Bayesian Quantile Regression R. Jun 15 2020 Estimation of low or high conditional quantiles is called for in many applications but commonly encountered data sparsity at the tails of distributions makes this a challenging task. Moyeed 2001 doi101016S0167-7152 0100124-9 Benoit.
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Moyeed 2001 doi101016S0167-7152 0100124-9 Benoit. The R package bayesQR contains a number of routines to estimate quantile regression parameters using a Bayesian approach based on the asymmetric Laplace distri-bution. Van den Poel 2012 doi101002jae1216.
The R package bayesQR contains a number of routines to estimate quantile regression parameters using a Bayesian approach based on the asymmetric Laplace distribution.
To improve the speed of the routine the Markov Chain Monte Carlo MCMC part of the algorithm is programmed in Fortran and is called from within the R function bayesQR. In addition this package implements the Bayesian Tobit and binary RQ with lasso and. Bayesian Function-on-Scalar Quantile Regression A method to perform Bayesian function-on-scalar quantile regression ie Bayesian FQR. Oct 15 2001 In this paper we have shown how Bayesian inference may be undertaken in the context of quantile regression.
