Journal article
Dual regression
- Abstract:
- We propose dual regression as an alternative to the quantile regression process for the global estimation of conditional distribution functions under minimal assumptions. Dual regression provides all the interpretational power of the quantile regression process while avoiding the need for repairing the intersecting conditional quantile surfaces that quantile regression often produces in practice. Our approach introduces a mathematical programming characterization of conditional distribution functions which, in its simplest form, is the dual program of a simultaneous estimator for linear location-scale models. We apply our general characterization to the specification and estimation of a flexible class of conditional distribution functions, and present asymptotic theory for the corresponding empirical dual regression process.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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Access Document
- Files:
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(Preview, Accepted manuscript, pdf, 5.2MB, Terms of use)
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- Publisher copy:
- 10.1093/biomet/asx074
Authors
- Publisher:
- Oxford University Press
- Journal:
- Biometrika More from this journal
- Volume:
- 105
- Issue:
- 1
- Pages:
- 1–18
- Publication date:
- 2018-01-19
- Acceptance date:
- 2017-11-13
- DOI:
- EISSN:
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1464-3510
- ISSN:
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0006-3444
- Keywords:
- Pubs id:
-
pubs:817187
- UUID:
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uuid:3c16d386-5aab-4afd-8c1e-81a8a50b9c7f
- Local pid:
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pubs:817187
- Source identifiers:
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817187
- Deposit date:
-
2018-01-08
- ARK identifier:
Terms of use
- Copyright holder:
- Biometrika Trust
- Copyright date:
- 2018
- Notes:
- © 2018 Biometrika Trust. This is the accepted manuscript version of the article. The final version is available online from Oxford University Press at: https://doi.org/10.1093/biomet/asx074
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