Chao Chang
Dept. of Mathematics, Washington University in St.Louis
Title :
Bayesian Quantile Regression
via Dirichlet Process Mixture of
Logistic Distributions
Date and Time: May 1, 2014 - 11 am. to 12 pm.
Location: Cupples I, Room 6
Abstract:
Abstract: In this talk I will present a new nonparametric Bayesian
approach to solve quantile regression for a single quantile. One
innovation is that the error distribution is modeled by using the
Dirichlet process mixture of logistic distributions, which have the
desired feature of being smooth and having a close-formed quantile
function. Further, with logistic distributions as the kernel, our
mixture model can provide great flexibility by mixing over both the
location parameter and the scale parameter. As a result, the scenarios
of multi-modal error distributions and clustered data can be handled
by our model. Monte Carlo Markov chain algorithm
is provided to do posterior inference. The performance of our approach
is evaluated using simulated data and real data. I might also discuss
some theories about convergence of Dirichlet process mixture if time
permits.