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ISBN 10: 1009410067 ISBN 13: 9781009410069
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Añadir al carritoHardcover. Condición: 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.
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Añadir al carritoHardback. Condición: 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.
Idioma: Inglés
Publicado por Cambridge University Press, 2024
ISBN 10: 1009410067 ISBN 13: 9781009410069
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ISBN 10: 1009410067 ISBN 13: 9781009410069
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Añadir al carritoHardcover. Condición: Brand New. 299 pages. 10.00x7.00x0.75 inches. In Stock.
Idioma: Inglés
Publicado por CAMBRIDGE UNIVERSITY PRESS, 2024
ISBN 10: 1009410067 ISBN 13: 9781009410069
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Idioma: Inglés
Publicado por Cambridge University Press, GB, 2024
ISBN 10: 1009410067 ISBN 13: 9781009410069
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Añadir al carritoHardback. Condición: 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.
Idioma: Inglés
Publicado por Cambridge University Press Feb 2024, 2024
ISBN 10: 1009410067 ISBN 13: 9781009410069
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Añadir al carritoBuch. Condición: Neu. Neuware - 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.
Idioma: Inglés
Publicado por Cambridge University Press, 2024
ISBN 10: 1009410067 ISBN 13: 9781009410069
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Idioma: Inglés
Publicado por Cambridge University Press, 2024
ISBN 10: 1009410067 ISBN 13: 9781009410069
Librería: Revaluation Books, Exeter, Reino Unido
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Añadir al carritoHardcover. Condición: Brand New. 299 pages. 10.00x7.00x0.75 inches. In Stock. This item is printed on demand.
Idioma: Inglés
Publicado por Cambridge University Press, 2024
ISBN 10: 1009410067 ISBN 13: 9781009410069
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Idioma: Inglés
Publicado por Cambridge University Press, Cambridge, 2024
ISBN 10: 1009410067 ISBN 13: 9781009410069
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Añadir al carritoHardcover. Condición: 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 our UK warehouse or from our Australian or US warehouses, depending on stock availability.