Generalized additive models for location, scale, and shape: a distributional regression approach, with applications
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 regr...
Gespeichert in:
| Beteilige Person: | |
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| Weitere beteiligte Personen: | , , , |
| Format: | E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Cambridge ; New York, NY
Cambridge University Press
2024
|
| Schriftenreihe: | Cambridge series in statistical and probabilistic mathematics
56 |
| Links: | https://doi.org/10.1017/9781009410076 |
| Zusammenfassung: | 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. |
| Umfang: | 1 Online-Ressource (xx, 285 Seiten) |
| ISBN: | 9781009410076 |
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| spelling | Stasinopoulos, Mikis D. Generalized additive models for location, scale, and shape a distributional regression approach, with applications Mikis D. Stasinopoulos, Thomas Kneib, Nadja Klein, Andreas Mayr, Gillian Z. Heller Cambridge ; New York, NY Cambridge University Press 2024 1 Online-Ressource (xx, 285 Seiten) txt c cr Cambridge series in statistical and probabilistic mathematics 56 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. Heller, Gillian Z. Klein, Nadja 1987- Kneib, Thomas Mayr, Andreas 1983- Erscheint auch als Druck-Ausgabe 9781009410069 |
| spellingShingle | Stasinopoulos, Mikis D. Generalized additive models for location, scale, and shape a distributional regression approach, with applications |
| title | Generalized additive models for location, scale, and shape a distributional regression approach, with applications |
| title_auth | Generalized additive models for location, scale, and shape a distributional regression approach, with applications |
| title_exact_search | Generalized additive models for location, scale, and shape a distributional regression approach, with applications |
| title_full | Generalized additive models for location, scale, and shape a distributional regression approach, with applications Mikis D. Stasinopoulos, Thomas Kneib, Nadja Klein, Andreas Mayr, Gillian Z. Heller |
| title_fullStr | Generalized additive models for location, scale, and shape a distributional regression approach, with applications Mikis D. Stasinopoulos, Thomas Kneib, Nadja Klein, Andreas Mayr, Gillian Z. Heller |
| title_full_unstemmed | Generalized additive models for location, scale, and shape a distributional regression approach, with applications Mikis D. Stasinopoulos, Thomas Kneib, Nadja Klein, Andreas Mayr, Gillian Z. Heller |
| title_short | Generalized additive models for location, scale, and shape |
| title_sort | generalized additive models for location scale and shape a distributional regression approach with applications |
| title_sub | a distributional regression approach, with applications |
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