Skip to main content

Generalized additive models for location, scale and shape

Buy Article:

$43.00 plus tax (Refund Policy)


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.
No References
No Citations
No Supplementary Data
No Article Media
No Metrics

Keywords: Beta–binomial distribution; Box–Cox transformation; Centile estimation; Cubic smoothing splines; Generalized linear mixed model; LMS method; Negative binomial distribution; Non-normality; Nonparametric models; Overdispersion; Penalized likelihood; Random effects; Skewness and kurtosis

Document Type: Research Article

Affiliations: London Metropolitan University, UK

Publication date: 01 June 2005

  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more