Bayesian joint inference for multivariate quantile regression model with L$$_{1/2}$$ penalty
| Autor: | Man-Lai Tang, Yuzhu Tian, Maozai Tian |
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| Rok vydání: | 2021 |
| Předmět: |
Statistics and Probability
Multivariate statistics Variables media_common.quotation_subject Bayesian probability Regression analysis Feature selection Conditional probability distribution Statistics::Computation Computational Mathematics Statistics Linear regression Statistics Probability and Uncertainty Mathematics media_common Quantile |
| Zdroj: | Computational Statistics. 36:2967-2994 |
| ISSN: | 1613-9658 0943-4062 |
| DOI: | 10.1007/s00180-021-01158-4 |
| Popis: | 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 $$_{1/2}$$ 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. |
| Databáze: | OpenAIRE |
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