Efficient class of estimators for finite population mean using auxiliary attribute in stratified random sampling
Housila P. Singh, Anurag Gupta, Rajesh Tailor
Abstract
The aim of this paper is to develop more effective methods for estimating population means in sample surveys using auxiliary attributes. To achieve this goal, we introduce a modified version of the estimators proposed by Koyuncu (2013b) and Shahzad et al. (2019), as well as a new class of estimators. We derive expressions for the bias and mean squared error of these new estimators up to the first degree of approximation. Our results show that the suggested classes of estimators perform better than other existing methods, with the lowest mean squared error under optimal conditions. We also conduct an empirical investigation to support our findings.
Topics & Concepts
EstimatorStratified samplingMean squared errorPopulation meanClass (philosophy)MathematicsPopulationSample mean and sample covarianceSimple random sampleStatisticsSampling (signal processing)Sample (material)Computer scienceApplied mathematicsArtificial intelligenceChromatographyFilter (signal processing)SociologyDemographyComputer visionChemistrySurvey Sampling and Estimation TechniquesHIV, Drug Use, Sexual RiskSARS-CoV-2 detection and testing