Bayesian Quantile Regression. A non-Markovian simulation-based algorithm was proposed in Rahman 2013. Bayesian inference provides a flexible way of combining data with prior information.
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A few gener-20 alization and extension of quantile regression were proposed in 4 5 6. This makes it necessary to specify linear regression as a distribution as well. However the tails of the response distribution are as important as the center in many subs Bayesian quantile nonhomogeneous hidden Markov models Stat Methods Med Res.
3 introduced Bayesian quantile regression for independent data.
Off-Canvas Navigation Menu Toggle. Jun 12 2013 While frequentist treatments of quantile regression are typically completely nonparametric a Bayesian formulation relies on assuming the asymmetric Laplace distribution as auxiliary error distribution that yields posterior modes equivalent to frequentist estimates. For a Bayesian approach to quantile regression you form the likelihood function based on the asymmetric Laplace distribution regardless of the actual distribution of the data. A non-Markovian simulation-based algorithm was proposed in Rahman 2013.
