IDEAS home Printed from https://ideas.repec.org/a/spr/stpapr/v53y2012i3p549-562.html
   My bibliography  Save this article

Combining two-parameter and principal component regression estimators

Author

Listed:
  • Xinfeng Chang
  • Hu Yang

Abstract

No abstract is available for this item.

Suggested Citation

  • Xinfeng Chang & Hu Yang, 2012. "Combining two-parameter and principal component regression estimators," Statistical Papers, Springer, vol. 53(3), pages 549-562, August.
    • Handle: RePEc:spr:stpapr:v:53:y:2012:i:3:p:549-562
    DOI: 10.1007/s00362-011-0364-7
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s00362-011-0364-7
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s00362-011-0364-7?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. Yalian Li & Hu Yang, 2010. "A new stochastic mixed ridge estimator in linear regression model," Statistical Papers, Springer, vol. 51(2), pages 315-323, June.
    2. Özkale, M. Revan & KaçIranlar, Selahattin, 2007. "Superiority of the r-d class estimator over some estimators by the mean square error matrix criterion," Statistics & Probability Letters, Elsevier, vol. 77(4), pages 438-446, February.
    3. Sarkar, Nityananda, 1996. "Mean square error matrix comparison of some estimators in linear regressions with multicollinearity," Statistics & Probability Letters, Elsevier, vol. 30(2), pages 133-138, October.
    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. R. Salmerón & J. García & C. B. García & M. M. López Martín, 2017. "A note about the corrected VIF," Statistical Papers, Springer, vol. 58(3), pages 929-945, September.
    2. Heewon Park & Sadanori Konishi, 2017. "Principal component selection via adaptive regularization method and generalized information criterion," Statistical Papers, Springer, vol. 58(1), pages 147-160, March.
    3. Jiewu Huang & Hu Yang, 2015. "On a principal component two-parameter estimator in linear model with autocorrelated errors," Statistical Papers, Springer, vol. 56(1), pages 217-230, February.
    4. Gargi Tyagi & Shalini Chandra, 2017. "A Note on the Performance of Biased Estimators with Autocorrelated Errors," International Journal of Mathematics and Mathematical Sciences, Hindawi, vol. 2017, pages 1-12, January.
    5. Marconi, Gabriele, 2014. "European higher education policies and the problem of estimating a complex model with a small cross-section," MPRA Paper 87600, University Library of Munich, Germany.
    6. Shuichi Kawano, 2021. "Sparse principal component regression via singular value decomposition approach," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 15(3), pages 795-823, 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. Deniz Inan, 2015. "Combining the Liu-type estimator and the principal component regression estimator," Statistical Papers, Springer, vol. 56(1), pages 147-156, February.
    2. Hongchang Hu & Weifu Hu & Xinxin Yu, 2021. "Pseudo-maximum likelihood estimators in linear regression models with fractional time series," Statistical Papers, Springer, vol. 62(2), pages 639-659, April.
    3. 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.
    4. Shalini Chandra & Nityananda Sarkar, 2015. "Comparison of the r - (k, d) Class Estimator with some Estimators for Multicollinearity under the Mahalanobis Loss Function," International Econometric Review (IER), Econometric Research Association, vol. 7(1), pages 1-12, April.
    5. F. Ghapani & A. R. Rasekh & B. Babadi, 2018. "The weighted ridge estimator in stochastic restricted linear measurement error models," Statistical Papers, Springer, vol. 59(2), pages 709-723, June.
    6. Sivarajah Arumairajan & Pushpakanthie Wijekoon, 2017. "The generalized preliminary test estimator when different sets of stochastic restrictions are available," Statistical Papers, Springer, vol. 58(3), pages 729-747, September.
    7. Chandra Shalini & Tyagi Gargi, 2017. "On the Performance of Some Biased Estimators in a Misspecified Model with Correlated Regressors," Statistics in Transition New Series, Polish Statistical Association, vol. 18(1), pages 27-52, March.
    8. M. Revan Özkale & Atif Abbasi, 2022. "Iterative restricted OK estimator in generalized linear models and the selection of tuning parameters via MSE and genetic algorithm," Statistical Papers, Springer, vol. 63(6), pages 1979-2040, December.
    9. Gülesen Üstündagˇ Şiray & Selahattin Kaçıranlar & Sadullah Sakallıoğlu, 2014. "r − k Class estimator in the linear regression model with correlated errors," Statistical Papers, Springer, vol. 55(2), pages 393-407, May.
    10. Fikri Akdeniz & Mahdi Roozbeh, 2019. "Generalized difference-based weighted mixed almost unbiased ridge estimator in partially linear models," Statistical Papers, Springer, vol. 60(5), pages 1717-1739, October.
    11. 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.
    12. Jan Pablo Burgard & Joscha Krause & Ralf Münnich, 2019. "Penalized Small Area Models for the Combination of Unit- and Area-level Data," Research Papers in Economics 2019-05, University of Trier, Department of Economics.
    13. Özkale, M. Revan & KaçIranlar, Selahattin, 2007. "Superiority of the r-d class estimator over some estimators by the mean square error matrix criterion," Statistics & Probability Letters, Elsevier, vol. 77(4), pages 438-446, February.
    14. Shalini Chandra & Gargi Tyagi, 2017. "On the Performance of Some Biased Estimators in a Misspecified Model with Correlated Regressors," Statistics in Transition New Series, Polish Statistical Association, vol. 18(1), pages 27-52, March.
    15. 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:3:p:549-562. 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.