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Dual regression

Research output: Contribution to journalArticle

Original languageEnglish
Article numberasx074
Pages (from-to)1-18
Number of pages18
JournalBiometrika
Volume105
Issue number1
Early online date19 Jan 2018
DOIs
DateAccepted/In press - 13 Nov 2017
DateE-pub ahead of print - 19 Jan 2018
DatePublished (current) - 1 Mar 2018

Abstract

We propose dual regression as an alternative to quantile regression for the global estimation of conditional distribution functions. Dual regression provides the interpretational power of quantile regression while avoiding the need to repair intersecting conditional quantile surfaces. We introduce 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, and use it to specify and estimate a flexible class of conditional distribution functions. We present asymptotic theory for the corresponding empirical dual regression process.

    Structured keywords

  • ECON Econometrics

    Research areas

  • Conditional distribution, Duality, Monotonicity, Quantile regression, Method of moments, Mathematical programming, Convex approximation

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  • Full-text PDF (accepted author manuscript)

    Rights statement: This is the author accepted manuscript (AAM). The final published version (version of record) is available online via Oxford University Press at https://academic.oup.com/biomet/advance-article/doi/10.1093/biomet/asx074/4817511 . Please refer to any applicable terms of use of the publisher.

    Accepted author manuscript, 5.2 MB, PDF document

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