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Generalized Additive Models for Location, Scale and Shape
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.
Book Details
- Publisher: Cambridge University Press
- Publish Date: Feb 29th, 2024
- Pages: 306
- Language: English
- Edition: undefined - undefined
- Dimensions: 10.00in - 7.00in - 0.75in - 1.65lb
- EAN: 9781009410069
- Categories: • Probability & Statistics - General
About the Author
Stasinopoulos, Mikis D.: - 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.
Kneib, Thomas: - Thomas Kneib is a Professor of Statistics at the University of Göttingen, Germany, where he is the Spokesperson of the interdisciplinary Centre for Statistics and Vice-Spokesperson of the Campus Institute Data Science. His main research interests include semiparametric regression, spatial statistics, and distributional regression.
Klein, Nadja: - Nadja Klein is Emmy Noether Research Group Leader in Statistics and Data Science and Professor for Uncertainty Quantification and Statistical Learning at TU Dortmund University and the Research Center Trustworthy Data Science and Security. Nadja is member of the Junge Akademie and associate editor for 'Biometrics, ' 'JABES, ' and 'Dependence Modeling.' Her. Her research interests include Bayesian methods, statistical and machine learning, and spatial statistics.
Praise for this book
'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 Netherlands