Simulation Study of Ratio Type Estimators in Stratified Random Sampling Using Multi-Auxiliary Information

  • Vishwantra Sharma Department of statistics, University of Jammu, Jammu, India
  • Sunil Kumar Department of statistics, University of Jammu, Jammu, India
Keywords: Ratio estimator, bias, mean squared error, multi-auxiliary variables

Abstract

The paper addresses the problem of estimating the population mean of the study variable in stratified random sampling by using multi-auxiliary variable. In this paper, we proposed a ratio type estimator for estimating the population mean of the study variable by using multi-auxiliary variables.  Stratified random sampling is taken into consideration. The expressions for the bias and mean square error (MSE) of the proposed estimator have been derived. The proposed estimator is compared with other existing estimators in terms of efficiency.  An empirical study with the aid of simulation has also been carried out to validate the theoretical results obtained. The theoretical and empirical studies reveal that the proposed estimator performs better than existing estimators in the literature.

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Author Biography

Vishwantra Sharma, Department of statistics, University of Jammu, Jammu, India

The paper addresses the problem of estimating the population mean of the study variable in stratified random sampling by using multi-auxiliary variable. In this paper, we proposed a ratio type estimator for estimating the population mean of the study variable by using multi-auxiliary variables.  Stratified random sampling is taken into consideration. The expressions for the bias and mean square error (MSE) of the proposed estimator have been derived. The proposed estimator is compared with other existing estimators in terms of efficiency.  An empirical study with the aid of simulation has also been carried out to validate the theoretical results obtained. The theoretical and empirical studies reveal that the proposed estimator performs better than existing estimators in the literature.

Published
2020-06-30
Section
Articles