IDEAS home Printed from https://ideas.repec.org/a/spr/aodasc/v10y2023i1d10.1007_s40745-020-00287-9.html
   My bibliography  Save this article

Estimation of Domain Mean Using Conventional Synthetic Estimator with Two Auxiliary Characters

Author

Listed:
  • Ashutosh

    (Banaras Hindu University)

Abstract

The estimation of domain mean is being accelerated applied to draft program policy in the government and private sectors. The use of two auxiliary characters is better choice as compared to single auxiliary character. The main interest is to consist information about an additional auxiliary character z in auxiliary character x and utilize for interested domain. This paper has investigated conventional generalized synthetic estimator for domain mean using two auxiliary characters x and z, and also discussed its properties. A comparative study of the proposed estimator has been made with the conventional ratio and conventional generalized estimators in terms of absolute relative bias and simulated relative standard error. It has evaluated, the proposed estimator is more efficient than the relevant estimators.

Suggested Citation

  • Ashutosh, 2023. "Estimation of Domain Mean Using Conventional Synthetic Estimator with Two Auxiliary Characters," Annals of Data Science, Springer, vol. 10(1), pages 153-166, February.
  • Handle: RePEc:spr:aodasc:v:10:y:2023:i:1:d:10.1007_s40745-020-00287-9
    DOI: 10.1007/s40745-020-00287-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s40745-020-00287-9
    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/s40745-020-00287-9?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. Tanin Sirimongkolkasem & Reza Drikvandi, 2019. "On Regularisation Methods for Analysis of High Dimensional Data," Annals of Data Science, Springer, vol. 6(4), pages 737-763, December.
    2. Havva Alizadeh Noughabi & Mohsen Kahani & Alireza Shakibamanesh, 2018. "Enhancing Situation Awareness Using Semantic Web Technologies and Complex Event Processing," Annals of Data Science, Springer, vol. 5(3), pages 487-496, September.
    3. K. K. Pandey & P. K. Rai, 2013. "Synthetic estimators using auxiliary information in small domains," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 14(1), pages 31-44, March.
    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. Reza Drikvandi & Olamide Lawal, 2023. "Sparse Principal Component Analysis for Natural Language Processing," Annals of Data Science, Springer, vol. 10(1), pages 25-41, February.
    2. David A. Alilah & C. O. Ouma & E. O. Ombaka, 2023. "Efficiency of Domain Mean Estimators in the Presence of Non-response Using Two-Stage Sampling with Non-linear and Linear Cost Function," Annals of Data Science, Springer, vol. 10(2), pages 291-316, April.
    3. Brij Behari Khare & Ashutosh Ashutosh & Piyush Kant Rai, 2021. "A comparative study of a class of direct estimators for domain mean with a direct ratio estimator for domain mean using auxiliary character," Statistics in Transition New Series, Polish Statistical Association, vol. 22(2), pages 189-200, June.
    4. Khare Brij Behari & Ashutosh & Rai Piyush Kant, 2021. "A comparative study of a class of direct estimators for domain mean with a direct ratio estimator for domain mean using auxiliary character," Statistics in Transition New Series, Polish Statistical Association, vol. 22(2), pages 189-200, 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:aodasc:v:10:y:2023:i:1:d:10.1007_s40745-020-00287-9. 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.