Bøger i Chapman & Hall/CRC Interdisciplinary Statistics serien

Statistical and mathematical models are defined by parameters that describe different characteristics of those models. This book explains why parameter redundancy and non-identifiability is a problem and the different methods that can be used for detection, including in a Bayesian context.

Capture-recapture methods have recently become popular in the social and medical sciences to estimate the size of elusive populations such as illicit drug users or people with a drinking problem. This book brings together important developments which allow the application of these methods with contributions from more than 40 researchers.

This is the first book focusing specifically on the design of experiments for GLMs. Much of the research literature on this topic is at a high mathematical level, and without any information on computation.

This book provides practical methods for analyzing missing data along with the heuristic reasoning for understanding the theoretical underpinnings. The author presents both frequentist and Bayesian perspectives. He describes easy-to-implement approaches, the underlying assumptions, and practical means for assessing these assumptions. Actual and

This book fills a gap by covering the principles of data and information essential for every researcher. These topics are firmly planted in existing graduate curricula, and covered in the research methods courses required of most master's level programs, yet a comprehensive and authoritative text did not exist until now.

This book describes new data-driven methodologies and case studies.The primary targeted audience is researchers in statistics, as well as those in fundamental roles such as earth sciences, environmetrics, astrophysics, machine learning, image analysis and bioinformatics.

The importance of Bayesian signal processing methods have grown over the past decade. A wealth of Bayesian tools are available for solving highly complex inference problems, including particle filters, Markov chain Monte Carlo, and variational Bayes. These methods can be utilized to solve some of the area's major challenges, from state and parameter estimation to decision/control. This book provides full coverage of the background material, including models, inference methods and case studies/examples in an accessible but not overly mathematical style.

Multiple Imputation of Missing Data in Practice: Basic Theory and Analysis Strategies provides a comprehensive introduction to the multiple imputation approach to missing data problems that are often encountered in data analysis.

Methods to monitor geographic patterns have received growing interest in recent years. Using an integrated approach, Statistical Detection and Monitoring of Geographic Clusters provides a thorough review of methods for cluster detection, organized according to the different types of hypotheses that can be investigated using these t

This book provides a detailed account on some of the newest methods for dealing with directional data. Directional data naturally arises in diverse domains such as earth sciences (in particular geology), meteorology, astronomy, studies of animal behavior, image analysis, neurosciences, medicine, machine learning, bioinformatics, and cosmology.

Develops a methodology that addresses the importance of scientific relevance, biological variability, and invariance of the statistical and scientific inferences with respect to the arbitrary choice of the coordinate system.

Presents a look at medical imaging and statistics, ranging from the statistical aspects of imaging technology to the statistical analysis of images. This book provides technicians and students with the statistical principles that underlay medical imaging and offers reference material for researchers involved in the design of technology.

Focusing on the specific features of QoL data, this book details the relevant issues and presents a range of solutions. It includes SAS and S-PLUS programs, checklists, figures, and a presentation that combine to provide readers with the tools and skills they need to design, conduct, analyze, and report their own studies.

Statistical and mathematical models are defined by parameters that describe different characteristics of those models. This book explains why parameter redundancy and non-identifiability is a problem and the different methods that can be used for detection, including in a Bayesian context.

Describing tools commonly used in the field, this textbook provides an understanding of a broad range of analytical tools required to solve transportation problems. It includes a wide breadth of examples and case studies in various aspects of transportation planning, engineering, safety, and economics.

This is the first book focusing specifically on the design of experiments for GLMs. Much of the research literature on this topic is at a high mathematical level, and without any information on computation.

This new edition of a bestseller provides a nontechnical and thoroughly up-to-date review of methods and issues related to clinical trials. The authors emphasize the importance of proper study design, analysis, and data management and identify the pitfalls inherent in these processes. The book has been restructured with separate chapters and expanded discussions on general clinical trial issues and issues specific to Phases I, II, and III. New sections cover innovations in Phase I designs, randomized Phase II designs, and overcoming the challenges of array data.

This book provides a detailed account on some of the newest methods for dealing with directional data. Directional data naturally arises in diverse domains such as earth sciences (in particular geology), meteorology, astronomy, studies of animal behavior, image analysis, neurosciences, medicine, machine learning, bioinformatics, and cosmology.