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- Publisher Website: 10.1109/WSC.2018.8632388
- Scopus: eid_2-s2.0-85062618048
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Conference Paper: Data-driven ranking and selection: High-dimensional covariates and general dependence
| Title | Data-driven ranking and selection: High-dimensional covariates and general dependence |
|---|---|
| Authors | |
| Issue Date | 2019 |
| Publisher | IEEE. |
| Citation | 2018 Winter Simulation Conference (WSC 2018), Gothenburg, Sweden, 9-12 December 2018. In Proceedings - Winter Simulation Conference, 2019, p. 1933-1944 How to Cite? |
| Abstract | © 2018 IEEE This paper considers the problem of ranking and selection with covariates and aims to identify a decision rule that stipulates the best alternative as a function of the observable covariates. We propose a general data-driven framework to accommodate (i) high-dimensional covariates and (ii) general (nonlinear) dependence between the mean performance of an alternative and the covariates. For both scenarios, we design new selection procedures and provide certain statistical guarantees, by leveraging the data-intensive environment and various statistical learning tools. The performances of our procedures are exhibited through simulation experiments. |
| Persistent Identifier | http://hdl.handle.net/10722/271503 |
| ISSN | 2023 SCImago Journal Rankings: 0.272 |
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Li, Xiaocheng | - |
| dc.contributor.author | Zhang, Xiaowei | - |
| dc.contributor.author | Zheng, Zeyu | - |
| dc.date.accessioned | 2019-07-02T07:16:15Z | - |
| dc.date.available | 2019-07-02T07:16:15Z | - |
| dc.date.issued | 2019 | - |
| dc.identifier.citation | 2018 Winter Simulation Conference (WSC 2018), Gothenburg, Sweden, 9-12 December 2018. In Proceedings - Winter Simulation Conference, 2019, p. 1933-1944 | - |
| dc.identifier.issn | 0891-7736 | - |
| dc.identifier.uri | http://hdl.handle.net/10722/271503 | - |
| dc.description.abstract | © 2018 IEEE This paper considers the problem of ranking and selection with covariates and aims to identify a decision rule that stipulates the best alternative as a function of the observable covariates. We propose a general data-driven framework to accommodate (i) high-dimensional covariates and (ii) general (nonlinear) dependence between the mean performance of an alternative and the covariates. For both scenarios, we design new selection procedures and provide certain statistical guarantees, by leveraging the data-intensive environment and various statistical learning tools. The performances of our procedures are exhibited through simulation experiments. | - |
| dc.language | eng | - |
| dc.publisher | IEEE. | - |
| dc.relation.ispartof | Proceedings - Winter Simulation Conference | - |
| dc.title | Data-driven ranking and selection: High-dimensional covariates and general dependence | - |
| dc.type | Conference_Paper | - |
| dc.description.nature | link_to_subscribed_fulltext | - |
| dc.identifier.doi | 10.1109/WSC.2018.8632388 | - |
| dc.identifier.scopus | eid_2-s2.0-85062618048 | - |
| dc.identifier.spage | 1933 | - |
| dc.identifier.epage | 1944 | - |
| dc.identifier.issnl | 0891-7736 | - |