Using Randomized Response to Estimate the Population Mean of a Sensitive Variable under the Influence of Measurement Error

Tiwari, Kuldeep Kumar; Bhougal, Sandeep; Kumar, Sunil; Rather, Khalid Ul Islam

doi:10.1007/s42519-022-00251-1

Cited by 9 publications

(3 citation statements)

References 33 publications

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“…The outcomes of the numerical answers revealed that the estimation of generalized estimations behaved better than the established formula for sampling designs with a double stage of simple and stratified sampling. Tiwari et al (2022) checked the influence of non-response on estimations of mean by applying RRT. Another investigation was replicated to check the efficiency of the proposed estimates.…”

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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“…The effect of measurement error has been studied in quantitative RRT models by many authors including [4,5]. Ref.…”

Section: Introductionmentioning

confidence: 99%

Accounting for Measurement Error and Untruthfulness in Binary RRT Models

Meche,

Poruri,

Gupta

et al. 2024

Mathematics

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This study examines the effect of measurement error on binary Randomized Response Technique models. We discuss a method for estimating and accounting for measurement error and untruthfulness in two basic models and one comprehensive model. Both theoretical and empirical results show that not accounting for measurement error leads to inaccurate estimates. We introduce estimators that account for the effect of measurement error. Furthermore, we introduce a new measure of model privacy using an odds ratio statistic, which offers better interpretability than traditional methods.

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“…Furthermore, Zahid et al [18] have also been proposed a generalized class of estimators for a sensitive variable in the presence of nonresponse and measurement errors under stratifed sampling. Furthermore, Tiwari et al [19,20], Kumar and Kour [21], and others have addressed the issue of nonresponse and measurement error in various sampling strategies, using prior studies as inspiration and realizing the essence of how to handle these faws in a sample survey. Furthermore, the exponential estimator(s) proposed by Bahl and Tuteja [22] are known to perform better than the corresponding usual ratio and product-type estimators under certain efciency conditions.…”

Section: Introductionmentioning

confidence: 99%

Using ORRT Models for Mean Estimation under Nonresponse and Measurement Errors in Stratified Successive Sampling

Choudhary

Kour

Kumar

et al. 2023

Journal of Probability and Statistics

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In the context of a sample survey, the collection of information on a sensitive variable is difficult, which may cause nonresponse and measurement errors. Due to this, the estimates can be biased and the variation may increase. To overcome this difficulty, we propose an estimator for the estimation of a sensitive variable by using auxiliary information in the presence of nonresponse and measurement errors simultaneously. The properties of the proposed estimators have been studied, and the results have been compared with those of the usual complete response estimator. Theoretical results have been verified through a simulation study using an artificial population and two real-life applications. With the outcomes of the proposed estimator, a suitable recommendation has been made to the survey statisticians for their real-life application.

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Using Randomized Response to Estimate the Population Mean of a Sensitive Variable under the Influence of Measurement Error

Cited by 9 publications

References 33 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

Accounting for Measurement Error and Untruthfulness in Binary RRT Models

Using ORRT Models for Mean Estimation under Nonresponse and Measurement Errors in Stratified Successive Sampling

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