Robust ratio‐type estimators for finite population mean in simple random sampling: A simulation study
Tolga Zaman, Hasan Bulut, Subhash Kumar Yadav
Abstract
Summary In this study, ratio estimators are proposed by utilizing some robust techniques to get the maximum benefit of the auxiliary variable for the estimation of the population mean in simple random sampling. The expressions for mean squared error are derived for the first degree of approximation. Theoretical comparisons demonstrate that the suggested estimators having robust regression estimates perform better than the existing estimators under certain conditions. Theoretical findings are supported with the aid of the original dataset in an application. In addition, a simulation study is also conducted to evaluate the performance of the suggested estimators.
Topics & Concepts
EstimatorSimple random samplePopulation meanStatisticsMean squared errorMathematicsRatio estimatorM-estimatorSimple (philosophy)PopulationSampling (signal processing)Extremum estimatorComputer scienceEfficient estimatorMinimum-variance unbiased estimatorDemographySociologyPhilosophyEpistemologyFilter (signal processing)Computer visionSurvey Sampling and Estimation TechniquesAdvanced Statistical Methods and ModelsAdvanced Statistical Process Monitoring