Generalized exponential type estimator for the mean of sensitive variable in the presence of non-sensitive auxiliary variable

Waseem, Zara; Khan, Hina; Shabbir, Javid

doi:10.1080/03610926.2019.1708399

Cited by 8 publications

(4 citation statements)

References 14 publications

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“…They showed that their proposed estimators performed better than other difficult assessors. Waseem et al (2020) used one or double variables of auxiliary type to generalize the exponential-type estimation for scrambled responses in SRS design. They also discussed special cases of the suggested estimator with privacy protection.…”

Section: Literaturementioning

confidence: 99%

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

Hussain,

Mahmood,

Mahmood

et al. 2023

Nat. App. Sci. Int. J.

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The theory of parameter estimation was developed a long time ago and is currently a widely accepted scientific approach. In some situations where the mean has to deal with the effect of extreme values, the median can be used instead. As there is almost little literature available on median estimation in the existence of non-responsive with the help of the Randomized Response Technique (RRT), the main objective of this paper was to develop a basic theoretical framework for median estimation in the existence of non-responsive with the help of RRT. In this work, we suggested median estimation for delicate variables in auxiliary information using a randomised response model. We have suggested a basic median estimator, product, ratio, exponential product, exponential ratio, and regression cum ratio estimation of the median for non-response by utilising RRT. The mathematical derivations for optimum values of constants, biasness, and Mean Square Error (MSE) of purposed estimators result from the application of well-known Taylor and exponential expansions. The performance of mentioned estimators is evaluated through the numerical study of two populations which discovered the regression cum ratio estimate is more proficient than the remaining estimations mentioned in this article.

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Section: Literaturementioning

confidence: 99%

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

Hussain,

Mahmood,

Mahmood

et al. 2023

Nat. App. Sci. Int. J.

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“…v) [8] proposed the exponential-type estimator created on generalized two-stage optional-scrambled reply method which is given as…”

Section: Few Existing Estimators In Simple Random Samplingmentioning

confidence: 99%

“…Here we have a scrambling variable S with zero (0) mean and variance σ 2 s and let K(-1 ≤ K ≤ 1) is suitably chosen scalar. [8] proposed the ORRT for the estimation of population mean in the case of sensitive study variables. Their suggested scenario states: Let the sensitive study variable be denoted by Y having the population mean µ y and unknown population variance σ 2 y .…”

Section: Mean Estimator For Generalized Two-stage Optional Scramble R...mentioning

confidence: 99%

An efficient application of scrambled response approach to estimate the population mean of the sensitive variables

Zahid

Masood

Mubarik

et al. 2022

mmnsa

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In the presence of one auxiliary variable and two auxiliary variables, we analyze various exponential estimators. The ranks of the auxiliary variables are also connected with the study variables, and there is a linkage between the study variables and the auxiliary variables. These ranks can be used to improve an estimator's accuracy. The Optional Randomized Response Technique (ORRT) and the Quantitative Randomized Response Technique are two techniques we utilize to estimate the sensitive variables from the population mean (QRRT). We used the scrambled response technique and checked the proposed estimators up to the first-order of approximation. The mean square error (MSE) equations are obtained for all the proposed ratio exponential estimators and show that our proposed exponential type estimator is more efficient than ratio estimators. The expression of mean square error is obtained up to the first degree of approximation. The empirical and theoretical comparison of the proposed estimators with existing estimators is also be carried out. We have shown that the proposed optional randomized response technique and quantitative randomized response model are always better than existing estimators. The simulation study is also carried out to determine the performance of the estimators. Few real-life data sets are also be applied in support of proposed estimators. It is observed that our suggested estimator is more efficient as compared to an existing estimator.

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“…Quantitative scrambled randomised response models are considerably improved by Saleem et al [18] by assessing the performance of the population mean estimator. Waseem et al [19] presented a generalized exponential type estimator for the mean of a sensitive variable using information from a nonsensitive auxiliary variable. Singh et al [20] provided some alternative additive randomised response models by offering a blank card option for predicting the population mean of a quantitative sensitive 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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Generalized exponential type estimator for the mean of sensitive variable in the presence of non-sensitive auxiliary variable

Cited by 8 publications

References 14 publications

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

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

An efficient application of scrambled response approach to estimate the population mean of the sensitive variables

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

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