B0366
Title: Uncertainty in heteroscedastic Bayesian model averaging
Authors: Sebastien Jessup - Concordia University (Canada) [presenting]
Mathieu Pigeon - Université du Québec à Montréal (UQAM) (Canada)
Melina Mailhot - Concordia (Canada)
Abstract: Bayesian model averaging (BMA) is a widely used tool for model combination using Bayesian inference. Different versions of an expectation-maximisation (EM) algorithm are frequently used to apply BMA, typically in a homoscedastic context. In many situations, such as climate risk modelling or actuarial reserving, the homoscedasticity assumption does not hold. Moreover, the EM algorithm has the well-known issue of convergence to a single model. Considering these issues, the EM algorithm is adapted to a heteroscedastic context. A numerical error integration approach is also proposed which considers data uncertainty and addresses convergence to a single model. The two methods are compared through a simulation study and a property and casualty insurance simulated dataset. Finally, some advantages of the proposed methods are discussed.
