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555 p. : ill.
  • INTRODUCTION, Adrian E. Raftery, Martin A. Tanner, and Martin T. Wells STATISTICS IN THE LIFE AND MEDICAL SCIENCES, Guest Editor Norman E. Breslow, University of Washington, USA Survival Analysis, David Oakes Causal Analysis in the Health Sciences, Sander Greenland Environmental Statistics, Peter Guttorp Capture-Recapture Models, Kenneth H. Pollock Statistics in Animal Breeding, Daniel Gianola Some Issues in Assessing Human Fertility, Clarice R. Weinberg and David B. Dunson Statistical Issues in Toxicology, Louise M. Ryan Receiver Operating Characteristic Methodology, Margaret Sullivan Pepe The Randomized Clinical Trial, David P. Harrington Some Contributions of Statistics to Environmental Epidemiology, Duncan C. Thomas Challenges Facing Statistical Genetics, B. S. Weir Computational Molecular Biology, Wing Hung Wong STATISTICS IN BUSINESS AND SOCIAL SCIENCE, Guest Editor Mark P. Becker, University of Michigan, USA Finance: A Selective Survey, Andrew W. Lo Statistics and Marketing, Peter E. Rossi and Greg M. Allenby Time Series and Forecasting: Brief History and Future Research, Ruey S. Tsay Contingency Tables and Log-Linear Models: Basic Results and New Developments, Stephen E. Fienberg Causal Inference in the Social Sciences, Michael E. Sobel Political Methodology: A Welcoming Discipline, Nathaniel L. Beck Statistics in Sociology, 1950-2000, Adrian E. Raftery Psychometrics, Michael W. Browne Empirical Methods and the Law, Theodore Eisenberg Demography: Past, Present, and Future, Yu Xie STATISTICS IN THE PHYSICAL SCIENCES AND ENGINEERING, Guest Editor Diane Lambert, Statistics Research Department, Bell Labs, USA Challenges in Understanding the Atmosphere, Doug Nychka Seismology: A Statistical Vignette, David Vere-Jones Internet Traffic Data, William S. Cleveland and Don X. Sun Coding and Compression: A Happy Union of Theory and Practice, Jorma Rissanen and Bin Yu Statistics in Reliability, Jerry Lawless The State of Statistical Process Control as We Proceed into the 21st Century, Zachary G. Stoumbos, Marion R. Reynolds, Jr., Thomas P. Ryan, and William H. Woodall Statistics in Preclinical Pharmaceutical Research and Development, Bert Gunter and Dan Holder Statistics in Advanced Manufacturing, Vijay Nair, Mark Hansen, and Jan Shi THEORY AND METHODS, Guest Editor George Casella, University of Florida, USA Bayesian Analysis: A Look at Today and Thoughts of Tomorrow, James O. Berger An Essay on Statistical Decision Theory, Lawrence D. Brown Markov Chain Monte Carlo: 10 Years and Still Running, Olivier Cappe and Christian P. Robert Empirical Bayes: Past, Present and Future, Bradley P. Carlin and Thomas A. Louis Linear and Log-Linear Models, Ronald Christensen The Bootstrap and Modern Statistics, Bradley Efron Prospects of Nonparametric Modeling, Jianquing Fan Gibbs Sampling, Alan E. Gelfand The Variable Selection Problem, Edward I. George Robust Nonparametric Methods, Thomas P. Hettmansperger, Joseph W. McKean, and Simon J. Sheather Hierarchical Models: A Current Computational Perspective, James P. Hobert Hypothesis Testing: From p Values to Bayes Factors, John I. Marden Generalized Linear Models, Charles E. McCulloch Missing Data: Dial M for ???, Xiao-Li Meng A Robust Journey in the New Millennium, Stephen Portnoy and Xuming He Likelihood, Nancy Reid Conditioning, Likelihood, and Coherence: A Review of Some Foundational Concepts, James Robins and Larry Wasserman The End of Time Series, V. Solo Principal Information Theoretic Approaches, Ehsan S. Soofi Measurement Error Models, L. A. Stefanski Higher Order Asymptotic Approximation: Laplace, Saddlepoint, and Related Methods, Robert L. Strawderman Minimaxity, William E. Strawderman Afterword, George Casella AUTHOR INDEX SUBJECT INDEX.
  • (source: Nielsen Book Data)9781584882725 20160604
Exactly what is the state of the art in statistics as we move forward into the 21st century? What promises, what trends does its future hold? Through the reflections of 70 of the world's leading statistical methodologists, researchers, theorists, and practitioners, Statistics in the 21st Century answers those questions. Originally published in the Journal of the American Statistical Association, this collection of vignettes examines our statistical past, comments on our present, and speculates on our future. Although the coverage is broad and the topics diverse, it reveals the essential intellectual unity of the field as we see the same themes recurring in different contexts. We see how the development of statistics has been driven by the unprecedented and still growing range of applications, by the explosion in computer technology, and by the new types of data that continue to emerge and advance the discipline. Organized around major areas of application and leading up to vignettes on theory and methods, Statistics in the 21st Century forms a landmark record of the progress and perceived future of the discipline. No student, researcher, or practitioner of statistics should miss this extraordinary opportunity to view the past, present, and future world of statistics through the eyes of its foremost thinkers.
(source: Nielsen Book Data)9781584882725 20160604
xxii, 429 p. : ill. ; 24 cm.
  • Introduction to Longitudinal Data.- Plots.- Simple Analyses.- Critiques of Simple Analyses.- The Multivariate Normal Linear Model.- Tools and Concepts.- Specifying Covariates.- Modeling the Covariance Matrix.- Random Effects Models.- Residuals and Case Diagnostics.- Discrete Longitudinal Data.- Missing Data.- Analyzing Two Longitudinal Variables.- Further Reading.
  • (source: Nielsen Book Data)9780387402710 20160528
Longitudinal data are ubiquitous across medicine, public health, public policy, psychology, political science, biology, sociology and education, yet many longitudinal data sets remain improperly analysed. This book teaches the art and statistical science of modern longitudinal data analysis. The author emphasizes specifying, understanding, and interpreting longitudinal data models. He inspects the longitudinal data graphically, analyses the time trend and covariates, models the covariance matrix, and then draws conclusions. Covariance models covered include random effects, autoregressive, autoregressive moving average, antedependence, factor analytic, and completely unstructured models among others.Longer expositions explore: an introduction to and critique of simple non-longitudinal analyses of longitudinal data, missing data concepts, diagnostics, and simultaneous modelling of two longitudinal variables. Applications and issues for random effects models cover estimation, shrinkage, clustered data, models for binary and count data and residuals and residual plots. Shorter sections include a general discussion of how computational algorithms work, handling transformed data, and basic design issues. This book requires a solid regression course as background and is particularly intended for the final year of a Biostatistics or Statistics Masters degree curriculum. The mathematical prerequisite is generally low, mainly assuming familiarity with regression analysis in matrix form.Doctoral students in Biostatistics or Statistics, applied researchers and quantitative doctoral students in disciplines such as medicine, public health, public policy, psychology, political science, biology, sociology and education will find this book invaluable. The book has many figures and tables illustrating longitudinal data and numerous homework problems. The associated web site contains many longitudinal data sets, examples of computer code, and labs to re-enforce the material. From the reviews: '...This book is extremely well presented and it has been written in a style that makes its reading really pleasant and enjoyable...I highly recommend "Modeling Longitudinal Data" as a good reference book for anyone interested in looking into the art and statistical science of modern longitudinal data analysis' - "Journal of Applied Statistics", December 2005. 'The book is clearly written and well presented. The author's accumulated experience in presenting the material comes over. On balance, this is one of the books which anyone about to teach a practical course in longitudinal data analysis should consider adopting as the course text' - "Short Book Reviews of the ISI", June 2006. '"Modeling Longitudinal Data" is a welcome addition to the vast literature on longitudinal data analysis. The book requires little in terms of prerequisites but offers a great deal' - Zhigang Zhang for the Journal of the American Statistical Association, December 2006.'Overall, Robert Weiss' book can be used as an excellent textbook for a first master-level course in longitudinal data analysis in a statistics or biostatistics program, or as a self-study book for applied researchers interested in this area...The style is very clear, concepts are explained in an engaging way and amply illustrated, and the chapters on covariate selection and modelling the variance-covariance matrix are definite assets' - Ralitza Gueorgueiva for "Biostatistics", September 2006.
(source: Nielsen Book Data)9780387402710 20160528
Science Library (Li and Ma)
xii, 224 p. : ill. ; 24 cm.
  • Introduction.- Balanced Ranked Set Sampling I: Nonparametric.- Balanced Ranked Set Sampling II: Parametric.- Unbalanced Ranked Set Sampling and Optimal Designs.- Distribution-Free Tests with Ranked Set Sampling.- Ranked Set Sampling with Concomitant Variables.- Ranked Set Sampling as Data Reduction Tools.- Case Studies.
  • (source: Nielsen Book Data)9780387402635 20160528
This is the first book on the concept and applications of ranked set sampling. It provides a comprehensive review of the literature, and it includes many new results and novel applications. Scientists and researchers on this subject will find a balanced presentation of theory and applications. The mathematical rigour of the theoretical foundations makes it beneficial to researchers. The detailed description of various methods illustrated by real or simulated data makes it useful for scientists and practitioners in application areas such as agriculture, forestry, sociology, ecological and environmental science, and medical studies. It can serve as a reference book and as a textbook for a short course at the graduate level. Zehua Chen is Associate Professor of Statistics at the National University of Singapore.Zhidong Bai is Professor of Statistics at the National University of Singapore; he is a Fellow of the Institute of Mathematical Statistics. Bimal Sinha is the Presidential Research Professor at University of Maryland Baltimore County; he is a Fellow of the Institute of Mathematical Statistics and the American Statistical Association. 'This comprehensive and up-to-date monograph covers, systematically and in simple language, the theory and applications of ranked set sampling (RSS), an improved technique related to traditional simple random sampling (SRS). Strong emphasis is placed on theoretical developments in RSS. In the meanwhile, the practical orientation and broad coverage will appeal to researchers and scientists working in sampling techniques, experimental designs, nonparametric statistics, and related fields...I would highly recommend this well-written and reasonably priced book' - "Techometrics", February 2005.'"Ranked Set Sampling: Theory and Applications" represents a major achievement, providing an up-to-date account of major research in ranked set sampling...this book would be a good addition to the library of anyone involved in statistical, environmental, and ecological research' - "Journal of the American Statistical Association", September 2005. 'The book is well laid out with concepts well explained...Those who are recently getting interested in the topic will find it an excellent start' - "Biometrics", December 2005.
(source: Nielsen Book Data)9780387402635 20160528
SAL3 (off-campus storage)
xv, 496 p. : ill. ; 24 cm.
  • Introduction.- Gaussian-Based Data Analysis.- Gaussian-Based Model Building.- Categorical Data and Goodness-of-Fit.- Regression Models for Count Data.- Analyzing Two-Way Tables.- Tables with More Structure.- Multidimensional Contingency Tables.- Regression Models for Binary Data.- Regression Models for Multiple Category Response Data.
  • (source: Nielsen Book Data)9780387007496 20160528
Categorical data arise often in many fields, including biometrics, economics, management, manufacturing, marketing, psychology, and sociology. This book provides an introduction to the analysis of such data. The coverage is broad, using the loglinear Poisson regression model and logistic binomial regression models as the primary engines for methodology. The topics covered include count regression models, such as Poisson, negative binomial, zero-inflated, and zero-truncated models; loglinear models for two-dimensional and multidimensional contingency tables, including for square tables and tables with ordered categories; and regression models for two-category (binary) and multiple-category target variables, such as logistic and proportional odds models. All methods are illustrated with analyses of real data examples, many from recent subject area journal articles.These analyses are highlighted in the text, and are more detailed than is typical, providing discussion of the context and background of the problem, model checking, and scientific implications. More than 200 exercises are provided, many also based on recent subject area literature. Data sets and computer code are available at a web site devoted to the text.'Jeff Simonoff's book is at the top of the heap of categorical data analysis textbooks...The examples are superb. Student reactions in a class I taught from this text were uniformly positive, particularly because of the examples and exercises. Additional materials related to the book, particularly code for S-Plus, SAS, and R, useful for analysis of examples, can be found at the author's Web site at New York University. I liked this book for this reason, and recommend it to you for pedagogical purposes' - Stanley Wasserman, "The American Statistician", August 2006, Vol. 60, No. 3.'The book has various noteworthy features. The examples used are from a variety of topics, including medicine, economics, sports, mining, weather, as well as social aspects like needle-exchange programs. The examples motivate the theory and also illustrate nuances of data analytical procedures. The book also incorporates several newer methods for analyzing categorical data, including zero-inflated Poisson models, robust analysis of binomial and poisson models, sandwich estimators, multinomial smoothing, ordinal agreement tables - this is definitely a good reference book for any researcher working with categorical data' - "Technometrics", May 2004.'This guide provides a practical approach to the appropriate analysis of categorical data and would be a suitable purchase for individuals with varying levels of statistical understanding' - "Paediatric and Perinatal Epidemiology", 2004, 18. 'This book gives a fresh approach to the topic of categorical data analysis. The presentation of the statistical methods exploits the connection to regression modeling with a focus on practical features rather than formal theory...There is much to learn from this book. Aside from the ordinary materials such as association diagrams, Mantel-Haenszel estimators, or overdispersion, the reader will also find some less-often presented but interesting and stimulating topics...[ T]his is an excellent book, giving an up-to-date introduction to the wide field of analyzing categorical data' - "Biometrics", September 2004.'...It is of great help to data analysts, practitioners and researchers who deal with categorical data and need to get a necessary insight into the methods of analysis as well as practical guidelines for solving problems' - "International Journal of General Systems", August 2004. 'The author has succeeded in writing a useful and readable textbook combining most of general theory and practice of count data' - "Kwantitatieve Methoden". 'The book especially stresses how to analyze and interpret data...In fact, the highly detailed multi-page descriptions of analysis and interpretation make the book stand out' - "Mathematical Geology", February 2005.'Overall, this is a competent and detailed text that I would recommend to anyone dealing with the analysis of categorical data' - "Journal of the Royal Statistical Society". 'This important work allows for clear analogies between the well-known linear models for Gaussian data and categorical data problems. Jeffrey Simonoff's "Analyzing Categorical Data" provides an introduction to many of the important ideas and methods for understanding counted data and tables of counts. Some readers will find Simonoff's style very much to their liking due to reliance on extended real data examples to illuminate ideas. I think the extensive examples will appeal to most students' - Sanford Weisberg, "SIAM Review", Vol. 47 (4), 2005.'It is clear that the focus of Simonoff's book is different from other books on categorical data analysis. As an introductory textbook, the book is comprehensive enough since all basic topics in categorical data analysis are discussed. I think Simonoff's book is a valuable addition to the literature because it discusses important models for counts' - Jeroen K. Vermunt, "Statistics in Medicine", Vol. 24, 2005.'The author based this book on his notes for a class with a very diverse pool of students. The material is presented in such a way that a very heterogeneous group of students could grasp it. All methods are illustrated with analyses of real data examples. The author provides a detailed discussion of the context and background of the problem. The book is very interesting and can be warmly recommended to people working with categorical data' - EMS - "European Mathematical Society Newsletter", December, 2004.'Categorical data arise often in many fields. This book provides an introduction to the analysis of such data. All methods are illustrated with analyses of real data examples, many from recent subject-area journal articles. These analyses are highlighted in the text and are more detailed than is typical. More than 200 exercises are provided, including many based on recent subject-area literature. Data sets and computer code are available at a Web site devoted to this text' - T. Postelnicu, "Zentralblatt MATH", Vol. 1028, 2003.'This book grew out of notes prepared by the author for classes in categorical data analysis. The presentation is fresh and compelling to read. Regression ideas are used to motivate the modelling presented. The book focuses on applying methods to real problems; many of these will be novel to readers of statistics texts. All chapters end with a section providing references to books or articles for the inquiring reader' - C.M. O'Brien, "Short Book Reviews", Vol. 23 (3), 2003.
(source: Nielsen Book Data)9780387007496 20160528
Science Library (Li and Ma)

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