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A Comparison of Three Randomized Response Models for Quantitative Data

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Pages 884-886 | Received 01 Apr 1976, Published online: 05 Apr 2012

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G. N. Singh, Amod Kumar & Gajendra K. Vishwakarma. (2020) Some alternative additive randomized response models for estimation of population mean of quantitative sensitive variable in the presence of scramble variable. Communications in Statistics - Simulation and Computation 49:11, pages 2785-2807.
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Garib Nath Singh, Amod Kumar & Gajendra K. Vishwakarma. (2020) Estimation of population mean of sensitive quantitative character using blank cards in randomized device. Communications in Statistics - Simulation and Computation 49:6, pages 1603-1630.
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Mahnaz Makhdum, Aamir Sanaullah & Muhammad Hanif. (2020) A modified regression-cum-ratio estimator of population mean of a sensitive variable in the presence of non-response in simple random sampling. Journal of Statistics and Management Systems 23:3, pages 495-510.
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M. Rueda, B. Cobo & P. F. Perri. (2020) Randomized response estimation in multiple frame surveys. International Journal of Computer Mathematics 97:1-2, pages 189-206.
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Usman Shahzad, Pier Francesco Perri & Muhammad Hanif. (2019) A new class of ratio-type estimators for improving mean estimation of nonsensitive and sensitive variables by using supplementary information. Communications in Statistics - Simulation and Computation 48:9, pages 2566-2585.
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Iram Saleem, Aamir Sanaullah & Muhammad Hanif. (2019) Double-sampling regression-cum-exponential estimator of the mean of a sensitive variable. Mathematical Population Studies 26:3, pages 163-182.
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Kumari Priyanka & Pidugu Trisandhya. (2019) A composite class of estimators using scrambled response mechanism for sensitive population mean in successive sampling. Communications in Statistics - Theory and Methods 48:4, pages 1009-1032.
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G. N. Singh, S. Suman, M. Khetan & C. Paul. (2018) Some estimation procedures of sensitive character using scrambled response techniques in successive sampling. Communications in Statistics - Theory and Methods 47:8, pages 1830-1841.
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Shakeel Ahmed, Javid Shabbir & Sat Gupta. (2017) Use of scrambled response model in estimating the finite population mean in presence of non response when coefficient of variation is known. Communications in Statistics - Theory and Methods 46:17, pages 8435-8449.
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Muhammad Imran Shahid & Zawar Hussain. (2016) Improved randomized response models in additive scrambling. Journal of Statistics and Management Systems 19:5, pages 701-720.
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Graeme Blair, Kosuke Imai & Yang-Yang Zhou. (2015) Design and Analysis of the Randomized Response Technique. Journal of the American Statistical Association 110:511, pages 1304-1319.
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Giancarlo Diana & Pier Francesco Perri. (2012) A calibration-based approach to sensitive data: a simulation study. Journal of Applied Statistics 39:1, pages 53-65.
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Giancarlo Diana & Pier Francesco Perri. (2010) New scrambled response models for estimating the mean of a sensitive quantitative character. Journal of Applied Statistics 37:11, pages 1875-1890.
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Zawar Hussain, Javid Shabbir & Sat Gupta. (2007) An alternative to Ryu et al. randomized response model. Journal of Statistics and Management Systems 10:4, pages 511-517.
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JONG-MIN KIM & WILLIAMD. WARDE. (2005) Some New Results on the Multinomial Randomized Response Model. Communications in Statistics - Theory and Methods 34:4, pages 847-856.
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Sarjinder Singh, N.S. angat & Ravindra Singh. (1997) Estimation of size and mean of a sensitive quantitative variable for a sub-group of a population. Communications in Statistics - Theory and Methods 26:7, pages 1793-1804.
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DavidR. Bellhouse. (1980) Linear Models for Randomized Response Designs. Journal of the American Statistical Association 75:372, pages 1001-1004.
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William McCance, Sat Gupta, Sadia Khalil & Wenhao Shou. Binary Randomized Response Technique (RRT) models under measurement error. Communications in Statistics - Simulation and Computation 0:0, pages 1-8.
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Sunil Kumar, Sanam Preet Kour & Housila P. Singh. Applying ORRT for the estimation of population variance of sensitive variable. Communications in Statistics - Simulation and Computation 0:0, pages 1-11.
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G. N. Singh, D. Bhattacharyya & A. Bandyopadhyay. Non-randomized scrambling models for sensitive quantitative attribute using innocuous characteristics. Journal of Statistical Computation and Simulation 0:0, pages 1-17.
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