Books in the Chapman & Hall/CRC Interdisciplinary Statistics series

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.

Discusses variable models, including multilevel or generalized linear mixed models, longitudinal or panel models, item response or factor models, latent class or finite mixture models, and structural equation models. This work explains a range of estimation and prediction methods from biostatistics, psychometrics, econometrics and statistics.

This work introduces Markov chain Monte Carlo methodology at a level suitable for applied statisticians. It explains the methodology and its theoretical background, summarizes application areas, and presents illustrative applications in many areas including archaeology and astronomy.

Emphasizing model choice and model averaging, this book presents Bayesian methods for analyzing complex ecological data. It provides a basic introduction to Bayesian methods that assumes no prior knowledge. It includes descriptions of methods that deal with covariate data and covers techniques at the forefront of research.

Offers a review of methods for cluster detection, organized according to the different types of hypotheses that can be investigated using these techniques. This book presents various methods that allow for detection of emergent geographic clusters. It includes actual datasets and simplified examples to illustrate key concepts.

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.

Due to recent advances in methodology that offer significant improvements over conventional methods, there is increasing interest in the use of time series models for the study of neuroscience data such as EEG, MEG, fMRI, and NIRS. Written by one of the pioneers of these methods, this book presents an overview of time series models for the study of neuroscience data. It is accessible to applied statisticians working with neuroscience data as well as quantitatively trained neuroscientists. The book is supported by many real examples to illustrate the methods provides computational toolbox on the web, which enables readers to apply the methods to real data.

Addresses statistical challenges posed by inaccurately measuring explanatory variables, a common problem in biostatistics and epidemiology. This book explores both measurement error in continuous variables and misclassification in categorical variables. It is suitable for biostatisticians, epidemiologists, and students.