Hierarchically penalized quantile regression with multiple responses
| Autor: | Sungwan Bang, Jongkyeong Kang, Seung Jun Shin, Jaeshin Park |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Statistics and Probability
Statistics::Theory Property (programming) 05 social sciences Feature selection Bayesian inference 01 natural sciences Regularization (mathematics) Oracle Statistics::Computation Quantile regression Statistics::Machine Learning 010104 statistics & probability 0502 economics and business Linear regression Statistics::Methodology 0101 mathematics Algorithm 050205 econometrics Mathematics |
| Zdroj: | Journal of the Korean Statistical Society. 47:471-481 |
| ISSN: | 1226-3192 |
| DOI: | 10.1016/j.jkss.2018.05.004 |
| Popis: | We study variable selection in quantile regression with multiple responses. Instead of applying conventional penalized quantile regression to each response separately, it is desired to solve them simultaneously when the sparsity patterns of the regression coefficients for different responses are similar, which is often the case in practice. In this paper, we propose employing a hierarchical penalty that enables us to detect a common sparsity pattern shared between different responses as well as additional sparsity patterns within the selected variables. We establish the oracle property of the proposed method and demonstrate it offers better performance than existing approaches. |
| Databáze: | OpenAIRE |
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