A simulation study: Robust ratio double sampling estimator of finite population mean in the presence of outliers

Abstract

In this study, we suggest a family of ratio estimators for the population mean parameter using various robust regression techniques. These robust regressions techniques are Huber MM, LTS, and LMS estimates. We evaluate the performance of estimators in terms of the mean square error (MSE), and we compare the efficiency of our proposed robust-regression-ratio-type estimators with existing estimators under the optimal conditions. These comparisons show that our robust ratio-type estimators give more efficient results than the existing estimators under double sampling. In addition, the simulation and the empirical studies based on a data set that includes unusual observations show that our proposed estimators have a lower MSE than the existing estimators.

Robust calibration for estimating the population mean using stratified random sampling

“…Zaman and Bulut [20], Zaman et al [21] Grover and Kaur [22], and Zaman and Bulut [23] have used the robust regression methods, for minimizing the impact of the extreme values in ratio estimators of the mean. In sampling theory, information about the auxiliary variable used in rate estimators is used to increase the efficiency of the population mean of the study variable.…”

A new class of robust ratio estimators for finite population variance

“…Grover and Kaur [18] developed the regression-type estimators of population mean with two auxiliary variables using the robust regression technique. Zaman and Bulut [19] proposed the robust ratio double sampling estimator of finite population mean in the presence of outliers. Unlike other studies, this study proposes regression-ratiotype estimators of population variance-based MCD and MVE covariance estimates for simple random sampling.…”

A simulation study: Improved ratio-in-regression type variance estimator based on dual use of auxiliary variable under simple random sampling

“…For proper treatment, a doctor must have a detailed realization of differences in human hypertension and fever. Many authors suggested different types of estimators for variance including Das [1], Prasad and Singh [2], Swain and Mishra [3], Ahmad and Shabbir [4], Kadilar and Cingi [5], Subramani and Kumarapandiyan [6], Singh and Solanki [7], Yadav et al [8], Singh et al [9], Yadav and Kadilar [10], Adichwal et al [11,12], Yasmeen et al [13], Zaman [14,15], Zaman and Bulut [16], Zaman et al [17,18], Bhushan et al [19], Shahzad et al [20], Ahmad et al [21], Ozturk [22], Ozturk and Demirel [23], Frey and Feeman [24], Zamanzade and Vock [25], Mahdizadeh and Zamanzade [26,27], Bhushan et al [28], Bhushan et al [29], Zaman and Bulut [30,31], Yadav and Zaman [32], Zaman and Kadilar [33], Zaman and Dunder [34] and Singh and Khalid [35]. We requisite to estimate population parameters for numerous variables in multidisciplinary surveys, such as house lengths, income level, and the number of unemployed people, in socio-economic surveys.…”