A regression estimator for finite population mean of a sensitive variable using an optional randomized response model
| Autor: | Javid Shabbir, Sat Gupta, Geeta Kalucha |
|---|---|
| Rok vydání: | 2015 |
| Předmět: |
Statistics and Probability
Hodges–Lehmann estimator Mean squared error 010102 general mathematics Estimator Trimmed estimator 01 natural sciences 010104 statistics & probability Minimum-variance unbiased estimator Efficient estimator Bias of an estimator Modeling and Simulation Statistics Consistent estimator 0101 mathematics Mathematics |
| Zdroj: | Communications in Statistics - Simulation and Computation. 46:2393-2405 |
| ISSN: | 1532-4141 0361-0918 |
| DOI: | 10.1080/03610918.2015.1044614 |
| Popis: | Kalucha et al. (Kalucha G., Gupta S., Dass B. K. (accepted). Ratio estimation of finite population mean using optional randomized response models. Journal of Statistical Theory and Practice) introduced an additive ratio estimator for finite population mean of a sensitive variable in simple random sampling without replacement and showed that this estimator performs better than the ordinary mean estimator based on an optional randomized response technique (RRT). In this paper, we introduce a regression estimator that performs better than the ratio estimator even for the modest correlation between the study and the auxiliary variables. A comparison of the proposed estimator with the corresponding ratio estimator and the ordinary RRT mean estimator is carried out theoretically, and is also illustrated with a simulation study. |
| Databáze: | OpenAIRE |
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