IDEAS home Printed from https://ideas.repec.org/a/spr/aistmt/v48y1996i3p451-467.html
   My bibliography  Save this article

Minimax kernels for density estimation with biased data

Author

Listed:
  • Colin Wu
  • Andrew Mao

Abstract

No abstract is available for this item.

Suggested Citation

  • Colin Wu & Andrew Mao, 1996. "Minimax kernels for density estimation with biased data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 48(3), pages 451-467, September.
    • Handle: RePEc:spr:aistmt:v:48:y:1996:i:3:p:451-467
    DOI: 10.1007/BF00050848
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/BF00050848
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/BF00050848?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Ahmad, Ibrahim A., 1995. "On multivariate kernel estimation for samples from weighted distributions," Statistics & Probability Letters, Elsevier, vol. 22(2), pages 121-129, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. José Cristóbal & José Alcalá, 2001. "An overview of nonparametric contributions to the problem of functional estimation from biased data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 10(2), pages 309-332, December.
    2. Dauxois, Jean-Yves & Guilloux, Agathe, 2008. "Nonparametric inference under competing risks and selection-biased sampling," Journal of Multivariate Analysis, Elsevier, vol. 99(4), pages 589-605, April.
    3. E. Brunel & F. Comte & A. Guilloux, 2009. "Nonparametric density estimation in presence of bias and censoring," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 18(1), pages 166-194, May.
    4. Wu, Colin O., 1997. "A Cross-Validation Bandwidth Choice for Kernel Density Estimates with Selection Biased Data," Journal of Multivariate Analysis, Elsevier, vol. 61(1), pages 38-60, April.

    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. Junke Kou & Youming Liu, 2018. "Wavelet regression estimations with strong mixing data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 27(4), pages 667-688, December.
    2. Wu, Colin O., 1997. "A Cross-Validation Bandwidth Choice for Kernel Density Estimates with Selection Biased Data," Journal of Multivariate Analysis, Elsevier, vol. 61(1), pages 38-60, April.
    3. J. Cristóbal & J. Ojeda & J. Alcalá, 2004. "Confidence bands in nonparametric regression with length biased data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 56(3), pages 475-496, September.
    4. Ying Qing Chen, 2010. "Semiparametric Regression in Size-Biased Sampling," Biometrics, The International Biometric Society, vol. 66(1), pages 149-158, March.
    5. Yogendra P. Chaubey & Christophe Chesneau & Fabien Navarro, 2017. "Linear wavelet estimation of the derivatives of a regression function based on biased data," Working Papers 2017-70, Center for Research in Economics and Statistics.
    6. de Uña-Álvarez, Jacobo, 2003. "Large sample results under biased sampling when covariables are present," Statistics & Probability Letters, Elsevier, vol. 63(3), pages 287-293, July.
    7. Wu, Colin O., 1997. "The effects of kernel choices in density estimation with biased data," Statistics & Probability Letters, Elsevier, vol. 34(4), pages 373-383, June.

    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:spr:aistmt:v:48:y:1996:i:3:p:451-467. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.