Stasinopoulos / Rigby / Heller Flexible Regression and Smoothing

Using GAMLSS in R
Erscheinungsjahr 2017
ISBN: 978-1-351-98038-8
Verlag: Taylor & Francis
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 571 Seiten

Reihe: Chapman & Hall/CRC The R Series

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.

Autoren/Hrsg.

Stasinopoulos, Mikis D.

Rigby, Robert A.

Heller, Gillian Z.

Voudouris, Vlasios

De Bastiani, Fernanda

Fachgebiete

• Mathematik | Informatik Mathematik Stochastik

Inhaltsverzeichnis

Part I Introduction to models and packages

Why GAMLSS?

Introduction to the gamlss packages

Part II The R implementation: algorithms and functions

The Algorithms

The gamlss() function

Methods for fitted gamlss objects

Part III Distributions

The gamlss.family of distributions

Finite mixture distributions

Part IV Additive terms

Linear parametric additive terms

Additive Smoothing Terms

Random effects

Part V Model selection and diagnostics

Model selection techniques

Diagnostics

Part VI Applications

Centile Estimation

Further Applications