Seminars are held from 4:00 p.m. – 5:00 p.m. in Griffin-Floyd 100 unless otherwise noted. Refreshments are available before the seminars from 3:30 p.m. – 4:00 p.m. in Griffin-Floyd Hall 103.
|Feb 25||Kelly Christina,|
Federal University of Rio de Janeiro, Brazil
|Bayesian hierarchical dynamic quantile linear models|
|Apr 9||Leah Johnson|
|Apr 16||Bei Jiang|
Bayesian hierarchical dynamic quantile linear models, Kelly Cristina (Federal University of Rio de Janeiro)
The main aim of this talk is to present a new class of models, named dynamic
quantile linear models. It combines dynamic linear models with distribution free quantile regression producing a robust statistical method. This class of models provides richer information on the effects of the predictors than does the traditional mean regression and it is very insensitive to heteroscedasticity and outliers, accommodating the non-normal errors often encountered in practical applications. Bayesian inference for quantile regression proceeds by forming the likelihood function based on the asymmetric Laplace distribution and a location-scale mixture representation of it allows finding analytical expressions for the conditional posterior densities of the model. Thus, Bayesian inference for dynamic quantile linear models can be performed using an efficient Markov chain Monte Carlo algorithm or a fast sequential procedure suited for high-dimensional predictive modeling applications with massive data. Finally, a hierarchical extension, useful to account for structural features in the dataset, will be also presented.