Bücher der Reihe Chapman & Hall/CRC Texts in Statistical Science

Suitable for graduate students and researchers in statistics and biostatistics as well as those in the medical field, epidemiology, and social sciences, this book introduces univariate survival analysis and extends it to the multivariate case. It also covers competing risks and counting processes and provides many real-world examples, exercises, and R code. The text discusses survival data, survival distributions, frailty models, parametric methods, multivariate data and distributions, copulas, continuous failure, parametric likelihood inference, and non- and semi-parametric methods.

Focusing on Bayesian approaches and computations using analytic and simulation-based methods for inference, Time Series: Modeling, Computation, and Inference, Second Edition integrates mainstream approaches for time series modeling with significant recent developments in methodology and applications of time series analysis. It encompasses a graduate-level account of Bayesian time series modeling, analysis and forecasting, a broad range of references to state-of-the-art approaches to univariate and multivariate time series analysis, and contacts research frontiers in multivariate time series modeling and forecasting.It presents overviews of several classes of models and related methodology for inference, statistical computation for model fitting and assessment, and forecasting. It explores the connections between time- and frequency-domain approaches and develop various models and analyses using Bayesian formulations and computation, including use of computations based on Markov chain Monte Carlo (MCMC) and sequential Monte Carlo (SMC) methods. It illustrates the models and methods with examples and case studies from a variety of fields, including signal processing, biomedicine, environmental science, and finance.Along with core models and methods, the book represents state-of-the art approaches to analysis and forecasting in challenging time series problems. It also demonstrates the growth of time series analysis into new application areas in recent years, and contacts recent and relevant modeling developments and research challenges.New in the second edition:Expanded on aspects of core model theory and methodology.Multiple new examples and exercises.Detailed development of dynamic factor models.Updated discussion and connections with recent and current research frontiers.

This text shows how to use multivariate analysis to extract useful information from multivariate data and understand the structure of random phenomena. Along with the basic concepts of various procedures in traditional multivariate analysis, the book covers nonlinear techniques for clarifying phenomena behind observed multivariate data. It prima

This text provides graduate students with a rigorous treatment of probability theory, with an emphasis on results central to theoretical statistics. It presents classical probability theory motivated with illustrative examples in biostatistics, such as outlier tests, monitoring clinical trials, and using adaptive methods to make design changes b

This book defines and investigates the concept of a random object. To accomplish this task in a natural way, it brings together three major areas; statistical inference, measure-theoretic probability theory and stochastic processes. This point of view has not been explored by existing textbooks

Designed to provide a good balance of theory and computational methods that will appeal to students and practitioners with minimal mathematical and statistical background and no experience in Bayesian statistics to students and practitioners looking for advanced methodologies.

Probability and Statistical Inference: From Basic Principles to Advanced Models covers aspects of probability, distribution theory, and inference that are fundamental to a proper understanding of data analysis and statistical modelling. It presents these topics in an accessible manner without sacrificing mathematical rigour, bridging the gap between the many excellent introductory books and the more advanced, graduate-level texts. The book introduces and explores techniques that are relevant to modern practitioners, while being respectful to the history of statistical inference. It seeks to provide a thorough grounding in both the theory and application of statistics, with even the more abstract parts placed in the context of a practical setting.Features:¿Complete introduction to mathematical probability, random variables, and distribution theory.¿Concise but broad account of statistical modelling, covering topics such as generalised linear models, survival analysis, time series, and random processes.¿Extensive discussion of the key concepts in classical statistics (point estimation, interval estimation, hypothesis testing) and the main techniques in likelihood-based inference.¿Detailed introduction to Bayesian statistics and associated topics.¿Practical illustration of some of the main computational methods used in modern statistical inference (simulation, boostrap, MCMC).This book is for students who have already completed a first course in probability and statistics, and now wish to deepen and broaden their understanding of the subject. It can serve as a foundation for advanced undergraduate or postgraduate courses. Our aim is to challenge and excite the more mathematically able students, while providing explanations of statistical concepts that are more detailed and approachable than those in advanced texts. This book is also useful for data scientists, researchers, and other applied practitioners who want to understand the theory behind the statistical methods used in their fields.

This book takes a first step in developing a full theory of richly parameterized models, which would allow statisticians to better understand their analysis results.

Offering deep insight into the connections between design choice and the resulting statistical analysis, this text explores how experiments are designed using the language of linear statistical models. It presents an organized framework for understanding the statistical aspects of experimental design as a whole within the structure provided by general linear models. The text describes specific forms or classes of experimental designs, incorporates actual experiments drawn from the scientific and technical literature, and includes many end-of-chapter exercises. Calculations are performed using R, with commands provided in an appendix. A solutions manual is available upon qualified course adoption.

Suitable for graduate students and researchers in statistics and biostatistics as well as those in the medical field, epidemiology, and social sciences, this book introduces univariate survival analysis and extends it to the multivariate case. It also covers competing risks and counting processes and provides many real-world examples, exercises, and R code. The text discusses survival data, survival distributions, frailty models, parametric methods, multivariate data and distributions, copulas, continuous failure, parametric likelihood inference, and non- and semi-parametric methods.

Providing a self-contained exposition of the theory of linear models, this treatise strikes a compromise between theory and practice, providing a sound theoretical basis while putting the theory to work in important cases.

Offers a practical treatment of the techniques and applications of the commonly used procedures for sampling from finite populations. This title includes various topics with illustrations, constructed from practical situations covering a variety of topics, from scholastic aptitude tests to healthcare expenditures and presidential elections.

A practical guide to statistical methods which reflects developments in the field. It includes a chapter introducing mixed models for binary data analysis and another on methods for modelling binary data. It also includes material on modelling ordered categorical data and provides a summary of the leading software packages.

Offering insight into the connections between design choice and the resulting statistical analysis, this book explores how experiments are designed using the language of linear statistical models. It also discusses factorial group screening experiments, regression model design, and an introduction to optimal design.

This is a reissue of the book by statistician R.G. Miller. The text has proved useful in understanding the level of the subject of applied statistics and includes numerous exericises.

Presents an overview of the logistic models, including binary, proportional, ordered, partially ordered, and unordered categorical response regression procedures. This book discusses other topics such as panel, survey, skewed, penalized, and exact logistic models. It illustrates how to apply the various models to health, and social science data.

Presents a set of useful tools that allow readers to understand the theoretical underpinnings of statistical methodology. This title emphasizes inferential procedures within the framework of parametric models.

Based on the authors¿ lecture notes, this text presents concise yet complete coverage of statistical inference theory, focusing on the fundamental classical principles. Unlike related textbooks, it combines the theoretical basis of statistical inference with a useful applied toolbox that includes linear models. Suitable for a second semester undergraduate course on statistical inference, the text offers proofs to support the mathematics and does not require any use of measure theory. It illustrates core concepts using cartoons and provides solutions to all examples and problems.

This new edition of this classic title, now in its seventh edition, presents a balanced and comprehensive introduction to the theory, implementation, and practice of time series analysis.

This classic textbook is suitable for a first course in the theory of statistics for students with a background in calculus, multivariate calculus, and the elements of matrix algebra.

Rev. ed. of: Computer-aided multivariate analysis. 4th ed. c2004.

Intended for those involved in complex processes in any industry, this book covers basic and more advanced techniques of data analysis. It also discusses experimental design and the so-called "Taguchi methods". Throughout the emphasis is on quality improvement and process capability.