4.4 Article

Dynamic Quantile Linear Models: A Bayesian Approach

期刊

BAYESIAN ANALYSIS
卷 15, 期 2, 页码 335-362

出版社

INT SOC BAYESIAN ANALYSIS
DOI: 10.1214/19-BA1156

关键词

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

资金

  1. Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ)
  2. Fundacao de Amparo a Pesquisa do Estado do Rio de Janeiro (FAPERJ)

向作者/读者索取更多资源

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.

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