ENAR Would Like to Acknowledge the Generous Support of the 2010 Local
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September 2010
THE ISBA BULLETIN Vol. 17 No. 3 September 2010 The official bulletin of the International Society for Bayesian Analysis AMESSAGE FROM THE membership renewal (there will be special provi- PRESIDENT sions for members who hold multi-year member- ships). Peter Muller¨ The Bayesian Nonparametrics Section (IS- ISBA President, 2010 BA/BNP) is already up and running, and plan- [email protected] ning the 2011 BNP workshop. Please see the News from the World section in this Bulletin First some sad news. In August we lost two and our homepage (select “business” and “mee- big Bayesians. Julian Besag passed away on Au- tings”). gust 6, and Arnold Zellner passed away on Au- gust 11. Arnold was one of the founding ISBA ISBA/SBSS Educational Initiative. Jointly presidents and was instrumental to get Bayesian with ASA/SBSS (American Statistical Associa- started. Obituaries on this issue of the Analysis tion, Section on Bayesian Statistical Science) we Bulletin and on our homepage acknowledge Juli- launched a new joint educational initiative. The an and Arnold’s pathbreaking contributions and initiative formalized a long standing history of their impact on the lives of many people in our collaboration of ISBA and ASA/SBSS in related research community. They will be missed dearly! matters. Continued on page 2. ISBA Elections 2010. Please check out the elec- tion statements of the candidates for the upco- In this issue ming ISBA elections. We have an amazing slate ‰ A MESSAGE FROM THE BA EDITOR of candidates. Thanks to the nominating commit- *Page 2 tee, Mike West (chair), Renato Martins Assuncao,˜ Jennifer Hill, Beatrix Jones, Jaeyong Lee, Yasuhi- ‰ 2010 ISBA ELECTION *Page 3 ro Omori and Gareth Robert! ‰ SAVAGE AWARD AND MITCHELL PRIZE 2010 *Page 8 Sections. -
IMS Bulletin 39(4)
Volume 39 • Issue 4 IMS1935–2010 Bulletin May 2010 Meet the 2010 candidates Contents 1 IMS Elections 2–3 Members’ News: new ISI members; Adrian Raftery; It’s time for the 2010 IMS elections, and Richard Smith; we introduce this year’s nominees who are IMS Collections vol 5 standing for IMS President-Elect and for IMS Council. You can read all the candi- 4 IMS Election candidates dates’ statements, starting on page 4. 9 Amendments to This year there are also amendments Constitution and Bylaws to the Constitution and Bylaws to vote Letter to the Editor 11 on: they are listed The candidate for IMS President-Elect is Medallion Preview: Laurens on page 9. 13 Ruth Williams de Haan Voting is open until June 26, so 14 COPSS Fisher lecture: Bruce https://secure.imstat.org/secure/vote2010/vote2010.asp Lindsay please visit to cast your vote! 15 Rick’s Ramblings: March Madness 16 Terence’s Stuff: And ANOVA thing 17 IMS meetings 27 Other meetings 30 Employment Opportunities 31 International Calendar of Statistical Events The ten Council candidates, clockwise from top left, are: 35 Information for Advertisers Krzysztof Burdzy, Francisco Cribari-Neto, Arnoldo Frigessi, Peter Kim, Steve Lalley, Neal Madras, Gennady Samorodnitsky, Ingrid Van Keilegom, Yazhen Wang and Wing H Wong Abstract submission deadline extended to April 30 IMS Bulletin 2 . IMs Bulletin Volume 39 . Issue 4 Volume 39 • Issue 4 May 2010 IMS members’ news ISSN 1544-1881 International Statistical Institute elects new members Contact information Among the 54 new elected ISI members are several IMS members. We congratulate IMS IMS Bulletin Editor: Xuming He Fellow Jon Wellner, and IMS members: Subhabrata Chakraborti, USA; Liliana Forzani, Assistant Editor: Tati Howell Argentina; Ronald D. -
Tea: a High-Level Language and Runtime System for Automating
Tea: A High-level Language and Runtime System for Automating Statistical Analysis Eunice Jun1, Maureen Daum1, Jared Roesch1, Sarah Chasins2, Emery Berger34, Rene Just1, Katharina Reinecke1 1University of Washington, 2University of California, 3University of Seattle, WA Berkeley, CA Massachusetts Amherst, femjun, mdaum, jroesch, [email protected] 4Microsoft Research, rjust, [email protected] Redmond, WA [email protected] ABSTRACT Since the development of modern statistical methods (e.g., Though statistical analyses are centered on research questions Student’s t-test, ANOVA, etc.), statisticians have acknowl- and hypotheses, current statistical analysis tools are not. Users edged the difficulty of identifying which statistical tests people must first translate their hypotheses into specific statistical should use to answer their specific research questions. Almost tests and then perform API calls with functions and parame- a century later, choosing appropriate statistical tests for eval- ters. To do so accurately requires that users have statistical uating a hypothesis remains a challenge. As a consequence, expertise. To lower this barrier to valid, replicable statistical errors in statistical analyses are common [26], especially given analysis, we introduce Tea, a high-level declarative language that data analysis has become a common task for people with and runtime system. In Tea, users express their study de- little to no statistical expertise. sign, any parametric assumptions, and their hypotheses. Tea A wide variety of tools (such as SPSS [55], SAS [54], and compiles these high-level specifications into a constraint satis- JMP [52]), programming languages (e.g., R [53]), and libraries faction problem that determines the set of valid statistical tests (including numpy [40], scipy [23], and statsmodels [45]), en- and then executes them to test the hypothesis. -
INFORMS Roundtable Summer Meeting Agenda July 19-20, 2015 Jackson Lake Lodge Grand Teton National Park, Moran, WY Theme: “Uncertainty Quantification”
INFORMS Roundtable Summer Meeting Agenda July 19-20, 2015 Jackson Lake Lodge Grand Teton National Park, Moran, WY Theme: “Uncertainty Quantification” Sunday, July 19 2:30 PM Meet in Jackson Lake Lodge Lobby 3:15 PM Outing: Jackson Lake Scenic Cruise Colter Bay We will meet in Jackson Lake Lodge Lobby to take a shuttle bus to the Colter Bay Marina, 5 miles north. We will return to Jackson Lake Lodge by 5:30 pm. http://www.gtlc.com/activities/marina-lake-cruise.aspx 6:30 PM Reception Sunset Terrace 7:30 PM Dinner Trapper’s Room 9:30 PM Adjourn Monday, July 20 7:00 AM Breakfast Trapper’s Room 8:15 AM Roundtable Introductions Wrangler/Prospector/Homesteader (WPH) 8:30 AM The Role of Uncertainty Quantification in Predictive Models WPH We increasingly rely on mathematical and statistical models to predict phenomena ranging from nuclear power plant design to profits made in financial markets. When assessing the feasibility of these predictions, it is critical to quantify uncertainties associated with the models, inputs to the models, and data used to calibrate the models. The synthesis of statistical and mathematical techniques, which can be used to quantify input and response uncertainties for simulation codes that can take hours to days to run, comprises the evolving field of uncertainty quantification. The use of data, to improve the predictive accuracy of models, is central to uncertainty quantification so we will begin by providing an overview of how Bayesian techniques can be used to construct distributions for Summer 2015 Roundtable Agenda 1 model inputs. -
December 2000
THE ISBA BULLETIN Vol. 7 No. 4 December 2000 The o±cial bulletin of the International Society for Bayesian Analysis A WORD FROM already lays out all the elements mere statisticians might have THE PRESIDENT of the philosophical position anything to say to them that by Philip Dawid that he was to continue to could possibly be worth ISBA President develop and promote (to a listening to. I recently acted as [email protected] largely uncomprehending an expert witness for the audience) for the rest of his life. defence in a murder appeal, Radical Probabilism He is utterly uncompromising which revolved around a Modern Bayesianism is doing in his rejection of the realist variant of the “Prosecutor’s a wonderful job in an enormous conception that Probability is Fallacy” (the confusion of range of applied activities, somehow “out there in the world”, P (innocencejevidence) with supplying modelling, data and in his pragmatist emphasis P ('evidencejinnocence)). $ analysis and inference on Subjective Probability as Contents procedures to nourish parts that something that can be measured other techniques cannot reach. and regulated by suitable ➤ ISBA Elections and Logo But Bayesianism is far more instruments (betting behaviour, ☛ Page 2 than a bag of tricks for helping or proper scoring rules). other specialists out with their What de Finetti constructed ➤ Interview with Lindley tricky problems – it is a totally was, essentially, a whole new ☛ Page 3 original way of thinking about theory of logic – in the broad ➤ New prizes the world we live in. I was sense of principles for thinking ☛ Page 5 forcibly struck by this when I and learning about how the had to deliver some brief world behaves. -
Department of Statistics Academic Program Review Spring 2015
Department of Statistics Academic Program Review Spring 2015 Department of Statistics • Texas A&M University • 3143 TAMU • College Station, TX 77843-3143 (979) 845-3141 • www.stat.tamu.edu Table of Contents I. Introduction Brief History of the Department……………………………………………………. 4 Mission and Goals………………………………………………………………….. 8 Administrative Structure of the Department………………………………………... 11 Resources…………………………………………………………………………... 13 Analysis……………………………………………………………………………. 14 Undergraduate Program……………………………………………………………. 15 Graduate Program………………………………………………………………….. 16 Professional Education……………………………………………………………... 19 Challenges and Opportunities……………………………………………………… 20 Assessment………………………………………………………………………… 22 II. Student Report Graduate Program in Statistics……………………………………………………… 28 Master of Science Program…………………………………………………………. 29 Doctor of Philosophy Program……………………………………………………... 32 Internship Program…………………………………………………………………. 35 Undergraduate Course Offerings…………………………………………………… 36 Graduate Course Offerings………………………………………………………… 37 Scheduling Coursework…………………………………………………………….. 42 Graduate Program Admissions Criteria……………………………………………... 43 GRE Scores for First Time Students………………………………………………... 44 Graduate Students Applied, Admitted, and Enrolled ………………………………. 45 Students by GPR Range……………………………………………………………. 48 Institutional Support for Full Time Students……………………………………….. 50 Honors & Awards Received by Students…………………………………………… 51 Statistics Graduate Student Association…………………………………………….. 54 Student Publications……………………………………………………………….. -
The BUGS Project: Evolution, Critique and Future Directions
STATISTICS IN MEDICINE Statist. Med. 2009; 28:3049–3067 Published online 24 July 2009 in Wiley InterScience (www.interscience.wiley.com) DOI: 10.1002/sim.3680 The BUGS project: Evolution, critique and future directions 1, , 1 2 3 David Lunn ∗ †, David Spiegelhalter ,AndrewThomas and Nicky Best 1Medical Research Council Biostatistics Unit, Institute of Public Health, University Forvie Site, Robinson Way, Cambridge CB2 0SR, U.K. 2School of Mathematics and Statistics, Mathematical Institute, North Haugh, St. Andrews, Fife KY16 9SS, U.K. 3Department of Epidemiology and Public Health, Imperial College London, St. Mary’s Campus, Norfolk Place, London W2 1PG, U.K. SUMMARY BUGS is a software package for Bayesian inference using Gibbs sampling. The software has been instru- mental in raising awareness of Bayesian modelling among both academic and commercial communities internationally, and has enjoyed considerable success over its 20-year life span. Despite this, the software has a number of shortcomings and a principal aim of this paper is to provide a balanced critical appraisal, in particular highlighting how various ideas have led to unprecedented flexibility while at the same time producing negative side effects. We also present a historical overview of the BUGS project and some future perspectives. Copyright 2009 John Wiley & Sons, Ltd. KEY WORDS: BUGS; WinBUGS; OpenBUGS; Bayesian modelling; graphical models 1. INTRODUCTION BUGS 1 is a software package for performing Bayesian inference using Gibbs sampling 2, 3 . The BUGS[ ] project began at the Medical Research Council Biostatistics Unit in Cambridge in[ 1989.] Since that time the software has become one of the most popular statistical modelling packages, with, at the time of writing, over 30000 registered users of WinBUGS (the Microsoft Windows incarnation of the software) worldwide, and an active on-line community comprising over 8000 members. -
Design and Analysis Issues in Family-Based Association
Emerging Challenges in Statistical Genetics Duncan Thomas University of Southern California Human Genetics in the Big Science Era • “Big Data” – large n and large p and complexity e.g., NIH Biomedical Big Data Initiative (RFA-HG-14-020) • Large n: challenge for computation and data storage, but not conceptual • Large p: many data mining approaches, few grounded in statistical principles • Sparse penalized regression & hierarchical modeling from Bayesian and frequentist perspectives • Emerging –omics challenges Genetics: from Fisher to GWAS • Population genetics & heritability – Mendel / Fisher / Haldane / Wright • Segregation analysis – Likelihoods on complex pedigrees by peeling: Elston & Stewart • Linkage analysis (PCR / microsats / SNPs) – Multipoint: Lander & Green – MCMC: Thompson • Association – TDT, FBATs, etc: Spielman, Laird – GWAS: Risch & Merikangas – Post-GWAS: pathway mining, next-gen sequencing Association: From hypothesis-driven to agnostic research Candidate pathways Candidate Hierarchical GWAS genes models (ht-SNPs) Ontologies Pathway mining MRC BSU SGX Plans Objectives: – Integrating structural and prior information for sparse regression analysis of high dimensional data – Clustering models for exposure-disease associations – Integrating network information – Penalised regression and Bayesian variable selection – Mechanistic models of cellular processes – Statistical computing for large scale genomics data Targeted areas of impact : – gene regulation and immunological response – biomarker based signatures – targeting -
Stan User's Guide 2.27
Stan User’s Guide Version 2.27 Stan Development Team Contents Overview 9 Part 1. Example Models 11 1. Regression Models 12 1.1 Linear regression 12 1.2 The QR reparameterization 14 1.3 Priors for coefficients and scales 16 1.4 Robust noise models 16 1.5 Logistic and probit regression 17 1.6 Multi-logit regression 19 1.7 Parameterizing centered vectors 21 1.8 Ordered logistic and probit regression 24 1.9 Hierarchical logistic regression 25 1.10 Hierarchical priors 28 1.11 Item-response theory models 29 1.12 Priors for identifiability 32 1.13 Multivariate priors for hierarchical models 33 1.14 Prediction, forecasting, and backcasting 41 1.15 Multivariate outcomes 42 1.16 Applications of pseudorandom number generation 48 2. Time-Series Models 51 2.1 Autoregressive models 51 2.2 Modeling temporal heteroscedasticity 54 2.3 Moving average models 55 2.4 Autoregressive moving average models 58 2.5 Stochastic volatility models 60 2.6 Hidden Markov models 63 3. Missing Data and Partially Known Parameters 70 1 CONTENTS 2 3.1 Missing data 70 3.2 Partially known parameters 71 3.3 Sliced missing data 72 3.4 Loading matrix for factor analysis 73 3.5 Missing multivariate data 74 4. Truncated or Censored Data 77 4.1 Truncated distributions 77 4.2 Truncated data 77 4.3 Censored data 79 5. Finite Mixtures 82 5.1 Relation to clustering 82 5.2 Latent discrete parameterization 82 5.3 Summing out the responsibility parameter 83 5.4 Vectorizing mixtures 86 5.5 Inferences supported by mixtures 87 5.6 Zero-inflated and hurdle models 90 5.7 Priors and effective data size in mixture models 94 6. -
A Joint Spatio-Temporal Model of Opioid Associated Deaths and Treatment Admissions in Ohio
A JOINT SPATIO-TEMPORAL MODEL OF OPIOID ASSOCIATED DEATHS AND TREATMENT ADMISSIONS IN OHIO BY YIXUAN JI A Thesis Submitted to the Graduate Faculty of WAKE FOREST UNIVERSITY GRADUATE SCHOOL OF ARTS AND SCIENCES in Partial Fulfillment of the Requirements for the Degree of MASTER OF ARTS Mathematics and Statistics May 2019 Winston-Salem, North Carolina Approved By: Staci Hepler, Ph.D., Advisor Miaohua Jiang, Ph.D., Chair Robert Erhardt, Ph.D. Acknowledgments First and foremost, I would like to express my sincere gratitude and appreciation to my advisor, Dr. Staci A. Hepler. Without your continuous support and inspiration, I could not have achieved this thesis research. Thank you so much for your patience, motivation, enthusiasm, and immense knowledge, which made this research even more enjoyable. In addition to my advisor, I would also like to thank Dr. Miaohua Jiang and Dr. Robert Erhardt, for the constant encouragement and advice, and for serving on my thesis committee. Furthermore, this thesis benefited greatly from various courses that I took, including Bayesian statistics from Dr. Hepler, factor analysis and Markov Chain from Dr. Berenhaut, poisson linear model from Dr. Erhardt's Generalized Linear Model class, R programming skills from Dr. Nicole Dalzell and also reasoning skills from Dr. John Gemmer's Real Analysis class. Additionally, I would like to extend my thanks to Dr. Ellen Kirkman, Dr. Stephen Robinson and Dr. Sarah Raynor for their special attention and support to me, as an international student. Last but not least, I would like to thank my family for their sincere mental support throughout my life. -
October Amstat News
October 2012 • Issue #424 AMSTATNEWS The Membership Magazine of the American Statistical Association • http://magazine.amstat.org Recollections Thoughts AboutAbout JSMJSM inin& & SanSan DiegoDiego Many Honored at Presidential Address, Awards Ceremony ALSO: NSF Graduate Research Fellowship Statistician Greg Ridgeway New Deputy Director of National Institute of Justice AMSTATNews OctOber 2012 • Issue #424 features Executive Director 3 President’s Corner Ron Wasserstein: [email protected] Associate Executive Director and Director of Operations 5 Member Spotlight Stephen Porzio: [email protected] Susan Gruber Director of Science Policy 6 ASA Commends NSF for Initiative, Commitment to Increasing Steve Pierson: [email protected] Statistics Profile Director of Education 6 ISS-2012 on Longitudinal Data Analysis a Success Rebecca Nichols [email protected] 7 Statistics for Policymakers Managing Editor Megan Murphy: [email protected] Statistical Modeling to Address the Problem of Illegal Immigration Production Coordinators/Graphic Designers Melissa Muko Gotherman: [email protected] 8 Statistician Greg Ridgeway New Deputy Director of National Kathryn Wright: [email protected] Institute of Justice Publications Coordinator 10 NSF Renews SAMSI for Five Years Val Nirala: [email protected] 11 ASA Records Viewable Online Advertising Manager Claudine Donovan: [email protected] 12 Meet NCHS Director, Edward Sondik Contributing Staff Members 14 JASA Highlights Kathleen Wert • Rebecca Nichols • Eric Sampson Studies of Immune Response and False Discovery Rate Amstat News welcomes news items and letters from readers on matters Featured in September Issue of interest to the association and the profession. Address correspondence to Managing Editor, Amstat News, American Statistical Association, 732 North 16 Statistician's View Washington Street, Alexandria VA 22314-1943 USA, or email amstat@ A Tale of Two Researchers amstat.org. -
David L. Banks
June 10, 2021 DAVID L. BANKS Department of Statistical Science Box 90251, Duke University, Durham, NC 27708-0251 phone: (919) 684-3743 fax: (919) 684-8594 email: [email protected] PERSONAL: Born: Baton Rouge, Louisiana Home Address: 9 Altmont Court Status: U.S. Citizen Durham, NC 27705 EDUCATION: 1979 - 1984 Virginia Polytechnic Institute and State University, Blacksburg, Virginia Statistics Ph.D., May 1984. Dissertation: Nonparametric Bayesian Inference Applied Mathematics M.S., June 1982. Statistics M.S., July 1980. 1973 - 1977 University of Virginia, Charlottesville, Virginia Majors: Anthropology, Mathematics. B.A. June, 1977 (cum laude). EMPLOYMENT: Jan. 2018 - present Director, Statistical and Applied Mathematical Sciences Institute Research Triangle Park, North Carolina July 2003 - present Professor of the Practice, Department of Statistical Science Duke University, Durham, North Carolina. Feb. 2002 - June 2003 Special Assistant to the Director of the Office of Biostatistics Center for Biologics Evaluation and Research U.S. Food and Drug Administration. June 1999 - Feb. 2002 Acting Chief Statistician/Director of the Office of Advanced Studies, Bureau of Transportation Statistics U.S. Department of Transportation. June 1997 - June 1999 Mathematical Statistician, Statistical Engineering Division National Institute of Standards and Technology. June 1993 - May 1997 Associate Professor, Department of Statistics Carnegie Mellon University. Aug. 1987 - May, 1993 Assistant Professor, Department of Statistics Carnegie Mellon University. Sept. 1986 - July 1987 Visiting Assistant Lecturer, Department of Pure Mathematics and Mathematical Statistics, University of Cambridge. July 1984 - Aug. 1986 NSF Postdoctoral Research Fellow, Department of Statistics, University of California, Berkeley. 1 MONOGRAPHS: 1. Banks, D., Rios, J., and R´ıos Insua, D. (2015).