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Some efficient classes of estimators under stratified sampling

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  • Shashi Bhushan
  • Anoop Kumar
  • Saurabh Singh

Abstract

The essence of this paper is to propose some efficient combined and separate classes of estimators for estimating population mean Y¯ under stratified simple random sampling. The bias and mean square error of the proposed classes of estimators are obtained. The proposed estimators are theoretically justified over the conventional mean estimator, classical ratio and regression estimators, Kadilar and Cingi estimators, Shabbir and Gupta estimators, Singh and Vishwakarma estimators, Koyuncu and Kadilar estimators, Singh and Solanki estimator, Yadav et al. estimator, Solanki and Singh estimator and Shahzad et al. estimator. The theoretical findings are extended with a simulation study accomplished over some artificially generated symmetric and asymmetric populations.

Suggested Citation

  • Shashi Bhushan & Anoop Kumar & Saurabh Singh, 2023. "Some efficient classes of estimators under stratified sampling," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 52(6), pages 1767-1796, March.
    • Handle: RePEc:taf:lstaxx:v:52:y:2023:i:6:p:1767-1796
    DOI: 10.1080/03610926.2021.1939052
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