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Table of Contents: “…; 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.…”
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