Special Talk

Bayesian quantile regression for discrete observations

Wednesday, 2018/07/04, 16:00 h

Prof. Haavard Rue

Quantile regression, i.e. modelling conditional quantiles of some covariates and other effects through the linear predictor, has typically been carried out exploiting the asymmetric Laplace distribution (ALD) as a working «likelihood». In the Bayesian framework, this is highly questionable as the posterior variance is affected by the artificial ALD «likelihood». With continuous responses, we can reparameterize the likelihood in terms of a $\alpha$-quantile, and let the $\alpha$-quantile depend on the linear predictor. We can then do model based quantile regression with little effort using the R-INLA package doing approximate Bayesian inference for latent Gaussian models, and trust the quantile regression posterior in the same way as when doing parametric mean regression.

Event organizer: ISPM Bern
Speaker: Prof. Haavard Rue, King Abdullah University of Science and Technology, Saudi Arabia
Date: 2018/07/04
Time: 16:00 - 17:00 h
Locality: room 220, 2nd floor
Mittelstrasse 43
3012 Bern
Characteristics: open to the public
free of charge