IDEAS home Printed from https://ideas.repec.org/a/spr/stpapr/v53y2012i2p265-280.html
   My bibliography  Save this article

Efficiency of the modified jackknifed Liu-type estimator

Author

Listed:
  • Esra Akdeniz Duran
  • Fikri Akdeniz

Abstract

No abstract is available for this item.

Suggested Citation

  • Esra Akdeniz Duran & Fikri Akdeniz, 2012. "Efficiency of the modified jackknifed Liu-type estimator," Statistical Papers, Springer, vol. 53(2), pages 265-280, May.
    • Handle: RePEc:spr:stpapr:v:53:y:2012:i:2:p:265-280
    DOI: 10.1007/s00362-010-0334-5
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s00362-010-0334-5
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s00362-010-0334-5?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. Fikri Akdeniz, 2004. "New biased estimators under the LINEX loss function," Statistical Papers, Springer, vol. 45(2), pages 175-190, April.
    2. Nyquist, Hans, 1988. "Applications of the jackknife procedure in ridge regression," Computational Statistics & Data Analysis, Elsevier, vol. 6(2), pages 177-183, March.
    3. Hu Yang & Jianwen Xu, 2009. "An alternative stochastic restricted Liu estimator in linear regression," Statistical Papers, Springer, vol. 50(3), pages 639-647, June.
    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. Roozbeh, Mahdi, 2018. "Optimal QR-based estimation in partially linear regression models with correlated errors using GCV criterion," Computational Statistics & Data Analysis, Elsevier, vol. 117(C), pages 45-61.
    2. M. Arashi & T. Valizadeh, 2015. "Performance of Kibria’s methods in partial linear ridge regression model," Statistical Papers, Springer, vol. 56(1), pages 231-246, February.
    3. Mohammad Arashi & Mina Norouzirad & S. Ejaz Ahmed & Bahadır Yüzbaşı, 2018. "Rank-based Liu regression," Computational Statistics, Springer, vol. 33(3), pages 1525-1561, September.

    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. Akdeniz Duran, Esra & Härdle, Wolfgang Karl & Osipenko, Maria, 2012. "Difference based ridge and Liu type estimators in semiparametric regression models," Journal of Multivariate Analysis, Elsevier, vol. 105(1), pages 164-175.
    2. Murat Genç, 2022. "A new double-regularized regression using Liu and lasso regularization," Computational Statistics, Springer, vol. 37(1), pages 159-227, March.
    3. Luis Mendo, 2012. "Estimation of a probability in inverse binomial sampling under normalized linear-linear and inverse-linear loss," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 21(4), pages 656-675, December.
    4. Ning Li & Hu Yang, 2021. "Nonnegative estimation and variable selection under minimax concave penalty for sparse high-dimensional linear regression models," Statistical Papers, Springer, vol. 62(2), pages 661-680, April.
    5. M. Revan Özkale & Engin Arıcan, 2019. "A first-order approximated jackknifed ridge estimator in binary logistic regression," Computational Statistics, Springer, vol. 34(2), pages 683-712, June.
    6. Semra Türkan & Gamze Özel, 2016. "A new modified Jackknifed estimator for the Poisson regression model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(10), pages 1892-1905, August.
    7. Kristofer Månsson & B. M. Golam Kibria, 2021. "Estimating the Unrestricted and Restricted Liu Estimators for the Poisson Regression Model: Method and Application," Computational Economics, Springer;Society for Computational Economics, vol. 58(2), pages 311-326, August.

    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:stpapr:v:53:y:2012:i:2:p:265-280. 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.