  Regression classes modeling more than the mean of the response have found a
lot of attention in the last years. Expectile regression is a special and
computationally convenient case of this family of models. Expectiles offer a
quantile-like characterisation of a complete distribution and include the mean
as a special case. In the frequentist framework the impact of a lot of
covariates with very different structures have been made possible. We propose
Bayesian expectile regression based on the asymmetric normal distribution. This
renders possible incorporating for example linear, nonlinear, spatial and
random effects in one model. Furthermore a detailed inference on the estimated
parameters can be conducted. Proposal densities based on iterativly weighted
least squares updates for the resulting Markov chain Monte Carlo (MCMC)
simulation algorithm are proposed and the potential of the approach for
extending the flexibility of expectile regression towards complex
semiparametric regression specifications is discussed.
