Open AccessChain Ratio Type Estimators Using Known Parameters of Auxiliary Variates in Double Sampling
Author(s) -
Priya Mehta,
Rajesh Tailor
Publication year - 2020
Publication title -
journal of reliability and statistical studies
Language(s) - English
Resource type - Journals
eISSN - 2229-5666
pISSN - 0974-8024
DOI - 10.13052/jrss0974-8024.13242
Subject(s) - estimator , mean squared error , bias of an estimator , efficient estimator , stein's unbiased risk estimate , mathematics , minimum variance unbiased estimator , ratio estimator , statistics , consistent estimator , efficiency , invariant estimator , trimmed estimator , extremum estimator , population , sampling (signal processing) , m estimator , computer science , demography , sociology , filter (signal processing) , computer vision
This paper discusses chain ratio type estimator for estimation of population mean in double sampling. The developed estimator uses two auxiliary variates associated with study variate in order to increases its efficiency. The developed estimator has been compared with usual unbiased estimator and other existing estimators. The expression for the bias and mean squared error of the developed estimator is obtained under large sample approximation. We have considered the natural population data set to examine the merits of the developed estimator and carried out the empirical study in support of theoretical findings. Numerical illustration shows that the proposed estimator is more efficient.