Multivariate analysis is a popular area in statistics and data. This book provides a good balance between conceptual understanding, key theoretical presentation, and detailed implementation with software R for commonly usedmultivariate analysis models and methods in practice.
Multivariate analysis is a popular area in statistics and data. This book provides a good balance between conceptual understanding, key theoretical presentation, and detailed implementation with software R for commonly used multivariate analysis models and methods in practice.
Multivariate analysis is a popular area in statistics and data. This book provides a good balance between conceptual understanding, key theoretical presentation, and detailed implementation with software R for commonly used multivariate analysis models and methods in practice.
Presenting effective approaches to address missing data, measurement errors, censoring, and outliers in longitudinal data, this book covers linear, nonlinear, generalized linear, nonparametric, and semiparametric mixed effects models. It links each mixed effects model with the c…
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 Complex Data discusses commonly used mixed effects models and presents appropr…
"The book introduces linear mixed effects (LME) models, generalized linear mixed models (GLMMs), nonlinear mixed effects (NLME) models, and semiparametric and nonparametric mixed effects models. It also includes general approaches for the analysis of complex data with missing va…
"The book introduces linear mixed effects (LME) models, generalized linear mixed models (GLMMs), nonlinear mixed effects (NLME) models, and semiparametric and nonparametric mixed effects models. It also includes general approaches for the analysis of complex data with missing va…
