Distributions for Modeling Location, Scale, and Shape: Using GAMLSS in R

1st Edition

Robert A. Rigby, Mikis D. Stasinopoulos, Gillian Z. Heller, Fernanda De Bastiani

Chapman and Hall/CRC
Published September 20, 2019
Reference - 560 Pages - 191 B/W Illustrations
ISBN 9780367278847 - CAT# 294917
Series: Chapman & Hall/CRC The R Series

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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.

Key features:

  • Describes over 100 distributions, (implemented in the GAMLSS packages in R), including continuous, discrete and mixed distributions.

  • Comprehensive summary tables of the properties of the distributions.

  • Discusses properties of distributions, including skewness, kurtosis, robustness and an important classification of tail heaviness.

  • Includes mixed distributions which are continuous distributions with additional specific values with point probabilities.

  • Includes many real data examples, with R code integrated in the text for ease of understanding and replication.

  • Supplemented by the gamlss website.

This book will be useful for applied statisticians and data scientists in selecting a distribution for a univariate response variable and modelling its dependence on explanatory variables, and to those interested in the properties of distributions.


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