ABSTRACT

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.

In particular, the GAMLSS statistical framework enables flexible regression and smoothing models to be fitted to the data. The GAMLSS model assumes that the response variable has any parametric (continuous, discrete or mixed) distribution which might be heavy- or light-tailed, and positively or negatively skewed. In addition, all the parameters of the distribution (location, scale, shape) can be modelled as linear or smooth functions of explanatory variables.

Key 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.

This book aims to help readers understand how to learn from data encountered in many fields. It will be useful for practitioners and researchers who wish to understand and use the GAMLSS models to learn from data and also for students who wish to learn GAMLSS through practical examples.

part I|2 pages

Introduction to models and packages

chapter 1|28 pages

Why Gamlss?

chapter 2|26 pages

Introduction to the gamlss packages

part II|2 pages

Algorithms, functions and inference

chapter 3|28 pages

The algorithms

chapter 4|26 pages

The gamlss() function

chapter 5|38 pages

Inference and prediction

part III|2 pages

Distributions

chapter 6|38 pages

The GAMLSS family of distributions

chapter 7|30 pages

Finite mixture distributions

part IV|2 pages

Model terms

chapter 8|32 pages

Linear parametric additive terms

chapter 9|66 pages

Additive smoothing terms

chapter 10|54 pages

Random effects

part V|2 pages

Model selection and diagnostics

chapter 11|40 pages

Model selection techniques

chapter 12|30 pages

Diagnostics

part VI|2 pages

Applications

chapter 13|48 pages

Centile estimation

chapter 14|26 pages

Further applications