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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 f...

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Publication status:
Published
Peer review status:
Peer reviewed
Version:
Accepted manuscript

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Publisher copy:
10.1093/biomet/asx074

Authors


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Institution:
University of Oxford
Division:
Colleges and Halls
Department:
Nuffield College
Oxford college:
Nuffield College
Role:
Author
Economic and Social Research Council More from this funder
Royal Economic Society More from this funder
Publisher:
Oxford University Press Publisher's website
Journal:
Biometrika Journal website
Volume:
105
Issue:
1
Pages:
1–18
Publication date:
2018-01-19
Acceptance date:
2017-11-13
DOI:
EISSN:
1464-3510
ISSN:
0006-3444
Pubs id:
pubs:817187
URN:
uri:3c16d386-5aab-4afd-8c1e-81a8a50b9c7f
UUID:
uuid:3c16d386-5aab-4afd-8c1e-81a8a50b9c7f
Local pid:
pubs:817187

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