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Improved Regression-Cum-Ratio Estimators Using Information on Two Auxiliary Variables Dealing with Subsampling Technique of Non-Response

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Abstract

In the present study, we have suggested some improved regression-cum-ratio-type estimators to estimate the population mean using two auxiliary variables in two different situations of non-response. The bias and mean square error are obtained under large sample approximation. After analysis of a numerical illustration, it has been found that the proposed class of estimators is more efficient than Hansen and Hurwitz (J Am Stat Assoc 41:517–529, 1946) usual unbiased estimator, conventional ratio and regression estimators, Singh and Kumar (Braz J Probab Stat 25(2):205–217, 2011) estimators, Muneer et. al. (Commun Stat Theory Methods 46(5):2181–2192, 2017) estimators, Kumar et. al. (J Stat Comput Simul 88(18):3694–3707, 2018) estimators and Akingbade and Okafer (Pak J Stat Oper Res 15(2):329–340, 2019) estimators. We also consider a simulation study under which the estimated performance of the proposed class of estimators is evaluated.

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Acknowledgements

Authors are very thankful to the Indian Institute of Technology (Indian School of Mines), Dhanbad, for providing financial assistance and necessary infrastructure to accomplish the present research work.

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Correspondence to M. Usman.

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Singh, G.N., Usman, M. Improved Regression-Cum-Ratio Estimators Using Information on Two Auxiliary Variables Dealing with Subsampling Technique of Non-Response. J Stat Theory Pract 14, 14 (2020) doi:10.1007/s42519-019-0082-3

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Keywords

  • Population mean
  • Study variable
  • Two auxiliary variables
  • Efficiency
  • Non-response

Mathematics Subject Classification

  • Primary 62D05
  • Secondary 62P20