Título: Modern distributional regression using GAMLSS

Plenarista: Fernanda De Bastiani (UFPE)

The generalized additive models for location, scale and shape, GAMLSS, are univariate distributional regression models, where all the parameters of the assumed distribution for the response variable can be modelled as additive function of the explanatory variables. GAMLSS provides a framework to address problems like the choice of an appropriate distribution for the response variable and explaining how this distributions, and it parameters, varies over different values of the explanatory variables.

We will introduce GAMLSS and its statistical modelling philosophy, and its implementation in the software R. GAMLSS can be used for modern distributional regression. It considers different additive terms for modelling the parameters of the distribution such as linear, non-parametric smoothing and random effects terms. And contains different modelling selection techniques and diagnostics for checking the model adequacy.