TITLE:
Mixture Regression Estimators Using Multi-Auxiliary Variables and Attributes in Two-Phase Sampling
AUTHORS:
John John Kung’u, Grace Chumba, Leo Odongo
KEYWORDS:
Regression Estimator, Multiple Auxiliary Variables, Multiple Auxiliary Attributes, Two-Phase Sampling, Bi-Serial Correlation Coefficient
JOURNAL NAME:
Open Journal of Statistics,
Vol.4 No.5,
August
15,
2014
ABSTRACT:
In this paper, we
have developed estimators of finite population mean using Mixture Regression
estimators using multi-auxiliary variables and attributes in two-phase sampling
and investigated its finite sample properties in full, partial and no
information cases. An empirical study using natural data is given to compare
the performance of the proposed estimators with the existing estimators that
utilizes either auxiliary variables or attributes or both for finite population
mean. The Mixture Regression estimators in full information case using multiple
auxiliary variables and attributes are more efficient than mean per unit,
Regression estimator using one auxiliary variable or attribute, Regression
estimator using multiple auxiliary variable or attributes and Mixture
Regression estimators in both partial and no information case in two-phase
sampling. A Mixture Regression estimator in partial information case is more
efficient than Mixture Regression estimators in no information case.