Linear and Generalized Linear Mixed Models and Their Applications

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Focusing on mixed effects models, this second edition provides a comprehensive overview of linear and generalized linear mixed models, emphasizing their theoretical foundations and practical applications across various disciplines. It systematically addresses inference for non-Gaussian models and incorporates recent advancements, including high-dimensional models in genome-wide association studies, new methods for prediction, selection, and diagnostics. The book serves as an essential resource for understanding the evolving landscape of mixed model analysis.