This volume discusses an important area of statistics and highlights the most important statistical advances. It is divided into four sections: statistics in the life and medical sciences, business and social science, the physical sciences and engineering, and theory and methods of statistics.

Introduction.Big data for twenty-first-century economic statistics: the future is now /Katharine G. Abraham, Ron S. Jarmin, Brian C. Moyer, and Matthew D. Shapiro --Toward comprehensive use of big data in economic statistics.Reengineering key national economic indicators /Gabriel Ehrlich, John Haltiwanger, Ron S. Jarmin, David Johnson, and Matthew D. Shapiro ;Big data in the US consumer price index: experiences and plans /Crystal G. Konny, Brendan K. Williams, and David M. Friedman ;Improving retail trade data products using alternative data sources /Rebecca J. Hutchinson ;From transaction data to economic statistics: constructing real-time, high-frequency, geographic measures of consumer spending /Aditya Aladangady, Shifrah Aron-Dine, Wendy Dunn, Laura Feiveson, Paul Lengermann, and Claudia Sahm ;Improving the accuracy of economic measurement with multiple data sources: the case of payroll employment data /Tomaz Cajner, Leland D. Crane, Ryan A. Decker, Adrian Hamins-Puertolas, and Christopher Kurz --Uses of big data for classification.Transforming naturally occurring text data into economic statistics: the case of online job vacancy postings /Arthur Turrell, Bradley Speigner, Jyldyz Djumalieva, David Copple, and James Thurgood ;Automating response evaluation for franchising questions on the 2017 economic census /Joseph Staudt, Yifang Wei, Lisa Singh, Shawn Klimek, J. Bradford Jensen, and Andrew Baer ;Using public data to generate industrial classification codes /John Cuffe, Sudip Bhattacharjee, Ugochukwu Etudo, Justin C. Smith, Nevada Basdeo, Nathaniel Burbank, and Shawn R. Roberts --Uses of big data for sectoral measurement.Nowcasting the local economy: using Yelp data to measure economic activity /Edward L. Glaeser, Hyunjin Kim, and Michael Luca ;Unit values for import and export price indexes: a proof of concept /Don A. Fast and Susan E. Fleck ;Quantifying productivity growth in the delivery of important episodes of care within the Medicare program using insurance claims and administrative data /John A. Romley, Abe Dunn, Dana Goldman, and Neeraj Sood ;Valuing housing services in the era of big data: a user cost approach leveraging Zillow microdata /Marina Gindelsky, Jeremy G. Moulton, and Scott A. Wentland --Methodological challenges and advances.Off to the races: a comparison of machine learning and alternative data for predicting economic indicators /Jeffrey C. Chen, Abe Dunn, Kyle Hood, Alexander Driessen, and Andrea Batch ;A machine learning analysis of seasonal and cyclical sales in weekly scanner data /Rishab Guha and Serena Ng ;Estimating the benefits of new products /W. Erwin Diewert and Robert C. Feenstra.

A selection of articles presented at the Eighth Lukacs Symposium held at the Bowling Green State University, Ohio. They discuss consistency and accuracy of the sequential bootstrap, hypothesis testing, geometry in multivariate analysis, the classical extreme value model, the analysis of cross-classified data, diffusion models for neural activity, e

This book explores official statistics and their social function in modern societies. Digitisation and globalisation are creating completely new opportunities and risks, a context in which facts (can) play an enormously important part if they are produced with a quality that makes them credible and purpose-specific. In order for this to actually happen, official statistics must continue to actively pursue the modernisation of their working methods. This book is not about the technical and methodological challenges associated with digitisation and globalisation; rather, it focuses on statistical sociology, which scientifically deals with the peculiarities and pitfalls of governing-by-numbers, and assigns statistics a suitable position in the future informational ecosystem. Further, the book provides a comprehensive overview of modern issues in official statistics, embodied in a historical and conceptual framework that endows it with different and innovative perspectives. Central to this work is the quality of statistical information provided by official statistics. The implementation of the UN Sustainable Development Goals in the form of indicators is another driving force in the search for answers, and is addressed here. This book will be of interest to a broad readership. The topics of sociology, epistemology, statistical history and the management of production processes, which are important for official statistics and their role in social decision-making processes, are generally not dealt with in statistics books. The book is primary intended for official statisticians, but researchers and advanced students in statistics, economics, sociology and the political sciences will find the book equally stimulating. Last but not least, it offers a valuable source of reflection for policymakers and stakeholders.

It is not news that each of us grows old. What is relatively new, however, is that the average age of the American population is increasing. More and better information is required to assess, plan for, and meet the needs of a graying population. The Aging Population in the Twenty-First Century examines social, economic, and demographic changes among the aged, as well as many health-related topics: health promotion and disease prevention; quality of life; health care system financing and use; and the quality of careâ€"especially long-term care. Recommendations for increasing and improving the data availableâ€"as well as for ensuring timely access to themâ€"are also included.

What are the grand dynamics that drive the accumulation and distribution of capital? Questions about the long-term evolution of inequality, the concentration of wealth, and the prospects for economic growth lie at the heart of political economy. But satisfactory answers have been hard to find for lack of adequate data and clear guiding theories. In this work the author analyzes a unique collection of data from twenty countries, ranging as far back as the eighteenth century, to uncover key economic and social patterns. His findings transform debate and set the agenda for the next generation of thought about wealth and inequality. He shows that modern economic growth and the diffusion of knowledge have allowed us to avoid inequalities on the apocalyptic scale predicted by Karl Marx. But we have not modified the deep structures of capital and inequality as much as we thought in the optimistic decades following World War II. The main driver of inequality--the tendency of returns on capital to exceed the rate of economic growth--today threatens to generate extreme inequalities that stir discontent and undermine democratic values if political action is not taken. But economic trends are not acts of God. Political action has curbed dangerous inequalities in the past, the author says, and may do so again. This original work reorients our understanding of economic history and confronts us with sobering lessons for today.

The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and in influence. 'Big data', 'data science', and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? This book takes us on an exhilarating journey through the revolution in data analysis following the introduction of electronic computation in the 1950s. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. The book ends with speculation on the future direction of statistics and data science.

The public relations industry is undergoing a revolution in using data to define promotional programs, to measure influence and to address the needs of clients with more precision than ever. Applying tools that range from online surveys to social-media listening to applying big data with sophisticated algorithms, today’s PR professionals are data-driven in virtually everything they do. Data-Driven Public Relations Research is the first book for PR students and practitioners to offer an overview of these new practices as well as a glimpse into the future of these new applications, including "big data" and some of the applications from real-world PR campaigns and strategic planning. It includes contemporary cases involving brand name companies who are blazing new trails in the use of metrics in public relations. This book presents a practical, accessible approach that requires no prior training or experience, with easy to follow, step-by-step measurement examples from existing campaigns. Using Excel, the book enables readers to export lessons from the classroom to the office, where use of statistical packages is rare and can give PR practitioners the advantage over competitors. This pragmatic approach helps readers apply metrics to PR problems such as: Finding the best target audiences Understanding audience communication needs and preferences How best to present research outcomes How to manage major projects with specialized research firms. Accompanying electronic resources for the book include sample answers to the book’s discussion questions, PowerPoint lecture slides for instructors and sample research exercises using Excel.

This report explores how data and digital technology can help achieve policy objectives and drive positive transformation in the health sector while managing new risks such as privacy, equity and implementation costs. It examines the following topics: improving service delivery models; empowering people to take an active role in their health and their care; improving public health; managing biomedical technologies; enabling better collaboration across borders; and improving health system governance and stewardship. It also examines how health workforces should be equipped to make the most of digital technology. The report contains findings from surveys of OECD countries and shares a range of examples that illustrate the potential benefits as well as challenges of the digital transformation in the health sector. Findings and recommendations are relevant for policymakers, health care providers, payers, industry as well as patients, citizens and civil society.

The National Science Foundation's National Center for Science and Engineering Statistics (NCSES), one of the nation's principal statistical agencies, is charged to collect, acquire, analyze, report, and disseminate statistical data related to the science and engineering enterprise in the United States and other nations that is relevant and useful to practitioners, researchers, policymakers, and to the public. NCSES data, based primarily on several flagship surveys, have become the major evidence base for American science and technology policy, and the agency is well respected globally for these data. This report assesses and provides guidance on NCSES's approach to measuring the science and engineering workforce population in the United States. It also proposes a framework for measuring the science and engineering workforce in the next decade and beyond, with flexibility to examine emerging issues related to this unique population while at the same time allowing for stability in the estimation of key trends