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About monotone regression quantiles

Author

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  • Poiraud-Casanova, Sandrine
  • Thomas-Agnan, Christine

Abstract

The aim of this paper is to prove the equivalence between the regression quantile under monotonicity constraint and the Min-Max formula introduced by Casady and Cryer (1976, Ann. Math. Statist. 4 (3), 532-541). The proof of this result uses an original probability density which does not appear in classical books.

Suggested Citation

  • Poiraud-Casanova, Sandrine & Thomas-Agnan, Christine, 2000. "About monotone regression quantiles," Statistics & Probability Letters, Elsevier, vol. 48(1), pages 101-104, May.
    • Handle: RePEc:eee:stapro:v:48:y:2000:i:1:p:101-104
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    Citations

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    Cited by:

    1. Catania, Leopoldo & Luati, Alessandra, 2023. "Semiparametric modeling of multiple quantiles," Journal of Econometrics, Elsevier, vol. 237(2).
    2. Jayanta K. Pokharel & Gokarna Aryal & Netra Khanal & Chris P. Tsokos, 2024. "Probability Distributions for Modeling Stock Market Returns—An Empirical Inquiry," IJFS, MDPI, vol. 12(2), pages 1-27, May.
    3. Bajgiran, Amirsaman H. & Mardikoraem, Mahsa & Soofi, Ehsan S., 2021. "Maximum entropy distributions with quantile information," European Journal of Operational Research, Elsevier, vol. 290(1), pages 196-209.
    4. Leopoldo Catania & Alessandra Luati & Pierluigi Vallarino, 2021. "Economic vulnerability is state dependent," CREATES Research Papers 2021-09, Department of Economics and Business Economics, Aarhus University.
    5. Tomasz Kozubowski & Saralees Nadarajah, 2010. "Multitude of Laplace distributions," Statistical Papers, Springer, vol. 51(1), pages 127-148, January.
    6. Samuel Kotz & Tomasz Kozubowski & Krzysztof Podgórski, 2002. "Maximum Likelihood Estimation of Asymmetric Laplace Parameters," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 54(4), pages 816-826, December.

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