1st Edition

Flexible Regression and Smoothing
Using GAMLSS in R





  • Available for pre-order. Item will ship after September 30, 2020
ISBN 9780367658069
September 30, 2020 Forthcoming by Chapman and Hall/CRC
549 Pages

USD $54.95

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Book 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:







  • Provides a broad overview of flexible regression and smoothing techniques to learn from data whilst also focusing on the practical application of methodology using GAMLSS software in R.






  • Includes a comprehensive collection of real data examples, which reflect the range of problems addressed by GAMLSS models and provide a practical illustration of the process of using flexible GAMLSS models for statistical learning.






  • R code integrated into the text for ease of understanding and replication.






  • Supplemented by a website with code, data and extra materials.






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.

Table of Contents

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

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Author(s)

Biography

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