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

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-cumratio 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 singlephase sampling for finite population.

Median estimation in the presence of non-response using randomised response technique

“…The regression-cum-ratio median estimator has been found to perform better than all other estimators mentioned in the research. This is supported by Mutembei et al (2014), who proposed a regression-cum-ratio estimator that uses data from multiple auxiliary variables and characteristics simultaneously to estimate the population mean (Koyuncu et al, 2019).…”