Improved Regression-Cum-Ratio Estimators Using Information on Two Auxiliary Variables Dealing with Subsampling Technique of Non-Response
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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.
KeywordsPopulation mean Study variable Two auxiliary variables Efficiency Non-response
Mathematics Subject ClassificationPrimary 62D05 Secondary 62P20
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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