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.
Highlights
The work of Neyman [1] may be referred to as the initial works where auxiliary information has been used
Shahbaz and HanIf [21] proposed a class of mixture ratio and regression estimators for single-phase sampling for estimating population mean by using information on auxiliary variables and attributes simultaneously
The mixture ratio estimator based on multiple auxiliary variables and attributes by Moeen, Shahbaz and HanIf
Summary
The work of Neyman [1] may be referred to as the initial works where auxiliary information has been used. The estimator had a smaller MSE compared to that of Jhajj, Sharma and Grover [9] They extended their work to ratio estimator which was generalization of Naik and Gupta [11] estimator in single and double phase sampling with full information, partial information and no information. Muhhamad and Munir [18] suggested a generalized regression-cum-ratio estimator for two-phase sampling using multiple auxiliary variables in full, partial and no information case. Shahbaz and HanIf [21] proposed a class of mixture ratio and regression estimators for single-phase sampling for estimating population mean by using information on auxiliary variables and attributes simultaneously. We will incorporate both multiple auxiliary variables and attributes in regression-cum-ratio estimator to form mixture regression-cum-ratio estimator in single-phase sampling and incorporate Arora and Bansi [22] approach in writing down the mean squared error
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