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An efficient new approach for estimating the general parameter using auxiliary variable in sample surveys

Abstract

This paper suggests a general class of estimators for unknown general population parameter of the study variable using auxiliary information in sample surveys. A large number of estimators of different parameters of the study variable based on auxiliary information can be viewed as a member of the suggested class of estimators. We have derived the bias and mean squared error of the suggested class of estimators under large sample approximation. Optimum conditions are obtained for which the proposed class of estimator has the minimum mean squared error. In particular, a class of estimators for the population mean of the study variable based on the coefficient variation of the auxiliary variable has been derived. The bias and mean squared error of the proposed class of estimators are obtained under large sample approximation. It has been shown that the proposed class of estimators is better than the usual unbiased estimator \(\overline{y}\) and Das and Tripathi’s (Sankhya C 42(1–2):76–86, 1980) estimator in the context of bivariate normal and bivariate non-normal populations. In support of the present study, an empirical is given.

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Acknowledgements

The author wish to thank anonymous learned referee for his careful reading and constructive suggestions regarding improvement of the paper.

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Correspondence to Surya K. Pal.

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Pal, S.K., Singh, H.P. An efficient new approach for estimating the general parameter using auxiliary variable in sample surveys. Afr. Mat. 33, 70 (2022). https://doi.org/10.1007/s13370-022-00983-0

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  • DOI: https://doi.org/10.1007/s13370-022-00983-0

Keywords

  • Auxiliary information
  • Study variable
  • Simple random sampling without replacement
  • Bias
  • Mean squared error

Mathematics Subject Classification

  • 62D05