Focusing on large sample techniques in statistics, this guide emphasizes the development of analytical skills and the importance of understanding motivation in statistical analysis. It provides readers with practical methods and insights to effectively handle large datasets, making complex statistical concepts accessible and applicable to real-world scenarios.
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.
Theory, Applications, and Open Problems
Mixed effects models are crucial in statistics, encompassing various types such as linear, generalized linear, non-linear, and non-parametric models. This monograph offers an in-depth exploration of these complex models, highlighting their practical applications across diverse fields. It serves as a thorough resource for understanding the intricacies and methodologies associated with mixed effects models, making it valuable for both practitioners and researchers in statistics.
Mixed-effects models have found broad applications in various fields. As a result, the interest in learning and using these models is rapidly growing.