Expectile Regression on Distributed Large-Scale Data
Large-scale data presents great challenges to data analysis due to the limited computer storage capacity and the heterogeneous data structure. In this article, we propose a distributed expectile regression model to resolve the challenges of large-scale data by designing a surrogate loss function and...
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9131766/ |
| _version_ | 1818853388295602176 |
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| author | Aijun Hu Chujin Li Jing Wu |
| author_facet | Aijun Hu Chujin Li Jing Wu |
| author_sort | Aijun Hu |
| collection | DOAJ |
| description | Large-scale data presents great challenges to data analysis due to the limited computer storage capacity and the heterogeneous data structure. In this article, we propose a distributed expectile regression model to resolve the challenges of large-scale data by designing a surrogate loss function and using the Iterative Local Alternating Direction Method of the Multipliers (IL-ADMM) algorithm, which is developed for the calculation of the proposed estimator. To obtain nice performance only after fewer rounds of communications, the proposed method only needs to solve an M-estimation problem on the master machine while the other working machines only to compute the gradients based on local data. Moreover, we show the consistency and the asymptotic normality of the proposed estimator, and illustrate the efficient proof by numerical simulations and positive analysis on the superconductor data. |
| first_indexed | 2024-12-19T07:36:01Z |
| format | Article |
| id | doaj.art-0078e8bf6e0a4fd59be706c0f75e6cdf |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T07:36:01Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-0078e8bf6e0a4fd59be706c0f75e6cdf2022-12-21T20:30:35ZengIEEEIEEE Access2169-35362020-01-01812227012228010.1109/ACCESS.2020.30065269131766Expectile Regression on Distributed Large-Scale DataAijun Hu0Chujin Li1https://orcid.org/0000-0002-0969-8965Jing Wu2School of Mathematics and Statistics, Huazhong University of Science and Technology, Wuhan, ChinaSchool of Mathematics and Statistics, Huazhong University of Science and Technology, Wuhan, ChinaElectronic Information School, Wuhan University, Wuhan, ChinaLarge-scale data presents great challenges to data analysis due to the limited computer storage capacity and the heterogeneous data structure. In this article, we propose a distributed expectile regression model to resolve the challenges of large-scale data by designing a surrogate loss function and using the Iterative Local Alternating Direction Method of the Multipliers (IL-ADMM) algorithm, which is developed for the calculation of the proposed estimator. To obtain nice performance only after fewer rounds of communications, the proposed method only needs to solve an M-estimation problem on the master machine while the other working machines only to compute the gradients based on local data. Moreover, we show the consistency and the asymptotic normality of the proposed estimator, and illustrate the efficient proof by numerical simulations and positive analysis on the superconductor data.https://ieeexplore.ieee.org/document/9131766/Expectile regressionsurrogate lossdistributed statistical learningIL-ADMM algorithm |
| spellingShingle | Aijun Hu Chujin Li Jing Wu Expectile Regression on Distributed Large-Scale Data IEEE Access Expectile regression surrogate loss distributed statistical learning IL-ADMM algorithm |
| title | Expectile Regression on Distributed Large-Scale Data |
| title_full | Expectile Regression on Distributed Large-Scale Data |
| title_fullStr | Expectile Regression on Distributed Large-Scale Data |
| title_full_unstemmed | Expectile Regression on Distributed Large-Scale Data |
| title_short | Expectile Regression on Distributed Large-Scale Data |
| title_sort | expectile regression on distributed large scale data |
| topic | Expectile regression surrogate loss distributed statistical learning IL-ADMM algorithm |
| url | https://ieeexplore.ieee.org/document/9131766/ |
| work_keys_str_mv | AT aijunhu expectileregressionondistributedlargescaledata AT chujinli expectileregressionondistributedlargescaledata AT jingwu expectileregressionondistributedlargescaledata |