IDEAS home Printed from https://ideas.repec.org/p/zbw/kondp2/254.html
   My bibliography  Save this paper

Kreuzvalidierung in der nichtparametrischen Quantilsregression

Author

Listed:
  • Abberger, Klaus

Abstract

Nichtparametrische Verfahren zur Dichte- und Regressionsschätzung setzen die Wahl eines Glättungsparameters voraus. Ein oft verwendetes Verfahren zu dessen Bestimmung ist die Kreuzvalidierung. Die Übertragung dieser Methode auf die Quantiisregression ist Gegenstand der vorliegenden Arbeit. Es werden ein Kreuz validierungsverfahren definiert und einige Eigenschaften dieser Vorgehensweise aufgezeigt. Simulationen für verschiedene Verteilungsannahmen sollen weitere Einblicke in die Anwendbarkeit der vorliegenden Prozedur geben.

Suggested Citation

  • Abberger, Klaus, 1995. "Kreuzvalidierung in der nichtparametrischen Quantilsregression," Discussion Papers, Series II 254, University of Konstanz, Collaborative Research Centre (SFB) 178 "Internationalization of the Economy".
    • Handle: RePEc:zbw:kondp2:254
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/101524/1/737086041.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Abberger, Klaus, 1994. "Nichtparametrische Schätzung bedingter Quantile in Finanzmarktdaten," Discussion Papers, Series II 225, University of Konstanz, Collaborative Research Centre (SFB) 178 "Internationalization of the Economy".
    2. Jones, M. C. & Hall, Peter, 1990. "Mean squared error properties of kernel estimates or regression quantiles," Statistics & Probability Letters, Elsevier, vol. 10(4), pages 283-289, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jürgen Franke & Peter Mwita & Weining Wang, 2015. "Nonparametric estimates for conditional quantiles of time series," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 99(1), pages 107-130, January.
    2. Chen, Xirong & Li, Degui & Li, Qi & Li, Zheng, 2019. "Nonparametric estimation of conditional quantile functions in the presence of irrelevant covariates," Journal of Econometrics, Elsevier, vol. 212(2), pages 433-450.
    3. Ould-SaI¨d, Elias, 2006. "A strong uniform convergence rate of kernel conditional quantile estimator under random censorship," Statistics & Probability Letters, Elsevier, vol. 76(6), pages 579-586, March.
    4. De Gooijer, Jan G. & Gannoun, Ali & Zerom, Dawit, 2002. "Mean squared error properties of the kernel-based multi-stage median predictor for time series," Statistics & Probability Letters, Elsevier, vol. 56(1), pages 51-56, January.
    5. Toshio Honda, 2000. "Nonparametric Estimation of a Conditional Quantile for α-Mixing Processes," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 52(3), pages 459-470, September.
    6. Heiler, Siegfried, 1999. "A Survey on Nonparametric Time Series Analysis," CoFE Discussion Papers 99/05, University of Konstanz, Center of Finance and Econometrics (CoFE).
    7. Racine, Jeffrey S. & Li, Kevin, 2017. "Nonparametric conditional quantile estimation: A locally weighted quantile kernel approach," Journal of Econometrics, Elsevier, vol. 201(1), pages 72-94.
    8. Abberger, Klaus, 1995. "Volatility and conditional distribution in financial markets," Discussion Papers, Series II 252, University of Konstanz, Collaborative Research Centre (SFB) 178 "Internationalization of the Economy".
    9. Gannoun, Ali & Girard, Stephane & Guinot, Christiane & Saracco, Jerome, 2004. "Sliced inverse regression in reference curves estimation," Computational Statistics & Data Analysis, Elsevier, vol. 46(1), pages 103-122, May.
    10. Yu, Keming, 2002. "Quantile regression using RJMCMC algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 40(2), pages 303-315, August.
    11. Yu, Keming & Jones, M. C., 1997. "A comparison of local constant and local linear regression quantile estimators," Computational Statistics & Data Analysis, Elsevier, vol. 25(2), pages 159-166, July.
    12. Sékou Falil Doumbouya & Aboubacar Kaba & Mama Kéita & Ousmane Bah & Mohamed Lamine Doumbouya M.L. & Koulako Camara, 2009. "Services sociaux d'éducation et de santé en Guinée: Effets redistributifs de la politique gouvernementale," Working Papers PMMA 2009-02, PEP-PMMA.
    13. Wang, Qiao, 2023. "A simple nonparametric conditional quantile estimator for time series with thin tails," Economics Letters, Elsevier, vol. 232(C).
    14. Qi Li & Jeffrey Scott Racine, 2006. "Nonparametric Econometrics: Theory and Practice," Economics Books, Princeton University Press, edition 1, volume 1, number 8355.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:zbw:kondp2:254. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://edirc.repec.org/data/fwkonde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.