Generalized Additive Models: Penalized Iterative Weighted Least Squares

This lecture covers the introduction to generalized additive models, focusing on iterative weighted least squares, model checking, and various types of data models. The instructor explains the concept of penalized iterative weighted least squares (P-IWLS) algorithm and its application in generalized additive models. The lecture also delves into the inference for smooth fits, mixed models, and the Laplace approximation method. Different algorithms for choosing the smoothing parameter are discussed, along with the relation with least squares and the Laplace approximation for integrals. The lecture concludes with a numerical example and approaches to iteration in the context of model estimation.