Flexible regression and smoothing : using GAMLSS in R

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

• Publisher: Chapman and Hall/CRC
• Year: 2017
• Tongue: English
• Leaves: 572
• Series: Chapman & Hall/CRC the R series (CRC Press)
• Edition: 1
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Content: 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

✦ Subjects

Regression analysis;Data processing;Linear models (Statistics);Smoothing (Statistics);Big data;R (Computer program language)