A CONVERSATION with MONROE SIRKEN 3 Charge of the Program Was Professor Z
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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. -
IGDA Online Games White Paper Full Version
IGDA Online Games White Paper Full Version Presented at the Game Developers Conference 2002 Created by the IGDA Online Games Committee Alex Jarett, President, Broadband Entertainment Group, Chairman Jon Estanislao, Manager, Media & Entertainment Strategy, Accenture, Vice-Chairman FOREWORD With the rising use of the Internet, the commercial success of certain massively multiplayer games (e.g., Asheron’s Call, EverQuest, and Ultima Online), the ubiquitous availability of parlor and arcade games on “free” game sites, the widespread use of matching services for multiplayer games, and the constant positioning by the console makers for future online play, it is apparent that online games are here to stay and there is a long term opportunity for the industry. What is not so obvious is how the independent developer can take advantage of this opportunity. For the two years prior to starting this project, I had the opportunity to host several roundtables at the GDC discussing the opportunities and future of online games. While the excitement was there, it was hard not to notice an obvious trend. It seemed like four out of five independent developers I met were working on the next great “massively multiplayer” game that they hoped to sell to some lucky publisher. I couldn’t help but see the problem with this trend. I knew from talking with folks that these games cost a LOT of money to make, and the reality is that only a few publishers and developers will work on these projects. So where was the opportunity for the rest of the developers? As I spoke to people at the roundtables, it became apparent that there was a void of baseline information in this segment. -
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. -
Causal Inference in Statistics: an Overview∗†‡
Statistics Surveys Vol. 3 (2009) 96–146 ISSN: 1935-7516 DOI: 10.1214/09-SS057 Causal inference in statistics: An overview∗†‡ Judea Pearl Computer Science Department University of California, Los Angeles, CA 90095 USA e-mail: [email protected] Abstract: This review presents empirical researcherswith recent advances in causal inference, and stresses the paradigmatic shifts that must be un- dertaken in moving from traditional statistical analysis to causal analysis of multivariate data. Special emphasis is placed on the assumptions that un- derly all causal inferences, the languages used in formulating those assump- tions, the conditional nature of all causal and counterfactual claims, and the methods that have been developed for the assessment of such claims. These advances are illustrated using a general theory of causation based on the Structural Causal Model (SCM) described in Pearl (2000a), which subsumes and unifies other approaches to causation, and provides a coher- ent mathematical foundation for the analysis of causes and counterfactuals. In particular, the paper surveys the development of mathematical tools for inferring (from a combination of data and assumptions) answers to three types of causal queries: (1) queries about the effects of potential interven- tions, (also called “causal effects” or “policy evaluation”) (2) queries about probabilities of counterfactuals, (including assessment of “regret,” “attri- bution” or “causes of effects”) and (3) queries about direct and indirect effects (also known as “mediation”). Finally, the paper defines the formal and conceptual relationships between the structural and potential-outcome frameworks and presents tools for a symbiotic analysis that uses the strong features of both. Keywords and phrases: Structuralequation models, confounding,graph- ical methods, counterfactuals, causal effects, potential-outcome, mediation, policy evaluation, causes of effects. -
Variance Estimation of Imputed Survey Data
NATIONAL CENTER FOR EDUCATION STATISTICS Working Paper Series The Working Paper Series was created in order to preserve the information contained in these documents and to promote the sharing of valuable work experience and knowledge. However, these documents were prepared under different formats and did not undergo vigorous NCES publication review and editing prior to their inclusion in the series. U. S. Department of Education Office of Educational Research and Improvement NATIONAL CENTER FOR EDUCATION STATISTICS Working Paper Series Variance Estimation of Imputed Survey Data Working Paper No. 98-14 October 1998 Contact: Steven Kaufman Surveys and Cooperative Systems Group email: [email protected] U. S. Department of Education Office of Educational Research and Improvement U.S. Department of Education Richard W. Riley Secretary Office of Educational Research and Improvement C. Kent McGuire Assistant Secretary National Center for Education Statistics Pascal D. Forgione, Jr. Commissioner The National Center for Education Statistics (NCES) is the primary federal entity for collecting, analyzing, and reporting data related to education in the United States and other nations. It fulfills a congressional mandate to collect, collate, analyze, and report full and complete statistics on the condition of education in the United States; conduct and publish reports and specialized analyses of the meaning and significance of such statistics; assist state and local education agencies in improving their statistical systems; and review and report on education activities in foreign countries. NCES activities are designed to address high priority education data needs; provide consistent, reliable, complete, and accurate indicators of education status and trends; and report timely, useful, and high quality data to the U.S. -
Mobile Smart Fundamentals Mma Members Edition July 2015
MOBILE SMART FUNDAMENTALS MMA MEMBERS EDITION JULY 2015 messaging . advertising . apps . mcommerce www.mmaglobal.com NEW YORK • LONDON • SINGAPORE • SÃO PAULO MOBILE MARKETING ASSOCIATION JULY 2015 REPORT The Inspiration Hub As you’ll have likely seen last week, we announced the shortlist for this year’s Global Smarties Awards, which you can read about here. The last 3 years have seen considerable growth for this program, supported in part by our expansion with specific regional and country programs to better represent our diverse global audience and the growing use of mobile. This celebration of, by equal measures, strategy, execution, creativity and results for individual campaigns allows us to recognize the leaders and innovators who put mobile at the heart of what they do to drive closer consumer engagement. Just as importantly, it also allows us to develop a unique and invaluable resource for the industry and our members. By curating each of the finalist submissions into case studies, the MMA continue to build a Case Study Hub that provides inspiration and benchmarks for all our members and the wider industry. Having a unique resource with this much depth allows us to constantly update what it means to be successful with mobile. By providing a stage for those willing to continuously push the envelope and show the industry what’s possible, the Hub serves as a beacon for the rest of the industry, who are figuring out how to get there – as quickly as possible. If you haven’t yet checked out the Case Study Hub, I’d encourage you to do so. -
No 30 the Use of Surveys by Central Banks Proceedings of the IFC Workshops in Pune June 2007, Buenos Aires December 2007 and Vienna March 2008
Irving Fisher Committee on Central Bank Statistics IFC Bulletin No 30 The use of surveys by central banks Proceedings of the IFC Workshops in Pune June 2007, Buenos Aires December 2007 and Vienna March 2008 July 2009 The IFC Bulletins contain proceedings of meetings of the Irving Fisher Committee on Central Bank Statistics. Papers in this volume were prepared for IFC-sponsored workshops entitled “The use of surveys by central banks”, held in Pune June 2007, Buenos Aires December 2007 and Vienna March 2008. Not all papers presented at the workshop are included in this volume. The views expressed are those of the respective contributors and do not necessarily reflect the views of the IFC, the BIS, the central banks or other institutions represented at the meeting. Individual papers (or excerpts thereof) may be reproduced or translated with the authorisation of the authors concerned. Copies of publications are available from: Bank for International Settlements Press & Communications CH 4002 Basel, Switzerland E-mail: [email protected] Fax: +41 61 280 9100 and +41 61 280 8100 This publication is available on the BIS website (www.bis.org). © Bank for International Settlements 2009. All rights reserved. Brief excerpts may be reproduced or translated provided the source is cited. ISSN 1991-7279 (print) ISBN 92-9131-796-9 (print) ISSN 1991-7511 (online) ISBN 92-9197-796-9 (online) Contents Foreword by the IFC Chair Manuel Marfán..........................................................................................................................1 Overall summary Paul Van den Bergh...................................................................................................................3 Session 1: Overview of central bank data collection practices Background note on data collection techniques by central banks: trends and issues Chatwaruth Musigchai, Bank of Thailand .................................................................................7 Surveys conducted by Reserve Bank of India C.L.