KaydedilenlerDerginizi ekleyin

Dergiler / Hacettepe Journal of Mathematics and Statistics / 2026 / Volume: 45 Issue: 2

An Improved Bar - Lev, Bobovitch and Boukai randomized response model using moments ratios of scrambling variable

Housila P. Singh
Tanveer A Tarray
Sayfa
—
Yayın Tarihi
2016-04-01
DOI
—
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Numerical illustrations are also given to support the present study. 

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:In this paper, we have suggested a new randomized response model and its properties have been studied.
📊
Temel Bulgular & Veri:The proposed model is found to be more efficient than the randomized response models studied by Bar – Lev et al. (2004) and Eichhorn and Hayre (1983).
💡
Sonuç & Katkı:Numerical illustrations are also given to support the present study. 
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:2004 Verileri
⚙️Değişkenler:Randomized response sampling · Estimation of mean · Respondents protection

Abstract

In this paper, we have suggested a new randomized response model and its properties have been studied. The proposed model is found to be more efficient than the randomized response models studied by Bar – Lev et al. (2004) and Eichhorn and Hayre (1983). The relative efficiency of the proposed model has been studied with respect to the Bar – Lev et al.’s (2004) and Eichhorn and Hayre’s (1983) models. Numerical illustrations are also given to support the present study. 

🔖

Bu Makaleye Atıf Yap

Singh, H. P., Tarray, T. A. (2016). An Improved Bar - Lev, Bobovitch and Boukai randomized response model using moments ratios of scrambling variable. Hacettepe Journal of Mathematics and Statistics, Volume: 45 Issue: 2. https://ajindex.com/articles/1403181

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (35)

  1. Ahsanullah M. and Eichhorn B.H. (1988). On estimation of response from scrambled quantitative data. Pakistan Journal of Statistics, 4(2), 83-91.
  2. Bar –Lev S.K., Bobovitch E. and Boukai B. (2004). A note on Randomized response models for quantitative data. Metrika, 60, 225-250.
  3. Chaudhuri A. and Mukerjee R. (1988). Randomized Response: Theory and Techniques. Marcel- Dekker, New York, USA.
  4. Chaudhuri A. and Christofides T. (2013). Indirect Questioning in Sample Surveys. DOI 10.1007/978-3-642-36276-7-3, Springer – Verlag Berlin Heidelberg.
  5. Eichhorn B.H. and Hayre, L.S. (1983). Scrambled randomized response methods for obtaining sensitive quantitative data. Journal of Statistics Planning and Inference,7,307-316.
  6. Fox J.A. and Tracy P.E. (1986). Randomized Response: A method of Sensitive Surveys. Newbury Park, CA: SEGE Publications.
  7. Gjestvang C.R. and Singh S. (2006). A new randomized response model. Journal of the Royal Statistical Society, 68, 523-530.
  8. Gjestvang C.R. and Singh S. (2009). An improved randomized response model: Estimation of mean, Journal of Applied Statistics, 36(12), 1361-1367.
  9. Gleser L.J. and Healy J.D. (1976). Estimating the mean of a normal distribution with known coefficient of variation. Journal of the American Statistical Association, 71,977-981.
  10. Govindaragulu Z. and Sahai H. (1972). Estimation of the parameters of a normal distribution with known coefficient of variation. Republic Statistics and Applications and Research, JUSU, 19,86-98.
  11. Gupta S. and Thornton B. (2002). Circumventing social desirability response bias in personal interview surveys. American Journal of Mathematics Managment and Sciences, 22, 369-383.
  12. Hussain Z., Hamraz M. and shabbier J. (2013). On alternative estimation technique for randomized response model. Pakistan Journal of Statistics, 29(3), 283-306.
  13. Hussain Z., Mashail, A.M., Bander A., Singh H.P. and Tarray T.A.(2015). Improved randomized response approaches for additive scrambling models. Mathematical Population Studiies. (In Press).
  14. Khan RA (1968). A note on estimating the mean of a normal distribution with known coefficient of variation. Journal of the American Statistical Association, 63,1039-1041.
  15. Kerkvliet J.(1994). Estimating a logit model with randomized data: the case of cocaine use. Austrialian Journal of Statistics,36,9-20.
  16. Mangat N.S. and Singh R. (1990). An alternative randomized procedure. Biometrika, 77, 439-442.
  17. Odumade O. and Singh S. (2009). Improved Bar – lev, Bobovitch and Boukai randomized response models. Communication in Statistics Theory and Methods, 38(3), 473-502.
  18. Searls D. T. (1964). The utilization of a known coefficient of variation in the estimation procedure. Journal of the American Statistical Association, 59, 12225-12226.
  19. Sen A.R. (1978). Estimation of the mean when the coefficient of variation is known. Communication in Statistics Theory and Methods, 7(7), 657-672.
  20. Sen A.R. (1979). Relative efficiency of the estimators of the mean of a normal distribution when coefficient of variation is known. Biometrical. Journal,21(2), 131-137.
  21. Singh S. and Cheng S.C. (2009). Utilization of higher order moments of scrambling variables in randomized response sampling. Journal of Statististics Planning and Inference,139, 3377- 3380.
  22. Singh H.P. and Katiyar N.P. (1988). A generalized class of estimators for common parameters of two normal distributions with known coefficient of variation. Journal of the Indian Society of the Agricultural Statistics, 40, (2), 127-149.
  23. Singh H.P. and Mathur N. (2005). Estimation of population mean when coefficient of variation is known using scrambled response technique. Journal of Statististics Planning and Inference,131 (1), 135-144.
  24. Singh H.P. and Tarray T.A. (2013). A modified survey technique for estimating the proportion and sensitivity in a dichotomous finite population. International Journal of the Advanved Science and and Technology Research, 3(6), 459 – 472.
  25. Singh H.P. and Tarray T.A. (2014). An alternative to stratified Kim and Warde’s randomized response model using optimal (Neyman) allocation. Model Assisted Statistical Applications, 9, 37-62.
  26. Singh H.P. and Tarray T.A. (2015). An efficient use of moment’s ratios of scrambling variables in randomized response sampling. Communication in Statistics Theory and Methods, (In Press).
  27. Tarray T.A. and Singh H.P. and (2015). An improved new additive model. Gazi University Journal of Sciences, (In Press).
  28. Tarray T.A., Singh H.P. and Zaizai Y. (2015). A stratified optional randomized response model. Sociological Methods and Research, 1-15.
  29. Tripathi T.P., Maiti P. and Sharma S.D. (1983). Use of prior information on some parameters in estimating population mean. Sankhya, 45, A, 3, 372-376.
  30. Upadhyaya L.N. and Singh H.P. (1984). On the estimation of the population mean with known coefficient variation. Biometrical Journal, 26, (8), 915-922.
  31. Warner S.L. (1965). Randomized response: A survey technique for eliminating evasive answer bias. Journal of the American Statistical Association, 60, 63-69.
  32. Hussain Z., Hamraz M. and shabbier J. (2013). On alternative estimation technique for randomized response model. Pakistan Journal of Statistics, 29(3), 283-306.
  33. Hussain Z., Mashail, A.M., Bander A., Singh H.P. and Tarray T.A.(2015). Improved randomized response approaches for additive scrambling models. Mathematical Population Studiies. (In Press).
  34. Tripathi T.P., Maiti P. and Sharma S.D. (1983). Use of prior information on some parameters in estimating population mean. Sankhya, 45, A, 3, 372-376.
  35. Upadhyaya L.N. and Singh H.P. (1984). On the estimation of the population mean with known coefficient variation. Biometrical Journal, 26, (8), 915-922.