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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 - 2015

by Mikis D. Stasinopoulos; Thomas Kneib; Nadja Klein

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1st edition NO-PA16APR2015-KAP. Hardback. New.
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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 Mikis D. Stasinopoulos; Thomas Kneib; Nadja Klein
  • Binding Hardback
  • Condition New
  • Pages 306
  • Volumes 1
  • Language ENG
  • Publisher Cambridge University Press
  • Publication date 1st edition NO-PA16APR2015-
  • Features Bibliography, Index
  • Bookseller's Inventory # 6399012940
  • 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)
  • 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

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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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