Bayesian Analysis

Dynamic Quantile Linear Models: A Bayesian Approach

Kelly C. M. Gonçalves, Hélio S. Migon, and Leonardo S. Bastos

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Abstract

The paper introduces a new class of models, named dynamic quantile linear models, which combines dynamic linear models with distribution-free quantile regression producing a robust statistical method. Bayesian estimation for the dynamic quantile linear model is performed using an efficient Markov chain Monte Carlo algorithm. The paper also proposes a fast sequential procedure suited for high-dimensional predictive modeling with massive data, where the generating process is changing over time. The proposed model is evaluated using synthetic and well-known time series data. The model is also applied to predict annual incidence of tuberculosis in the state of Rio de Janeiro and compared with global targets set by the World Health Organization.

Article information

Source
Bayesian Anal., Advance publication (2018), 28 pages.

Dates
First available in Project Euclid: 26 April 2019

Permanent link to this document
https://projecteuclid.org/euclid.ba/1556244057

Digital Object Identifier
doi:10.1214/19-BA1156

Keywords
asymmetric Laplace distribution Bayes linear Bayesian quantile regression dynamic models Gibbs sampling

Rights
Creative Commons Attribution 4.0 International License.

Citation

Gonçalves, Kelly C. M.; Migon, Hélio S.; Bastos, Leonardo S. Dynamic Quantile Linear Models: A Bayesian Approach. Bayesian Anal., advance publication, 26 April 2019. doi:10.1214/19-BA1156. https://projecteuclid.org/euclid.ba/1556244057


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