Conditional empirical likelihood for quantile regression models

Wu Wang and Zhongyi Zhu ()
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Wu Wang: Fudan University
Zhongyi Zhu: Fudan University

Metrika: International Journal for Theoretical and Applied Statistics, 2017, vol. 80, issue 1, No 1, 16 pages

Abstract: Abstract In this paper, we propose a new Bayesian quantile regression estimator using conditional empirical likelihood as the working likelihood function. We show that the proposed estimator is asymptotically efficient and the confidence interval constructed is asymptotically valid. Our estimator has low computation cost since the posterior distribution function has explicit form. The finite sample performance of the proposed estimator is evaluated through Monte Carlo studies.

Keywords: Quantile regression; Bayesian analysis; Conditional empirical likelihood (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/s00184-016-0588-6

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