Distributions for modeling location, scale, and shape: using GAMLSS in R
This is a book about statistical distributions, their properties, and their application to modelling the dependence of the location, scale, and shape of the distribution of a response variable on explanatory variables. It will be especially useful to applied statisticians and data scientists in a wi...
| Main Authors: | , , , |
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| Format: | Book |
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Chapman & Hall/CRC
2019
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| _version_ | 1825625383892942848 |
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| author | Rigby, Robert A. Stasinopoulos, Dimitrios Heller, Gillian Z. De Bastiani, Fernanda |
| author_facet | Rigby, Robert A. Stasinopoulos, Dimitrios Heller, Gillian Z. De Bastiani, Fernanda |
| author_sort | Rigby, Robert A. |
| collection | LMU |
| description | This is a book about statistical distributions, their properties, and their application to modelling the dependence of the location, scale, and shape of the distribution of a response variable on explanatory variables. It will be especially useful to applied statisticians and data scientists in a wide range of application areas, and also to those interested in the theoretical properties of distributions. This book follows the earlier book ‘Flexible Regression and Smoothing: Using GAMLSS in R’, [Stasinopoulos et al., 2017], which focused on the GAMLSS model and software. GAMLSS (the Generalized Additive Model for Location, Scale, and Shape, [Rigby and Stasinopoulos, 2005]), is a regression framework in which the response variable can have any parametric distribution and all the distribution parameters can be modelled as linear or smooth functions of explanatory variables. The current book focuses on distributions and their application. |
| first_indexed | 2024-07-09T03:58:34Z |
| format | Book |
| id | oai:repository.londonmet.ac.uk:4993 |
| institution | London Metropolitan University |
| last_indexed | 2024-07-09T03:58:34Z |
| publishDate | 2019 |
| publisher | Chapman & Hall/CRC |
| record_format | eprints |
| spelling | oai:repository.londonmet.ac.uk:49932021-07-22T08:17:28Z https://repository.londonmet.ac.uk/4993/ Distributions for modeling location, scale, and shape: using GAMLSS in R Rigby, Robert A. Stasinopoulos, Dimitrios Heller, Gillian Z. De Bastiani, Fernanda 510 Mathematics This is a book about statistical distributions, their properties, and their application to modelling the dependence of the location, scale, and shape of the distribution of a response variable on explanatory variables. It will be especially useful to applied statisticians and data scientists in a wide range of application areas, and also to those interested in the theoretical properties of distributions. This book follows the earlier book ‘Flexible Regression and Smoothing: Using GAMLSS in R’, [Stasinopoulos et al., 2017], which focused on the GAMLSS model and software. GAMLSS (the Generalized Additive Model for Location, Scale, and Shape, [Rigby and Stasinopoulos, 2005]), is a regression framework in which the response variable can have any parametric distribution and all the distribution parameters can be modelled as linear or smooth functions of explanatory variables. The current book focuses on distributions and their application. Chapman & Hall/CRC 2019-10 Book NonPeerReviewed Rigby, Robert A., Stasinopoulos, Dimitrios, Heller, Gillian Z. and De Bastiani, Fernanda (2019) Distributions for modeling location, scale, and shape: using GAMLSS in R. The R Series . Chapman & Hall/CRC, Boca Raton, Florida. ISBN 9780367278847 https://www.crcpress.com/Distributions-for-Modelling-Location-Scale-and-Shape-Using-GAMLSS-in/Rigby-Stasinopoulos-Heller-Bastiani/p/book/9780367278847 |
| spellingShingle | 510 Mathematics Rigby, Robert A. Stasinopoulos, Dimitrios Heller, Gillian Z. De Bastiani, Fernanda Distributions for modeling location, scale, and shape: using GAMLSS in R |
| title | Distributions for modeling location, scale, and shape: using GAMLSS in R |
| title_full | Distributions for modeling location, scale, and shape: using GAMLSS in R |
| title_fullStr | Distributions for modeling location, scale, and shape: using GAMLSS in R |
| title_full_unstemmed | Distributions for modeling location, scale, and shape: using GAMLSS in R |
| title_short | Distributions for modeling location, scale, and shape: using GAMLSS in R |
| title_sort | distributions for modeling location scale and shape using gamlss in r |
| topic | 510 Mathematics |
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