Mixture Regression Estimators Using Multi-Auxiliary Variables and Attributes in Two-Phase Sampling
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Date
2014
Authors
Kung’u, John
Chumba, Grace
Odongo, Leo
Journal Title
Journal ISSN
Volume Title
Publisher
Scientific Research Publishing
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.
Description
Full Article
Keywords
Regression Estimator, Multiple Auxiliary Variables, Multiple Auxiliary Attributes, Two-Phase Sampling, Bi-Serial Correlation Coefficient
Citation
Open Journal of Statistics, 2014, 4, 355-366