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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)

Generalized Additive Models for Location, Scale and Shape: A Distributional Regression Approach, with Applications (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 56)

Generalized Additive Models for Location, Scale and Shape: A Distributional
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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) Hardback - 2024

by Stasinopoulos, Mikis D.,Kneib, Thomas,Heller, Gillian Z.,Mayr, Andreas,Klein, Nadja

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Cambridge University Press, 2/29/2024 12:00:01 A. hardcover. Very Good. 1.1024 10.3150 7.2441.
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  • Title Generalized Additive Models for Location, Scale and Shape: A Distributional Regression Approach, with Applications (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 56)
  • Author Stasinopoulos, Mikis D.,Kneib, Thomas,Heller, Gillian Z.,Mayr, Andreas,Klein, Nadja
  • Binding Hardback
  • Condition Used - Very good
  • Pages 306
  • Volumes 1
  • Language ENG
  • Publisher Cambridge University Press
  • Publication date 2/29/2024 12:00:01 A
  • Features Bibliography, Index
  • Bookseller's Inventory # mon0004010574
  • ISBN 9781009410069 / 1009410067
  • Weight 1.65 lbs (0.75 kg)
  • Dimensions 10 x 7 x 0.75 in (25.40 x 17.78 x 1.91 cm)
  • Size 1.1024 10.3150 7.2441
  • Category Mathematics
  • Library of Congress subjects Theory of distributions (Functional analysis), Regression analysis - Mathematical models
  • Library of Congress Catalogue Number 2023035047
  • Dewey Decimal Code 519.536
  • Quantity available 1
  • Bookseller catalogues Book

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From the publisher

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.

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