On Friday, November 10th, 2023, Dr. Kevin Burke, Lecturer in Statistics at the University of Limerick, presented about automating variable selection in distributional regression. Details for the talk are below.
Title
Automating variable selection in distributional regression
Abstract
Variable selection is an important scientific endeavour as it identifies important associations. Of course, this is more challenging than simply fitting a model for a given pre-specified set of covariates. From a scientific perspective, “distributional regression” models allow us to better understand the phenomenon under study compared to the classical mean-view of the world; for example, we can discover how covariates impact both the mean and variance of the response. However, variable selection is even more challenging in this setting since there is a regression equation for each of the distributional parameters. Stepwise regression procedures are quite computationally intensive in general, but so too are penalised regression procedures due to the need to select the penalty tuning parameter(s); the issue is compounded in distributional regression models due to the fact that there are multiple regression equations. Therefore, we introduce a tuning-parameter-free (and, hence, automated) procedure for selecting variables based on a differentiable approximation to an information criterion that we optimise directly. This method is especially advantageous in the distributional-regression setting, but is also useful in classical regression settings. For further details, see https://doi.org/10.1007/s11222-023-10204-8.