Robust ratio‐type estimators for finite population mean in simple random sampling: A simulation study

Abstract

In this study, ratio estimators are proposed by utilizing some robust techniques to get the maximum benefit of the auxiliary variable for the estimation of the population mean in simple random sampling. The expressions for mean squared error are derived for the first degree of approximation. Theoretical comparisons demonstrate that the suggested estimators having robust regression estimates perform better than the existing estimators under certain conditions. Theoretical findings are supported with the aid of the original dataset in an application. In addition, a simulation study is also conducted to evaluate the performance of the suggested estimators.

Improved estimation of population distribution function using twofold auxiliary information under simple random sampling

“…discussed ranked set sampling for the DF. The [ 31 , 32 ] discussed robust ratios to estimate the mean of a finite population.…”

Some efficient ratio-type exponential estimators using the Robust regression’s Huber M-estimation function

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A new improved generalized class of estimators for population distribution function using auxiliary variable under simple random sampling

“…Shahzad et al 11 suggested quantile regression-ratio-type estimators for mean estimation under complete and partial auxiliary information. Zaman et al 12 discussed robust ratio‐type estimators for finite population mean in simple random sampling. Singh et al 13 discussed some efficient classes of estimators of population mean in two-phase successive sampling under random non response.…”