Effective Rotation Patterns Under Non Response in Two-Occasion Successive Sampling

Singh, G. N.; Majhi, D.; Prasad, Shakti; Homa, Fozia

doi:10.1080/03610926.2012.762401

Cited by 4 publications

(10 citation statements)

References 9 publications

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“…The simulation study has been carried out by considering 5000 different samples using Monte Carlo simulation for Population-I. The simulated PRE of the proposed estimator with respect to the sample mean estimator, natural successive sampling estimator and Singh and Sharma 18 estimators have been computed using the data set .…”

Section: Monte Carlo Simulation Studymentioning

confidence: 99%

“…To show the performances of the proposed estimator , we compare with sample mean estimator (when there is no matching), natural successive sampling estimator where and the Singh and Sharma 18 estimator . The estimator suggested by Singh and Sharma 18 is as follows…”

Section: Empirical Studymentioning

confidence: 99%

“…The variance of , optimum variance of and the optimum mean square error of Singh and Sharma 18 estimators are as follows…”

Section: Empirical Studymentioning

confidence: 99%

“…Utilizing the above data, the percent relative efficiencies of the proposed estimator with respect to , and the Singh and Sharma 18 estimators are computed using the formula given below and presented in Tables 7 and 8 .…”

Section: Empirical Studymentioning

confidence: 99%

“…In real-life problems, there are well-documented situations where sensitive studies need to be monitored over time to understand the problem in a better way. To handle such situations, Jessen, 9 Priyanka et al 14 and Singh and Sharma 18 among others used successive sampling. Arnab and Singh 1 and Priyanka and Trisandhya 12 among others used a scrambled response technique to handle sensitive issues on successive occasions.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Study to Alter the Nuisance Effect of Non-Response Using Scrambled Mechanism

Singh¹,

Kamal²,

Singh³

et al. 2021

RMHP

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Introduction In biometric sample surveys, our objective is to get ready-made information for future planning and policy implementations related to the subject matters of highly sensitive issues. In such situations, we apply randomized response/scrambled response techniques. There are many highly sensitive issues which need to be examined over time as they may have a tendency to change. To get rid of these types of practical cases we need a scrambled response technique on successive occasions. Methods Using an additive and multiplicative technique, we proposed new effective scrambled response models to estimate the population mean of quantitative sensitive characteristics. Degree of privacy protection and unified measure approaches are used to examine the efficacy of the proposed models. Efficiency of the proposed models has been checked using MATLAB software. The utility of the proposed models under two occasions of successive sampling has been also explored using exponential-type estimators. Empirical and simulation studies are carried out to justify the proposition of the proposed estimators using MATLAB software. Results The percent relative efficiencies of the proposed models are always greater than 100 with respect to the well-known Bar-Lev et al model. In terms of degree of privacy protection, most of the values are greater than 0.5 and closer to 1. Similarly, the values of the proposed models are smaller with respect to the Bar-Lev et al model in terms of a unified measure approach. When the proposed scrambled response models are used on successive occasions, the percent relative efficiency is always found greater than 100 for all cases over its competitors. Discussion In this study, after deeply examining the properties of the proposed models, we found that the proposed models performed better over the well-known existing model. The proposed models may be used in human survey when we deal with highly sensitive issues. The proposed models also performed better when we utilized them in successive sampling. Hence, if sensitive characteristics change with time, the proposed estimators may be the best alternative to deal with these types of situations. Mathematics Subject Classification 62D05.

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Section: Monte Carlo Simulation Studymentioning

confidence: 99%