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http://dx.doi.org/10.29220/CSAM.2019.26.2.175

Item sum techniques for quantitative sensitive estimation on successive occasions  

Priyanka, Kumari (Department of Mathematics, Shivaji College, University of Delhi)
Trisandhya, Pidugu (Department of Mathematics, Shivaji College, University of Delhi)
Publication Information
Communications for Statistical Applications and Methods / v.26, no.2, 2019 , pp. 175-189 More about this Journal
Abstract
The problem of the estimation of quantitative sensitive variable using the item sum technique (IST) on successive occasions has been discussed. IST difference, IST regression, and IST general class of estimators have been proposed to estimate quantitative sensitive variable at the current occasion in two occasion successive sampling. The proposed new estimators have been elaborated under Trappmann et al. (Journal of Survey Statistics and Methodology, 2, 58-77, 2014) as well as Perri et al. (Biometrical Journal, 60, 155-173, 2018) allocation designs to allocate long list and short list samples of IST. The properties of all proposed estimators have been derived including optimum replacement policy. The proposed estimators have been mutually compared under the above mentioned allocation designs. The comparison has also been conducted with a direct method. Numerical applications through empirical as well as simplistic simulation has been used to show how the illustrated IST on successive occasions may venture in practical situations.
Keywords
sensitive variable; successive occasions; class of estimators; population mean; variance; bias; mean squared error; optimum matching fraction;
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  • Reference
1 Arnab R and Singh S (2013). Estimation of mean of sensitive characteristics for successive sampling, Communications in Statistics - Theory and Methods, 42, 2499-2524.   DOI
2 Chaudhuri A and Christofides TC (2013). Indirect Questioning in Sample Surveys, Springer-Verlag, Berlin, Heidelberg, De.
3 Hussian Z, Shabbir N, and Shabbir J (2015). An alternative item sum technique for improved estimators of population mean in sensitive surveys, Hacettepe University Bulletin of Natural Sciences and Engineering Series B: Mathematics and Statistics, 46, 1-30.
4 Jessen RJ (1942). Statistical investigation of a sample survey for obtaining farm facts, Iowa Agriculture and Home Economics Experiment Station. Research Bulletin, 26, 1-104.
5 Kosmidis I (2014). Bias in parametric estimation: reduction and useful side-effects, WIREs Computational Statistics, 6, 185-196.   DOI
6 Miller JD (1984). A new survey technique for studying deviant behavior (Ph.D. thesis), The George Washington University, Washington DC.
7 Naeem N and Shabbir J (2016). Use of scrambled responses on two occasions successive sampling under non-response, Hacettepe University Bulletin of Natural Sciences and Engineering Series B: Mathematics and Statistics, 46. Available from: http://www.hjms.hacettepe.edu.tr/uploads/32c18b80-275f-4b5a-a28d-6a5890eecac3.pdf
8 Perri PF, Rueda Garcia M, and Cobo Rodriguez B (2018). Multiple sensitive estimation and optimal sample size allocation in the item sum technique, Biometrical Journal, 60, 155-173.   DOI
9 Priyanka K and Trisandhya P (2018). A composite class of estimators using scrambled response mechanism for sensitive population mean in successive sampling, Communications in Statistics - Theory and Methods, DOI: 10.1080/03610926.2017.1422762   DOI
10 Priyanka K, Trisandhya P, and Mittal R (2018). Dealing sensitive characters on successive occasions through a general class of estimators using scrambled response techniques, Metron, 76, 203-230.   DOI
11 Rueda Garcia M, Perri PF, and Cobo Rodriguez B (2017). Advances in estimation by the item sum technique using Auxiliary information in complex surveys, Advances in Statistical Analysis, 102, 455-478.
12 Trappmann M, Krumpal I, Kirchner A, and Jann B (2014). Item sum: a new technique for asking quantative sensitive questions, Journal of Survey Statistics and Methodology, 2, 58-77.   DOI
13 Singh GN, Suman S, Khetan M, and Paul C (2017). Some estimation procedures of sensitive character using scrambled response techniques in successive sampling, Communications in Statistics - Theory and Methods, DOI:10.1080/03610926.2017.1327073.   DOI
14 Srivastava SK and Jhajj HS (1980). A class of estimators using auxiliary information for estimating finite population variance, Sankhya, C42, 87-96.
15 Tracy DS, Singh HP, and Singh R (1996). An alternative to the ratio-cum-product estimator in sample surveys, Journal of Statistical Planning and Inference, 53, 375-397.   DOI
16 Tian GL and Tang ML (2014). Incomplete Categorical Data Design: Non-Randomized Response Techniques for Sensitive Questions in Surveys, Chapman & Hall/CRC, FL.
17 Warner SL (1965). Randomized response: a survey technique for eliminating evasive answer bias, Journal of the American Statistical Association, 60, 63-69.   DOI
18 Yu B, Jin Z, Tian J, and Gao G (2015). Estimation of sensitive proportion by randomized response data in successive sampling, Computational and Mathematical Methods in Medicine, 2015, 1-6.