This book is designed to be a practical study in infectious disease dynamics. The book offers an easy to follow implementation and analysis of mathematical epidemiology. The book focuses on recent cas
Offering a clear set of workable examples with data and explanations, Interaction Effects in Linear and Generalized Linear Models is a comprehensive and accessible text that provides a unified approac
This book provides a new analytical approach for dynamic data repeatedly measured from multiple subjects over time. Random effects account for differences across subjects. Auto-regression in response
When data consist of grouped observations or clusters, and there is a risk that measurements within the same group are not independent, group-specific random effects can be added to a regression model
Faraway surveys statistical techniques that grow from a linear model, describing three extensions. A practicing statistician needs to be aware of and familiar with the basic use of a broad range of id
This book presents wide-ranging coverage of parametric modeling in linear and nonlinear mixed-effects models. Using these models, readers can perform parameter estimation across a whole population of
This second edition updates the case studies using the latest versions of the software procedures, covers additional topics in the application of linear mixed models, and provides up-to-date informati
Linear models are widely used in applications in physical science, engineering, social science and business. The world of R has expanded in the ten years that have passed since this text’s first editi
"This book covers a wide range of statistical models, including hierarchical, hierarchical generalized linear, linear mixed, dynamic linear, smoothing, spatial, and longitudinal. It presents a framewo
Mixed modeling is one of the most promising and exciting areas of statistical analysis, enabling the analysis of nontraditional, clustered data that may come in the form of shapes or images. This book
Linear mixed-effects models (LMMs) are an important class of statistical models that can be used to analyze correlated data. Such data are encountered in a variety of fields including biostatistics, p
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 fram
"Focusing on user-developed programming, An R Companion to Linear Statistical Models serves two audiences: Those who are familiar with the theory and applications of linear statistical models and wish
Multivariate Generalized Linear Mixed Models Using R presents robust and methodologically sound models for analyzing large and complex data sets, enabling readers to answer increasingly complex resear
Although standard mixed effects models are useful in a range of studies, other approaches must often be used in correlation with them when studying complex or incomplete data. Mixed Effects Models for
State space models have gained tremendous popularity in recent years in as disparate fields as engineering, economics, genetics and ecology. After a detailed introduction to general state space models
This set features:Variance Components by Shayle R. Searle, George Casella, Charles E. McCulloch (978-0-470-00959-8)Matrix Algebra for Useful Statistics by Shayle R. Searle (978-0-470-00961-1)Linear Mo
Presents a comprehensive treatment of linear mixed models for continuous longitudinal data. This book puts major emphasis on exploratory data analysis for all aspects of the model, such as the margina
