Some Efficient Methods to Remove Bias in Ratio and Product Types Estimators in Ranked Set Sampling

Authors

  • Nitu Mehta Dept. of Agricultural Economics & Management, Rajasthan College of Agriculture, Maharana Pratap University of Agriculture and Technology, Udaipur, Rajasthan (313 001), India https://orcid.org/0000-0002-0883-3467
  • V. L. Mandowara Dept. of Mathematics & Statistics, University College of Science, M. L. Sukhadia University, Udaipur, Rajasthan (313 001), India

Keywords:

RSS, unbiased estimators, ratio product estimators, auxiliary variables

Abstract

Ranked set sampling is one method to potentially increase precision and reduce costs by using quantitative or qualitative information to obtain a more representative sample. Use of auxiliary information has shown its significance in improvement of efficiency of estimators of unknown population parameters. Ratio estimator is used when auxiliary information in the form of population mean of auxiliary variable at estimation stage for the estimation of population parameters when study and auxiliary variable are positively correlated. In case of negative correlation between study variable and auxiliary variable, Product estimator is defined for the estimation of population mean. This paper proposed the problem of reducing the bias of the ratio and product estimators of the population mean in ranked set sampling (RSS). This paper suggested several type unbiased estimators of the finite population mean using information on known population parameters of the auxiliary variable in ranked set sampling. An important objective in any statistical estimation procedure is to obtain the estimators of parameters of interest with more precision. The Variance of the proposed unbiased ratio and product estimators are obtained up to first degree of approximation. Theoretically, it is shown that these suggested estimators are more efficient than the unbiased estimators in Simple random sampling. A numerical illustration is also carried out to demonstrate the merits of the proposed estimators using RSS over the usual estimators in SRS.

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Published

2022-03-31

How to Cite

1.
Mehta N, Mandowara VL. Some Efficient Methods to Remove Bias in Ratio and Product Types Estimators in Ranked Set Sampling. IJBSM [Internet]. 2022 Mar. 31 [cited 2024 May 22];13(Mar, 3):276-82. Available from: https://ojs.pphouse.org/index.php/IJBSM/article/view/4211

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