TITLE:
Generalized Ratio-Cum-Product Estimators for Two-Phase Sampling Using Multi-Auxiliary Variables
AUTHORS:
John Kung’u, Joseph Nderitu
KEYWORDS:
Ratio-Cum-Product Estimator, Multiple Auxiliary Variables, Two-Phase Sampling
JOURNAL NAME:
Open Journal of Statistics,
Vol.6 No.4,
August
16,
2016
ABSTRACT: In this paper, we have proposed estimators
of finite population mean using generalized Ratio- cum-product estimator for
two-Phase sampling using multi-auxiliary variables under full, partial and no
information cases and investigated their finite sample properties. An empirical
study is given to compare the performance of the proposed estimators with the
existing estimators that utilize auxiliary variable(s) for finite population
mean. It has been found that the generalized Ra-tio-cum-product estimator in full
information case using multiple auxiliary variables is more efficient than mean
per unit, ratio and product estimator using one auxiliary variable, ratio and
product estimator using multiple auxiliary variable and ratio-cum-product
estimators in both partial and no information case in two phase sampling. A
generalized Ratio-cum-product estimator in partial information case is more
efficient than Generalized Ratio-cum-product estimator in No information case.