Generalized Additive Models for Location, Scale and Shape
A Distributional Regression Approach, with Applications
Published on 29. February 2024
Book
Hardback
306 pages
978-1-009-41006-9 (ISBN)
Description
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 the book's companion website, allowing for replication and further study.
Reviews / Votes
'In a relatively short time, GAMLSS has become very popular. The driving force was the quality of the R package that made this powerful model easily accessible for applied statisticians. Despite the popularity of the model, the literature on GAMLSS is relatively small. This book fills a gap: it carefully presents the existing theory and adds extensions like Bayesian inference and boosting as well as new tools for interpreting GAMLSS models. In addition, it contains a large section with new and inspiring applications.' Paul Eilers, Erasmus University Medical Center, Rotterdam, the NetherlandsMore details
Series
Language
English
Place of publication
Cambridge
United Kingdom
Product notice
sewn/stitched
Cloth over boards
Illustrations
Worked examples or Exercises
Dimensions
Height: 259 mm
Width: 186 mm
Thickness: 26 mm
Weight
760 gr
ISBN-13
978-1-009-41006-9 (9781009410069)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Mikis D. Stasinopoulos | Thomas Kneib | Nadja Klein
Generalized Additive Models for Location, Scale and Shape
A Distributional Regression Approach, with Applications
E-Book
02/2024
Cambridge University Press
€67.99
Available for download
Persons
Mikis D. Stasinopoulos is Professor of Statistics at the School of Computing and Mathematical Sciences, University of Greenwich. He is, together with Professor Bob Rigby, coauthor of the original Royal Statistical Society article on GAMLSS. He has also coauthored three books on distributional regression, and in particular the theoretical and computational aspects of the GAMLSS framework.
Content
Preface; Notation and Termanology; Part I. Introduction and Basics: 1. Distributional Regression Models; 2. Distributions; 3. Additive Model Terms; Part II. Statistical Inference in GAMLSS: 4. Inferential Methods; 5. Penalized Maximum Likelihood Inference; 6. Bayesian Inference; 7. Statistical Boosting for GAMLSS; Part. III Applications and Case Studies: 8. Fetal Ultrasound; 9. Speech Intelligibility Testing; 10. Social Media Post Performance; 11. Childhood Undernutrition in India; 12. Socioeconomic Determinants of Federal Election Outcomes in Germany; 13. Variable Selection for Gene Expression Data; Appendix A. Continuous Distributions; Appendix B. Discrete Distributions; Bibliography; Index.