Generalized Additive Models for Location, ...

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Beschreibung

"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"--

Über den Autor

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.

Inhaltsverzeichnis

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.

Erscheinungsjahr:	2024
Fachbereich:	Kommunikationswissenschaften
Genre:	Medienwissenschaften
Rubrik:	Wissenschaften
Medium:	Buch
Seiten:	306
ISBN-13:	9781009410069
ISBN-10:	1009410067
Einband:	Gebunden
Autor:	Mayr, Andreas Heller, Gillian Z. Stasinopoulos, Mikis D. Klein, Nadja Kneib, Thomas
Hersteller:	Cambridge University Press
Maße:	259 x 186 x 26 mm
Von/Mit:	Andreas Mayr (u. a.)
Erscheinungsdatum:	29.02.2024
Gewicht:	0,76 kg

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