TITLE:
Mixture Regression-Cum-Ratio Estimator Using Multi-Auxiliary Variables and Attributes in Single-Phase Sampling
AUTHORS:
Teresio Mutembei, John Kung’u, Christopher Ouma
KEYWORDS:
Regression-Cum-Ratio Estimator, Multiple Auxiliary Variables and Attributes, Single-Phase Sampling
JOURNAL NAME:
Open Journal of Statistics,
Vol.4 No.5,
August
15,
2014
ABSTRACT: In this paper, we have proposed a class of mixture
regression-cum-ratio estimator for estimating population mean by using information on multiple auxiliary
variables and attributes simultaneously in single-phase
sampling and analyzed the properties of the estimator. An empirical
was carried out to compare the performance of the proposed estimator with the
existing estimators of finite population mean using simulated population. It
was found that the mixture regression-cum-ratio
estimator was more efficient than ratio and regression estimators using one
auxiliary variable and attribute, ratio
and regression estimators using multiple auxiliary variables and attributes and regression-cum-ratio estimators
using multiple auxiliary variables and attributes in single-phase
sampling for finite population.