GAMLSS - A distributional regression approach

The Generalized Additive Models for Location, Scale and Shape (GAMLSS) introduced almost 20 year ago by Rigby and Stasinopoulos (2005), are a very general framework for univariate regression. Their novelty arise from the fact that all the parameters of the assumed parametric distribution of the response (target) can be modelled as functions of the explanatory variables (features). This allows modelling a response variable with high skewness or kurtosis. The assumed distribution can be any theoretical distribution as described by Rigby et al. (2019). There are different ways for modelling all the distribution parameters using explanatory variables. These include linear terms, smoothing terms and any sensible machine learning technique like neural networks, LASSO, principal component regression etc., see Stasinopoulos et al. (2017). There are also different ways of fitting the model which includes the classical (using penalized likelihood), the Bayesian using MCMC, or boosting, see Stasinopoulos et al. (2024).

03/06/2024 - 05/06/2024

3 sessions (June 3rd, 4th and 5th)
9:30 am to 12.30 pm; 1.30 pm to 4.30 pm.

Deadline for registration

Obs.:Reduced fee (20%) applies for students from a specialization course in Public Health, master’s students in Public Health, PhD students in Public Health, Global Public Health, or Applied Mathematics, and members of SPE or APE, upon presentation of the respective proof to the email

This short course is designed for practitioners and applied statisticians which would like to know how to go about modelling their data set using GAMLSS. The short course will discuss exploratory checking of the data, selection of the right distribution, and selection of the right explanatory variables modelling each parameter of the distribution of the response. Interpretation and the use of the GAMLSS model in prediction will be discussed. A specials section will devoted to the creation of references interval curves (centile estimation).

The course is targeted at researchers as well as master’s and PhD students in statistics, mathematics, medicine, public health, and related areas.

Participant Limit:
Registration Fee:
Reduced fee:
Teaching Staff:
Mikis Stasinopoulos
GAMLSS - A distributional regression approach
3 sessions
Course format
Face to face sessions
Contact information