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.
|Place of Publication||Boca Raton, Florida|
|Publisher||Chapman & Hall/CRC|
|Number of pages||549|
|Publication status||Published - 2017|
|Name||Chapman and Hall/CRC the R Series|
Stasinopoulos, D. M., Rigby, R., Heller, G., Voudouris, V., & De Bastiani, F. (2017). Flexible regression and smoothing: using GAMLSS in R. (Chapman and Hall/CRC the R Series). Boca Raton, Florida: Chapman & Hall/CRC. https://doi.org/10.1201/b21973