Mikis D. Stasinopoulos (University of Greenwich), Thomas Kneib (Georg-August-Universitat, Nadja Klein (Technische Universitat Dortmund), Andreas Mayr (Rheinische Friedrich-Wilhelms-Universitat Bonn), Gillian Z. Heller (University of Sydney)

Generalized Additive Models for Location, Scale and Shape (eBook, PDF)

A Distributional Regression Approach, with Applications

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: Cambridge University Press
• Erscheinungstermin: 28. Februar 2024
• ISBN-13: 9781009410052
• Artikelnr.: 70911688

Autorenporträt

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.

Inhaltsangabe

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.