Bayesian joint inference for multivariate quantile regression model with L $$_{1/2}$$ 1 / 2 penalty

Yu-Zhu Tian (), Man-Lai Tang and Mao-Zai Tian
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Yu-Zhu Tian: Northwest Normal University
Man-Lai Tang: The Hang Seng University of Hong Kong
Mao-Zai Tian: Renmin University of China

Computational Statistics, 2021, vol. 36, issue 4, No 25, 2967-2994

Abstract: Abstract This paper considers a Bayesian approach for joint estimation of the marginal conditional quantiles from several dependent variables under a linear regression framework. This approach incorporates the dependence among different dependent variables in the regression model which studies how the relationship between dependent variables and a set of explanatory variables can vary across different quantiles of the marginal conditional distribution of the dependent variables. A Bayesian regularization approach with L $$_{1/2}$$ 1 / 2 penalty is adopted to conduct high-dimensional variable selection. Some simulation studies are conducted to evaluate the performance of our proposed method. We illustrate the proposed estimation approach using a real data set on energy efficiency with two responses.

Keywords: Multi-response regression; The MAL distribution; Bayesian variable selection; Joint quantile inference; Energy performance of buildings (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s00180-021-01158-4

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