Böcker i Chapman & Hall/CRC Interdisciplinary Statistics-serien

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 shows scientific researchers and applied statisticians from a wide range of fields how to analyze their spatial point pattern data. Making the techniques accessible to non-mathematicians, the authors draw on their 25 years of software development experiences, methodological research, and broad scientific collaborations to deliver a book that clearly and succinctly explains concepts and addresses real scientific questions. The book uses the authors? R package spatstat throughout to process and analyze spatial point pattern data.

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 simulated data sets illustrate important concepts, with the data sets and codes available online. The book also includes practical examples, simulation studies, projects, and end-of-chapter exercises.

This book presents special statistical methods for analyzing data collected by questionnaires. It takes an applied approach to testing and measurement tasks, mirroring the growing use of statistical methods and software in education, psychology, sociology, and other fields. The authors cover classical test theory (CTT) and item response theory (IRT) basics, explore the latest IRT extensions, and describe estimation methods and diagnostic instruments. Stata and R software codes are included for each method and example datasets are available on the authors¿ web page.

Power Analysis of Trials with Multilevel Data is a valuable reference for anyone who wants to perform power calculations on trials with hierarchical data. It provides a thorough overview of power analysis, familiarizing you with terminology and notation, outlining the key concepts of statistical power and power analysis, and covering all common hierarchical designs.

Written for researchers and students in statistics, ecology, demography, and the social sciences, this book covers many modern developments of capture-recapture and related methods. With an emphasis on ecology, it helps readers understand model formulation and applications, including the technicalities of model diagnostics and checking. A wide range of real examples demonstrates the complexities that arise when describing extensive modern data. The book contains 130 exercises that extend the text and offers data sets, computer programs, and more online.

This book explores the ways in which statistical models, methods, and research designs can be used to open new possibilities for APC analysis. Within a single, consistent HAPC-GLMM statistical modeling framework, the authors synthesize APC models and methods for three research designs: age-by-time period tables of population rates or proportions, repeated cross-section sample surveys, and accelerated longitudinal panel studies. They show how the empirical application of the models to various problems leads to many fascinating findings on how outcome variables develop along the age, period, and cohort dimensions.

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.

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.

The third edition of the bestselling Clinical Trials in Oncology provides a concise, nontechnical, and thoroughly up-to-date review of methods and issues related to cancer clinical trials. The authors emphasize the importance of proper study design, analysis, and data management and identify the pitfalls inherent in these processes. In addition, the book has been restructured to have separate chapters and expanded discussions on general clinical trials 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. Although this book focuses on cancer trials, the same issues and concepts are important in any clinical setting. As always, the authors use clear, lucid prose and a multitude of real-world examples to convey the principles of successful trials without the need for a strong statistics or mathematics background. Armed with Clinical Trials in Oncology, Third Edition, clinicians and statisticians can avoid the many hazards that can jeopardize the success of a trial.

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.

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.

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.

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.

Presents developments in statistical methodology with a number of worked examples that outline how these methods can be applied. This title provides key tools needed to understand and model the genetic variation for drug response and equips statisticians with an understanding of this complex field and how computational skills can be employed.

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 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.

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.