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Dergiler / Hacettepe Journal of Mathematics and Statistics / 2011 / Cilt: 40 - Sayı: 1

Improved estimation of mean in randomized response models

Zawar HUSSAİN
Javid SHABBIR
Sayfa
91–104
Yayın Tarihi
2022-10-18
DOI
—
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

The proposed estimator has been compared with the Ryu et al. and Hussain and Shabbir (Improved estimation procedure for the mean of a sensitive variable using randomized response model, Pakistan Journal of Statistics 25(2), 205–220, 2009) estimators assuming simple random sampling with replacement.

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Amaç & Hipotez:The present investigation considers the problem of estimating the mean of a sensitive quantitative variable μA in a human population survey, using the scrambled response technique suggested by Ryu, Kim, Heo and Park (On stratified randomized response sampling, Model Assisted Statistics and Application 1(1), 31–36, 2005–2006).
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Temel Bulgular & Veri:The optimum value of the de- gree k(0 $geq k geq$ 1) of the belief in the prior estimate depends, besides others, on the unknown population parameters, e.g. mean and vari- ance, so the proposed family of estimators may have limited practical applications.
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Sonuç & Katkı:The proposed estimator has been compared with the Ryu et al. and Hussain and Shabbir (Improved estimation procedure for the mean of a sensitive variable using randomized response model, Pakistan Journal of Statistics 25(2), 205–220, 2009) estimators assuming simple random sampling with replacement.
🔬Tür:Uygulamalı Anket & Alan Araştırması
📍Kapsam:2005–2006 Verileri
⚙️Değişkenler:Alan Parametreleri

Abstract

The present investigation considers the problem of estimating the mean of a sensitive quantitative variable μA in a human population survey, using the scrambled response technique suggested by Ryu, Kim, Heo and Park (On stratified randomized response sampling, Model Assisted Statistics and Application 1(1), 31–36, 2005–2006). Specifically, using the prior estimate (or guessed mean) of the mean of a population, a family of estimators μAk is presented to estimate the population mean μA, and its properties are examined. The optimum value of the de- gree k(0 $geq k geq$ 1) of the belief in the prior estimate depends, besides others, on the unknown population parameters, e.g. mean and vari- ance, so the proposed family of estimators may have limited practical applications. In an attempt to overcome this problem, another esti- mator based on the estimated optimum value of k has been proposed. The proposed estimator has been compared with the Ryu et al. and Hussain and Shabbir (Improved estimation procedure for the mean of a sensitive variable using randomized response model, Pakistan Journal of Statistics 25(2), 205–220, 2009) estimators assuming simple random sampling with replacement.

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Bu Makaleye Atıf Yap

HUSSAİN, Z., SHABBIR, J. (2022). Improved estimation of mean in randomized response models. Hacettepe Journal of Mathematics and Statistics, Cilt: 40 - Sayı: 1, 91–104. https://ajindex.com/articles/530586

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Kaynakça (17)

  1. Ahmed, S. E. and Rohatgi, V.K. Shrinkage estimation of proportion in randomized response, Metrika 43, 17–30, 1996
  2. Arnab, R. and Dorffner, G. Randomized response technique for complex survey designs, Statistical Papers 48, 131–141, 2006
  3. Bar-Lev, S.K., Bobovitch. E. and Boukai, B. A note on randomized response models, Metrika 60, 255-260, 2004
  4. Eichhorn, B.H. and Hayre, L. S. Scrambled randomized response methods for obtaining sensitive quantitative data, Journal of Statistical Planning and inference 7, 307–316, 1983
  5. Espejo R,M. and Singh, H.P. Protection of privacy with objective prior distribution in randomized response, Statistica Anno LXIII (4), 697–701, 2003
  6. Greenberg, B.G., Kuebler, R.R. Jr., Abernathy, J.R. and Hovertiz, D.G. Application of the randomized response techniques in obtaining quantitative data, Journal of the American Statistical Associations 66, 243–250, 1971
  7. Gupta S., Gupta, B. and Singh, S. Estimation of sensitivity level of personal interview survey questions, Journal of Statistical Planning and Inference 100, 239–247, 2002
  8. Gupta S. and Shabbir J. Sensitivity estimation for personal interview survey question, Sta- tistica Anno LXIV (4), 643–653, 2004
  9. Hussain, Z. and Shabbir, J. On estimation of mean of a sensitive quantitative variable, InterStat. July (6), 1–14, 2007
  10. Hussain, Z. and Shabbir, J. Improved estimation procedure for the mean of a sensitive variable using randomized response model, Pakistan Journal of Statistics 25 (2), 205–220, 2009
  11. Hussain, Z., Shabbir, J. and Gupta, S. An alternative to Ryu randomized response model, Journal of Statistics & Management Systems 10 (4), 511–517, 2007
  12. Mangat, N. S. and Singh, R. An alternative randomized response procedure, Biometrika 77, 439–442, 1990
  13. Mathur, N. and Singh, H.P. Estimation of population mean with prior information using scrambled response technique, Brazilian Journal of Probability and Statistics 22 (2), 165– 181, 2008
  14. Mehta, J. S. and Srinivasan, R. Estimation of mean by shrinkage to a point, Journal of the American Statistical Association 66, 86–90, 1971
  15. Ryu, J. -B., Kim, J. -M., Heo, T. -Y. and Park, C.G. On stratified randomized response sampling, Model Assisted Statistics and Application 1 (1), 31–36, 2005–2006
  16. Saxana, S. Estimation of exponential mean life in complete and failure censored samples with prior information, The Philippine Statistician 53 (1-4), 35–45, 2004
  17. Searls, D.