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A simulation approach to modified Searls estimation of population mean under two-phase random sampling

Author

Listed:
  • S.K. Yadav
  • Dinesh K. Sharma
  • Madhulika Dube

Abstract

A new extended Searls type estimator is suggested in this paper to assess the population mean of an essential variable more efficiently. The suggested estimator utilises a known supplementary variable in a two-phase (double sampling) design for improved estimation. The bias and mean squared error (MSE) of the introduced estimator are retained to approximate degree one for studying the sampling distribution properties. The Searls constants' optimal values that provide minimum MSE of the novel introduced estimator have also been acquired. The MSE of the introduced estimator was compared with the MSEs of other estimators in competition under double sampling, and the conditions of efficiency are obtained. The efficiency conditions obtained in theoretical comparison for the proposed estimator are verified using four natural datasets. A simulation study has also been performed using the natural populations' parameters to see the results' consistency.

Suggested Citation

  • S.K. Yadav & Dinesh K. Sharma & Madhulika Dube, 2022. "A simulation approach to modified Searls estimation of population mean under two-phase random sampling," International Journal of Mathematics in Operational Research, Inderscience Enterprises Ltd, vol. 22(2), pages 235-251.
    • Handle: RePEc:ids:ijmore:v:22:y:2022:i:2:p:235-251
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