Transfer Learning of High-Dimensional Stochastic Frontier Model via Elastic Net
In this paper, the high-dimensional stochastic frontier model problem is explored via Elastic Net under the transfer learning framework. When the target data is limited, transfer learning improves the accuracy of model estimation and prediction by transferring the source data. When the transferable...
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MDPI AG
2025-06-01
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| Online Access: | https://www.mdpi.com/2075-1680/14/7/507 |
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| author | Jiahao Chen Wenjun Chen Yunquan Song |
| author_facet | Jiahao Chen Wenjun Chen Yunquan Song |
| author_sort | Jiahao Chen |
| collection | DOAJ |
| description | In this paper, the high-dimensional stochastic frontier model problem is explored via Elastic Net under the transfer learning framework. When the target data is limited, transfer learning improves the accuracy of model estimation and prediction by transferring the source data. When the transferable source is known, a transfer learning algorithm for a high-dimensional stochastic frontier model is proposed based on Elastic Net. In addition, based on the prior knowledge of the parameters, this paper introduces linear constraints to improve the estimation accuracy in transfer learning. When the transferable source is unknown, this paper designs a corresponding algorithm to detect the transferable source. Finally, the effectiveness of the method is proved by simulation experiments and actual cases. |
| format | Article |
| id | doaj-art-2acc7a595ff84d76a5c96fa20dcdfb62 |
| institution | DOAJ |
| issn | 2075-1680 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Axioms |
| spelling | doaj-art-2acc7a595ff84d76a5c96fa20dcdfb622025-08-20T02:45:33ZengMDPI AGAxioms2075-16802025-06-0114750710.3390/axioms14070507Transfer Learning of High-Dimensional Stochastic Frontier Model via Elastic NetJiahao Chen0Wenjun Chen1Yunquan Song2College of Science, China University of Petroleum, Qingdao 266580, ChinaStudent Career Guidance Center, China University of Petroleum (East China), Qingdao 266580, ChinaCollege of Science, China University of Petroleum, Qingdao 266580, ChinaIn this paper, the high-dimensional stochastic frontier model problem is explored via Elastic Net under the transfer learning framework. When the target data is limited, transfer learning improves the accuracy of model estimation and prediction by transferring the source data. When the transferable source is known, a transfer learning algorithm for a high-dimensional stochastic frontier model is proposed based on Elastic Net. In addition, based on the prior knowledge of the parameters, this paper introduces linear constraints to improve the estimation accuracy in transfer learning. When the transferable source is unknown, this paper designs a corresponding algorithm to detect the transferable source. Finally, the effectiveness of the method is proved by simulation experiments and actual cases.https://www.mdpi.com/2075-1680/14/7/507high-dimensional stochastic frontier modeltransfer learninglinear constraintsElastic Net |
| spellingShingle | Jiahao Chen Wenjun Chen Yunquan Song Transfer Learning of High-Dimensional Stochastic Frontier Model via Elastic Net Axioms high-dimensional stochastic frontier model transfer learning linear constraints Elastic Net |
| title | Transfer Learning of High-Dimensional Stochastic Frontier Model via Elastic Net |
| title_full | Transfer Learning of High-Dimensional Stochastic Frontier Model via Elastic Net |
| title_fullStr | Transfer Learning of High-Dimensional Stochastic Frontier Model via Elastic Net |
| title_full_unstemmed | Transfer Learning of High-Dimensional Stochastic Frontier Model via Elastic Net |
| title_short | Transfer Learning of High-Dimensional Stochastic Frontier Model via Elastic Net |
| title_sort | transfer learning of high dimensional stochastic frontier model via elastic net |
| topic | high-dimensional stochastic frontier model transfer learning linear constraints Elastic Net |
| url | https://www.mdpi.com/2075-1680/14/7/507 |
| work_keys_str_mv | AT jiahaochen transferlearningofhighdimensionalstochasticfrontiermodelviaelasticnet AT wenjunchen transferlearningofhighdimensionalstochasticfrontiermodelviaelasticnet AT yunquansong transferlearningofhighdimensionalstochasticfrontiermodelviaelasticnet |