Bu-821-M a Partial Bibliography on Statistical Design
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Alberto Abadie
ALBERTO ABADIE Office Address Massachusetts Institute of Technology Department of Economics 50 Memorial Drive Building E52, Room 546 Cambridge, MA 02142 E-mail: [email protected] Academic Positions Massachusetts Institute of Technology Cambridge, MA Professor of Economics, 2016-present IDSS Associate Director, 2016-present Harvard University Cambridge, MA Professor of Public Policy, 2005-2016 Visiting Professor of Economics, 2013-2014 Associate Professor of Public Policy, 2004-2005 Assistant Professor of Public Policy, 1999-2004 University of Chicago Chicago, IL Visiting Assistant Professor of Economics, 2002-2003 National Bureau of Economic Research (NBER) Cambridge, MA Research Associate (Labor Studies), 2009-present Faculty Research Fellow (Labor Studies), 2002-2009 Non-Academic Positions Amazon.com, Inc. Seattle, WA Academic Research Consultant, 2020-present Education Massachusetts Institute of Technology Cambridge, MA Ph.D. in Economics, 1995-1999 Thesis title: \Semiparametric Instrumental Variable Methods for Causal Response Mod- els." Centro de Estudios Monetarios y Financieros (CEMFI) Madrid, Spain M.A. in Economics, 1993-1995 Masters Thesis title: \Changes in Spanish Labor Income Structure during the 1980's: A Quantile Regression Approach." 1 Universidad del Pa´ıs Vasco Bilbao, Spain B.A. in Economics, 1987-1992 Specialization Areas: Mathematical Economics and Econometrics. Honors and Awards Elected Fellow of the Econometric Society, 2016. NSF grant SES-1756692, \A General Synthetic Control Framework of Estimation and Inference," 2018-2021. NSF grant SES-0961707, \A General Theory of Matching Estimation," with G. Imbens, 2010-2012. NSF grant SES-0617810, \The Economic Impact of Terrorism: Lessons from the Real Estate Office Markets of New York and Chicago," with S. Dermisi, 2006-2008. -
Miguel De Carvalho
School of Mathematics Miguel de Carvalho Contact M. de Carvalho T: +44 (0) 0131 650 5054 Information The University of Edinburgh B: [email protected] School of Mathematics : mb.carvalho Edinburgh EH9 3FD, UK +: www.maths.ed.ac.uk/ mdecarv Personal Born September 20, 1980 in Montijo, Lisbon. Details Portuguese and EU citizenship. Interests Applied Statistics, Biostatistics, Econometrics, Risk Analysis, Statistics of Extremes. Education Universidade de Lisboa, Portugal Habilitation in Probability and Statistics, 2019 Thesis: Statistical Modeling of Extremes Universidade Nova de Lisboa, Portugal PhD in Mathematics with emphasis on Statistics, 2009 Thesis: Extremum Estimators and Stochastic Optimization Advisors: Manuel Esqu´ıvel and Tiago Mexia Advisors of Advisors: Jean-Pierre Kahane and Tiago de Oliveira. Nova School of Business and Economics (Triple Accreditation), Portugal MSc in Economics, 2009 Thesis: Mean Regression for Censored Length-Biased Data Advisors: Jos´eA. F. Machado and Pedro Portugal Advisors of Advisors: Roger Koenker and John Addison. Universidade Nova de Lisboa, Portugal `Licenciatura'y in Mathematics, 2004 Professional Probation Period: Statistics Portugal (Instituto Nacional de Estat´ıstica). Awards & ISBA (International Society for Bayesian Analysis) Honours Lindley Award, 2019. TWAS (Academy of Sciences for the Developing World) Young Scientist Prize, 2015. International Statistical Institute Elected Member, 2014. American Statistical Association Young Researcher Award, Section on Risk Analysis, 2011. National Institute of Statistical Sciences j American Statistical Association Honorary Mention as a Finalist NISS/ASA Best y-BIS Paper Award, 2010. Portuguese Statistical Society (Sociedade Portuguesa de Estat´ıstica) Young Researcher Award, 2009. International Association for Statistical Computing ERS IASC Young Researcher Award, 2008. 1 of 13 p l e t t si a o e igulctoson Applied Statistical ModelingPublications 1. -
A Nonparametric Estimator of Heterogeneity Variance with Applications to SMR- and Proportion-Data
Biometrical Journal 42 )2000) 3, 321±334 A Nonparametric Estimator of Heterogeneity Variance with Applications to SMR- and Proportion-Data Dankmar BoÈhning Department of Epidemiology Institute for Social Medicine and Medical Psychology Free University Berlin Germany Jesus Sarol Jr. Department of Epidemiology and Biostatistics The College of Public Health University of the Philippines Manila Philippines Summary In this paper the situation of extra population heterogeneity is discussed from a analysis of variance point of view. We first provide a non-iterative way of estimating the variance of the heterogeneity distribution without estimating the heterogeneity distribution itself for Poisson and binomial counts. The consequences of the presence of heterogeneity in the estimation of the mean are discussed. We show that if the homogeneity assumption holds, the pooled mean is optimal while in the presence of strong heterogeneity, the simple )arithmetic) mean is an optimal estimator of the mean SMR or mean proportion. These results lead to the problem of finding an optimal estimator for situations not repre- sented by these two extreme cases. We propose an iterative solution to this problem. Illustrations for the application of these findings are provided with examples from various areas. Key words: Population heterogeneity; Random effects model; Moment estimator; Variance separation; Confidence interval estimation adjusted for unob- served heterogeneity. 1. Introduction In a variety of biometric applications the situation of extra-population heteroge- neity occurs. In particular, this is the case if there is good reason to model the variable of interest Y through a density of parametric form p)y, q) with a scalar parameter q. -
A Simple Method for Correcting for the Will Rogers Phenomenon with Biometrical Applications
University of Plymouth PEARL https://pearl.plymouth.ac.uk Faculty of Science and Engineering School of Engineering, Computing and Mathematics 2020-01-20 A simple method for correcting for the Will Rogers phenomenon with biometrical applications Stander, M http://hdl.handle.net/10026.1/15227 10.1002/bimj.201900199 Biometrical Journal: journal of mathematical methods in biosciences Wiley-VCH Verlag All content in PEARL is protected by copyright law. Author manuscripts are made available in accordance with publisher policies. Please cite only the published version using the details provided on the item record or document. In the absence of an open licence (e.g. Creative Commons), permissions for further reuse of content should be sought from the publisher or author. Biometrical Journal 52 (2010) 61, zzz–zzz / DOI: 10.1002/bimj.200100000 A simple method for correcting for the Will Rogers phenomenon with biometrical applications Mark Stander∗ 1 and Julian Stander 2 1 BAE Systems 6 Carlton Gardens LONDON SW1Y 5AD United Kingdom Email: [email protected] 2 School of Engineering, Computing and Mathematics University of Plymouth Drake Circus PLYMOUTH PL4 8AA United Kingdom Email: [email protected] Received zzz, revised zzz, accepted zzz In its basic form the Will Rogers phenomenon takes place when an increase in the average value of each of two sets is achieved by moving an element from one set to another. This leads to the conclusion that there has been an improvement, when in fact essentially nothing has changed. Extended versions of this phenomenon can occur in epidemiological studies, rendering their results unreliable. -
Department of Statistics and Data Science Promotion and Tenure Guidelines
Approved by Department 12/05/2013 Approved by Faculty Relations May 20, 2014 UFF Notified May 21, 2014 Effective Spring 2016 2016-17 Promotion Cycle Department of Statistics and Data Science Promotion and Tenure Guidelines The purpose of these guidelines is to give explicit definitions of what constitutes excellence in teaching, research and service for tenure-earning and tenured faculty. Research: The most common outlet for scholarly research in statistics is in journal articles appearing in refereed publications. Based on the five-year Impact Factor (IF) from the ISI Web of Knowledge Journal Citation Reports, the top 50 journals in Probability and Statistics are: 1. Journal of Statistical Software 26. Journal of Computational Biology 2. Econometrica 27. Annals of Probability 3. Journal of the Royal Statistical Society 28. Statistical Applications in Genetics and Series B – Statistical Methodology Molecular Biology 4. Annals of Statistics 29. Biometrical Journal 5. Statistical Science 30. Journal of Computational and Graphical Statistics 6. Stata Journal 31. Journal of Quality Technology 7. Biostatistics 32. Finance and Stochastics 8. Multivariate Behavioral Research 33. Probability Theory and Related Fields 9. Statistical Methods in Medical Research 34. British Journal of Mathematical & Statistical Psychology 10. Journal of the American Statistical 35. Econometric Theory Association 11. Annals of Applied Statistics 36. Environmental and Ecological Statistics 12. Statistics in Medicine 37. Journal of the Royal Statistical Society Series C – Applied Statistics 13. Statistics and Computing 38. Annals of Applied Probability 14. Biometrika 39. Computational Statistics & Data Analysis 15. Chemometrics and Intelligent Laboratory 40. Probabilistic Engineering Mechanics Systems 16. Journal of Business & Economic Statistics 41. -
Rank Full Journal Title Journal Impact Factor 1 Journal of Statistical
Journal Data Filtered By: Selected JCR Year: 2019 Selected Editions: SCIE Selected Categories: 'STATISTICS & PROBABILITY' Selected Category Scheme: WoS Rank Full Journal Title Journal Impact Eigenfactor Total Cites Factor Score Journal of Statistical Software 1 25,372 13.642 0.053040 Annual Review of Statistics and Its Application 2 515 5.095 0.004250 ECONOMETRICA 3 35,846 3.992 0.040750 JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION 4 36,843 3.989 0.032370 JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY 5 25,492 3.965 0.018040 STATISTICAL SCIENCE 6 6,545 3.583 0.007500 R Journal 7 1,811 3.312 0.007320 FUZZY SETS AND SYSTEMS 8 17,605 3.305 0.008740 BIOSTATISTICS 9 4,048 3.098 0.006780 STATISTICS AND COMPUTING 10 4,519 3.035 0.011050 IEEE-ACM Transactions on Computational Biology and Bioinformatics 11 3,542 3.015 0.006930 JOURNAL OF BUSINESS & ECONOMIC STATISTICS 12 5,921 2.935 0.008680 CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 13 9,421 2.895 0.007790 MULTIVARIATE BEHAVIORAL RESEARCH 14 7,112 2.750 0.007880 INTERNATIONAL STATISTICAL REVIEW 15 1,807 2.740 0.002560 Bayesian Analysis 16 2,000 2.696 0.006600 ANNALS OF STATISTICS 17 21,466 2.650 0.027080 PROBABILISTIC ENGINEERING MECHANICS 18 2,689 2.411 0.002430 BRITISH JOURNAL OF MATHEMATICAL & STATISTICAL PSYCHOLOGY 19 1,965 2.388 0.003480 ANNALS OF PROBABILITY 20 5,892 2.377 0.017230 STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT 21 4,272 2.351 0.006810 JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS 22 4,369 2.319 0.008900 STATISTICAL METHODS IN -
An Information Theoretical Method for Analyzing Unreplicated Designs with Binary Response
REVSTAT – Statistical Journal Volume 17, Number 3, July 2019, 383–399 AN INFORMATION THEORETICAL METHOD FOR ANALYZING UNREPLICATED DESIGNS WITH BINARY RESPONSE Authors: Krystallenia Drosou – Department of Mathematics, National Technical University of Athens, Athens, Greece [email protected] Christos Koukouvinos – Department of Mathematics, National Technical University of Athens, Athens, Greece [email protected] Received: February 2017 Revised: March 2017 Accepted: April 2017 Abstract: • The analysis of unreplicated factorial designs constitutes a challenging but difficult issue since there are no degrees of freedom so as to estimate the error variance. In the present paper we propose a method for screening active effects in such designs, as- suming Bernoulli distributed data rather than linear; something that hasn’t received much attention yet. Specifically, we develop an innovating algorithm based on an information theoretical measure, the well-known symmetrical uncertainty, so that it can measure the relation between the response variable and each factor separately. The powerfulness of the proposed method is revealed via both, a thorough simulation study and a real data set analysis. Key-Words: • two-level factorial designs; unreplicated experiments; generalized linear models; symmetrical uncertainty. AMS Subject Classification: • 62-07, 62K15, 62J12. 384 Krystallenia Drosou and Christos Koukouvinos Analysis of Unreplicated Designs with Binary Response 385 1. INTRODUCTION Factorial designs constitute a powerful tool especially in screening experi- ments where the goal is to identify the factors with a significant impact on the response of interest. Although two-level factorial designs are commonly used as experimental plans, the number of runs grows exponentially as the number of factors increases; thus, in case when the replication of the experiment is pro- hibitive due to economical or technical issues, unreplicated designs constitute an appropriate choice. -
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. -
Ten Things We Should Know About Time Series
KIER DISCUSSION PAPER SERIES KYOTO INSTITUTE OF ECONOMIC RESEARCH Discussion Paper No.714 “Great Expectatrics: Great Papers, Great Journals, Great Econometrics” Michael McAleer August 2010 KYOTO UNIVERSITY KYOTO, JAPAN Great Expectatrics: Great Papers, Great Journals, Great Econometrics* Chia-Lin Chang Department of Applied Economics National Chung Hsing University Taichung, Taiwan Michael McAleer Econometric Institute Erasmus School of Economics Erasmus University Rotterdam and Tinbergen Institute The Netherlands and Institute of Economic Research Kyoto University Japan Les Oxley Department of Economics and Finance University of Canterbury New Zealand Rervised: August 2010 * The authors wish to thank Dennis Fok, Philip Hans Franses and Jan Magnus for helpful discussions. For financial support, the first author acknowledges the National Science Council, Taiwan; the second author acknowledges the Australian Research Council, National Science Council, Taiwan, and the Japan Society for the Promotion of Science; and the third author acknowledges the Royal Society of New Zealand, Marsden Fund. 1 Abstract The paper discusses alternative Research Assessment Measures (RAM), with an emphasis on the Thomson Reuters ISI Web of Science database (hereafter ISI). The various ISI RAM that are calculated annually or updated daily are defined and analysed, including the classic 2-year impact factor (2YIF), 5-year impact factor (5YIF), Immediacy (or zero-year impact factor (0YIF)), Eigenfactor score, Article Influence, C3PO (Citation Performance Per Paper Online), h-index, Zinfluence, and PI-BETA (Papers Ignored - By Even The Authors). The ISI RAM data are analysed for 8 leading econometrics journals and 4 leading statistics journals. The application to econometrics can be used as a template for other areas in economics, for other scientific disciplines, and as a benchmark for newer journals in a range of disciplines. -
Curriculum Vitae
Curriculum Vitae PERSONAL INFORMATION Norbert Benda WORK EXPERIENCE February 2010- Present Head of Biostatistics and Special Pharmacokinetics Unit BfArM (Federal Institute for Drugs and Medical Devices) (Germany) Managing Biostatistics and Special Pharmacokinetics Unit, biostatistical assessment of European and national German drug applications, biostatistical support in scientific advices, biostatistical input to guidelines, biostatistical input to clinical and methodological research projects November 2006-January 2010 (Senior) Expert Statistical Methodologist Novartis Pharma AG (Switzerland) Development and application of novel statistical methodologies (dose-finding, adaptive designs, longitudinal data analysis, missing data, optimal design), methodological support for Trial and Program Statisticians, clinical scenario evaluations (simulations) for clinical trial planning, internal training courses in statistics July 1997-October 2006 Statistician Schering AG (Germany) Statistical design and analysis of clinical studies (phase I - IV), statistical consultant for preclinical and clinical development, project statistician for international drug development projects, member of the Global Pharmacometrics Team October 1993-July 1997 Research Assistant University of Tübingen, Department of Medical Biometry (Germany) Statistical consultant in statistics for medical PhD students and researchers, lectures in medical statistics, design and evaluation of medical studies, research projects in ophthalmology April 1989-September 1993 Research Assistant -
HHS Public Access Author Manuscript
HHS Public Access Author manuscript Author Manuscript Author ManuscriptBiometrika Author Manuscript. Author manuscript; Author Manuscript available in PMC 2016 June 01. Published in final edited form as: Biometrika. 2015 June ; 102(2): 281–294. doi:10.1093/biomet/asv011. On random-effects meta-analysis D. ZENG and D. Y. LIN Department of Biostatistics, CB #7420, University of North Carolina, Chapel Hill, North Carolina 27599, U.S.A D. ZENG: [email protected]; D. Y. LIN: [email protected] Summary Meta-analysis is widely used to compare and combine the results of multiple independent studies. To account for between-study heterogeneity, investigators often employ random-effects models, under which the effect sizes of interest are assumed to follow a normal distribution. It is common to estimate the mean effect size by a weighted linear combination of study-specific estimators, with the weight for each study being inversely proportional to the sum of the variance of the effect-size estimator and the estimated variance component of the random-effects distribution. Because the estimator of the variance component involved in the weights is random and correlated with study-specific effect-size estimators, the commonly adopted asymptotic normal approximation to the meta-analysis estimator is grossly inaccurate unless the number of studies is large. When individual participant data are available, one can also estimate the mean effect size by maximizing the joint likelihood. We establish the asymptotic properties of the meta-analysis estimator and the joint maximum likelihood estimator when the number of studies is either fixed or increases at a slower rate than the study sizes and we discover a surprising result: the former estimator is always at least as efficient as the latter. -
Professor Robert Kohn
Professor Robert Kohn Curriculum Vitae Education Ph.D. Econometrics, Australian National University. Master of Economics, Australian National University. Academic Experience 2003 to present. Professor,Australian School of Business. Joint appointment between School of Economics and School of Banking and Finance. 1987 to 2003 Professor, Australian Graduate School of Management, University of New South Wales 1985-1987 Associate Professor, Australian Graduate School of Management, University of NSW 1982-1986 Associate Professor of Statistics, Graduate School of Business, University of Chicago 1979 -1982 Assistant Professor in Statistics, Graduate School of Business, University of Chicago Related Academic Experience 1991-2002 Head, Statistics and Operations Group, Australian Graduate School of Management, University New South Wales 1995-January 2002. Director, PhD Program Australian Graduate School of Management 1996-1998 Director of Research, Australian Graduate School of Management 1998- 2002 Associate Dean for Research, Australian Graduate School of Management 1998 to 2002 1 Member of the Committee on Research, University of New South Wales 1992 to 2002 Member, Higher Degree Committee, Australian Graduate School of Management 1991 to 2002 Member, Executive Committee, Australian Graduate School of Management. Awards Fellow of the Journal of Econometrics 2000 Fellow of the Institute of Mathematical Statistics 2002 Fellow of the American Statistical Association 2005 Scientia Professor, University of New South Wales, 2007-2012 Fellow Academy Social Sciences of Australia 2007 Associate Editor. 1996- Australia and New Zealand Journal of Statistics Associate Editor 1999-2001. Journal of the American Statistical Association. Associate Editor 2004- Bayesian Analysis Journal Associate Editor 2006 -2007 Sankhya Professional Affiliations Member of American Statistical Association, Econometric Society, Institute of Mathematical Statistics, Royal Statistical Society and Australian Statistical Society.