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
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
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
Linear models are central to the practice of statistics and form the foundation of a vast range of statistical methodologies. Julian J. Faraway's critically acclaimed Linear Models with R examined reg
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
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
Simplifying the often confusing array of software programs for fitting linear mixed models (LMMs), Linear Mixed Models: A Practical Guide Using Statistical Software provides a basic introduction to pr
This book discusses advanced statistical methods that can be used to analyse ecological data. Most environmental collected data are measured repeatedly over time, or space and this requires the use of
This book discusses advanced statistical methods that can be used to analyse ecological data. Most environmental collected data are measured repeatedly over time, or space and this requires the use o
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 textbook focuses on the practice of regression and analysis of variance. Readers will learn which methods are available and the various situations in which they can be applied. Numerous examples
"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
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
This book provides a comprehensive treatment of linear mixed models for continuous longitudinal data. Next to model formulation, this edition puts major emphasis on exploratory data analysis for all a
An advanced discussion of linear models with mixed or random effects.In recent years a breakthrough has occurred in our ability to draw inferences from exact and optimum tests of variance component mo
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
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
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
An accessible and self-contained introduction to statistical models-now in a modernized new edition Generalized, Linear, and Mixed Models, Second Edition provides an up-to-date treatment of the essen
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
A precise and accessible presentation of linear model theory, illustrated with data examplesStatisticians often use linear models for data analysis and for developing new statistical methods. Most boo
"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
An overview of the theory and application of linear and nonlinear mixed-effects models in the analysis of grouped data, such as longitudinal data, repeated measures, and multilevel data. The authors 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
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
