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• Ahsanullah M. and Eichhorn B.H. (1988). On estimation of response from scrambled quantitative data. Pakistan Journal of Statistics, 4(2), 83-91.
• Bar –Lev S.K., Bobovitch E. and Boukai B. (2004). A note on Randomized response models for quantitative data. Metrika, 60, 225-250.
• Chaudhuri A. and Mukerjee R. (1988). Randomized Response: Theory and Techniques. Marcel- Dekker, New York, USA.
• Chaudhuri A. and Christofides T. (2013). Indirect Questioning in Sample Surveys. DOI 10.1007/978-3-642-36276-7-3, Springer – Verlag Berlin Heidelberg.
• 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.
• Fox J.A. and Tracy P.E. (1986). Randomized Response: A method of Sensitive Surveys. Newbury Park, CA: SEGE Publications.
• Gjestvang C.R. and Singh S. (2006). A new randomized response model. Journal of the Royal Statistical Society, 68, 523-530.
• Gjestvang C.R. and Singh S. (2009). An improved randomized response model: Estimation of mean, Journal of Applied Statistics, 36(12), 1361-1367.
• 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.
• 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.
• 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.
• Hussain Z., Hamraz M. and shabbier J. (2013) . On alternative estimation technique for randomized response model. Pakistan Journal of Statistics, 29(3), 283-306.
• 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).
• 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.
• Kerkvliet J.(1994). Estimating a logit model with randomized data: the case of cocaine use. Austrialian Journal of Statistics,36,9-20.
• Mangat N.S. and Singh R. (1990). An alternative randomized procedure. Biometrika, 77, 439-442.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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).
• Tarray T.A. and Singh H.P. and (2015). An improved new additive model. Gazi University Journal of Sciences, (In Press).
• Tarray T.A., Singh H.P. and Zaizai Y. (2015). A stratified optional randomized response model. Sociological Methods and Research, 1-15.
• 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.
• 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.
• Warner S.L. (1965). Randomized response: A survey technique for eliminating evasive answer bias. Journal of the American Statistical Association, 60, 63-69.