首页    期刊浏览 2024年11月08日 星期五
登录注册

文章基本信息

  • 标题:Mixture Regression-Cum-Ratio Estimator Using Multi-Auxiliary Variables and Attributes in Single-Phase Sampling
  • 本地全文:下载
  • 作者:Teresio Mutembei ; John Kung’u ; Christopher Ouma
  • 期刊名称:Open Journal of Statistics
  • 印刷版ISSN:2161-718X
  • 电子版ISSN:2161-7198
  • 出版年度:2014
  • 卷号:04
  • 期号:05
  • 页码:367-376
  • DOI:10.4236/ojs.2014.45036
  • 语种:English
  • 出版社:Scientific Research Publishing
  • 摘要:In this paper, we have proposed a class of mixture regression-cum-ratio estimator for estimating population mean by using information on multiple auxiliary variables and attributes simultaneously in single-phase sampling and analyzed the properties of the estimator. An empirical was carried out to compare the performance of the proposed estimator with the existing estimators of finite population mean using simulated population. It was found that the mixture regression-cum-ratio estimator was more efficient than ratio and regression estimators using one auxiliary variable and attribute, ratio and regression estimators using multiple auxiliary variables and attributes and regression-cum-ratio estimators using multiple auxiliary variables and attributes in single-phase sampling for finite population.
  • 关键词:Regression-Cum-Ratio Estimator; Multiple Auxiliary Variables and Attributes; Single-Phase Sampling
国家哲学社会科学文献中心版权所有