Article,
Generalized additive models for location, scale and shape
Journal of the Royal Statistical Society: Series C (Applied Statistics), 54 (3): 507--554 (2005)
Abstract
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.
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
Tags
- binomial
- distribution,
- effects,
- kurtosis,
- negative
- overdispersion,
- random
- semiparametric,
- skew
- \_tablet\_modified,
