Incluye referencias bibliográficas.

Copyright Page; Dedication; Contents; Preface; I: Introduction to models and packages; 1 Why GAMLSS?; 1.1 Introduction; 1.2 The 1980s Munich rent data; 1.3 The linear regression model (LM); 1.4 The generalized linear model (GLM); 1.5 The generalized additive model (GAM); 1.6 Modelling the scale parameter; 1.7 The generalized additive model for location, scale and shape (GAMLSS); 1.8 Bibliographic notes; 1.9 Exercises; 2 Introduction to the gamlss packages; 2.1 Introduction; 2.2 The gamlss packages; 2.3 A simple example using the gamlss packages.
3.5 Bibliographic notes3.6 Exercises; 4 The gamlss() function; 4.1 Introduction to the gamlss() function; 4.2 The arguments of the gamlss() function; 4.2.1 The algorithmic control functions; 4.2.2 Weighting out observations: the weights and data=subset() arguments; 4.3 The refit and update functions; 4.3.1 refit(); 4.3.2 update(); 4.4 The gamlss object; 4.5 Methods and functions for gamlss objects; 4.6 Bibliographic notes; 4.7 Exercises; 5 Inference and prediction; 5.1 Introduction; 5.1.1 Asymptotic behaviour of a parametric GAMLSS model; 5.1.2 Types of inference in a GAMLSS model.
5.1.3 Likelihood-based inference5.1.4 Bootstrapping; 5.2 Functions to obtain standard errors; 5.2.1 The gen.likelihood() function; 5.2.2 The vcov() and rvcov() functions; 5.2.3 The summary() function; 5.3 Functions to obtain confidence intervals; 5.3.1 The confint() function; 5.3.2 The prof.dev() function; 5.3.3 The prof.term() function; 5.4 Functions to obtain predictions; 5.4.1 The predict() function; 5.4.2 The predictAll() function; 5.5 Appendix: Some theoretical properties of GLM and GAMLSS; 5.6 Bibliographic notes; 5.7 Exercises; III: Distributions; 6 The GAMLSS family of distributions.

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.