Bøger i Chapman & Hall/CRC Handbooks of Modern Statistical Methods serien

This book explores various state-of-the-art aspects behind the statistical analysis of neuroimaging data. It examines the development of novel statistical approaches to model brain data. Designed for researchers in statistics, biostatistics, computer science, cognitive science, computer engineering, biomedical engineering, applied mathematics, physics, and radiology, the book can also be used as a textbook for graduate-level courses in statistics and biostatistics or as a self-study reference for Ph.D. students in statistics, biostatistics, psychology, neuroscience, and computer science.

This handbook provides a comprehensive and unified account of the main research developments in cluster analysis. Written by active, distinguished researchers in this area, the book helps readers make informed choices of the most suitable clustering approach for their problem and make better use of existing cluster analysis tools. It covers the traditional core approaches to cluster analysis, along with additional approaches, such as constrained and semi-supervised clustering. It also explores other relevant issues, such as cluster validation.

This handbook explains how to model epidemiological problems and improve inference about disease etiology from a geographical perspective. Top epidemiologists, geographers, and statisticians share interdisciplinary viewpoints on analyzing spatial data and space¿time variations in disease incidences. The book explores the use of GIS and spatial statistics as tools for the analysis of spatial epidemiological data. It covers methodological advances as well as applications in human and veterinary epidemiology. A supplementary website provides color figures, program code, and datasets.

This carefully edited collection synthesizes the state of the art in the theory and applications of designed experiments and their analyses. It provides a detailed overview of the tools required for the optimal design of experiments and their analyses. The handbook covers many recent advances in the field, including designs for nonlinear models and algorithms applicable to a wide variety of design problems. It also explores the extensive use of experimental designs in marketing, the pharmaceutical industry, engineering, and other areas.

This handbook presents many methodological advances and the latest applications of missing data methods in empirical research. It outlines a general taxonomy of missing data mechanisms and their implications for analysis and describes alternatives for estimating models when data are missing. The book covers a range of approaches that assess the sensitivity of inferences to alternative assumptions about the missing data process. It also discusses how to handle missing data in clinical trials and sample surveys.

Incorporating more than 20 years of the editors¿ and contributors¿ statistical work in mixed membership modeling, this handbook shows 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, readers will discover how to characterize complex multivariate data in a range of areas.

Survival analysis is an area of statistics that deals with time-to-event data that is complicated by censoring and the dynamic nature of events occurring in time. With chapters written by leading researchers in the field, this handbook presents modern techniques and research problems in lifetime data analysis. The book focuses on advances in survival analysis techniques, covering classical and Bayesian approaches. It gives a complete overview of the current status of survival analysis and should inspire further research in the field.

Brings together the major advances that have occurred over the years while incorporating enough introductory material for new users of Markov Chain Monte Carlo. Along with coverage of the theoretical foundations and algorithmic and computational methodology, this handbook also includes case studies that demonstrate the application of MCMC methods.

Offers an introduction detailing the evolution of the field of spatial statistics. This title focuses on the three main branches of spatial statistics: continuous spatial variation (point referenced data); discrete spatial variation, including lattice and areal unit data; and, spatial point patterns.

Focuses on the assorted challenges that arise in analyzing longitudinal data. This book explores four broad themes: parametric modeling, nonparametric and semi parametric methods, joint models, and incomplete data. It is suitable for those involved in the development of statistical methodology or the analysis of longitudinal data.