Linear Mixed-Effects Models Using R

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

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

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

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

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

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

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

This textbook for students in the health and social sciences covers the basics of linear model methods with a minimum of mathematics, assuming only a pre-calculus background. Numerous examples drawn from the news and current events with an emphasis on health issues, illustrate the concepts in an immediately accessible way. Methods covered include linear regression models, Poisson regression, logistic regression, proportional hazards regression, survival analysis, and nonparametric regression. The author emphasizes interpretation of computer output in terms of the motivating example. All of the R code is provided and carefully explained, allowing readers to quickly apply the methods to their own data. Plenty of exercises help students think about the issues involved in the analysis and its interpretation. Code and datasets are available for download from the book's website at www.cambridge.org/zelterman

The goal of this text for students and professionals is to help readers who have worked with linear mixed models make the transition to generalized linear mixed models. Benefits and challenges of gene

Understand deep learning, the nuances of its different models, and where these models can be applied.The abundance of data and demand for superior products/services have driven the development of adva

Mixed effects models, including linear mixed models, generalized linear mixed models, nonlinear mixed effects models, and non-parametric mixed effects models, are complex models by nature; yet, these

This comprehensive guide to Bayesian methods in astronomy enables hands-on work by supplying complete R, JAGS, Python, and Stan code, to use directly or to adapt. It begins by examining the normal model from both frequentist and Bayesian perspectives and then progresses to a full range of Bayesian generalized linear and mixed or hierarchical models, as well as additional types of models such as ABC and INLA. The book provides code that is largely unavailable elsewhere and includes details on interpreting and evaluating Bayesian models. Initial discussions offer models in synthetic form so that readers can easily adapt them to their own data; later the models are applied to real astronomical data. The consistent focus is on hands-on modeling, analysis of data, and interpretations that address scientific questions. A must-have for astronomers, its concrete approach will also be attractive to researchers in the sciences more generally.

This book provides the application of generalized linear mixed-effects models and its related models in the statistical design and analysis of repeated measures adopted in randomized controlled trials

This is the second edition of a monograph on generalized linear models with random effects that extends the classic work of McCullagh and Nelder. It has been thoroughly updated, with around 80 pages a

This book presents a greatly enlarged statistical framework compared to generalized linear models (GLMs) with which to approach regression modelling. Comprising of about half-a-dozen major classes of

Hidden Markov Models (HMMs) remains a vibrant area of research in statistics, with many new applications appearing since publication of the first edition. The second edition of this popular reference

This book presents a greatly enlarged statistical framework compared to generalized linear models (GLMs) with which to approach regression modelling. Comprising of about half-a-dozen major classes of

Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and STANintroduces the Bayesian and frequentist methods of conducting data analyses. The book provides the theoretical background in

This book is a comprehensive introduction to financial modeling that teaches advanced undergraduate and graduate students in finance and economics how to use R to analyze financial data and implement

"This useful guide helps pharmaceutical scientists and students learn the requisite information needed to perform mixed effect modeling of pharmacologic data using the NONMEM software package"--Provid

Edward F. Vonesh's Generalized Linear and Nonlinear Models for Correlated Data: Theory and Applications Using SAS is devoted to the analysis of correlated response data using SAS, with special emphasi

Reveals How HMMs Can Be Used as General-Purpose Time Series ModelsImplements all methods in RHidden Markov Models for Time Series: An Introduction Using R applies hidden Markov models (HMMs) to a wide

This book provides a protocol for conducting gauge repeatability and reproducibility (R&R) experiments, which are required whenever a new test system is developed to monitor a manufacturing proces