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A Comparative Study on Calibration Approach Based Estimators for Domain Estimation Utilizing Power Function: Revisited

Author

Listed:
  • Ashutosh

    (MGKVP)

  • Piyush Kant Rai

    (Banaras Hindu University)

  • Ajeet Kumar Singh

    (University of Rajasthan)

Abstract

The calibration approach based estimators of the domain mean have growing demand during past couple of decades. Estimation of domains is another challenging task for surveyors and several efforts have been made to produce the reliable estimators for this purpose. Prominently the power function based estimators in the sample surveys are having dual advantages for the selection and their application to produce an improved estimation at any stage in the terms of efficiency without much complexity. In the domain estimation utilization of the power function in the development of calibration based estimators are also very promising and provide considerable results. A simulation study has examined for the comparison of several calibration estimators along with the proposed estimator in terms of the absolute relative bias and simulated relative standard error.

Suggested Citation

  • Ashutosh & Piyush Kant Rai & Ajeet Kumar Singh, 2023. "A Comparative Study on Calibration Approach Based Estimators for Domain Estimation Utilizing Power Function: Revisited," Annals of Data Science, Springer, vol. 10(6), pages 1559-1569, December.
    • Handle: RePEc:spr:aodasc:v:10:y:2023:i:6:d:10.1007_s40745-021-00365-6
    DOI: 10.1007/s40745-021-00365-6
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    References listed on IDEAS

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    1. Chandra, Hukum & Salvati, Nicola & Chambers, Ray, 2018. "Small area estimation under a spatially non-linear model," Computational Statistics & Data Analysis, Elsevier, vol. 126(C), pages 19-38.
    2. Claudia Baldermann & Nicola Salvati & Timo Schmid, 2018. "Robust Small Area Estimation under Spatial Non†stationarity," International Statistical Review, International Statistical Institute, vol. 86(1), pages 136-159, April.
    Full references (including those not matched with items on IDEAS)

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