Open AccessA New Generalized Class of Mixture Estimators for Estimating the Population Mean Under Single and Double Phase Sampling Schemes
Author(s) -
Sara Azam,
Hina Khan,
Mustansar Aatizaz
Publication year - 2021
Publication title -
statistics, computing and interdisciplinary research
Language(s) - English
Resource type - Journals
eISSN - 2788-7464
pISSN - 2707-7101
DOI - 10.52700/scir.v3i2.52
Subject(s) - estimator , population mean , mathematics , mean squared error , class (philosophy) , sampling (signal processing) , statistics , extremum estimator , population , phase (matter) , m estimator , computer science , artificial intelligence , chemistry , demography , organic chemistry , filter (signal processing) , sociology , computer vision
A new class of mixture estimators is proposed by combining the ratio and regression estimators when the nature of auxiliary variable is qualitative under single and double phase sampling schemes for estimating the population mean. The mean square errors (MSE,s) of the purposed estimators are derived up to the first order of approximation. Finally, we use some real life applications and simulated results to prove that the proposed estimators are more efficient as compared to the several existing estimates.