Open AccessOn Some Improved Classes of Estimators Under Stratified Sampling Using Attribute
Author(s) -
Shashi Bhushan,
Anoop Kumar,
Dushyant Tyagi,
S. P. Singh
Publication year - 2022
Publication title -
journal of reliability and statistical studies
Language(s) - English
Resource type - Journals
eISSN - 2229-5666
pISSN - 0974-8024
DOI - 10.13052/jrss0974-8024.1518
Subject(s) - estimator , mathematics , stratified sampling , mean squared error , extremum estimator , statistics , ratio estimator , simple random sample , sampling (signal processing) , efficiency , m estimator , efficient estimator , population , econometrics , minimum variance unbiased estimator , computer science , demography , filter (signal processing) , sociology , computer vision
This article establishes some improved classes of difference and ratio type estimators of population mean of study variable using information on auxiliary attribute under stratified simple random sampling. The usual mean estimator, classical ratio estimator, classical product estimator and classical regression estimator are identified as particular cases of the proposed classes of estimators for different values of the characterising scalars. The expression of mean square error of the suggested classes of estimators has been studied up to first order of approximation and their effective performances are likened with respect to the conventional as well as lately existing estimators. Subsequently, an empirical study has been carried out using a real data set in support of theoretical results. The empirical results justify the proposition of the proposed classes of estimators in terms of percent relative efficiency over all discussed work till date. Suitable suggestions are forwarded to the survey practitioners.