9781009410069 - generalized additive models for location, scale and shape: a distributional regression approach, with applications di stasinopoulos, mikis d.; kneib, thomas; klein, nadja; mayr, andreas; heller, gillian z. (20 risultati)
Generalized Additive Models for Location, Scale and Shape: A Distributional Regression Approach, with Applications (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 56)
Stasinopoulos, Mikis D.; Kneib, Thomas; Klein, Nadja; Mayr, Andreas; Heller, Gillian Z.
Lingua: Inglese
Editore: Cambridge University Press, 2024
Serie: Cambridge Series in Statistical and Probabilistic Mathematics, Libro 44 di 46. Libro 44 di 46 - Cambridge Series in Statistical and Probabilistic Mathematics
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Generalized Additive Models for Location, Scale and Shape : A Distributional Regression Approach, With Applications
Stasinopoulos, Mikis D.; Kneib, Thomas; Klein, Nadja; Mayr, Andreas; Heller, Gillian Z.
Lingua: Inglese
Editore: Cambridge University Press, 2024
Serie: Cambridge Series in Statistical and Probabilistic Mathematics, Libro 44 di 46. Libro 44 di 46 - Cambridge Series in Statistical and Probabilistic Mathematics
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Generalized Additive Models for Location, Scale and Shape: A Distributional Regression Approach, with Applications (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 56)
Stasinopoulos, Mikis D.; Kneib, Thomas; Klein, Nadja; Mayr, Andreas; Heller, Gillian Z.
Lingua: Inglese
Editore: Cambridge University Press, 2024
Serie: Cambridge Series in Statistical and Probabilistic Mathematics, Libro 44 di 46. Libro 44 di 46 - Cambridge Series in Statistical and Probabilistic Mathematics
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Generalized Additive Models for Location, Scale and Shape: A Distributional Regression Approach, with Applications (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 56)
Stasinopoulos, Mikis D.; Kneib, Thomas; Klein, Nadja; Mayr, Andreas; Heller, Gillian Z.
Lingua: Inglese
Editore: Cambridge University Press, 2024
Serie: Cambridge Series in Statistical and Probabilistic Mathematics, Libro 44 di 46. Libro 44 di 46 - Cambridge Series in Statistical and Probabilistic Mathematics
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Generalized Additive Models for Location, Scale and Shape: A Distributional Regression Approach, with Applications (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 56)
Stasinopoulos, Mikis D.; Kneib, Thomas; Klein, Nadja; Mayr, Andreas; Heller, Gillian Z.
Lingua: Inglese
Editore: Cambridge University Press, 2024
Serie: Cambridge Series in Statistical and Probabilistic Mathematics, Libro 44 di 46. Libro 44 di 46 - Cambridge Series in Statistical and Probabilistic Mathematics
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Generalized Additive Models for Location, Scale and Shape : A Distributional Regression Approach, With Applications
Stasinopoulos, Mikis D.; Kneib, Thomas; Klein, Nadja; Mayr, Andreas; Heller, Gillian Z.
Lingua: Inglese
Editore: Cambridge University Press, 2024
Serie: Cambridge Series in Statistical and Probabilistic Mathematics, Libro 44 di 46. Libro 44 di 46 - Cambridge Series in Statistical and Probabilistic Mathematics
- Rilegato
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Generalized Additive Models for Location, Scale and Shape: A Distributional Regression Approach, with Applications (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 56)
Stasinopoulos, Mikis D.; Kneib, Thomas; Klein, Nadja; Mayr, Andreas; Heller, Gillian Z.
Lingua: Inglese
Editore: Cambridge University Press, 2024
Serie: Cambridge Series in Statistical and Probabilistic Mathematics, Libro 44 di 46. Libro 44 di 46 - Cambridge Series in Statistical and Probabilistic Mathematics
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Generalized Additive Models for Location, Scale and Shape
Mikis D. Stasinopoulos, Thomas Kneib, Nadja Klein, Andreas Mayr, Gillian Z. Heller
Lingua: Inglese
Editore: Cambridge University Press, GB, 2024
Serie: Cambridge Series in Statistical and Probabilistic Mathematics, Libro 44 di 46. Libro 44 di 46 - Cambridge Series in Statistical and Probabilistic Mathematics
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Hardback. Condizione: New. 1st. 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 re…gression. 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.
Generalized Additive Models for Location, Scale and Shape: A Distributional Regression Approach, with Applications (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 56)
Stasinopoulos, Mikis D.; Kneib, Thomas; Klein, Nadja; Mayr, Andreas; Heller, Gillian Z.
Lingua: Inglese
Editore: Cambridge University Press, 2024
Serie: Cambridge Series in Statistical and Probabilistic Mathematics, Libro 44 di 46. Libro 44 di 46 - Cambridge Series in Statistical and Probabilistic Mathematics
- Rilegato
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Generalized Additive Models for Location, Scale and Shape : A Distributional Regression Approach, With Applications
Stasinopoulos, Mikis D.; Kneib, Thomas; Klein, Nadja; Mayr, Andreas; Heller, Gillian Z.
Lingua: Inglese
Editore: Cambridge University Press, 2024
Serie: Cambridge Series in Statistical and Probabilistic Mathematics, Libro 44 di 46. Libro 44 di 46 - Cambridge Series in Statistical and Probabilistic Mathematics
- Rilegato
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Serie: Cambridge Series in Statistical and Probabilistic Mathematics, Libro 44 di 46. Libro 44 di 46 - Cambridge Series in Statistical and Probabilistic Mathematics
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Condizione: New. 2024. hardcover. . . . . .
Generalized Additive Models for Location, Scale and Shape : A Distributional Regression Approach, With Applications
Stasinopoulos, Mikis D.; Kneib, Thomas; Klein, Nadja; Mayr, Andreas; Heller, Gillian Z.
Lingua: Inglese
Editore: Cambridge University Press, 2024
Serie: Cambridge Series in Statistical and Probabilistic Mathematics, Libro 44 di 46. Libro 44 di 46 - Cambridge Series in Statistical and Probabilistic Mathematics
- Rilegato
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Serie: Cambridge Series in Statistical and Probabilistic Mathematics, Libro 44 di 46. Libro 44 di 46 - Cambridge Series in Statistical and Probabilistic Mathematics
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Generalized Additive Models for Location, Scale and Shape
Mikis D. Stasinopoulos , Thomas Kneib , Nadja Klein , Andreas Mayr , Gillian Z. Heller
Lingua: Inglese
Editore: CAMBRIDGE UNIVERSITY PRESS, 2024
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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
Lingua: Inglese
Editore: Cambridge University Press, 2024
Serie: Cambridge Series in Statistical and Probabilistic Mathematics, Libro 44 di 46. Libro 44 di 46 - Cambridge Series in Statistical and Probabilistic Mathematics
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Generalized Additive Models for Location, Scale and Shape
Mikis D. Stasinopoulos, Thomas Kneib, Nadja Klein, Andreas Mayr, Gillian Z. Heller
Lingua: Inglese
Editore: Cambridge University Press, GB, 2024
Serie: Cambridge Series in Statistical and Probabilistic Mathematics, Libro 44 di 46. Libro 44 di 46 - Cambridge Series in Statistical and Probabilistic Mathematics
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Hardback. Condizione: New. 1st. 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 re…gression. 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.
Lingua: Inglese
Editore: Cambridge University Press, 2024
Serie: Cambridge Series in Statistical and Probabilistic Mathematics, Libro 44 di 46. Libro 44 di 46 - Cambridge Series in Statistical and Probabilistic Mathematics
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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
Lingua: Inglese
Editore: Cambridge University Press, 2024
Serie: Cambridge Series in Statistical and Probabilistic Mathematics, Libro 44 di 46. Libro 44 di 46 - Cambridge Series in Statistical and Probabilistic Mathematics
- Rilegato
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Lingua: Inglese
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Hardcover. Condizione: new. Hardcover. 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 distribution…al 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. 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
