A comprehensive presentation of generalized additive models for location, scale and shape linking methods with diverse applications.
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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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Hardcover. Zustand: new. Hardcover. 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. This text provides a state-of-the-art treatment of distributional regression, accompanied by real-world examples from diverse areas of application. Maximum likelihood, Bayesian and machine learning approaches are covered in-depth and contrasted, providing an integrated perspective on GAMLSS for researchers in statistics and other data-rich fields. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Bestandsnummer des Verkäufers 9781009410069
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Hardback. Zustand: New. 1st. 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. Bestandsnummer des Verkäufers LU-9781009410069
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