Major statistics book now out in paperback

The highly-commended book, 'Flexible Regression and Smoothing,' aims to help readers understand how to learn from data encountered in many fields.

Date: 10 December 2020

An important book co-authored by two London Met academics, Professors Robert  A. Rigby and Mikis D. Stasinopoulos, along with their colleagues Dr Fernanda de Bastiani, Professor Gillian Z. Heller, and Dr Vlasios Voudouris, has recently come out in paperback. 

Titled Flexible Regression and Smoothing: Using GAMLSS in R, the book is about learning from data using the Generalized Additive Models for Location, Scale and Shape (GAMLSS). 

GAMLSS extends the Generalized Linear Models and Generalized Additive Models to accommodate large complex datasets, which are increasingly prevalent. GAMLSS allows any parametric distribution for the response variable and modelling all the parameters of the distribution as linear or smooth functions of explanatory variables. GAMLSS was invented by Professors Rigby and Stasinopoulos and presented in a paper to the Royal Statistical Society.

GAMLSS was used by the World Health Organization to produce the WHO Child Growth Standards which were adopted by more than 150 countries. 

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 R code, and figures to illustrate the methods, and is supplemented by a website www.gamlss.com.

It aims to help readers understand how to learn from data encountered in many fields, and 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.

The European Central Bank’s Carli di Maio praised the book, saying, "that the authors succeed in communicating the process of learning from data using the GAMLSS suite of tools is due to the clear and effective organization of the book. The book is a complete introduction to GAMLSS models (and by extension GLMs and GAMs) as well as some newer techniques such as semi-parametric neural networks/deep learning and trees. I highly recommend it to any reader interested in advanced machine learning techniques."

The book is available from Taylor & Francis.  

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Pictured: detail from the book cover

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