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Generalized Additive Models for Location, Scale and Shape : A Distributional Regression Approach, with Applications

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

Cambridge Series in Statistical and Probabilistic Mathematics

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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.

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