In response to scientific needs for more diverse and structured explanations of statistical data, researchers have discovered how to model individual data points as belonging to multiple groups. Handbook of Mixed Membership Models and Their Applications shows you how to use these flexible modeling tools to uncover hidden patterns in modern high-dimensional multivariate data. It explores the use of the models in various application settings, including survey data, population genetics, text analysis, image processing and annotation, and molecular biology. Through examples using real data sets, you'll discover how to characterize complex multivariate data in: Studies involving genetic databases Patterns in the progression of diseases and disabilities Combinations of topics covered by text documents Political ideology or electorate voting patterns Heterogeneous relationships in networks, and much more The handbook spans more than 20 years of the editors' and contributors' statistical work in the field. Top researchers compare partial and mixed membership models, explain how to interpret mixed membership, delve into factor analysis, and describe nonparametric mixed membership models. They also present extensions of the mixed membership model for text analysis, sequence and rank data, and network data as well as semi-supervised mixed membership models.

In response to scientific needs for more diverse and structured explanations of statistical data, researchers have discovered how to model individual data points as belonging to multiple groups. Handbook of Mixed Membership Models and Their Applications shows you how to use these flexible modeling tools to uncover hidden patterns in modern high-dimensional multivariate data. It explores the use of the models in various application settings, including survey data, population genetics, text analysis, image processing and annotation, and molecular biology. Through examples using real data sets, you'll discover how to characterize complex multivariate data in: Studies involving genetic databases Patterns in the progression of diseases and disabilities Combinations of topics covered by text documents Political ideology or electorate voting patterns Heterogeneous relationships in networks, and much more The handbook spans more than 20 years of the editors' and contributors' statistical work in the field. Top researchers compare partial and mixed membership models, explain how to interpret mixed membership, delve into factor analysis, and describe nonparametric mixed membership models. They also present extensions of the mixed membership model for text analysis, sequence and rank data, and network data as well as semi-supervised mixed membership models.

One of the best known statisticians of the 20th century, Frederick Mosteller has inspired numerous statisticians and other scientists by his creative approach to statistics and its applications. This volume collects 40 of his most original and influential papers, capturing the variety and depth of his writings. It is hoped that sharing these writings with a new generation of researchers will inspire them to build upon his insights and efforts.

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.

A large number of Mostellar's friends, colleagues, collaborators, and former students have contributed to the preparation of this volume in honor of his 70th birthday. It provides a critical assessment of Mosteller's professional and research contributions to the field of statistics and its applications.

With increasing frequency, the proof of facts in legal proceedings en tails the use of quantitative methods. Judges, lawyers, statisticians, social scientists, and many others involved in judicial processes must address is sues such as the evaluation and interpretation of quantitative evidence, the ethical and professional obligations of expert witnesses, and the roles of court-appointed witnesses. The Panel on Statistical Assessments as Evi dence in the Courts was convened to help clarify these issues and provide some guidance in addressing the difficulties encountered in the use of quan titative assessments in legal proceedings. This report is the culmination of more than three years of research and deliberation. In it, we address a variety of issues that arise in federal and state court proceedings when statistical assessments such as quantitative descriptions, causal inferences, and predictions of events based on earlier occurrences are presented as evidence. We appraise the forms in which such assessments are presented, aspects of their admission into evidence, and the response to and evaluation of them by judges and juries."

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.

This book constitutes the thoroughly refereed post-proceedings of the International Workshop on Statistical Network Analysis: Models, Issues, and New Directions held in Pittsburgh, PA, USA in June 2006 as associated event of the 23th International Conference on Machine Learning, ICML 2006.

The 12 revised full papers and 4 invited lectures presented together with the summary of the closing panel discussion were carefully revised and selected during two rounds of reviewing and improvement from the presentations at the workshop. The papers focus on probabilistic methods for network analysis, paying special attention to model design and computational issues of learning and inference. The workshop brings together statistical network modeling researchers from different communities to create and motivate novel modeling approaches, diverse applications, and new research directions.

Frederick Mosteller has inspired numerous statisticians and other scientists by his creative approach to statistics and its applications. This volume brings together 40 of his most original and influential papers, capturing the variety and depth of his writings. The editors hope to share these with a new generation of researchers, so that they can build upon his insights and efforts. This volume of selected papers is a companion to the earlier volume A Statistical Model: Frederick Mosteller's Contributions to Statistics, Science, and Public Policy, edited by Stephen E. Fienberg, David C. Hoaglin, William H. Kruskal, and Judith M. Tanur (Springer-Verlag, 1990), and to Mosteller's forthcoming autobiography, which will also be published by Springer-Verlag. It includes a biography and a comprehensive bibliography of Mosteller's books, papers, and other writings. Stephen E. Fienberg is Maurice Falk University Professor of Statistics and Social Science, in the Departments of Statistics and Machine Learning at Carnegie Mellon University, Pittsburgh, PA. David C. Hoaglin is Principal Scientist at Abt Associates Inc., Cambridge, MA.

From the reviews: "This collection of essays surveys the most important of Fisher's papers in various areas of statistics. ... ... the monograph will be a useful source of reference to most of Fisher's major papers; it will certainly provide background material for much vigorous discussion." #"Australian Journal of Statistics"#1

A variety of biological and social science data come in the form of cross-classified tables of counts, commonly referred to as contingency tables. Until recent years the statistical and computational techniques available for the analysis of cross-classified data were quite limited. This book presents some of the recent work on the statistical analysis of cross-classified data using longlinear models, especially in the multidimensional situation.

A scientific response to the best-selling The Bell Curve which set off a hailstorm of controversy upon its publication in 1994. Much of the public reaction to the book was polemic and failed to analyse the details of the science and validity of the statistical arguments underlying the books conclusion. Here, at last, social scientists and statisticians reply to The Bell Curve and its conclusions about IQ, genetics and social outcomes.

Bilingual education has long been the subject of major disagreements in this country. This book provides a detailed critique of the two largest studies of U.S. bilingual education programs. It examines the goals of the studies and what can be learned from them. In addition, using these studies as cases, this book provides guidelines on how to plan large evaluation studies to achieve useful answers to major policy questions about education. Table of Contents Front Matter 1 Bilingual Education in the United States 2 Research Methodology and Bilingual Education 3 The Longitudinal Study 4 The Immersion Study 5 Lessons for the Future 6 Conclusions and Recommendations Appendix A: Bilingual Education in the United States Appendix B: Trajectory Analysis of Matched Percentiles Appendix C: Acronyms Appendix D: Biographical Sketches of Panel Members and Staff References

A Survey of Statistical Network Models aims to provide the reader with an entry point to the voluminous literature on statistical network modeling. It guides the reader through the development of key stochastic network models, touches upon a number of examples and commonalities across different parts of the network literature, and discusses major schools of thought in static and dynamic network modeling. Networks have found a prominent place in our everyday lives. In science, networks have been used to analyze interpersonal social relationships, communication, academic paper co- authorships and citations, protein interaction patterns, and much more. Popular books on networks and their analysis began to appear a decade ago, and online networking communities such as Facebook, MySpace, and LinkedIn now include millions of people from around the world. Formal statistical modeling for the analysis of network data has emerged as a major research topic in diverse areas of study. A Survey of Statistical Network Models aims to provide the reader with an entry point to the voluminous literature on statistical network modeling.It guides the reader through the development of key stochastic network models, touches upon a number of examples and commonalities across different parts of the network literature, and discusses major schools of thought in static and dynamic network modeling. In addition it illuminates the interconnections between existing models. Despite the rich and extensive network modeling literature, many statistical questions remain unanswered. It is hoped that the concluding discussion of gaps and challenges will help the interested reader deduce important future research directions.