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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.

Focuses on the analysis and modeling of a meta-analysis with individually pooled data. This book explores alternatives to the profile likelihood method, including approximated likelihood and multilevel models, and shows how the nonparametric profile maximum likelihood estimator can be computed via the EM algorithm with a gradient function update.

Covers many of the diverse methods in applied probability and statistics. This book also emphasizes the variety of practical situations in insurance and actuarial science where these techniques may be used. It examines generalized linear models, credibility theory, game theory, and simulation techniques and contains numerous examples and problems.

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.

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.

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.

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.

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.

Biology is at the beginning of a new era, promising significant discoveries that will be characterized by information-packed databases. This text offers a textbook treatment of the combinatorial and statistical problems that will arise in this new era.

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.