Flexible regression and smoothing: using GAMLSS in R

Stasinopoulos, Dimitrios, Rigby, Robert A., Heller, Gillian Z., Voudouris, Vlasios and De Bastiani, Fernanda (2017) Flexible regression and smoothing: using GAMLSS in R. The R Series . Chapman & Hall/CRC, Boca Raton, Florida. ISBN 978-1-138-19790-9


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.

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