Stasinopoulos, Dimitrios, Rigby, Robert A., Heller, Gillian Z., Voudouris, Vlasios and De Bastiani, Fernanda (2017) Flexible regression and smoothing: using GAMLSS in R. The R Series . Chapman & Hall/CRC, Boca Raton, Florida. ISBN 978-1-138-19790-9
Full text not available from this repository. (Request a copy)Abstract / Description
This book is about learning from data using the Generalized Additive Models for Location, Scale and Shape (GAMLSS). GAMLSS extends the Generalized Linear Models (GLMs) and Generalized Additive Models (GAMs) to accommodate large complex datasets, which are increasingly prevalent.
In particular, the GAMLSS statistical framework enables flexible regression and smoothing models to be fitted to the data. The GAMLSS model assumes that the response variable has any parametric (continuous, discrete or mixed) distribution which might be heavy- or light-tailed, and positively or negatively skewed. In addition, all the parameters of the distribution (location, scale, shape) can be modelled as linear or smooth functions of explanatory variables.
Item Type: | Book |
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Additional Information: | "Flexible Regression and Smoothing: Using GAMLSS in R" is a Book available on request for the Reviewer. |
Uncontrolled Keywords: | Generalized Additive Models for Location, Scale and Shape (GAMLSS); Generalized Linear Models (GLMs); Generalized Additive Models (GAMs); Regression analysis -- Data processing; Linear models (Statistics) |
Subjects: | 500 Natural Sciences and Mathematics > 510 Mathematics |
Department: | School of Computing and Digital Media |
Depositing User: | Robert Rigby |
Date Deposited: | 22 Jul 2019 08:42 |
Last Modified: | 27 Jul 2023 08:14 |
URI: | https://repository.londonmet.ac.uk/id/eprint/4992 |
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