Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/106857
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Applied Mathematics | en_US |
dc.creator | Shen, G | en_US |
dc.creator | Chen, K | en_US |
dc.creator | Huang, J | en_US |
dc.creator | Lin, Y | en_US |
dc.date.accessioned | 2024-06-06T06:05:18Z | - |
dc.date.available | 2024-06-06T06:05:18Z | - |
dc.identifier.issn | 0006-3444 | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/106857 | - |
dc.language.iso | en | en_US |
dc.publisher | Oxford University Press | en_US |
dc.rights | © The Author(s) 2022. Published by Oxford University Press on behalf of the Biometrika Trust. All rights reserved. | en_US |
dc.rights | This is a pre-copyedited, author-produced version of an article accepted for publication in Biometrika following peer review. The version of record Guohao Shen, Kani Chen, Jian Huang, Yuanyuan Lin, Linearized maximum rank correlation estimation, Biometrika, Volume 110, Issue 1, March 2023, Pages 187–203 is available online at: https://doi.org/10.1093/biomet/asac027. | en_US |
dc.subject | Censored data | en_US |
dc.subject | Closed-form solution | en_US |
dc.subject | Linearized maximum rank correlation | en_US |
dc.subject | Single-index model | en_US |
dc.title | Linearized maximum rank correlation estimation | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 187 | en_US |
dc.identifier.epage | 203 | en_US |
dc.identifier.volume | 110 | en_US |
dc.identifier.issue | 1 | en_US |
dc.identifier.doi | 10.1093/biomet/asac027 | en_US |
dcterms.abstract | We propose a linearized maximum rank correlation estimator for the single-index model. Unlike the existing maximum rank correlation and other rank-based methods, the proposed estimator has a closed-form expression, making it appealing in theory and computation. The proposed estimator is robust to outliers in the response and its construction does not need knowledge of the unknown link function or the error distribution. Under mild conditions, it is shown to be consistent and asymptotically normal when the predictors satisfy the linearity of the expectation assumption. A more general class of estimators is also studied. Inference procedures based on the plug-in rule or random weighting resampling are employed for variance estimation. The proposed method can be easily modified to accommodate censored data. It can also be extended to deal with high-dimensional data combined with a penalty function. Extensive simulation studies provide strong evidence that the proposed method works well in various practical situations. Its application is illustrated with the Beijing PM 2.5 dataset. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Biometrika, Mar. 2023, v. 110, no. 1, p. 187-203 | en_US |
dcterms.isPartOf | Biometrika | en_US |
dcterms.issued | 2023-03 | - |
dc.identifier.scopus | 2-s2.0-85150597700 | - |
dc.identifier.eissn | 1464-3510 | en_US |
dc.description.validate | 202406 bcch | en_US |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | a2752 | - |
dc.identifier.SubFormID | 48237 | - |
dc.description.fundingSource | RGC | en_US |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | The work of K. Chen is supported by the Hong Kong Research Grants Council (Grant 465 No. 16308221); The work of Y. Lin was supported by the Hong Kong Research Grants Council (Grant No. 14306219 and 14306620) and Direct Grants for Research, The Chinese University of Hong Kong | en_US |
dc.description.pubStatus | Published | en_US |
dc.description.oaCategory | Green (AAM) | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Shen_Linearized_Maximum_Rank_Main text.pdf | Pre-Published version | 365.87 kB | Adobe PDF | View/Open |
Shen_Linearized_Maximum_Rank_Supplement.pdf | Pre-Published version | 357.95 kB | Adobe PDF | View/Open |
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