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http://dx.doi.org/10.1017/S0021859600048012

has been cited by the following article:

• 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.