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- 7.000.000 titres dans notre catalogue
- Payer en toute sécurité
- Toujours un magasin près de chez vous
Flexible Regression and Smoothing
Using GAMLSS in R
Mikis D Stasinopoulos, Robert A Rigby, Gillian Z Heller
97,95 €
+ 195 points
Format
Description
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.
Key Features:
This book aims to help readers understand how to learn from data encountered in many fields. It will be useful for practitioners and researchers who wish to understand and use the GAMLSS models to learn from data and also for students who wish to learn GAMLSS through practical examples.
Spécifications
Parties prenantes
- Auteur(s) :
- Editeur:
Contenu
- Nombre de pages :
- 572
- Langue:
- Anglais
- Collection :
Caractéristiques
- EAN:
- 9780367658069
- Date de parution :
- 30-09-20
- Format:
- Livre broché
- Format numérique:
- Trade paperback (VS)
- Dimensions :
- 178 mm x 254 mm
- Poids :
- 984 g
