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...
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| Hauptverfasser: | , , , , |
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Cambridge, United Kingdom
Cambridge University Press
2024
|
| Schriftenreihe: | Cambridge series in statistical and probabilistic mathematics
56 |
| Schlagworte: | |
| Online-Zugang: | DE-12 DE-634 DE-92 DE-91 DE-473 DE-19 DE-945 Volltext |
| 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. |
| Beschreibung: | 1 Online-Ressource (xx, 285 Seiten) |
| ISBN: | 9781009410076 |
| DOI: | 10.1017/9781009410076 |
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Datensatz im Suchindex
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| author | Stasinopoulos, Mikis D. Kneib, Thomas 1976- Klein, Nadja 1987- Mayr, Andreas 1983- Heller, Gillian Z. |
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| dewey-full | 519.5/36 |
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| discipline | Mathematik |
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| illustrated | Not Illustrated |
| index_date | 2024-07-03T23:38:11Z |
| indexdate | 2024-07-20T06:10:04Z |
| institution | BVB |
| isbn | 9781009410076 |
| language | English |
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| spelling | Stasinopoulos, Mikis D. Verfasser (DE-588)1200420330 aut 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, United Kingdom Cambridge University Press 2024 © 2024 1 Online-Ressource (xx, 285 Seiten) txt rdacontent c rdamedia cr rdacarrier 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. Regression analysis / Mathematical models Theory of distributions (Functional analysis) Kneib, Thomas 1976- Verfasser (DE-588)131555332 aut Klein, Nadja 1987- Verfasser (DE-588)1035481014 aut Mayr, Andreas 1983- Verfasser (DE-588)1323832335 aut Heller, Gillian Z. Verfasser (DE-588)1310631409 aut Erscheint auch als Druck-Ausgabe, Hardcover 978-1-009-41006-9 Cambridge series in statistical and probabilistic mathematics 56 (DE-604)BV041460443 56 https://doi.org/10.1017/9781009410076 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Stasinopoulos, Mikis D. Kneib, Thomas 1976- Klein, Nadja 1987- Mayr, Andreas 1983- Heller, Gillian Z. Generalized additive models for location, scale and shape a distributional regression approach, with applications Cambridge series in statistical and probabilistic mathematics |
| 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_exact_search_txtP | 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 |
| url | https://doi.org/10.1017/9781009410076 |
| volume_link | (DE-604)BV041460443 |
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