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

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
Rilegato
ISBN 10: 1009410067 ISBN 13: 9781009410069
Casa editrice: Cambridge Univ Pr, 2024

Sinossi

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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Editore
Cambridge Univ Pr
Data di pubblicazione
2024
Lingua
Inglese
ISBN 10 
1009410067
ISBN 13 
9781009410069
Rilegatura
Copertina rigida
Numero di pagine
285
Contatto del produttore
non disponibile
Persona responsabile
Avery
https://www.avery.fr/

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