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An Agile Optimal Orthogonal Additive Randomized Response Model

Year 2019, Volume: 2 Issue: 1, 48 - 54, 22.03.2019
https://doi.org/10.33434/cams.443055

Abstract

In this paper, a new additive randomized response model has been proposed. The properties of the proposed model have been studied. It has been shown theoretically that the suggested additive model is better than the one envisaged by [1] under very realistic conditions. Numerical illustrations are also given in support of the present study.

References

  • [1] S. Singh, Proposed optimal orthogonal new additive model (POONAM), Statist., LXX(1) (2010), 73–81.
  • [2] S. L. Warner, Randomized response: A survey technique for eliminating evasive answer bias, J. Amer. Statist. Assoc., 60 (1965), 63-69.
  • [3] S. K. Bar –Lev, E. Bobovitch, B. Boukai, A note on Randomized response models for quantitative data, Metrika, 60 (2004), 225-250.
  • [4] J. A. Fox, P. E. Tracy, Randomized Response: A method of Sensitive Surveys, Newbury Park, CA: SEGE Publications 1986.
  • [5] C. R. Gjestvang, S. Singh, A new randomized response model, J. Roy. Statist. Soc., 68 (2006), 523-530.
  • [6] C. R. Gjestvang, S. Singh, An improved randomized response model: Estimation of mean, J. Appl. Statist., 36(12) (2009), 1361-1367.
  • [7] N. S. Mangat, R. Singh, An alternative randomized procedure, Biometrika, 77 (1990), 439-442.
  • [8] H. P. Singh, N. Mathur, Estimation of population mean when coefficient of variation is known using scrambled response technique, J. Statist. Plann. Infer., 131(1) (2005), 135-144.
  • [9] H. P. Singh, T. A. Tarray, An alternative to Kim and Warde’s mixed randomized response technique, Statistica, 73(3) (2013), 379-402.
  • [10] H. P. Singh, T. A. Tarray, An alternative to stratified Kim and Warde’s randomized response model using optimal (Neyman) allocation, Model Assist. Stat. Appl., 9 (2014), 37-62.
  • [11] H. P. Singh, T. A. Tarray, A stratified Mangat and Singh’s optional randomized response model using proportional and optimal allocation, Statistica, 74(1) (2014),65-83.
  • [12] H. P. Singh, T. A. Tarray, An improved randomized response additive model, Sri Lan. J. Appl. Statist., 15(2) (2014) ,131-138.
  • [13] T. A. Tarray, H. P. Singh, Y. Zaizai, A stratified optional randomized response model, Socio. Meth. Res., 45 (2015), 1-15.
  • [14] T. A. Tarray, H. P. Singh, An improved new additive model, Gazi Uni. J. Sci., 29(1) (2016), 159-165.
  • [15] T. A. Tarray, H. P. Singh, Y. Zaizai, A dexterous optional randomized response model, Socio. Meth. Res., 46(3) (2017),565- 585.
  • [16] T. A. Tarray, H. P. Singh, A simple way of improving the Bar–Lev, Bobovitch and Boukai Randomized response model, Kuwait J. Sci., 44(4) (2017), 83-90.
  • [17] T. A. Tarray, H. P. Singh, A randomization device for estimating a rare sensitive attribute in stratified sampling using Poisson distribution, Afri. Mat., 29(3) (2018), 407-423.
  • [18] T. A. Tarray, H.P. Singh, Missing data in clinical trials: stratified Singh and Grewal’s randomized response model using geometric distribution, Trends Bioinfor., 11(1) (2018), 63-69
Year 2019, Volume: 2 Issue: 1, 48 - 54, 22.03.2019
https://doi.org/10.33434/cams.443055

Abstract

References

  • [1] S. Singh, Proposed optimal orthogonal new additive model (POONAM), Statist., LXX(1) (2010), 73–81.
  • [2] S. L. Warner, Randomized response: A survey technique for eliminating evasive answer bias, J. Amer. Statist. Assoc., 60 (1965), 63-69.
  • [3] S. K. Bar –Lev, E. Bobovitch, B. Boukai, A note on Randomized response models for quantitative data, Metrika, 60 (2004), 225-250.
  • [4] J. A. Fox, P. E. Tracy, Randomized Response: A method of Sensitive Surveys, Newbury Park, CA: SEGE Publications 1986.
  • [5] C. R. Gjestvang, S. Singh, A new randomized response model, J. Roy. Statist. Soc., 68 (2006), 523-530.
  • [6] C. R. Gjestvang, S. Singh, An improved randomized response model: Estimation of mean, J. Appl. Statist., 36(12) (2009), 1361-1367.
  • [7] N. S. Mangat, R. Singh, An alternative randomized procedure, Biometrika, 77 (1990), 439-442.
  • [8] H. P. Singh, N. Mathur, Estimation of population mean when coefficient of variation is known using scrambled response technique, J. Statist. Plann. Infer., 131(1) (2005), 135-144.
  • [9] H. P. Singh, T. A. Tarray, An alternative to Kim and Warde’s mixed randomized response technique, Statistica, 73(3) (2013), 379-402.
  • [10] H. P. Singh, T. A. Tarray, An alternative to stratified Kim and Warde’s randomized response model using optimal (Neyman) allocation, Model Assist. Stat. Appl., 9 (2014), 37-62.
  • [11] H. P. Singh, T. A. Tarray, A stratified Mangat and Singh’s optional randomized response model using proportional and optimal allocation, Statistica, 74(1) (2014),65-83.
  • [12] H. P. Singh, T. A. Tarray, An improved randomized response additive model, Sri Lan. J. Appl. Statist., 15(2) (2014) ,131-138.
  • [13] T. A. Tarray, H. P. Singh, Y. Zaizai, A stratified optional randomized response model, Socio. Meth. Res., 45 (2015), 1-15.
  • [14] T. A. Tarray, H. P. Singh, An improved new additive model, Gazi Uni. J. Sci., 29(1) (2016), 159-165.
  • [15] T. A. Tarray, H. P. Singh, Y. Zaizai, A dexterous optional randomized response model, Socio. Meth. Res., 46(3) (2017),565- 585.
  • [16] T. A. Tarray, H. P. Singh, A simple way of improving the Bar–Lev, Bobovitch and Boukai Randomized response model, Kuwait J. Sci., 44(4) (2017), 83-90.
  • [17] T. A. Tarray, H. P. Singh, A randomization device for estimating a rare sensitive attribute in stratified sampling using Poisson distribution, Afri. Mat., 29(3) (2018), 407-423.
  • [18] T. A. Tarray, H.P. Singh, Missing data in clinical trials: stratified Singh and Grewal’s randomized response model using geometric distribution, Trends Bioinfor., 11(1) (2018), 63-69
There are 18 citations in total.

Details

Primary Language English
Subjects Mathematical Sciences
Journal Section Articles
Authors

Tanveer A. Tarray 0000-0003-4773-715X

Housial P. Singh This is me

Publication Date March 22, 2019
Submission Date July 12, 2018
Acceptance Date November 29, 2018
Published in Issue Year 2019 Volume: 2 Issue: 1

Cite

APA Tarray, T. A., & Singh, H. P. (2019). An Agile Optimal Orthogonal Additive Randomized Response Model. Communications in Advanced Mathematical Sciences, 2(1), 48-54. https://doi.org/10.33434/cams.443055
AMA Tarray TA, Singh HP. An Agile Optimal Orthogonal Additive Randomized Response Model. Communications in Advanced Mathematical Sciences. March 2019;2(1):48-54. doi:10.33434/cams.443055
Chicago Tarray, Tanveer A., and Housial P. Singh. “An Agile Optimal Orthogonal Additive Randomized Response Model”. Communications in Advanced Mathematical Sciences 2, no. 1 (March 2019): 48-54. https://doi.org/10.33434/cams.443055.
EndNote Tarray TA, Singh HP (March 1, 2019) An Agile Optimal Orthogonal Additive Randomized Response Model. Communications in Advanced Mathematical Sciences 2 1 48–54.
IEEE T. A. Tarray and H. P. Singh, “An Agile Optimal Orthogonal Additive Randomized Response Model”, Communications in Advanced Mathematical Sciences, vol. 2, no. 1, pp. 48–54, 2019, doi: 10.33434/cams.443055.
ISNAD Tarray, Tanveer A. - Singh, Housial P. “An Agile Optimal Orthogonal Additive Randomized Response Model”. Communications in Advanced Mathematical Sciences 2/1 (March 2019), 48-54. https://doi.org/10.33434/cams.443055.
JAMA Tarray TA, Singh HP. An Agile Optimal Orthogonal Additive Randomized Response Model. Communications in Advanced Mathematical Sciences. 2019;2:48–54.
MLA Tarray, Tanveer A. and Housial P. Singh. “An Agile Optimal Orthogonal Additive Randomized Response Model”. Communications in Advanced Mathematical Sciences, vol. 2, no. 1, 2019, pp. 48-54, doi:10.33434/cams.443055.
Vancouver Tarray TA, Singh HP. An Agile Optimal Orthogonal Additive Randomized Response Model. Communications in Advanced Mathematical Sciences. 2019;2(1):48-54.

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