Additive Mixed Model

Additive mixed models are statistical frameworks designed to analyze complex relationships between multiple factors and an outcome variable, accounting for both additive and interaction effects, and often incorporating random effects to handle correlated data. Current research emphasizes extending these models to high-dimensional data using techniques like gradient boosting trees, kernel-based neural networks, and Bayesian tensor regression, improving efficiency and handling non-linearity. These advancements are crucial for applications in diverse fields such as genetics, precision medicine, and survival analysis, enabling more accurate modeling of complex phenomena and facilitating better prediction and interpretation of results. The development of privacy-preserving distributed estimation methods further expands the applicability of these models to sensitive datasets.