Sir David Cox is among the most important statisticians of the past half-century. He has made pioneering and highly influential contributions to a uniquely wide range of topics in statistics and applied probability. His teaching has inspired generations of students, and many well-known researchers have begun as his graduate students or have worked with him at early stages of their careers. Legions of others have been stimulated and enlightened by the clear, concise, and direct exposition exemplified by his many books, papers, and lectures. This book presents a collection of chapters by major statistical researchers who attended a conference held at the University of Neuchatel in July 2004 to celebrate David Cox's 80th birthday. Each chapter is carefully crafted and collectively present current developments across a wide range of research areas from epidemiology, environmental science, finance, computing and medicine. Edited by Anthony Davison, Ecole Polytechnique Federale de Lausanne, Switzerland; Yadolah Dodge, University of Neuchatel, Switzerland; and N. Wermuth, Goteborg University, Sweden, with chapters by Ole E. Barndorff-Nielsen, Sarah C. Darby, Christina Davies, Peter J. Diggle, David Firth, Peter Hall, Valerie S. Isham, Kung-Yee Liang, Peter McCullagh, Paul McGale, Amilcare Porporato, Nancy Reid, Brian D. Ripley, Ignacio Rodriguez-Iturbe, Andrea Rotnitzky, Neil Shephard, Scott L. Zeger, and including a brief biography of David Cox, this book is suitable for students of statistics, epidemiology, environmental science, finance, computing and medicine, and academic and practising statisticians.

The International Statistical Institute was founded in 1885 and is therefore one of the world's oldest international scientific societies. The field of statistics is still expanding rapidly and possesses a rich variety of applications in many areas of human activity such as science, government, business, industry, and everyday affairs. In consequence, the celebration of the Institute's centenary in 1985 is of considerable interest not only to statisticians but also more widely to the international scientific community. As part of its centennial celebration planning the Institute decided to publish a volume of papers representing the immensely wide range of interests encompassed by statistics in its international context, viewed both from a historical and from a contemporary standpoint. We were fortunate in securing the services of Anthony Atkinson and Stephen Fienberg as Editors of this volume: they have worked hard over a period of several years to put together a most fascinating collection of papers. On behalf of the Institute it is my pleasant duty to thank them and the authors for their contributions. J. DURBIN, President International Statistical Institute Preface The papers in this volume were prepared to help celebrate the centenary of the International Statistical Institute. During the lSI's first 100 years statistics has matured, both as a scientific discipline and as a profession, in ways that the lSI's founders could not possibly have imagined.

This volume gathers papers presented at the LISA 2020 Sustainability Symposium in Kumasi, Ghana, May 2–6, 2022. They focus on sustainable methods and practices of using statistics and data science to address real-world problems. From utilizing social media for statistical collaboration to predicting obesity among rural women, and from analyzing inflation in Nigeria using machine learning to teaching data science in Africa, this book explores the intersection of data, statistics, and sustainability. With practical applications, code snippets, and case studies, this book offers valuable insights for researchers, policymakers, and data enthusiasts alike. The LISA 2020 Global Network aims to enhance statistical and data science capability in developing countries through the creation of a network of collaboration laboratories (also known as “stat labs”). These stat labs are intended to serve as engines for development by training the next generation of collaborative statisticians and data scientists, providing research infrastructure for researchers, data producers, and decision-makers, and enabling evidence-based decision-making that has a positive impact on society. The research conducted at LISA 2020 focuses on practical methods and applications for sustainable growth of statistical capacity in developing nations.

This book encompasses a wide range of important topics. The articles cover the following areas: asymptotic theory and inference, biostatistics, economics and finance, statistical computing and Bayesian statistics, and statistical genetics. Specifically, the issues that are studied include large deviation, deviation inequalities, local sensitivity of model misspecification in likelihood inference, empirical likelihood confidence intervals, uniform convergence rates in density estimation, randomized designs in clinical trials, MCMC and EM algorithms, approximation of p-values in multipoint linkage analysis, use of mixture models in genetic studies, and design and analysis of quantitative traits.

The book presents a nuanced narrative about statistical development in Africa since around the time of independence when emerging states needed statistics mainly to support their planning processes. It highlights challenges faced then, some of which have persisted, including institutional, organizational and technical challenges. These challenges manifest themselves in countries with different degrees of severity and are quite severe in post-conflict countries. Key statistical programmes to support statistical development in Africa in the 1970s, 1980s and 1990s are presented

From his unique perspective, renowned statistician and educator Frederick Mosteller describes many of the projects and events in his long career. From humble beginnings in western Pennsylvania to becoming the founding chairman of Harvard University’s Department of Statistics and beyond, he inspired many statisticians, scientists, and students with his unabashed pragmatism, creative thinking, and zest for both learning and teaching. This candid account offers fresh insights into the qualities that made Mosteller a superb teacher, a prolific scholar, a respected leader, and a valued advisor. A special feature of the book is its chapter-length insider accounts of work on the pre-election polls of 1948, statistical aspects of the Kinsey report on sexual behavior in the human male, mathematical learning theory, authorship of the disputed Federalist papers, safety of anesthetics, and a wide-ranging examination of the Coleman report on equality of educational opportunity. This volume is a companion to Selected Papers of Frederick Mosteller (Springer, 2006) and A Statistical Model: Frederick Mosteller’s Contributions to Statistics, Science, and Public Policy (Springer-Verlag, 1990). Frederick Mosteller (1916–2006) was Roger I. Lee Professor of Mathematical Statistics at Harvard University. His manuscript was unfinished at his death and has been updated.

Most are familiar with the adage "correlation does not imply causation." Since much of science is concerned with problems of causality and statistics is so widely used in research, one may wonder whether statistics possesses the tools to study such problems and contribute to their resolution. These were the questions posed over thirty years ago by Pearl, Robins, Rubin, Shafer, etc. when they set out to incorporate notions of causality into statistics theory and develop methods for estimating causal relationships. Since then, the schools of "statistical causality" they founded have produced interesting results and methods that help us think about causality and are potentially useful in real-life problems. Yet, despite its appeal, statistical causality is still disregarded by many "mainstream" statisticians, and its methods are not widely known. In part this is explained by the unorthodox and apparently disparate character of the various schools, in particular by the distinct languages they developed and that are not readily accessible. Thus, even some advanced researchers seemed startled by things like Rubin's "counterfactuals" that in one guise or another appear in all theories but that seem potentially incompatible with Kolmogorov's formalism, the very foundation of statistics. It turns out that statistical causality is firmly rooted in Kolmogorov's axiomatization of probability as the elements required by it are essentially those proposed a century ago by Steinhaus, and, perhaps surprisingly, that statistics has always engaged with causality. The present book makes this plain, providing a basis for statistical causality that subsumes and reconciles the theories of all other schools and that to a mainstream statistician will appear entirely familiar and natural.

Holidays are a key to helping us understand the transformation of national, regional, community and ethnic identities. In Celebrating Canada, Matthew Hayday and Raymond Blake situate Canada in an international context as they examine the history and evolution of our national and provincial holidays and annual celebrations. The contributors to this volume examine such holidays as Dominion Day, Victoria Day, Quebec’s Fête Nationale and Canadian Thanksgiving, among many others. They also examine how Canadians celebrate the national days of other countries (like the Fourth of July) and how Dominion Day was observed in the United Kingdom. Drawing heavily on primary source research, and theories of nationalism, identities and invented traditions, the essays in this collection deepen our understanding of how these holidays have influenced the evolution of Canadian identities.

This second edition has been completely revised and expanded to become the most up-to-date and thorough professional reference text in this fast-moving area of biostatistics. It contains an additional two chapters on fully parametric models for discrete repeated measures data and statistical models for time-dependent predictors.

“Fascinating.” —Jill Lepore, The New Yorker A sweeping history of data and its technical, political, and ethical impact on our world. From facial recognition—capable of checking people into flights or identifying undocumented residents—to automated decision systems that inform who gets loans and who receives bail, each of us moves through a world determined by data-empowered algorithms. But these technologies didn’t just appear: they are part of a history that goes back centuries, from the census enshrined in the US Constitution to the birth of eugenics in Victorian Britain to the development of Google search. Expanding on the popular course they created at Columbia University, Chris Wiggins and Matthew L. Jones illuminate the ways in which data has long been used as a tool and a weapon in arguing for what is true, as well as a means of rearranging or defending power. They explore how data was created and curated, as well as how new mathematical and computational techniques developed to contend with that data serve to shape people, ideas, society, military operations, and economies. Although technology and mathematics are at its heart, the story of data ultimately concerns an unstable game among states, corporations, and people. How were new technical and scientific capabilities developed; who supported, advanced, or funded these capabilities or transitions; and how did they change who could do what, from what, and to whom? Wiggins and Jones focus on these questions as they trace data’s historical arc, and look to the future. By understanding the trajectory of data—where it has been and where it might yet go—Wiggins and Jones argue that we can understand how to bend it to ends that we collectively choose, with intentionality and purpose.