Estimation of Mean of a Sensitive Quantitative Variable in Complex Survey: Improved Estimator and Scrambled Randomized Response Model

Estimation of Mean of a Sensitive Quantitative Variable in Complex Survey: Improved Estimator and Scrambled Randomized Response Model

With the intention to control a true swapping between the efficiency and the privacy protectionthis paper introduces a scrambled randomized response (SRR) model to be alternative of Saha’sscrambling mechanism. The basic initiative is to provide an assortment of the additive, thesubtractive and the multiplicative models. The simulation and the empirical studies are providedfor various sample sizes to compare the efficiency of the proposed model. The results obtainedfrom simulation showed that the proposed model performs better than Pollock and Bek’s additivemodel. Also, the proposed generalized estimator of mean has been studied using a new SRRmodel presented in this article and shown that the proposed estimator and its class of estimatorsare more efficient than existing estimators. It is also shown that gain in efficiency is more whenthe proposed SRR model is used. The efficiency of the proposed class of estimators over existingestimators using both models is also provided using real data and with a simulation study.

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  • Warner, S. L., “Randomized response: A survey technique for eliminating evasive answer bias”, Journal of the American Statistical Association, 60(309): 63-69, (1965).
  • Greenberg, B. G., Kuebler, R. R., Jr., Abernathy, J. R., Horvitz, D. G., “Application of the randomized response technique in obtaining quantitative data”, Journal of the American Statistical Association, 66(334): 243-250, (1971).
  • Pollock, K. H. and Bek, Y., “A comparison of three randomized response models for quantitative data”, Journal of the American Statistical Association, 71(356):884-886, (1976).
  • Himmelfarb, S., Edgell, S. E., “Additive constants model: A randomized response technique for eliminating evasiveness to quantitative response questions”, Psychological Bulletin, 87(3): 525, (1980).
  • Eichhorn, B.H., Hayre, L. S., “Scrambled randomized response models for obtaining sensitive quantitative data”, Journal of Statistical Planning and Inference, 7(4): 307-316, (1983).
  • Saha, A., “Optional randomized response in stratified unequal probability sampling-A simulation based numerical study with Kuk’s method”, Test, 16(2): 346-354, (2007).
  • Diana, G., Perri, P. F., “New scrambled response models for estimating the mean of a sensitive quantitative character”, Journal of Applied Statistics, 37(11): 1875-1890, (2010).
  • Diana, G., Perri, P. F., “A class of estimators for quantitative sensitive data”, Statistical Papers, 52(3): 633-650, (2011).
  • Hussain, Z., “Improvement of the Gupta and Thornton scrambling model through double use of randomization device”, International Journal Academic Research Business and Social Science, 2:91- 97, (2012).
  • Chen, C. C., Singh, S., “The Franklin's randomized response model for two sensitive attributes”, Section Survey Research Methods, 4171-4185, (2009).
  • Singh, H., Tarray, T., “An improved randomized response additive model”, Sri Lankan Journal of Applied Statistics, 15(2): 131-138, (2014).
  • Hussain, Z., Al-Zahrani, B., “Mean and sensitivity estimation of a sensitive variable through additive scrambling”, Communications in Statistics-Theory and Methods, 45(1): 182-193, (2016).
  • Bouza-Herrera, C. N., “Behavior of some scrambled randomized response models under simple random sampling, ranked set sampling and Rao–Hartley–Cochran designs”, Handbook of Statistics, 34, 209- 220, Elsevier, (2016).
  • Bouza-Herrera, C. N., “Data gathering, analysis and protection of privacy through randomized response techniques: qualitative and quantitative human traits”, Investigación Operacional, 39(1): 151-152, (2018).
  • Bouza, C. N., Singh, P., Singh, R., “Ranked set sampling and optional scrambling randomized response modeling”, Investigación Operacional, 39(1): 100-107, (2018).
  • Sousa, R., Shabbir, J., Real, P. C., Gupta, S., “Ratio estimation of the mean of a sensitive variable in the presence of auxiliary information”, Journal of Statistical Theory and Practice, 4(3): 495-507, (2010).
  • Gupta, S., Shabbir, J., Sousa, R., Corte-real, P., “Estimation of the mean of a sensitive variable in the presence of auxiliary information”, Communications in Statistics-Theory and Methods, 41(13-14): 2394–2404, (2012).
  • Tarray, T. A., Singh, H. P., “A general procedure for estimating the mean of a sensitive variable using auxiliary information”, Investigacion Operacionel, 36(3): 268-279, (2015).
  • Koyuncu, N., Gupta, S., Sousa, R., “Exponential-type estimators of the mean of a sensitive variable in the presence of non-sensitive auxiliary information”, Communications in Statistics-Simulation and Computation, 43(7): 1583-1594, (2014).
  • Saha, A., “A randomized response technique for quantitative data under unequal probability sampling”, Journal of Statistical Theory and Practice, 2(4): 589-596, (2008).
  • Gupta, S., Mehta, S., Shabbir, J., Khalil, S., “A unified measure of respondent privacy and model efficiency in quantitative RRT models”, Journal of Statistical Theory and Practice, 12(3): 506-511, (2018).
  • Yan, Z., Wang, J., Lai, J., “An efficiency and protection degree-based comparison among the quantitative randomized response strategies”, Communications in Statistics-Theory and Methods, 38(3): 400-408, (2008).
  • Rao, T. J., “On certail methods of improving ration and regression estimators”, Communications in Statistics-Theory and Methods, 20(10): 3325-3340, (1991).
  • Bahl, S., Tuteja, R. K., “Ratio and product type exponential estimators”, Journal of Information and Optimization Sciences”, 12(1): 159-164, (1991).
  • Horvitz, D.G., Thompson, D.J., “A generalization of sampling without replacement from a finite universe”, J. Amer. Statist. Assoc., 47(260): 663-685, (1952). [26] under stratified two-phase random sampling”, Applied Mathematics and Computation, 226: 541-547, (2014).
  • Asghar, A., Sanaullah, A., Hanif, M., “Generalized exponential type estimator for population variance in survey sampling”, Revista Colombiana de Estadística, 37(1): 213-224, (2014).
  • Sanaullah, A., Hanif, M., Asghar. A., “Generalized exponential estimators for population variance under two-phase sampling”, Int. J. Appl. Comput. Math., 2(1): 75-84, (2016).
  • Koyuncu, N., Kadilar, C., “Family of estimators of population mean using two auxiliary variables in stratified random sampling”, Communications in Statistics-Theory and Methods, 38(14): 2398–2417, (2009).
  • Jabeen, R., Sanaullah, A., Hanif, M., “Generalized estimator for estimating population mean under two stage sampling”, Pak. J. Statist., 30(4): 465-486, (2014).