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Ahmad, Z. (2008) Generalized Multivariate Ratio and Regression Estimators for Multi-Phase Sampling. Ph.D. Thesis, National College of Business Administration and Economics, Lahore.

has been cited by the following article:

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

  AUTHORS: Teresio Mutembei, John Kung’u, Christopher Ouma

  KEYWORDS: Regression-Cum-Ratio Estimator, Multiple Auxiliary Variables and Attributes, Single-Phase Sampling

  JOURNAL NAME: Open Journal of Statistics, Vol.4 No.5, August 15, 2014

  ABSTRACT: 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.