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  • 标题:Generalized Ratio-Cum-Product Estimators for Two-Phase Sampling Using Multi-Auxiliary Variables
  • 本地全文:下载
  • 作者:John Kung’u ; Joseph Nderitu
  • 期刊名称:Open Journal of Statistics
  • 印刷版ISSN:2161-718X
  • 电子版ISSN:2161-7198
  • 出版年度:2016
  • 卷号:06
  • 期号:04
  • 页码:616-627
  • DOI:10.4236/ojs.2016.64052
  • 语种:English
  • 出版社:Scientific Research Publishing
  • 摘要: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.
  • 关键词:Ratio-Cum-Product Estimator;Multiple Auxiliary Variables;Two-Phase Sampling
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