Introduction

This website is a companion to the book

Stasinopoulos DM, Kneib T, Klein N, Mayr A and Heller GZ (2024). Generalized Additive Models for Location, Scale and Shape: A Distributional Regression Approach, with Applications. Cambridge University Press.

An emerging field in statistics, distributional regression facilitates the modelling of the complete conditional distribution, rather than just the mean. This book introduces generalized additive models for location, scale and shape (GAMLSS) – one of the most important classes of distributional regression. Taking a broad perspective, the authors consider penalized likelihood inference, Bayesian inference, and boosting as potential ways of estimating models and illustrate their usage in complex applications. Written by the international team who developed GAMLSS, the text’s focus on practical questions and problems sets it apart. Case studies demonstrate how researchers in statistics and other data-rich disciplines can use the model in their work, exploring examples ranging from fetal ultrasounds to social media performance metrics. The R code and data sets for the case studies are available on this companion website, allowing for replication and further study.

Further resources

• Cambridge University Press page
• Most of the data used in the book is freely available in the R package gamlss.data or taken from other R packages (as indicated in the code provided on this website).
• The books Flexible Regression and Smoothing - Using GAMLSS in R and Distributions for Modeling Location, Scale, and Shape - Using GAMLSS in R can be seen as complements to this book, focusing on the R package gamlss on the one hand and the distributions available in this package on the other hand.
• The R packages gamlss, bamlss and gamboostLSS are most central to this book and are used throughout the case studies.
• The website gamlss.com provides a lot of additional material related to generalized additive models for location, scale and shape.