Generalized Additive Models For Location Scale And Shape
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Generalized Additive Models for Location, Scale, and Shape
Author | : Mikis D. Stasinopoulos |
Publisher | : |
Total Pages | : 0 |
Release | : 2024 |
Genre | : Regression analysis |
ISBN | : 9781009410076 |
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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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This book is about learning from data using the Generalized Additive Models for Location, Scale and Shape (GAMLSS). GAMLSS extends the Generalized Linear Models
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Now in widespread use, generalized additive models (GAMs) have evolved into a standard statistical methodology of considerable flexibility. While Hastie and Tib
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This book presents a greatly enlarged statistical framework compared to generalized linear models (GLMs) with which to approach regression modelling. Comprising