التفاصيل البيبلوغرافية
| العنوان: |
Distributed Bootstrap Simultaneous Inference for High-Dimensional Quantile Regression. |
| المؤلفون: |
Zhou, Xingcai1 (AUTHOR) xczhou@nau.edu.cn, Jing, Zhaoyang1 (AUTHOR) mg2108116@stu.nau.edu.cn, Huang, Chao2 (AUTHOR) chuang7@fsu.edu |
| المصدر: |
Mathematics (2227-7390). Mar2024, Vol. 12 Issue 5, p735. 53p. |
| مصطلحات موضوعية: |
*QUANTILE regression, *MACHINE learning, *STATISTICAL accuracy, *REGRESSION analysis, *DISTRIBUTED computing, *STATISTICAL learning |
| مستخلص: |
Modern massive data with enormous sample size and tremendous dimensionality are usually impossible to process with a single machine. They are typically stored and processed in a distributed manner. In this paper, we propose a distributed bootstrap simultaneous inference for a high-dimensional quantile regression model using massive data. Meanwhile, a communication-efficient (CE) distributed learning algorithm is developed via the CE surrogate likelihood framework and ADMM procedure, which can handle the non-smoothness of the quantile regression loss and the Lasso penalty. We theoretically prove the convergence of the algorithm and establish a lower bound on the number of communication rounds ι min that warrant statistical accuracy and efficiency. The distributed bootstrap validity and efficiency are corroborated by an extensive simulation study. [ABSTRACT FROM AUTHOR] |
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