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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Cambridge, United Kingdom
Cambridge University Press
2024
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Schriftenreihe: | Cambridge series in statistical and probabilistic mathematics
56 |
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490 | 1 | |a Cambridge series in statistical and probabilistic mathematics |v 56 | |
520 | 3 | |a 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. | |
653 | 0 | |a Regression analysis / Mathematical models | |
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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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author_sort | Stasinopoulos, Mikis D. |
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dewey-full | 519.5/36 |
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discipline | Mathematik |
discipline_str_mv | Mathematik |
doi_str_mv | 10.1017/9781009410076 |
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id | DE-604.BV049629394 |
illustrated | Not Illustrated |
index_date | 2024-09-19T15:57:26Z |
indexdate | 2024-11-21T16:45:44Z |
institution | BVB |
isbn | 9781009410076 |
language | English |
oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-034973254 |
oclc_num | 1429571832 |
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physical | 1 Online-Ressource (xx, 285 Seiten) |
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publishDate | 2024 |
publishDateSearch | 2024 |
publishDateSort | 2024 |
publisher | Cambridge University Press |
record_format | marc |
series | Cambridge series in statistical and probabilistic mathematics |
series2 | Cambridge series in statistical and probabilistic mathematics |
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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