Statistics on Spotlight: World Statistics Day 2015
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Publications Using the HMD in Years 1997 – 2013
Publications Using the HMD in Years 1997 – 2013 Table of Contents Official Reports ................................................................................................................................................... 1 Books and Book Chapters .............................................................................................................................. 4 Journal Articles ................................................................................................................................................ 12 Dissertations and Theses .............................................................................................................................. 63 Technical Reports, Working, Research and Discussion Papers ......................................................... 67 Introduction The following comprises a list of publications that rely on data from the Human Mortality Database. It resorts to the Google Scholar web search engine1 using “Human mortality database” and “Berkeley mortality database” as the search expressions. The expressions may appear anywhere in the publication (title, abstract, body). Works that used the BMD are identified by “[BMD]” at the end of the citation; all other publications used the HMD. This version of the HMD reference list concentrates on scholarly articles and books, dissertations, technical reports and working papers published from January 1997 up to the end of November 2013. The list also includes all publications by the HMD team members based on analyses of -
Appendix 2: Publications and Other Works Using Data from the Human
Publications Using the HMD or BMD Table of Contents Introduction.............................................................................................................................................1 Official Reports.......................................................................................................................................1 Books and Book Chapters......................................................................................................................2 Journal Articles.......................................................................................................................................8 Dissertations and Theses.....................................................................................................................27 Technical Reports and Working Papers...............................................................................................29 Introduction The following comprises a list of publications that rely on data from the Human Mortality Database (HMD) or the Berkeley Mortality Database (BMD). Works that used the BMD are identified by “[BMD]” at the end of the citation; all other publications used the HMD. This list is probably not a complete list of all publications based on the HMD, as there may be others that remain unknown to us. The publications are grouped into six categories: i) official reports, ii) books and book chapters, iii) journal articles, iv) dissertations and theses, v) technical reports and working papers. Official Reports 1. Balkwill, -
Report on Exact and Statistical Matching Techniques
Statistical Policy Working Papers are a series of technical documents prepared under the auspices of the Office of Federal Statistical Policy and Standards. These documents are the product of working groups or task forces, as noted in the Preface to each report. These Statistical Policy Working Papers are published for the purpose of encouraging further discussion of the technical issues and to stimulate policy actions which flow from the technical findings and recommendations. Readers of Statistical Policy Working Papers are encouraged to communicate directly with the Office of Federal Statistical Policy and Standards with additional views, suggestions, or technical concerns. Office of Joseph W. Duncan Federal Statistical Director Policy Standards For sale by the Superintendent of Documents, U.S. Government Printing Office Washington, D.C. 20402 Statistical Policy Working Paper 5 Report on Exact and Statistical Matching Techniques Prepared by Subcommittee on Matching Techniques Federal Committee on Statistical Methodology DEPARTMENT OF COMMERCE UNITED STATES OF AMERICA U.S. DEPARTMENT OF COMMERCE Philip M. Klutznick Courtenay M. Slater, Chief Economist Office of Federal Statistical Policy and Standards Joseph W. Duncan, Director Issued: June 1980 Office of Federal Statistical Policy and Standards Joseph W. Duncan, Director Katherine K. Wallman, Deputy Director, Social Statistics Gaylord E. Worden, Deputy Director, Economic Statistics Maria E. Gonzalez, Chairperson, Federal Committee on Statistical Methodology Preface This working paper was prepared by the Subcommittee on Matching Techniques, Federal Committee on Statistical Methodology. The Subcommittee was chaired by Daniel B. Radner, Office of Research and Statistics, Social Security Administration, Department of Health and Human Services. Members of the Subcommittee include Rich Allen, Economics, Statistics, and Cooperatives Service (USDA); Thomas B. -
Presentation
1 2 The U.S. Census Bureau’s COVID-19 Hub Several previous webinars on the hub www.census.gov/academy 3 Census Academy – Home Page 4 The U.S. Census Bureau’s COVID-19 Hub Data behind the COVID products Available Resources Practical Applications 5 6 The American Community Survey The Foundation The American Community Survey is on the leading edge of survey design, continuous improvement, and data quality • The nation’s most current, reliable, and accessible data source for local statistics on critical planning topics such as age, children, veterans, commuting, education, income, and employment • Surveys 3.5 million addresses and informs over $675 billion of Federal government spending each year • Covers 40+ topics, supports over 300 evidence-based Federal government uses, and produces 11 billion estimates each year • Three key annual data releases: • 1-year Estimates (for large populations) • 1-year Supplemental Estimates (for small populations) • 5-year Estimates (for very small populations) 7 8 The American Community Survey How is the American Community Survey Different from Decennial? ACS Census Purpose Sample estimates Official counts Detailed social, economic, Collects housing, and demographic Basic demographics characteristics Produces Population and housing Population and housing characteristics totals New Data Every Year 10 years Data Reflect Period of time Point in time 9 9 The American Community Survey Content Overview POPULATION HOUSING SOCIAL DEMOGRAPHIC Computer & Internet Use Age Ancestry Costs (Mortgage, Rent, Taxes, Hispanic -
Correlation of Salivary Immunoglobulin a Against Lipopolysaccharide of Porphyromonas Gingivalis with Clinical Periodontal Parameters
Correlation of salivary immunoglobulin A against lipopolysaccharide of Porphyromonas gingivalis with clinical periodontal parameters Pushpa S. Pudakalkatti, Abhinav S. Baheti Abstract Background: A major challenge in clinical periodontics is to find a reliable molecular marker of periodontal tissue destruction. Aim: The aim of the present study was to assess, whether any correlation exists between salivary immunoglobulin A (IgA) level against lipopolysaccharide of Porphyromonas gingivalis and clinical periodontal parameters (probing depth and clinical attachment loss). Materials and Methods: Totally, 30 patients with chronic periodontitis were included for the study based on clinical examination. Unstimulated saliva was collected from each study subject. Probing depth and clinical attachment loss were recorded in all selected subjects using University of North Carolina‑15 periodontal probe. Extraction and purification of lipopolysaccharide were done from the standard strain of P. gingivalis (ATCC 33277). Enzyme linked immunosorbent assay (ELISA) was used to detect the level of IgA antibodies against lipopolysaccharide of P. gingivalis in the saliva of each subject by coating wells of ELISA kit with extracted lipopolysaccharide antigen. Statistical Analysis: The correlation between salivary IgA and clinical periodontal parameters was checked using Karl Pearson’s correlation coefficient method and regression analysis. Results: The significant correlation was observed between salivary IgA level and clinical periodontal parameters in chronic -
D 27 April 1936 Summary. Karl Pearson Was Founder of The
Karl PEARSON1 b. 27 March 1857 - d 27 April 1936 Summary. Karl Pearson was Founder of the Biometric School. He made prolific contributions to statistics, eugenics and to the scientific method. Stimulated by the applications of W.F.R. Weldon and F. Galton he laid the foundations of much of modern mathematical statistics. Founder of biometrics, Karl Pearson was one of the principal architects of the modern theory of mathematical statistics. He was a polymath whose interests ranged from astronomy, mechanics, meteorology and physics to the biological sciences in particular (including anthropology, eugenics, evolution- ary biology, heredity and medicine). In addition to these scientific pursuits, he undertook the study of German folklore and literature, the history of the Reformation and German humanists (especially Martin Luther). Pear- son’s writings were prodigious: he published more than 650 papers in his lifetime, of which 400 are statistical. Over a period of 28 years, he founded and edited 6 journals and was a co-founder (along with Weldon and Galton) of the journal Biometrika. University College London houses the main set of Pearson’s collected papers which consist of 235 boxes containing family papers, scientific manuscripts and 16,000 letters. Largely owing to his interests in evolutionary biology, Pearson created, al- most single-handedly, the modern theory of statistics in his Biometric School at University College London from 1892 to 1903 (which was practised in the Drapers’ Biometric Laboratory from 1903-1933). These developments were underpinned by Charles Darwin’s ideas of biological variation and ‘sta- tistical’ populations of species - arising from the impetus of statistical and experimental work of his colleague and closest friend, the Darwinian zoolo- gist, W.F.R. -
2019-UNSD-Brochure.Pdf
1 The past year was full of milestones for the global statistical community. From the launch of the Global SDG Progress Report 2018, to the second UN World Data Forum, to the first UN World Geospatial Information Congress, we have remained committed to the advancement of the global statistical system, as well as to supporting countries’ efforts to strengthen their national statistical and geospatial information systems. We have engaged enthusiastically with all data communities, such as other government institutions, private sector, civil society, academia and media, and are very excited to see the results of these partnerships come to fruition. For example, we have advanced in the development of a federated system of data hubs for the exchange and dissemination of statistical and geographical information. In partnership with the private sector, we are testing this tool for SDG monitoring at national and subnational level in a number of countries and look forward to seeing it work in support of the 2030 Agenda of leaving no one behind. 2018 was also about change. As part of the overall UN reform, I have implemented a restructuring of the Division, which I am confident will make us more streamlined. I have made some strategic alignments: I created a new Environment and Geospatial Information Systems Branch. In addition, I have integrated trade statistics into an enlarged Economic Statistics Branch. Together with the Demographic and Social Statistics Branch, these three substantive branches now adequality reflect the three pillars of the 2030 Agenda for Sustainable Development. Furthermore, there is now a new Data Innovation and Capacity Branch, which brings together the ongoing work in various parts of the Division and gives more prominence to these workstreams, which include the strategic management of our extensive technical and institutional statistical capacity development programmes in support of the 2030 Agenda. -
Testing the Effectiveness of Consumer Financial Disclosure: Experimental Evidence from Savings Accounts
NBER WORKING PAPER SERIES TESTING THE EFFECTIVENESS OF CONSUMER FINANCIAL DISCLOSURE: EXPERIMENTAL EVIDENCE FROM SAVINGS ACCOUNTS Paul D. Adams Stefan Hunt Christopher Palmer Redis Zaliauskas Working Paper 25718 http://www.nber.org/papers/w25718 NATIONAL BUREAU OF ECONOMIC RESEARCH 1050 Massachusetts Avenue Cambridge, MA 02138 March 2019 For helpful comments, we thank our discussants Xiao Liu and Daniel Martin; participants at Advances with Field Experiments 2018 Conference, RAND Behavioral Finance Forum, CFPB Research Conference, Boulder Summer Conference on Consumer Financial Decision Making, NBER Summer Institute (Law & Economics); and Joseph Briggs, Michael Grubb, Arvind Krishnamurthy, Sheisha Kulkarni, David Laibson, Peter Lukacs, Brigitte Madrian, Adair Morse, Philipp Schnabl, and Jonathan Zinman. Matthew Ward and Tim Burrell provided invaluable help with execution. We thank Margarita Alvarez-Echandi, Sam Hughes, Tammy Lee, and Lei Ma for their research assistance. This research was conducted in conjunction with the Financial Conduct Authority, where Adams, Hunt, and Zaliauskas were employed during the trials. The views expressed in this paper are those of the authors and not the Financial Conduct Authority, which has reviewed the paper for human subjects compliance and the release of confidential information. All errors and omissions are the authors’ own. This RCT was registered in the American Economic Association Registry for randomized control trials under trial number AEARCTR-0004053. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research. At least one co-author has disclosed a financial relationship of potential relevance for this research. Further information is available online at http://www.nber.org/papers/w25718.ack NBER working papers are circulated for discussion and comment purposes. -
The Potential Use of Population Registers to Generate Socio- Demographic Statistics
The Potential Use of Population Registers to Generate Socio- Demographic Statistics Christophe Lefranc Asia and the Pacific Regional Office United Nations Population Fund (UNFPA) Asia-Pacific Stats Café October 27, 2020 Traditional Sources of Socio-Demographic Statistics 3 traditional pillars of socio-demographic statistics: – Population censuses: broad photographs of the population at different points in time: numbers and main characteristics of individuals and households at detailed geographical levels. Exhaustive coverage but limited number of variables of interest and long intervals between updates. – Household sample surveys: more detailed information on specific issues (employment, living standards…). Sample basis resulting in limitations in disaggregation. – Civil registration and vital statistics (CRVS) systems: use of administrative data to update on change in population numbers between censuses, using population projections (which are fairly robust in the short to medium term). Using administrative data to produce statistics is not new. Towards the Use of New Data Sources Several simultaneous trends result in the aspiration to mobilize new data sources, not specifically designed for statistical purposes: – Concerns on the cost of traditional modes of data collection for population censuses and sample surveys; – Growing difficulty in reaching respondents as a result of modern lifestyles and lack of cooperation from respondents; – Increasing need for fresher and more disaggregated data, particularly in the context of SDG implementation and monitoring; – Availability of new technologies making it possible to manage large amounts of data and to mobilize new data sources. Population registers are a possible way-forward in this context. The Potential Power of Population Registers With well-functioning CRVS systems, administrative (registration) information, which primarily aims at giving individuals an identity, is also used to produce vital statistics. -
Generalized Correlations and Kernel Causality Using R Package Generalcorr
Generalized Correlations and Kernel Causality Using R Package generalCorr Hrishikesh D. Vinod* October 3, 2017 Abstract Karl Pearson developed the correlation coefficient r(X,Y) in 1890's. Vinod (2014) develops new generalized correlation coefficients so that when r∗(Y X) > r∗(X Y) then X is the \kernel cause" of Y. Vinod (2015a) arguesj that kernelj causality amounts to model selection be- tween two kernel regressions, E(Y X) = g1(X) and E(X Y) = g2(Y) and reports simulations favoring kernelj causality. An Rj software pack- age called `generalCorr' (at www.r-project.org) computes general- ized correlations, partial correlations and plausible causal paths. This paper describes various R functions in the package, using examples to describe them. We are proposing an alternative quantification to extensive causality apparatus of Pearl (2010) and additive-noise type methods in Mooij et al. (2014), who seem to offer no R implementa- tions. My methods applied to certain public benchmark data report a 70-75% success rate. We also describe how to use the package to assess endogeneity of regressors. Keywords: generalized measure of correlation, non-parametric regres- sion, partial correlation, observational data, endogeneity. *Vinod: Professor of Economics, Fordham University, Bronx, New York, USA 104 58. E-mail: [email protected]. A version of this paper was presented at American Statistical Association's Conference on Statistical Practice (CSP) on Feb. 19, 2016, in San Diego, California, and also at the 11-th Greater New York Metro Area Econometrics Colloquium, Johns Hopkins University, Baltimore, Maryland, on March 5, 2016. I thank Dr Pierre Marie Windal of Paris for pointing out an error in partial correlation coefficients. -
Sabra Sultana Stat 2017 IUB Incomplete.Pdf
ON THE VISUALIZATION IN STATISTICS A THESIS SUBMITTED TO THE ISLAMIA UNIVERSITY OF BAHAWALPUR IN THE SUBJECT OF STATISTICS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY By SABRA SULTANA October 2011 Department of Statistics Bahawalpur 63100, PAKISTAN www.iub.edu.pk Chapter 1 Introduction 1.1 Data Visualization and Its Importance It has been noted years ago that visualization is the foundation for human understanding. Since the beginning of recorded history, human beings have seen images in natural wonders. Objects such as stars, rock formations, and clouds were being more easily explained through visualization. The sayings (i) “Seeing is believing”, (ii) “One picture is worth ten thousand words” and (iii) “A good sketch is better than a long speech” are true everywhere and in statistics have no exception. Visualization is an emerging field that has an important role to play in statistics. Playfair (1801) expressed, “For no study is less alluring or more dry and tedious than statistics unless the mind and imagination are set to work or that the person studying is particularly interested in the subject; which is seldom the case with men in any rank in life.” Data visualization is used to (i) present data in a summarized form, (ii) analyze large amounts of data to see patterns, trends, correlations, etc., (iii) get insights of the data to make decisions. Data visualization is very popular in business fields and considered to be necessary for the growth of business. Data recorded in experiments or surveys is to be displayed by a statistical graph for visualization. Usually, they are classified into different types such as Pictograph, Line Plot, Pie Chart, Histogram, Bar Graph, Line Graph, Frequency Polygon, Scatter Plot, Stem and Leaf Plot, and Box Plot etc. -
Integrated Information System for Demographic Statistics 'ESGRAON-TDS' in Bulgaria
Demographic Research a free, expedited, online journal of peer-reviewed research and commentary in the population sciences published by the Max Planck Institute for Demographic Research Doberaner Strasse 114 · D-18057 Rostock · GERMANY www.demographic-research.org DEMOGRAPHIC RESEARCH VOLUME 6, ARTICLE 12, PAGES 325-354 PUBLISHED 14 MAY 2002 www.demographic-research.org/Volumes/Vol6/12/ DOI: 10.4054/DemRes.2002.6.12 RESEARCH MATERIAL Integrated Information System for Demographic Statistics 'ESGRAON-TDS' in Bulgaria Iliana Kohler Jordan Kaltchev Mariana Dimova © 2002 Max-Planck-Gesellschaft. Table of Contents 1 Introduction 326 2 Integrated Information System for Demographic 327 Statistics 'ESGRAON-TDS' 2.1 Informational Basis of the System 327 2.2 Informational Coverage of the System 329 2.3 Organization of the 'ESGRAON-TDS' System 329 2.4 The Personal Identification Number - EGN 331 3 Vital Statistics (TDS) 334 3.1 Socio-demographic Indicators in the Vital Statistics 334 3.2 Methodological Criteria for the Organization of 336 Vital Statistics 3.3 National Demographic Database 336 3.4 Publication of Demographic Data 337 4 Population Censuses 338 4.1 The 2001 Population Census 339 4.1.1 Targets and units of the census enumeration 340 4.1.2 Census definitions and classifications 341 4.2 Post-enumeration Surveys on Coverage and 346 Accuracy of Registration in the Census 5 Specialized Demographic Studies 348 6 Data protection and Data Confidentiality of 349 Individual-Level Data in Bulgaria 7 Acknowledgements 351 Notes 352 References 353 Demographic Research - Volume 6, Article 12 Integrated Information System for Demographic Statistics ‘ESGRAON–TDS’ in Bulgaria Iliana Kohler1 Jordan Kaltchev2 Mariana Dimova3 Abstract In this paper we describe the Bulgarian statistical system that provides unique research possibilities for demographic analyses.