Estimation of mean using generalized optional scrambled responses in the presence of non-sensitive auxiliary variable

Noor‐ul‐Amin, Muhammad; Mushtaq, Nadia; Hanif, Muhammad

doi:10.1080/09720510.2018.1427029

Cited by 6 publications

(1 citation statement)

References 18 publications

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“…Di erent ratio, regression, and exponential estimators for estimating population parameters of sensitive variables based on scrambled responses were reported by Sousa et al [8], Koyuncu et al [9], Kalucha et al [10], and Gupta et al [11,12]. Using a generalized quantitative optional randomised response model, Noor-Ul-Amin et al [13] developed estimators based on generalized ratio and regression types, in which the nature of the auxiliary variable is nonsensitive. In the presence of measurement error, Khalil et al [14] and Zahid and Shabbir [15] developed improved mean estimators of a sensitive research variable.…”

Section: Introductionmentioning

confidence: 99%

Generalized Difference-Cum Exponential Class of Estimators for Estimating Population Parameters of the Sensitive Variable

Vishwakarma

Yadav

Bari

2022

Mathematical Problems in Engineering

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We propose a generalized class of estimators to estimate general population parameters of susceptible research variables using additional auxiliary information in SRSWOR. The population constants such as the coefficient of variation, the population mean, the standard deviation, and the population mean square are defined using a conventional estimator. The expression for the mean square errors of the proposed class is derived up to the first order of approximation. To compare the effectiveness of the proposed class of estimators, an empirical study is conducted utilizing real and simulated data sets. Theoretical and empirical research demonstrates that the suggested generalized class of estimators outperforms other current estimators.

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