Generalized Additive Models for Location, Scale and Shape: A Distributional Regression Approach, With Applications - Couverture rigide

Livre 44 sur 45: Cambridge Series in Statistical and Probabilistic Mathematics

Stasinopoulos, Mikis D.; Kneib, Thomas; Klein, Nadja; Mayr, Andreas; Heller, Gillian Z.

 
9781009410069: Generalized Additive Models for Location, Scale and Shape: A Distributional Regression Approach, With Applications
Couverture rigide
ISBN 10 : 1009410067 ISBN 13 : 9781009410069
Editeur : Cambridge University Press, 2024

Synopsis

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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À propos des auteurs

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.

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.

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.

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Éditeur
Cambridge University Press
Date d'édition
2024
Langue
anglais
ISBN 10 
1009410067
ISBN 13 
9781009410069
Reliure
Relié
Nombre de pages
306
Coordonnées du fabricant
non disponible
Personne responsable
Avery
https://www.avery.fr/

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