Flexible regression and smoothing : using GAMLSS in R

Flexible regression and smoothing : using GAMLSS in R

Mikis D. Stasinopoulos, Robert A. Rigby, Gillian Z. Heller, Vlasios Voudouris, Fernanda De Bastiani
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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.

Категории:
Година:
2017
Издание:
1
Издателство:
Chapman and Hall/CRC
Език:
english
Страници:
549
ISBN 10:
1315269872
ISBN 13:
9781315269870
Серия:
Chapman & Hall/CRC the R series (CRC Press)
Файл:
PDF, 18.36 MB
IPFS:
CID , CID Blake2b
english, 2017
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