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Flexible regression and smoothing : using GAMLSS in R
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 pa...
| Auteurs principaux : | , , , , |
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| Format : | Livre |
| Langue : | anglais |
| Titre complet : | Flexible regression and smoothing : using GAMLSS in R / Mikis D. Stasinopoulos, Robert A. Rigby, Gillian Z. Heller... [et al.] |
| Publié : |
Boca Raton :
CRC Press/Taylor & Francis Group
, C 2017 |
| Description matérielle : | 1 vol. (xxii-549 pages) |
| Collection : | Chapman & Hall/CRC the R series (Print) |
| Sujets : |
| Résumé : | 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.-- |
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| Notes : | Autres contributions (auteurs) : Vlasios Voudouris, Fernandan de Bastiani |
| Bibliographie : | Bibliogr. p. 523-542. Index. |
| ISBN : | 978-1-138-19790-9 |