Generalized Additive Models for Location, Scale and Shape

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ID: 290119
2005
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Ranked #1 of 46 articles by views in Journal of the Royal Statistical Society Series C (Applied Statistics)

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Abstract
Summary A general class of statistical models for a univariate response variable is presented which we call the generalized additive model for location, scale and shape (GAMLSS). The model assumes independent observations of the response variable y given the parameters, the explanatory variables and the values of the random effects. The distribution for the response variable in the GAMLSS can be selected from a very general family of distributions including highly skew or kurtotic continuous and discrete distributions. The systematic part of the model is expanded to allow modelling not only of the mean (or location) but also of the other parameters of the distribution of y, as parametric and/or additive nonparametric (smooth) functions of explanatory variables and/or random-effects terms. Maximum (penalized) likelihood estimation is used to fit the (non)parametric models. A Newton–Raphson or Fisher scoring algorithm is used to maximize the (penalized) likelihood. The additive terms in the model are fitted by using a backfitting algorithm. Censored data are easily incorporated into the framework. Five data sets from different fields of application are analysed to emphasize the generality of the GAMLSS class of models.
Reference Key
openalex_W2154065358 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Robert A. Rigby, D. Stasinopoulos
Journal Journal of the Royal Statistical Society Series C (Applied Statistics)
Year 2005
DOI
10.1111/j.1467-9876.2005.00510.x
URL
https://academic.oup.com/jrsssc/article-pdf/54/3/507/50016951/jrsssc_54_3_507.pdf
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