{"success":1,"msg":"","color":"rgb(28, 35, 49)","title":"Bayesian dynamic quantile linear models and some extensions<\/b>","description":"webinar","title2":"","start":"2021-09-24 14:00","end":"2021-09-24 15:00","responsable":"Isabelle Beaudry <\/i><\/a>","speaker":"Kelly C. M. Gon\u00e7alves (Universidade Federal do Rio de Janeiro)","id":"51","type":"webinar","timezone":"America\/Santiago","activity":"https:\/\/zoom.us\/j\/98319489993?pwd=VmhtT1pGY09YNjFSVUkvaFh5VGxhUT09\r\npassword: 561921","abstract":"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."}