2002 - 2005. 1 ACADEMIC HONORS Keynote talk, 11th Official Statistics and Methodology Symposium (Statistics Korea), 2021. Fellow of the Institute of Mathematical Statistics, 2020. Clifford C. Clogg Memorial Lecture, Pennsylvania State University, 2020 (postponed due to covid-19). -
Causality: Readings in Statistics and Econometrics Hedibert F
Causality: Readings in Statistics and Econometrics Hedibert F. Lopes, INSPER http://www.hedibert.org/current-teaching/#tab-causality Annotated Bibliography 1 Articles 1. Angrist and Imbens (1995) Two-Stage Least Squares Estimation of Average Causal Effects in Models With Variable Treatment Intensity. JASA, 90, 431-442. 2. Angrist and Krueger (1991) Does compulsory school attendance affect earnings? Quarterly Journal of Economic, 106, 979-1019. 3. Angrist, Imbens and Rubin (1996) Identification of causal effects using instrumental variables (with discussion). JASA, 91, 444-472. 4. Athey and Imbens (2015) Machine Learning Methods for Estimating Heterogeneous Causal Effects. 5. Balke and Pearl (1995). Counterfactuals and policy analysis in structural models. In Besnard and Hanks, Eds., Uncertainty in Artificial Intelligence, Proceedings of the Eleventh Conference. Morgan Kaufmann, San Francisco, 11-18. 6. Bareinboim and Pearl (2015) Causal inference from big data: Theoretical foundations and the data-fusion problem. Proceedings of the National Academy of Sciences. 7. Bollen and Pearl (2013) Eight myths about causality and structural equation models. In Morgan (Ed.) Handbook of Causal Analysis for Social Research, Chapter 15, 301-328. Springer. 8. Bound, Jaeger, and Baker (1995) Problems with Instrumental Variables Estimation when the Correlation Be- tween the Instruments and the Endogenous Regressors is Weak. JASA, 90, 443-450. 9. Brito and Pearl (2002) Generalized instrumental variables. In Darwiche and Friedman, Eds. Uncertainty in Artificial Intelligence, Proceedings of the Eighteenth Conference. Morgan Kaufmann, San Francisco, 85-93. 10. Brzeski, Taddy and raper (2015) Causal Inference in Repeated Observational Studies: A Case Study of eBay Product Releases. arXiv:1509.03940v1. 11. Chambaz, Drouet and Thalabard (2014) Causality, a Trialogue. -
Can P-Values Be Meaningfully Interpreted Without Random Sampling?
A Service of Leibniz-Informationszentrum econstor Wirtschaft Leibniz Information Centre Make Your Publications Visible. zbw for Economics Hirschauer, Norbert; Grüner, Sven; Mußhoff, Oliver; Becker, Claudia; Jantsch, Antje Article — Published Version Can p-values be meaningfully interpreted without random sampling? Statistics Surveys Provided in Cooperation with: Leibniz Institute of Agricultural Development in Transition Economies (IAMO), Halle (Saale) Suggested Citation: Hirschauer, Norbert; Grüner, Sven; Mußhoff, Oliver; Becker, Claudia; Jantsch, Antje (2020) : Can p-values be meaningfully interpreted without random sampling?, Statistics Surveys, ISSN 1935-7516, Cornell University Library, Ithaca, NY, Vol. 14, pp. 71-91, http://dx.doi.org/10.1214/20-SS129 , https://projecteuclid.org/euclid.ssu/1585274548 This Version is available at: http://hdl.handle.net/10419/215709 Standard-Nutzungsbedingungen: Terms of use: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Documents in EconStor may be saved and copied for your Zwecken und zum Privatgebrauch gespeichert und kopiert werden. personal and scholarly purposes. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle You are not to copy documents for public or commercial Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich purposes, to exhibit the documents publicly, to make them machen, vertreiben oder anderweitig nutzen. publicly available on the internet, or to distribute or otherwise use the documents in public. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, If the documents have been made available under an Open gelten abweichend von diesen Nutzungsbedingungen die in der dort Content Licence (especially Creative Commons Licences), you genannten Lizenz gewährten Nutzungsrechte. may exercise further usage rights as specified in the indicated licence. -
IMS Treasurer's Report
16 . IMS Bulletin Volume 49 . Issue 6 Treasurer’s Report 2019 Introduction Membership Data This report details membership and Table 1 presents the membership data back to 2015. Total individual paid membership in subscription data for the calendar year end the Institute as of December 31, 2019 increased by 15% from December 31, 2018. This 2019. The 2019 fiscal year-end audit report is largely due to an increase in members from mainland China. The total number of paid will be posted online separately in the Fall IMS members in 2019 was 2,788. The IMS had its peak in paid membership in 2008 with of 2020, after the auditors have completed 3,156 members. The IMS Executive Committee continues to look for ways to address our the annual process. membership numbers. In 2019, the total number of IMS mem- TABLE 1: Membership, by Calendar Year bers increased. Subscriptions by institutions 2015 2016 2017 2018 2019 % change decreased this past year by 3% overall, and Regular 1,587 1,565 1,447 1,384 1,397 0.9 % by 4% for IMS core journals. The financial Life/Retired Life 528 541 563 613 617 0.7 % status of the Institute continues to be stable Reduced Country/Retired/IMS China 376 337 370 331 704 100.6 % and strong, and actions are in place to New Graduate 58 113 213 76 70 -7.9 % ensure its long-term stability. Student 1,236 1,094 1,022 828 722 -15.3 % Details of the events of the past year, Total 3,785 3,650 3,615 3,217 3,510 7.1 % and membership, subscription and sales Total excluding free members (students) 2,549 2,556 2,593 2,389 2,788 15.0 % data, are given below. -
JAMES MAHONEY Departments of Political Science and Sociology Northwestern University Evanston, IL 60208-1006 [email protected]
October 2019 JAMES MAHONEY Departments of Political Science and Sociology Northwestern University Evanston, IL 60208-1006 [email protected] Professional Appointments Gordon Fulcher Professor in Decision-Making, Northwestern University (2012-present). Chair, Department of Sociology, Northwestern University (2014-2017). Associate Chair, Department of Political Science, Northwestern University (2010-13). Gerald F. and Marjorie G. Fitzgerald Professor of Economic History, Northwestern University (2009-2012). Associate (2005-7) to Full Professor (2007-present), Departments of Political Science (50%) and Sociology (50%), Northwestern University. Assistant (1997-2003) to Associate Professor (2003-5), Department of Sociology, Brown University. Education University of California, Berkeley, Department of Political Science. M.A. 1991; Ph.D. 1997. University of Minnesota, Department of Political Science (Minor in History). B.A. 1990, Summa cum Laude. Major Awards and Grants Aaron Wildavsky Enduring Contribution Award, Section on Public Policy, American Political Science Association, June 2019. Elected to the Sociological Research Association, June 2018. Leo Goodman Award, Section on Methodology, American Sociological Association, August 2012. Faculty Book Award, Section on Development, American Sociological Association. Received for Colonialism and Postcolonial Development: Spanish America in Comparative Perspective, August 2012. Gregory Luebbert Best Book Award, Section on Comparative Politics, American Political Science Association. -
Efficient Multiply Robust Imputation in the Presence of Influential Units In
Efficient multiply robust imputation in the presence of influential units in surveys Sixia Chen,∗ David Haziza† and Victoire Michal‡ Abstract Item nonresponse is a common issue in surveys. Because unad- justed estimators may be biased in the presence of nonresponse, it is common practice to impute the missing values with the objective of reducing the nonresponse bias as much as possible. However, com- monly used imputation procedures may lead to unstable estimators of population totals/means when influential units are present in the set of respondents. In this article, we consider the class of multiply ro- bust imputation procedures that provide some protection against the failure of underlying model assumptions. We develop an efficient ver- sion of multiply robust estimators based on the concept of conditional bias, a measure of influence. We present the results of a simulation study to show the benefits of the proposed method in terms of bias and efficiency. Key words: Conditional bias; Influential unit; Item nonresponse; Multiply robust arXiv:2010.01706v1 [stat.ME] 4 Oct 2020 imputation; Skewed distribution. ∗Department of Biostatistics and Epidemiology, University of Oklahoma Health Sci- ences Center, Oklahoma City, OK, U.S.A. †Department of Mathematics and Statistics, University of Ottawa, Ottawa, Canada; ‡Department of Epidemiology, Biostatistics and Occupational Health, McGill Univer- sity, Montreal, Canada. 1 1 Introduction Item nonresponse is ubiquitous in surveys conducted by National Statistical Offices. Most often, it is treated by some form of single imputation, whereby a missing value is replaced by some plausible value constructed under certain assumptions. The customary imputation process starts with specifying an imputation model describing the relationship between the variable y requir- ing imputation, and a set of fully observed variables, v; available for both respondents and nonrespondents. -
Abbreviations of Names of Serials
Abbreviations of Names of Serials This list gives the form of references used in Mathematical Reviews (MR). ∗ not previously listed The abbreviation is followed by the complete title, the place of publication x journal indexed cover-to-cover and other pertinent information. y monographic series Update date: January 30, 2018 4OR 4OR. A Quarterly Journal of Operations Research. Springer, Berlin. ISSN xActa Math. Appl. Sin. Engl. Ser. Acta Mathematicae Applicatae Sinica. English 1619-4500. Series. Springer, Heidelberg. ISSN 0168-9673. y 30o Col´oq.Bras. Mat. 30o Col´oquioBrasileiro de Matem´atica. [30th Brazilian xActa Math. Hungar. Acta Mathematica Hungarica. Akad. Kiad´o,Budapest. Mathematics Colloquium] Inst. Nac. Mat. Pura Apl. (IMPA), Rio de Janeiro. ISSN 0236-5294. y Aastaraam. Eesti Mat. Selts Aastaraamat. Eesti Matemaatika Selts. [Annual. xActa Math. Sci. Ser. A Chin. Ed. Acta Mathematica Scientia. Series A. Shuxue Estonian Mathematical Society] Eesti Mat. Selts, Tartu. ISSN 1406-4316. Wuli Xuebao. Chinese Edition. Kexue Chubanshe (Science Press), Beijing. ISSN y Abel Symp. Abel Symposia. Springer, Heidelberg. ISSN 2193-2808. 1003-3998. y Abh. Akad. Wiss. G¨ottingenNeue Folge Abhandlungen der Akademie der xActa Math. Sci. Ser. B Engl. Ed. Acta Mathematica Scientia. Series B. English Wissenschaften zu G¨ottingen.Neue Folge. [Papers of the Academy of Sciences Edition. Sci. Press Beijing, Beijing. ISSN 0252-9602. in G¨ottingen.New Series] De Gruyter/Akademie Forschung, Berlin. ISSN 0930- xActa Math. Sin. (Engl. Ser.) Acta Mathematica Sinica (English Series). 4304. Springer, Berlin. ISSN 1439-8516. y Abh. Akad. Wiss. Hamburg Abhandlungen der Akademie der Wissenschaften xActa Math. Sinica (Chin. Ser.) Acta Mathematica Sinica. -
Wiley Statsref: Statistics Reference Online, © 2014–2019 John Wiley & Sons, Ltd
Institute of Mathematical Statistics (IMS) Institute of Mathematical Statistics (IMS) By Elyse Gustafson1 and Edsel A. Peña2 Keywords: awards, fellowships, founding, lectures, meetings, outreach, publications Abstract: This article is about the formation, governance, membership, awards and fellow- ship, sponsored lectures, publications, and various activities of the premier organization of mathematical statisticians and probabilists in the world – The Institute of Mathematical Statistics (IMS). 1 Founding of IMS and its Mission The Institute of Mathematical Statistics, hereon referred to as the IMS or the Institute, is a nonprofit orga- nization and was formed on September 12, 1935, at Ann Arbor, Michigan. On its founding, its officers were H. L. Rietz as President; W. A. Shewhart as Vice-President; and A. T. Craig as Secretary-Treasurer. Prior to its founding, there was a feeling that the theory of statistics would be advanced in the United States by the formation of an organization composed of individuals especially interested in the mathematical aspects of the field of statistics. As such, the IMS mission is that of fostering the development and dissemination of the theory and applications of statistics and probability. The official journal of the IMS during its founding was the The Annals of Mathematical Statistics, hereon referred to as the Annals. The founding of this journal actually preceded the formation of the IMS, and the existence of this journal was instrumental in the founding of the IMS. Professor Stephen Stigler’s article entitled History of Statistics[1] chronicled some of the events during this period pertinent to the Annals and the founding of the IMS. -
Equi-Energy Sampler with Applications in Statistical Inference and Statistical Mechanics1,2,3
The Annals of Statistics 2006, Vol. 34, No. 4, 1581–1619 DOI: 10.1214/009053606000000515 © Institute of Mathematical Statistics, 2006 DISCUSSION PAPER EQUI-ENERGY SAMPLER WITH APPLICATIONS IN STATISTICAL INFERENCE AND STATISTICAL MECHANICS1,2,3 BY S. C. KOU,QING ZHOU AND WING HUNG WONG Harvard University, Harvard University and Stanford University We introduce a new sampling algorithm, the equi-energy sampler, for efficient statistical sampling and estimation. Complementary to the widely used temperature-domain methods, the equi-energy sampler, utilizing the temperature–energy duality, targets the energy directly. The focus on the energy function not only facilitates efficient sampling, but also provides a powerful means for statistical estimation, for example, the calculation of the density of states and microcanonical averages in statistical mechanics. The equi-energy sampler is applied to a variety of problems, including exponential regression in statistics, motif sampling in computational biology and protein folding in biophysics. 1. Introduction. Since the arrival of modern computers during World War II, the Monte Carlo method has greatly expanded the scientific horizon to study com- plicated systems ranging from the early development in computational physics to modern biology. At the heart of the Monte Carlo method lies the difficult problem of sampling and estimation: Given a target distribution, usually multidimensional and multimodal, how do we draw samples from it and estimate the statistical quan- tities of interest? In this article, we attempt to introduce a new sampling algorithm, the equi-energy sampler, to address the problem. Since the Monte Carlo method began from calculations in statistical physics and mechanics, to introduce the equi- energy sampler, we begin from statistical mechanics.