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Research on mixed models has been extensive over the most recent decade. This book differs from the authors' previous monographs on longitudinal data in that it focuses on mixed models of a linear, generalized linear and nonlinear type. The book pays attention to recent developments that include diagnostics, semi-parametric methodology, non-normal random effects, multivariate longitudinal data, high-dimensional outcomes, joint modeling of longitudinal and survival data and discrimination and classification based on longitudinal data using mixed models.
List of contents
Introductory Material. Mixed Models for Continuous Data. Generalized Linear Mixed Models. Nonlinear Models. Data Exploration, Model Building and Model Diagnostics. Extensions and Topics. Incomplete Data and Sensitivity Analysis.
About the author
Geert Verbeke is affiliated with KU Leuven.
Geert Molenberghs is affiliated with the University of Hasselt.
Verbeke and Molenberghs previously published two monographs on longitudinal data, dealing with continuous longitudinal data and the non-Gaussian counterpart.
Summary
Research on mixed models has been extensive over the most recent decade. This book differs from the authors' previous monographs on longitudinal data in that it focuses on mixed models of a linear, generalized linear and nonlinear type. The book pays attention to recent developments that include diagnostics, semi-parametric methodology, non-normal random effects, multivariate longitudinal data, high-dimensional outcomes, joint modeling of longitudinal and survival data and discrimination and classification based on longitudinal data using mixed models.
