Generalized Additive Models for Location, Scale and Shape: A Distributional Regression Approach, with Applications - Rilegato
Libro 44 di 46: Cambridge Series in Statistical and Probabilistic Mathematics
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A comprehensive presentation of generalized additive models for location, scale and shape linking methods with diverse applications.
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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)
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Cambridge University Press, 2024
ISBN 10: 1009410067
ISBN 13: 9781009410069
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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)
Editore:
Cambridge University Press, 2024
ISBN 10: 1009410067
ISBN 13: 9781009410069
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Hardcover. Condizione: Very Good. No dustjacket as issued. Very minor shelf wear, including a small dent to back cover. Binding tight. All pages intact and free of marks. Codice articolo 052425k07
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Generalized Additive Models for Location, Scale and Shape : A Distributional Regression Approach, With Applications
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Generalized Additive Models for Location, Scale and Shape (Hardcover)
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Cambridge University Press, Cambridge, 2024
ISBN 10: 1009410067
ISBN 13: 9781009410069
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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 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. 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 multiple locations in the US or from the UK, depending on stock availability. Codice articolo 9781009410069
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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)
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Generalized Additive Models for Location, Scale and Shape : A Distributional Regression Approach, With Applications
Editore:
Cambridge University Press, 2024
ISBN 10: 1009410067
ISBN 13: 9781009410069
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Generalized Additive Models for Location, Scale and Shape
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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)
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Generalized Additive Models for Location, Scale and Shape
Editore:
Cambridge University Press, GB, 2024
ISBN 10: 1009410067
ISBN 13: 9781009410069
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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 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. Codice articolo LU-9781009410069
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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)
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