Ratio-Cum-Product Estimator Using Multiple Auxiliary Attributes in Two-Phase Sampling

Abstract

In this paper, we have proposed three classes of ratio-cum-product estimators for estimating population mean of study variable for two-phase sampling using multi-auxiliary attributes for full information, partial information and no information cases. The expressions for mean square errors are derived. An empirical study is given to compare the performance of the estimator with the existing estimator that utilizes auxiliary attribute or multiple auxiliary attributes. The ratio-cum-product estimator in two-phase sampling for full information case has been found to be more efficient than existing estimators and also ratio-cum-product estimator in two-phase sampling for both partial and no information case. Finally, ratio-cum-product estimator in two-phase sampling for partial information case has been found to be more efficient than ratio-cum-product estimator in two-phase sampling for no information case.

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

“…Zahoor, 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. Kung'u and Odongo [19] and [20] proposed ratio-cum-product estimators using multiple auxiliary attributes in single phase sampling and two-phase sampling using multiple auxiliary attributes in full, partial and no information case. Moeen, 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.…”

Mixture Ratio Estimators Using Multi-Auxiliary Variables and Attributes for Two-Phase Sampling

“…They also extended their work to ratio estimator which was generalization of Naik and Gupta [12] estimator in single-and double-phase samplings with full information, partial information and no information. Kung'u and Odongo [15] and [16] proposed ratio-cum-product estimators using multiple auxiliary attributes in singleand two-phase sampling. Moeen, Shahbaz and Hanif [17] 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.…”

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

“…Moeen, Shahbaz and HanIf [16] 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. Kung'u and Odongo [17] and [18] proposed ratio-cum-product estimators using multiple auxiliary attributes in single and two-phase sampling.…”