Fully Bayesian L1/2-penalized linear quantile regression analysis with autoregressive errors
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AbstractIn the quantile regression framework, we incorporate Bayesian L1/2 and adaptive L1/2 penalties into quantile linear regression models with autoregressive (AR) errors to conduct statistical inference. A Bayesian joint hierarchical model is established using the working likelihood of the asymmetric Laplace distribution (ALD). On the basis of the mixture representations of ALD and the generalized Gaussian distribution priors of regression coefficients and AR parameters, a Markov chain Monte Carlo algorithm is developed to conduct posterior inference. Finally, the proposed Bayesian estimation procedures are demonstrated by simulation studies and applied to a real data application concerning the electricity consumption of residential customers.
All Author(s) ListTian Y. Z., Song X. Y.
Journal nameStatistics and Its Interface
Volume Number13
Issue Number3
PublisherInternational Press
Pages271 - 286
LanguagesEnglish-United States

Last updated on 2020-19-10 at 01:05