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Generic Law on Official Statistics for Latin America
Generic Law on Official Statistics for Latin America Statistical Conference of the Americas of ECLAC Thank you for your interest in this ECLAC publication ECLAC Publications Please register if you would like to receive information on our editorial products and activities. When you register, you may specify your particular areas of interest and you will gain access to our products in other formats. www.cepal.org/en/publications ublicaciones www.cepal.org/apps Generic Law on Official Statistics for Latin America Statistical Conference of the Americas of ECLAC The Generic Law on Official Statistics for Latin America was adopted by the Statistical Conference of the Americas of the Economic Commission for Latin America and the Caribbean at its tenth meeting, held in Santiago from 19 to 21 November 2019. Thanks are conveyed to the Inter-American Development Bank (IDB) for its support in the preparation of this document. IDB provided consultants who were involved throughout the process and financial support for the organization of the Regional workshop on legal frameworks for the production of official statistics, held in Bogotá, from 3 to 5 July 2018, and for the meeting at which the final text was discussed, in San Salvador, on 29 and 30 August 2019. United Nations publication LC/CEA.10/8 Distribution: L Copyright © United Nations, 2020 All rights reserved Printed at United Nations, Santiago S.20-00045 This publication should be cited as: Economic Commission for Latin America and the Caribbean (ECLAC), Generic Law on Official Statistics for Latin America (LC/CEA.10/8), Santiago, 2020. Applications for authorization to reproduce this work in whole or in part should be sent to the Economic Commission for Latin America and the Caribbean (ECLAC), Publications and Web Services Division, publicaciones. -
United Nations Fundamental Principles of Official Statistics
UNITED NATIONS United Nations Fundamental Principles of Official Statistics Implementation Guidelines United Nations Fundamental Principles of Official Statistics Implementation guidelines (Final draft, subject to editing) (January 2015) Table of contents Foreword 3 Introduction 4 PART I: Implementation guidelines for the Fundamental Principles 8 RELEVANCE, IMPARTIALITY AND EQUAL ACCESS 9 PROFESSIONAL STANDARDS, SCIENTIFIC PRINCIPLES, AND PROFESSIONAL ETHICS 22 ACCOUNTABILITY AND TRANSPARENCY 31 PREVENTION OF MISUSE 38 SOURCES OF OFFICIAL STATISTICS 43 CONFIDENTIALITY 51 LEGISLATION 62 NATIONAL COORDINATION 68 USE OF INTERNATIONAL STANDARDS 80 INTERNATIONAL COOPERATION 91 ANNEX 98 Part II: Implementation guidelines on how to ensure independence 99 HOW TO ENSURE INDEPENDENCE 100 UN Fundamental Principles of Official Statistics – Implementation guidelines, 2015 2 Foreword The Fundamental Principles of Official Statistics (FPOS) are a pillar of the Global Statistical System. By enshrining our profound conviction and commitment that offi- cial statistics have to adhere to well-defined professional and scientific standards, they define us as a professional community, reaching across political, economic and cultural borders. They have stood the test of time and remain as relevant today as they were when they were first adopted over twenty years ago. In an appropriate recognition of their significance for all societies, who aspire to shape their own fates in an informed manner, the Fundamental Principles of Official Statistics were adopted on 29 January 2014 at the highest political level as a General Assembly resolution (A/RES/68/261). This is, for us, a moment of great pride, but also of great responsibility and opportunity. In order for the Principles to be more than just a statement of noble intentions, we need to renew our efforts, individually and collectively, to make them the basis of our day-to-day statistical work. -
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). -
World Health Statistics 2007
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Jacob Oleson
JACOB J. OLESON The University of Iowa College of Public Health Department of Biostatistics Iowa City, IA 52242 [email protected] Citizenship: USA EDUCATION: Ph.D. Statistics University of Missouri – Columbia 2002 M.A. Statistics University of Missouri – Columbia 1999 B.A. Mathematics Central College (Pella, Iowa) 1997 EMPLOYMENT: 2015 – present Director of Graduate Studies, Department of Biostatistics, The University of Iowa 2014 – present Director – Center for Public Health Statistics – College of Public Health – The University of Iowa 2012 – present Associate Professor – Department of Biostatistics – College of Public Health – The University of Iowa 2004 – 2012 Assistant Professor – Department of Biostatistics – College of Public Health – The University of Iowa 2002 – 2004 Assistant Professor – Department of Mathematics & Statistics – Arizona State University 2000 – 2002 Research Assistant – University of Missouri - Columbia; Missouri Department of Conservation 1999 – 2002 Research Assistant – University of Missouri - Columbia; Biostatistics. 1997 – 2000 Graduate Instructor – University of Missouri - Columbia COURSES TAUGHT: The University of Iowa • 171:161 – Introduction to Biostatistics (3 terms) • 171:162 – Introduction to Biostatistics WWW (online) (4 terms; supervisor 10 terms) • 171:162 – Design and Analysis of Biomedical Studies (6 terms; supervisor 6 terms) • 171:241 – Applied Categorical Data Analysis (2 terms) • 171:202 – Biostatistical Methods II (3 terms) • 171:281 – Independent Study (Spatial Statistics, Survey Statistics, -
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. -
Official Statistics for the Next Decade-- Methodological Issues and Challenges
Official Statistics for the Next Decade-- Methodological Issues and Challenges Danny Pfeffermann Conference on New Techniques and Technologies for Statistics (NTTS) March, 2015 1 List of tough challenges A- Collection and management of big data for POS √ B- Integration of computer science for POS from big data C- Data accessibility, privacy and confidentiality D- Possible use of Internet panels √ E- How to deal with mode effects √ G- Future censuses and small area estimation √ F- Integration of statistics and geospatial information. Ques. Are Universities preparing students for NSOs? √ 2 Collection and management of big data for POS Exp. 1. Count of number of vehicles crossing road sections. Presently done in a very primitive way. Why not get the information and much more from cell phone companies? Available in principle for each time point. Exp. 2. Use the BPP, based on 5 million commodities sold on line to predict the CPI requires two costly surveys. 3 Big Data Big Problems Big headache Coverage/selection bias (we are talking of POS) Data accessibility New legislation Privacy (data protection) Disclosure control Computer storage Computation and Analysis Linkage of different files Risk of data manipulation 4 Two types of big data Type 1. Data obtained from sensors, cameras, cell phones…, - generally structured and accurate, Type 2. Data obtained from social networks, e-commerce etc.,- diverse, unstructured and appears irregularly. Type 1 measurements available continuously. Should POS publications be mostly in the form of graphs and pictures? If aggregate data needed, how should big data be transformed to monthly aggregates? By sampling? Will random sampling continue playing an important role when processing big data? 5 Other important issues Coverage bias- major concern in use of big data for POS. -
WP1105 Modeling Mortality with a Bayesian Vector
ARC Centre of Excellence in Population Ageing Research Working Paper 2011/5 Modeling Mortality with a Bayesian Vector Autoregression Carolyn Njenga and Michael Sherris* * Njenga is a doctoral student in the School of Risk and Actuarial at the University of New South Wales (UNSW) and a Research Assistant at the ARC Centre of Excellence in Population Ageing Research (CEPAR). Sherris is Professor of Actuarial Studies at UNSW and a CEPAR Chief Investigator. This paper can be downloaded without charge from the ARC Centre of Excellence in Population Ageing Research Working Paper Series available at www.cepar.edu.au Modeling Mortality with a Bayesian Vector Autoregression Carolyn Ndigwako Njenga Australian School of Business University of New South Wales, Sydney, NSW, 2052 Australia Email: [email protected] Michael Sherris Australian School of Business University of New South Wales, Sydney, NSW, 2052 Email: [email protected] 4th March 2011 Abstract Mortality risk models have been developed to capture trends and common fac- tors driving mortality improvement. Multiple factor models take many forms and are often developed and fitted to older ages. In order to capture trends from young ages it is necessary to take into account the richer age structure of mortality im- provement from young ages to middle and then into older ages. The Heligman and Pollard (1980) model is a parametric model which captures the main features of period mortality tables and has parameters that are interpreted according to age range and effect on rates. Although time series techniques have been applied to model parameters in various parametric mortality models, there has been limited analysis of parameter risk using Bayesian techniques. -
Big Data and Modernizing Federal Statistics: Update Bill Bostic Associate Director Economic Programs Directorate
Big Data and Modernizing Federal Statistics: Update Bill Bostic Associate Director Economic Programs Directorate Ron Jarmin Ph.D. Assistant Director, Research and Methodology Directorate August 2015 1 Big Data Trends and Challenges . Trends . Increasingly data-driven economy . Individuals are increasingly mobile . Technology changes data uses . Stakeholder expectations are changing . Agency budgets and staffing remain flat/reduced. The next generation of official statistics . Utilize broad sources of information . Increase granularity, detail, and timeliness . Reduce cost & burden . Maintain confidentiality and security . Measure impact of a global economy . Multi-disciplinary challenges : . Computation, statistics, informatics, social science, policy 2 MIT Workshop Series Objectives . Convene experts – . in computer science, social science, statistics, informatics and business . Explore . challenges to building the next generation of official statistics . Identify . new opportunities for using big data to augment official statistics . core computational and methodological challenges . ongoing research that should inform the Big Data research program 3 Workshop Organizers Series Conveners . Census: Cavan Capps, Ron Prevost . MIT: Micah Altman Workshop conveners . First Workshop: Ron Duych (DOT), Joy Sharp (DOT) . Second Workshop Amy O’Hara, Laura McKenna, Robin Bachman . Third Workshop: Peter Miller, Benjamin Reist, Michael Thieme Workshop Sponsors . William Bostic, Ron Jarmin 4 Workshop Coverage Approach . Examine a set of broad topical questions through a specific case . Link broad issues to specific approach case Acquisition . Link specific challenge of case to broad challenge Topics Big Data Access Retention Challenges Acquisition – Data Sources Using New forms of Information for Official Economic Statistics [August 3-4] Access -- Privacy Challenges Analysis Location Confidentiality and Official Surveys [October 5-6] Analysis – Inference Challenges Transparency and Inference [December 7-8] 5 Preliminary Observations from First Workshop . -
Cooperation Between a Statistical Bureau and an Academic
International Statistical Institute, 56th Session, 2007: Gad Nathan Cooperation between a statistical bureau and an academic department of statistics as a basis for teaching official statistics Gad Nathan Hebrew University, Department of Statistics Mt. Scopus 91905 Jerusalem, Israel E-mail: [email protected] 1. Introduction The teaching of official statistics was for long deemed to be outside the scope of academic curricula and was traditionally confined to the internal training programmes of the official statistical agencies themselves and to those of international agencies. The relevance of academic training in statistics for official statisticians was questioned, for instance, by Bishop (1964), and by Benjamin (1975), who emphasized the need for the training of official statisticians in administrative skills, rather than in statistics. In recent years, however, statistical sciences have developed considerably to cover a wide range of applications, each with its own specific methodology. Some applications, such as econometrics, psychometrics, biostatistics, operations research etc., have become disciplines in their own right and recognized as closely related to classical statistical sciences. The development of a vast range of statistical techniques, developed over the past fifty years, has resulted in a high degree of specialization and this is manifested in the diversity of graduate academic programmes and curricula in statistics. On the other hand, the functions and modes of operation of governmental statistical agencies have changed considerably over the past fifty years. The developments in statistical methodology, especially in sampling techniques and analysis, have shifted the emphasis from a primarily descriptive mode, mostly concerned with the tabulation and simple description of administrative data, to very sophisticated methods of collection by sample surveys, sometimes linked to administrative data, and to advanced methods of analysis, such as time series analysis and categorical data models. -
Working-Paper-Front-Cover.Pages
Assessing the accuracy of response propensities in longitudinal studies CCSR Working Paper 2010-08 Ian Plewis, Sosthenes Ketende, Lisa Calderwood Ian Plewis, Social Statistics, University of Manchester, Manchester M13 9PL, U.K. E-mail: [email protected]. Sosthenes Ketende and Lisa Calderwood, Centre for Longitudinal Studies, Institute of Education, London WC1H 0AL, U.K. The omnipresence of non-response in longitudinal studies is addressed by assessing the accuracy of statistical models constructed to predict different types of non-response. Particular attention is paid to summary measures derived from receiver operating characteristic curves and logit rank plots as ways of assessing accuracy. The ideas are applied to data from the first four waves of the UK Millennium Cohort Study and the results suggest that our ability to discriminate and predict non-response is not high. Changes in socio-economic circumstances do predict wave non-response with implications for the underlying missingness mechanism. Conclusions are drawn in terms of the potential of interventions to prevent non-response and methods of adjusting for it.. www.ccsr.ac.uk Abstract The omnipresence of non-response in longitudinal studies is addressed by assessing the accuracy of statistical models constructed to predict different types of non-response. Particular attention is paid to summary measures derived from receiver operating characteristic curves and logit rank plots as ways of assessing accuracy. The ideas are applied to data from the first four waves of the UK Millennium Cohort Study and the results suggest that our ability to discriminate and predict non-response is not high. Changes in socio-economic circumstances do predict wave non- response with implications for the underlying missingness mechanism.