Mixture Regression Estimators Using Multi-Auxiliary Variables and Attributes in Two-Phase Sampling

dc.contributor.authorKung’u, John
dc.contributor.authorChumba, Grace
dc.contributor.authorOdongo, Leo
dc.date.accessioned2015-08-03T09:05:34Z
dc.date.available2015-08-03T09:05:34Z
dc.date.issued2014
dc.descriptionFull Articleen_US
dc.description.abstractIn 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.en_US
dc.identifier.citationOpen Journal of Statistics, 2014, 4, 355-366en_US
dc.identifier.urihttp://ir-library.ku.ac.ke/handle/123456789/13282
dc.language.isoenen_US
dc.publisherScientific Research Publishingen_US
dc.subjectRegression Estimatoren_US
dc.subjectMultiple Auxiliary Variablesen_US
dc.subjectMultiple Auxiliary Attributesen_US
dc.subjectTwo-Phase Samplingen_US
dc.subjectBi-Serial Correlation Coefficienten_US
dc.titleMixture Regression Estimators Using Multi-Auxiliary Variables and Attributes in Two-Phase Samplingen_US
dc.typeArticleen_US
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