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

• 作者: Grace Chumba John John Kung’u Leo Odongo
• 来源期刊: 统计学期刊(英文)
• 学科: 医学
• 关键词: Regression ESTIMATOR MULTIPLE AUXILIARY VARIABLES MULTIPLE AUXILIARY Attributes TWO-PHASE Sampling Bi-Serial Correlation Coefficient
• 年，卷（期）: 2014,（5）
• 页码范围: 355-366
• 页数: 12页
• 分类号: R73

摘要

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.