Aimed at a nontechnical audience, with intuitive explanations instead of mathematical derivations, Analysis of Longitudinal Studies in Epidemiology covers a wide range of topics that include Poisson regression, survival analysis, repeated measure, clustered data, longitudinal observations, and …
Written for students and researchers in statistics and biostatistics, this book presents a modern introduction to categorical data analysis, with R integrated into the text as a data analysis method and a learning tool. The book includes many detailed worked examples to illustrate methods, with R …
One of the first texts to use R to illustrate the construction of experimental designs and analysis of data, this book covers both classical ideas in experimental design and the latest research topics. It clearly discusses the objectives of a research project that lead to an appropriate design …
Modelling, Inference and Data Analysis brings together key topics in mathematical statistics and presents them in a rigorous yet accessible manner. It covers aspects of probability, distribution theory and random processes that are fundamental to a proper understanding of inference. The book also …
Building an up-to-date understanding of the methodologies that can be used to shape public health policies, Epidemiology: Study Design and Data Analysis, Second Edition encompasses the study of epidemiology from the observation of associations between risk factors and disease to the use of …
This third edition of a classic textbook presents a comprehensive introduction to Bayesian data analysis. Written for students and researchers alike, the text is written in an easily accessible manner with chapters that contain many exercises as well as detailed worked examples taken from various …
Accessible to students in statistics and industrial engineering, this text presents a systematic introduction to traditional and modern SPC methods. Requiring some background in basic linear algebra, calculus, and introductory statistics, the book illustrates the methods using detailed worked …
This text emphasizes nonlinear models for a course in time series analysis. After introducing stochastic processes, Markov chains, Poisson processes, and ARMA models, the authors cover functional autoregressive, ARCH, threshold AR, and discrete time series models as well as several complementary …
Linear algebra and the study of matrix algorithms have become fundamental to the development of statistical models. Using a vector-space approach, this book provides an understanding of the major concepts that underlie linear algebra and matrix analysis. Each chapter introduces a key topic, such as …
The book gives a comprehensive coverage of most classical nonparametric statistical tests as well as nonparametric techniques in regression and generalized regression, time-to-event analysis, density estimations and resampling methods. All these techniques are widely used in modern day statistics. …
Exercises and Solutions in Statistical Theory helps students and scientists obtain an in-depth understanding of statistical theory by working on and reviewing solutions to interesting and challenging exercises of practical importance. Unlike similar books, this text incorporates many exercises that …
Designed for a one-semester advanced undergraduate or graduate course, Statistical Theory: A Concise Introduction clearly explains the underlying ideas and principles of major statistical concepts, including parameter estimation, confidence intervals, hypothesis testing, asymptotic analysis, …
Providing a much-needed bridge between elementary statistics courses and advanced research methods courses, Understanding Advanced Statistical Methods helps students grasp the fundamental assumptions and machinery behind sophisticated statistical topics, such as logistic regression, maximum …
Bayesian statistical methods have become widely used for data analysis and modelling in recent years, and the BUGS software has become the most popular software for Bayesian analysis worldwide. Authored by the team that originally developed this software, The BUGS Book provides a practical …
Intended for a second course in stationary processes, Stationary Stochastic Processes: Theory and Applications presents the theory behind the field’s widely scattered applications in engineering and science. In addition, it reviews sample function properties and spectral representations for …
Generalized Linear Mixed Models: Modern Concepts, Methods and Applications presents an introduction to linear modeling using the generalized linear mixed model (GLMM) as an overarching conceptual framework. For readers new to linear models, the book helps them see the big picture. It shows how …
Developed from the authors’ graduate-level biostatistics course, Applied Categorical and Count Data Analysis explains how to perform the statistical analysis of discrete data, including categorical and count outcomes. The authors describe the basic ideas underlying each concept, model, and approach …
Multivariate Survival Analysis and Competing Risks introduces univariate survival analysis and extends it to the multivariate case. It covers competing risks and counting processes and provides many real-world examples, exercises, and R code. The text discusses survival data, survival distributions …
Based on the authors’ lecture notes, Introduction to the Theory of Statistical Inference presents concise yet complete coverage of statistical inference theory, focusing on the fundamental classical principles. Suitable for a second-semester undergraduate course on statistical inference, the book …