Introduction | What are (polynomial) splines? | Normal GeD Spline Regression | Stage A | Stage B | Generalized GeD Spline Regression | The GNM (GLM) fitting problem | Generalized GeDS estimation | Poisson: | Gamma: | (Quasi)Binomial: | Bivariate GeD Spline Regression | Normal Bivariate GeDS: | Poisson Bivariate GeDS: | Multivariate Normal GeDS Regression

Why a joint smoother? | A four-dimensional Normal example | Repeated test-sample comparison | A four-dimensional generalized-response example | Moving to five dimensions

1. Introduction | 2. Functional Gradient Boosting | 2.1. Boosting with Splines | 2.2. Componentwise Functional Gradient Boosting | 3. Overcoming key limitations of Gradient Boosting with FGB-GeDS | 4. Functional Gradient Boosting with GeDS base-learners | 4.1. FGB-GeDS for regression | 4.2. FGB-GeDS for classification | 4.2.1. Two classes | 4.2.2. Multinomial classification

Introduction | What are Generalized Additive Models? | Linear Regression and GLM | Additive models and GAM | Generalized Additive Models fitting: Local-Scoring and Backfitting | 1. Backfitting | 2. Local Scoring | Generalized Additive Models with GeD Splines

Introduction | GeDS estimation method | Generalized additive models with GeDS | Functional gradient boosting | L2Boost Normal GeD spline regression | Numerical examples – non-additive models | Numerical examples – generalized additive models | Numerical examples – component-wise boosted models | Conclusions | GeDS Boost model update | Additional examples