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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) by Mikis D. Stasinopoulos, Thomas Kneib, Nadja Klein, Andreas Mayr, Gillian Z. Heller is a mathematics available to read on EtoBox.
What is Generalized Additive Models for Location, Scale and Shape: A Distributional Regression Approach, with Applications (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 56) about?
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 a
Who reads Generalized Additive Models for Location, Scale and Shape: A Distributional Regression Approach, with Applications (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 56)?
It is typically read by self-directed learners exploring a subject in depth.
Common subject areas: history, science, philosophy, social sciences.
- Author
- Mikis D. Stasinopoulos, Thomas Kneib, Nadja Klein, Andreas Mayr, Gillian Z. Heller
- Publisher
- Cambridge University Press
- Published
- 2024
- Language
- EN
- ISBN
- 9781009410069
- Category
- mathematics
- Subjects
- Mathematics, Stem
- Updated
- 2026-03-25
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