On Bayesian Quantile Regression Using a Pseudo-joint Asymmetric Laplace Likelihood
Karthik Sriram (karthiks@iimahd.ernet.in),
R. V. Ramamoorthi (ramamoor@stt.msu.edu) and
Pulak Ghosh (pulak.ghosh@iimb.ernet.in)
Additional contact information
Karthik Sriram: Indian Institute of Management Ahmedabad
R. V. Ramamoorthi: Michigan State University
Pulak Ghosh: Indian Institute of Management Bangalore
Sankhya A: The Indian Journal of Statistics, 2016, vol. 78, issue 1, No 5, 87-104
Abstract:
Abstract We consider a pseudo-likelihood for Bayesian estimation of multiple quantiles as a function of covariates. This arises as a simple product of multiple asymmetric Laplace densities (ALD), each corresponding to a particular quantile. The ALD has already been used in the Bayesian estimation of a single quantile. However, the pseudo-joint ALD likelihood is a way to incorporate constraints across quantiles, which cannot be done if each of the quantiles is modeled separately. Interestingly, we find that the normalized version of the likelihood turns out to be misleading. Hence, the pseudo-likelihood emerges as an alternative. In this note, we show that posterior consistency holds for the multiple quantile estimation based on such a likelihood for a nonlinear quantile regression framework and in particular for a linear quantile regression model. We demonstrate the benefits and explore potential challenges with the method through simulations.
Keywords: Asymmetric Laplace density; Bayesian quantile regression; Pseudo-likelihood; Primary 62J02; Secondary 62C10. (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1007/s13171-015-0079-2
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