[Statlist] Talk: Tuesday, 03.07.2018, with Håvard Rue, King Abdullah University of Science and Technology, Saudi Arabia

[malgorzata.roos at uzh.ch]

 Dear colleagues, 

We are glad to announce the following talk:

Speaker: Håvard Rue, King Abdullah University of Science and Technology, Saudi Arabia
Date: Tuesday, 03.07.2018 
Time: 14:00h 
Location: Room 1, first floor, AKI, Hirschengraben 86, Zürich
http://map.search.ch/zuerich/hirschengraben-86

Afterwards coffee and cakes will be served.
 
Title: Bayesian quantile regression for discrete observations

Abstract:

Quantile regression, i.e. modeling 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 (www.r-inla.org) doing approximate Bayesian inference for latent Gaussian models, and trust the quantile regression posterior in the same way as when doing parametric mean regression.

For discrete variables, like Poisson and (negative) Binomial, there is no continuous relationship between quantiles and distribution’s parameters, hence model based quantile regression seems no longer possible. In this talk I will discuss how to resolve this issue, so that we can do model based quantile regression also for discrete responses. I will present some examples that also demonstrate how the parametric approach almost resolves the quantile crossing problem.

This is joint work with Tullia Padellini, Sapienza University of Rome, Italy.

Keywords: Bayesian quantile-regression, asymmetric Laplace distribution

We are looking forward to meeting you.

Kind regards,
Leonhard Held, Torsten Hothorn, Malgorzata Roos

Department of Biostatistics, 
Epidemiology, Biostatistics and Prevention Institute,
University of Zurich,
Hirschengraben 84 
Zurich

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