FLEXIBLE REGRESSION AND SMOOTHING: USING GAMLSS IN R

• Part I Introduction to models and packages
Why GAMLSS?
Introduction to the gamlss packages
• Part II The R implementation: algorithms and functions
The Algorithms
The gamlss() function
Methods for fitted gamlss objects
• Part III Distributions
The gamlss.family of distributions
Finite mixture distributions
• Part IV Additive terms
Linear parametric additive terms
Additive Smoothing Terms
Random effects
Part V Model selection and diagnostics
Model selection techniques
Diagnostics
• Part VI Applications
Centile Estimation
Further Applications

This book is about learning from data using the Generalized Additive Models for Location, Scale and Shape (GAMLSS). GAMLSS extends the Generalized Linear Models (GLMs) and Generalized Additive Models (GAMs) to accommodate large complex datasets, which are increasingly prevalent. GAMLSS allows any parametric distribution for the response variable and modelling all the parameters (location, scale and shape) of the distribution as linear or smooth functions of explanatory variables. This book provides a broad overview of GAMLSS methodology and how it is implemented in R. It includes a comprehensive collection of real data examples, integrated code, and figures to illustrate the methods, and is supplemented by a website with code, data and additional materials.

Features
• Provides a broad overview of flexible regression and smoothing techniques to learn from data whilst also focusing on the practical application of methodology using GAMLSS software in R.
• Includes a comprehensive collection of real data examples, which reflect the range of problems addressed by GAMLSS models and provide a practical illustration of the process of using flexible GAMLSS models for statistical learning.
• R code integrated into the text for ease of understanding and replication.
• Supplemented by a website with code, data and extra materials.