Mixture Regression-Cum-Ratio Estimator Using Multi-Auxiliary Variables and Attributes in Single-Phase Sampling

• 作者: Christopher Ouma John Kung’u Teresio Mutembei
• 来源期刊: 统计学期刊(英文)
• 学科: 医学
• 关键词: Regression-Cum-Ratio ESTIMATOR Multiple AUXILIARY VARIABLES and Attributes SINGLE-PHASE Sampling
• 年，卷（期）: 2014,（5）
• 页码范围: 367-376
• 页数: 10页
• 分类号: R73

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.