Use of scrambled response for estimating mean of the sensitivity variable

Sanaullah, Aamir; Saleem, Iram; Shabbir, Javid

doi:10.1080/03610926.2019.1576898

Cited by 11 publications

(2 citation statements)

References 13 publications

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“…Sousa et al [5]. Gupta et al [6], Koyuncu et al [7], Sanaullah et al [8], Saleem et al [9] and Sanaullah et al [10] presented mean estimators to estimate the population mean based on randomized response technique. Mushtaq et al [11] introduced ratio, regression and general class of estimators for the estimation of population mean using non-sensitive variable under stratified random sampling.…”

Section: Original Research Articlementioning

confidence: 99%

Development of Estimation Procedure of Population Variance in Stratified Randomized Response Technique

Mushtaq¹,

Saleem²

2020

AJPAS

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Singh et al. (2016) presented a ratio and regression estimators of population variance of a sensitive variable using auxiliary information based on randomized response technique (RRT). In this article, the RRT is considered in stratified random sampling for the estimation of variance. A generalized class of estimators of variance in stratified RRT is proposed and derive the procedure of variance estimation in stratified RRT. The expression of the bias and mean square error are expressed. The empirical findings support the soundness of proposed scheme of variance estimation.

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Section: Original Research Articlementioning

confidence: 99%

Development of Estimation Procedure of Population Variance in Stratified Randomized Response Technique

Mushtaq¹,

Saleem²

2020

AJPAS

Self Cite

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“…Sanaullah et al [16] introduced a new family of di erence-cum-exponential-type estimators of the nite population mean of the susceptible research variable by using a single nonsensitive secondary information. In the case of nonresponse using RRT under two-phase sampling, Sanaullah et al [17] proposed a generalized family of estimators for the nite population mean of a susceptible research variable. Quantitative scrambled randomised response models are considerably improved by Saleem et al [18] by assessing the performance of the population mean estimator.…”

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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Generalized Double Sampling Family of Estimators for Population mean of Sensitive Variable Harnessing Non-sensitive Auxiliary Variable and Attribute

2023

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Use of scrambled response for estimating mean of the sensitivity variable

Cited by 11 publications

References 13 publications

Development of Estimation Procedure of Population Variance in Stratified Randomized Response Technique

Development of Estimation Procedure of Population Variance in Stratified Randomized Response Technique

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

Generalized Double Sampling Family of Estimators for Population mean of Sensitive Variable Harnessing Non-sensitive Auxiliary Variable and Attribute

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