STATISTICAL SCIENCE Volume 36, Number 3 August 2021
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Report on Statistical Disclosure Limitation Methodology
STATISTICAL POLICY WORKING PAPER 22 (Second version, 2005) Report on Statistical Disclosure Limitation Methodology Federal Committee on Statistical Methodology Originally Prepared by Subcommittee on Disclosure Limitation Methodology 1994 Revised by Confidentiality and Data Access Committee 2005 Statistical and Science Policy Office of Information and Regulatory Affairs Office of Management and Budget December 2005 The Federal Committee on Statistical Methodology (December 2005) Members Brian A. Harris-Kojetin, Chair, Office of William Iwig, National Agricultural Management and Budget Statistics Service Wendy L. Alvey, Secretary, U.S. Census Arthur Kennickell, Federal Reserve Board Bureau Nancy J. Kirkendall, Energy Information Lynda Carlson, National Science Administration Foundation Susan Schechter, Office of Management and Steven B. Cohen, Agency for Healthcare Budget Research and Quality Rolf R. Schmitt, Federal Highway Steve H. Cohen, Bureau of Labor Statistics Administration Lawrence H. Cox, National Center for Marilyn Seastrom, National Center for Health Statistics Education Statistics Robert E. Fay, U.S. Census Bureau Monroe G. Sirken, National Center for Health Statistics Ronald Fecso, National Science Foundation Nancy L. Spruill, Department of Defense Dennis Fixler, Bureau of Economic Analysis Clyde Tucker, Bureau of Labor Statistics Gerald Gates, U.S. Census Bureau Alan R. Tupek, U.S. Census Bureau Barry Graubard, National Cancer Institute G. David Williamson, Centers for Disease Control and Prevention Expert Consultant Robert Groves, University of Michigan and Joint Program in Survey Methodology Preface The Federal Committee on Statistical Methodology (FCSM) was organized by the Office of Management and Budget (OMB) in 1975 to investigate issues of data quality affecting Federal statistics. Members of the committee, selected by OMB on the basis of their individual expertise and interest in statistical methods, serve in a personal capacity rather than as agency representatives. -
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). -
The American Statistician
This article was downloaded by: [T&F Internal Users], [Rob Calver] On: 01 September 2015, At: 02:24 Publisher: Taylor & Francis Informa Ltd Registered in England and Wales Registered Number: 1072954 Registered office: 5 Howick Place, London, SW1P 1WG The American Statistician Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/utas20 Reviews of Books and Teaching Materials Published online: 27 Aug 2015. Click for updates To cite this article: (2015) Reviews of Books and Teaching Materials, The American Statistician, 69:3, 244-252, DOI: 10.1080/00031305.2015.1068616 To link to this article: http://dx.doi.org/10.1080/00031305.2015.1068616 PLEASE SCROLL DOWN FOR ARTICLE Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content. This article may be used for research, teaching, and private study purposes. -
Biometrical Applications in Biological Sciences-A Review on the Agony for Their Practical Efficiency- Problems and Perspectives
Biometrics & Biostatistics International Journal Review Article Open Access Biometrical applications in biological sciences-A review on the agony for their practical efficiency- Problems and perspectives Abstract Volume 7 Issue 5 - 2018 The effect of the three biological sciences- agriculture, environment and medicine S Tzortzios in the people’s life is of the greatest importance. The chain of the influence of the University of Thessaly, Greece environment to the form and quality of the agricultural production and the effect of both of them to the people’s health and welfare consists in an integrated system Correspondence: S Tzortzios, University of Thessaly, Greece, that is the basic substance of the human life. Agriculture has a great importance in Email [email protected] the World’s economy; however, the available resources for research and technology development are limited. Moreover, the environmental and productive conditions are Received: August 12, 2018 | Published: October 05, 2018 very different from one country to another, restricting the generalized transferring of technology. The statistical methods should play a paramount role to insure both the objectivity of the results of agricultural research as well as the optimum usage of the limited resources. An inadequate or improper use of statistical methods may result in wrong conclusions and in misuse of the available resources with important scientific and economic consequences. Many times, Statistics is used as a basis to justify conclusions of research work without considering in advance the suitability of the statistical methods to be used. The obvious question is: What importance do biological researchers give to the statistical methods? The answer is out of any doubt and the fact that most of the results published in specialized journals includes statistical considerations, confirms its importance. -
STATISTICAL SCIENCE Volume 35, Number 1 February 2020
STATISTICAL SCIENCE Volume 35, Number 1 February 2020 Special Issue on Statistics and Science IntroductiontotheSpecialIssue............................................................. 1 Model-Based Approach to the Joint Analysis of Single-Cell Data on Chromatin Accessibility andGeneExpression................Zhixiang Lin, Mahdi Zamanighomi, Timothy Daley, Shining Ma and Wing Hung Wong 2 Risk Models for Breast Cancer and Their Validation . Adam R. Brentnall and Jack Cuzick 14 SomeStatisticalIssuesinClimateScience..................................Michael L. Stein 31 A Tale of Two Parasites: Statistical Modelling to Support Disease Control Programmes in Africa............Peter J. Diggle, Emanuele Giorgi, Julienne Atsame, Sylvie Ntsame Ella, Kisito Ogoussan and Katherine Gass 42 QuantumScienceandQuantumTechnology..................Yazhen Wang and Xinyu Song 51 StatisticalMethodologyinSingle-MoleculeExperiments............Chao Du and S. C. Kou 75 Statistical Molecule Counting in Super-Resolution Fluorescence Microscopy: Towards QuantitativeNanoscopy..........Thomas Staudt, Timo Aspelmeier, Oskar Laitenberger, Claudia Geisler, Alexander Egner and Axel Munk 92 Data Denoising and Post-Denoising Corrections in Single Cell RNA Sequencing ...................................Divyansh Agarwal, Jingshu Wang and Nancy R. Zhang 112 Statistical Inference for the Evolutionary History of Cancer Genomes .....Khanh N. Dinh, Roman Jaksik, Marek Kimmel, Amaury Lambert and Simon Tavaré 129 Maximum Independent Component Analysis with Application to EEG Data ........................................Ruosi -
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. -
A Matching Based Theoretical Framework for Estimating Probability of Causation
A Matching Based Theoretical Framework for Estimating Probability of Causation Tapajit Dey and Audris Mockus Department of Electrical Engineering and Computer Science University of Tennessee, Knoxville Abstract The concept of Probability of Causation (PC) is critically important in legal contexts and can help in many other domains. While it has been around since 1986, current operationalizations can obtain only the minimum and maximum values of PC, and do not apply for purely observational data. We present a theoretical framework to estimate the distribution of PC from experimental and from purely observational data. We illustrate additional problems of the existing operationalizations and show how our method can be used to address them. We also provide two illustrative examples of how our method is used and how factors like sample size or rarity of events can influence the distribution of PC. We hope this will make the concept of PC more widely usable in practice. Keywords: Probability of Causation, Causality, Cause of Effect, Matching 1. Introduction Our understanding of the world comes from our knowledge about the cause-effect relationship between various events. However, in most of the real world scenarios, one event is caused by multiple other events, having arXiv:1808.04139v1 [stat.ME] 13 Aug 2018 varied degrees of influence over the first event. There are two major concepts associated with such relationships: Cause of Effect (CoE) and Effect of Cause (EoC) [1, 2]. EoC focuses on the question that if event X is the cause/one of the causes of event Y, then what is the probability of event Y given event Email address: [email protected], [email protected] (Tapajit Dey and Audris Mockus) Preprint submitted to arXiv July 2, 2019 X is observed? CoE, on the other hand, tries to answer the question that if event X is known to be (one of) the cause(s) of event Y, then given we have observed both events X and Y, what is the probability that event X was in fact the cause of event Y? In this paper, we focus on the CoE scenario. -
Statistical Inference Bibliography 1920-Present 1. Pearson, K
StatisticalInferenceBiblio.doc © 2006, Timothy G. Gregoire, Yale University http://www.yale.edu/forestry/gregoire/downloads/stats/StatisticalInferenceBiblio.pdf Last revised: July 2006 Statistical Inference Bibliography 1920-Present 1. Pearson, K. (1920) “The Fundamental Problem in Practical Statistics.” Biometrika, 13(1): 1- 16. 2. Edgeworth, F.Y. (1921) “Molecular Statistics.” Journal of the Royal Statistical Society, 84(1): 71-89. 3. Fisher, R. A. (1922) “On the Mathematical Foundations of Theoretical Statistics.” Philosophical Transactions of the Royal Society of London, Series A, Containing Papers of a Mathematical or Physical Character, 222: 309-268. 4. Neyman, J. and E. S. Pearson. (1928) “On the Use and Interpretation of Certain Test Criteria for Purposes of Statistical Inference: Part I.” Biometrika, 20A(1/2): 175-240. 5. Fisher, R. A. (1933) “The Concepts of Inverse Probability and Fiducial Probability Referring to Unknown Parameters.” Proceedings of the Royal Society of London, Series A, Containing Papers of Mathematical and Physical Character, 139(838): 343-348. 6. Fisher, R. A. (1935) “The Logic of Inductive Inference.” Journal of the Royal Statistical Society, 98(1): 39-82. 7. Fisher, R. A. (1936) “Uncertain inference.” Proceedings of the American Academy of Arts and Sciences, 71: 245-258. 8. Berkson, J. (1942) “Tests of Significance Considered as Evidence.” Journal of the American Statistical Association, 37(219): 325-335. 9. Barnard, G. A. (1949) “Statistical Inference.” Journal of the Royal Statistical Society, Series B (Methodological), 11(2): 115-149. 10. Fisher, R. (1955) “Statistical Methods and Scientific Induction.” Journal of the Royal Statistical Society, Series B (Methodological), 17(1): 69-78. -
Kshitij Khare
Kshitij Khare Basic Information Mailing Address: Telephone Numbers: Internet: Department of Statistics Office: (352) 273-2985 E-mail: [email protected]fl.edu 103 Griffin Floyd Hall FAX: (352) 392-5175 Web: http://www.stat.ufl.edu/˜kdkhare/ University of Florida Gainesville, FL 32611 Education PhD in Statistics, 2009, Stanford University (Advisor: Persi Diaconis) Masters in Mathematical Finance, 2009, Stanford University Masters in Statistics, 2004, Indian Statistical Institute, India Bachelors in Statistics, 2002, Indian Statistical Institute, India Academic Appointments University of Florida: Associate Professor of Statistics, 2015-present University of Florida: Assistant Professor of Statistics, 2009-2015 Stanford University: Research/Teaching Assistant, Department of Statistics, 2004-2009 Research Interests High-dimensional covariance/network estimation using graphical models High-dimensional inference for vector autoregressive models Markov chain Monte Carlo methods Kshitij Khare 2 Publications Core Statistics Research Ghosh, S., Khare, K. and Michailidis, G. (2019). “High dimensional posterior consistency in Bayesian vector autoregressive models”, Journal of the American Statistical Association 114, 735-748. Khare, K., Oh, S., Rahman, S. and Rajaratnam, B. (2019). A scalable sparse Cholesky based approach for learning high-dimensional covariance matrices in ordered data, Machine Learning 108, 2061-2086. Cao, X., Khare, K. and Ghosh, M. (2019). “High-dimensional posterior consistency for hierarchical non- local priors in regression”, Bayesian Analysis 15, 241-262. Chakraborty, S. and Khare, K. (2019). “Consistent estimation of the spectrum of trace class data augmen- tation algorithms”, Bernoulli 25, 3832-3863. Cao, X., Khare, K. and Ghosh, M. (2019). “Posterior graph selection and estimation consistency for high- dimensional Bayesian DAG models”, Annals of Statistics 47, 319-348. -
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. -
ALEXANDER PHILIP DAWID Emeritus Professor of Statistics University of Cambridge E-Mail: [email protected] Web
ALEXANDER PHILIP DAWID Emeritus Professor of Statistics University of Cambridge E-mail: [email protected] Web: http://www.statslab.cam.ac.uk/∼apd EDUCATION 1967{69 University of London (Imperial College; University College) 1963{67 Cambridge University (Trinity Hall; Darwin College) 1956{63 City of London School QUALIFICATIONS 1982 ScD (Cantab.) 1970 MA (Cantab.) 1967 Diploma in Mathematical Statistics (Cantab.: Distinction) 1966 BA (Cantab.): Mathematics (Second Class Honours) 1963 A-levels: Mathematics and Advanced Mathematics (Double Distinction), Physics (Distinction) State Scholarship HONOURS AND AWARDS 2018 Fellow of the Royal Society 2016 Fellow of the International Society for Bayesian Analysis 2015 Honorary Lifetime Member, International Society for Bayesian Analysis 2013 Network Scholar of The MacArthur Foundation Research Network on Law and Neuroscience 2002 DeGroot Prize for a Published Book in Statistical Science 2001 Royal Statistical Society: Guy Medal in Silver 1978 Royal Statistical Society: Guy Medal in Bronze 1977 G. W. Snedecor Award for Best Publication in Biometry EMPLOYMENT ETC. 2013{ Emeritus Professor of Statistics, Cambridge University 2013{ Emeritus Fellow, Darwin College Cambridge 2007{13 Professor of Statistics and Director of the Cambridge Statistics Initiative, Cambridge University 2011{13 Director of Studies in Mathematics, Darwin College Cambridge 2007{13 Professorial Fellow, Darwin College Cambridge 1989{2007 Pearson Professor of Statistics, University College London 1983{93 Head, Department of Statistical Science, University College London 1982{89 Professor of Probability and Statistics, University College London 1981{82 Reader in Probability and Statistics, University College London 1978{81 Professor of Statistics, Head of Statistics Section, and Director of the Statistical Laboratory, Department of Mathematics, The City University, London 1969{78 Lecturer in Statistics, University College London VISITING POSITIONS ETC. -
Theory and Applications of Proper Scoring Rules
Theory and Applications of Proper Scoring Rules A. Philip Dawid, University of Cambridge and Monica Musio, Università di Cagliari Abstract We give an overview of some uses of proper scoring rules in statistical inference, including frequentist estimation theory and Bayesian model selection with improper priors. 1 Introduction The theory of proper scoring rules (PSRs) originated as an approach to probability forecasting (Dawid 1986), the general enterprise of forming and evaluating probabilistic predictions. The fundamental idea is to develop ways of motivating a forecaster to be honest in the predictions he announces, and of assessing the performance of announced probabilities in the light of the outcomes that eventuate. The early applications of this methodology were to meteorology (Brier 1950) and subjective Bayesianism (Good 1952; de Finetti 1975). However, it is becoming clear that the mathematical theory of PSRs has a wide range of other applications in Statistics generally. The aim of this paper is to give a brief outline of some of these. Some further details will appear in Dawid et al. (2014). After setting out the basic ideas and properties of PSRs in § 2, in § 3 we describe a number of important special cases. Section 4 describes how a PSR supplies an alternative to the likelihood function of a statistical model, and how this can be used to develop new estimators that may have useful properties of robustness and/or computational feasability. In particular, in § 5 we show that, by using an appropriate PSR, it is possible to avoid difficulties associated with intractable normalising constants. In § 6 the same ability to ignore a normalising constant is shown to supply an approach to Bayesian model selection with improper priors.