IDEAS home Printed from https://ideas.repec.org/a/spr/stmapp/v15y2006i2d10.1007_s10260-006-0006-8.html
   My bibliography  Save this article

A Mixed Model-assisted Regression Estimator that Uses Variables Employed at the Design Stage

Author

Listed:
  • Giorgio E. Montanari

    (Università degli Studi di Perugia)

  • M. Giovanna Ranalli

    (Università degli Studi di Perugia)

Abstract

The Generalized regression estimator (GREG) of a finite population mean or total has been shown to be asymptotically optimal when the working linear regression model upon which it is based includes variables related to the sampling design. In this paper a regression estimator assisted by a linear mixed superpopulation model is proposed. It accounts for the extra information coming from the design in the random component of the model and saves degrees of freedom in finite sample estimation. This procedure combines the larger asymptotic efficiency of the optimal estimator and the greater finite sample stability of the GREG. Design based properties of the proposed estimator are discussed and a small simulation study is conducted to explore its finite sample performance.

Suggested Citation

  • Giorgio E. Montanari & M. Giovanna Ranalli, 2006. "A Mixed Model-assisted Regression Estimator that Uses Variables Employed at the Design Stage," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 15(2), pages 139-149, August.
    • Handle: RePEc:spr:stmapp:v:15:y:2006:i:2:d:10.1007_s10260-006-0006-8
    DOI: 10.1007/s10260-006-0006-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10260-006-0006-8
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10260-006-0006-8?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. F. J. Breidt & G. Claeskens & J. D. Opsomer, 2005. "Model-assisted estimation for complex surveys using penalised splines," Biometrika, Biometrika Trust, vol. 92(4), pages 831-846, December.
    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. Barranco-Chamorro, I. & Jiménez-Gamero, M.D. & Moreno-Rebollo, J.L. & Muñoz-Pichardo, J.M., 2012. "Case-deletion type diagnostics for calibration estimators in survey sampling," Computational Statistics & Data Analysis, Elsevier, vol. 56(7), pages 2219-2236.
    2. Särndal Carl-Erik & Traat Imbi & Lumiste Kaur, 2018. "Interaction Between Data Collection And Estimation Phases In Surveys With Nonresponse," Statistics in Transition New Series, Statistics Poland, vol. 19(2), pages 183-200, June.
    3. Sumanta Adhya & Tathagata Banerjee & Gaurangadeb Chattopadhyay, 2012. "Inference on finite population categorical response: nonparametric regression-based predictive approach," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 96(1), pages 69-98, January.
    4. Liu Bin & Yu Cindy Long & Price Michael Joseph & Jiang Yan, 2018. "Generalized Method of Moments Estimators for Multiple Treatment Effects Using Observational Data from Complex Surveys," Journal of Official Statistics, Sciendo, vol. 34(3), pages 753-784, September.
    5. Giorgio Montanari & M. Ranalli, 2006. "A Mixed Model-assisted Regression Estimator that Uses Variables Employed at the Design Stage," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 15(2), pages 139-149, August.
    6. Torsten Harms & Pierre Duchesne, 2010. "On kernel nonparametric regression designed for complex survey data," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 72(1), pages 111-138, July.
    7. Julian Wagner & Ralf Münnich & Joachim Hill & Johannes Stoffels & Thomas Udelhoven, 2017. "Non‐parametric small area models using shape‐constrained penalized B‐splines," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(4), pages 1089-1109, October.
    8. Carl-Erik Särndal & Imbi Traat & Kaur Lumiste, 2018. "Interaction Between Data Collection And Estimation Phases In Surveys With Nonresponse," Statistics in Transition New Series, Polish Statistical Association, vol. 19(2), pages 183-200, June.
    9. Sanjoy Sinha & Abdus Sattar, 2015. "Inference in semi-parametric spline mixed models for longitudinal data," METRON, Springer;Sapienza Università di Roma, vol. 73(3), pages 377-395, December.
    10. Sayed A. Mostafa & Ibrahim A. Ahmad, 2019. "Kernel density estimation from complex surveys in the presence of complete auxiliary information," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 82(3), pages 295-338, April.
    11. Wang, Li & Wang, Suojin, 2011. "Nonparametric additive model-assisted estimation for survey data," Journal of Multivariate Analysis, Elsevier, vol. 102(7), pages 1126-1140, August.
    12. Sumanta Adhya & Banerjee, Tathagata & Chattopadhyay, Gouranga, 2015. "A Note on Estimating Variance of Finite Population Distribution Function," IIMA Working Papers WP2015-08-02, Indian Institute of Management Ahmedabad, Research and Publication Department.

    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:stmapp:v:15:y:2006:i:2:d:10.1007_s10260-006-0006-8. 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.