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Efficient Estimation of Population Mean of a Sensitive Variable Under ORRT Models Using Two-Phase Sampling
Estimación eficiente de la media poblacional de una variable sensible según modelos ORRT utilizando un muestreo de dos fases
DOI:
https://doi.org/10.15446/rce.v48n2.113534Keywords:
Study variable, Auxiliary variable(s), Non-response, Measurement error, Mean squared error (MSE), Percent relative efficiency, Optional Randomized Response Technique (ORRT). (en)Variable de estudio, Variable(s) auxiliar(es), Falta de respuesta, Error de medición, Error cuadrático medio (EMC), Porcentaje de eficiencia relativa, Técnica de respuesta aleatoria opcional (ORRT). (es)
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During human survey, participants are asked highly personal questions about a sensitive variable. This paper focus on estimation of population mean of sensitive study variable in the existence of non-response and measurement error under Optional Randomized Response Technique model using two-phase sampling. The bias and mean squared error of the proposed and considered family of estimators have been derived up to first order of approximation. Further, the properties of the proposed estimator have been discussed, and efficiency conditions have been derived. To demonstrate the theoretical findings, simulation study is conducted under various conditions using data set from hypothetical population and it is clear that our proposed family of estimator is always better than the other considered family of estimators.
Durante una encuesta a personas, se les hacen preguntas muy personales a los participantes sobre una variable sensible. Este artículo se centra en la estimación de la media poblacional de la variable sensible del estudio en la existencia de falta de respuesta y error de medición bajo el modelo de técnica de respuesta aleatoria opcional utilizando un muestreo de dos fases. El sesgo y el error cuadrático medio de la familia de estimadores propuesta y considerada se han derivado hasta el primer orden de aproximación. Además, se han discutido las propiedades del estimador propuesto y se han derivado las condiciones de eficiencia. Para demostrar los hallazgos teóricos, se lleva a cabo un estudio de simulación en varias condiciones utilizando un conjunto de datos de una población hipotética y está claro que nuestra familia de estimadores propuesta es siempre mejor que la otra familia de estimadores considerada.
References
Allen, J., Singh, H. P. & Smarandache, F. (2003), `A family of estimators of population mean using multiauxiliary information in presence of measurement errors', International Journal of Social Economics 30(7), 837-848. DOI: https://doi.org/10.1108/03068290310478775
Azeem, M. (2014), On estimation of population mean in the presence of measurement error and non-response, Unpublished Ph.D. Thesis, National College of Business Administration and Economics, Lahore.
Azeem, M. & Hanif, M. (2017), `Joint influence of measurement error and nonresponse on the estimation of population mean', Communications in Statistics Theory and Methods 46(4), 1679-1693. DOI: https://doi.org/10.1080/03610926.2015.1026992
Azeem, M., Salahuddin, N., Hussain, S., Ijaz, M. & Salam, A. (2024), `An efficient estimator of population variance of a sensitive variable with a new randomized response technique', Heliyon 10, 1-11. DOI: https://doi.org/10.1016/j.heliyon.2024.e31690
Diana, G. & Perri, P. F. (2011), `A class of estimators for quantitative sensitive data', Statistical Papers 52, 633-650. DOI: https://doi.org/10.1007/s00362-009-0273-1
Eichhorn, B. H. & Hayre, L. S. (1983), `Scrambled randomized response methods for obtaining sensitive quantitative data', Journal of Statistical Planning and Inference 7(4), 307-316. DOI: https://doi.org/10.1016/0378-3758(83)90002-2
Greenberg, B. G., Kuebler, R. R., Abernathy, J. R. & Horvitz, D. G. (1971), `Application of the randomized response techniques in obtaining quantitative data', Journal of the American Statistical Association 66(334), 243-250. DOI: https://doi.org/10.1080/01621459.1971.10482248
Gupta, S., Gupta, B. & Singh, S. (2002), `Estimation of sensitivity level of personal interview survey question', Journal of Statistical Planning and inference 100, 239-247. DOI: https://doi.org/10.1016/S0378-3758(01)00137-9
Gupta, S., Kalucha, J., Shabbir, J. & Dass, B. K. (2014), `Estimation of Finite Population Mean Using Optional RRT Models in the Presence of Non-sensitive Auxiliary Information', American Journal of Mathematical and Management Sciences 33(2), 147-159. DOI: https://doi.org/10.1080/01966324.2014.908332
Gupta, S., Mehta, S., Shabbir, J. & Khalil, S. (2018), `A unified measure of respondent privacy and model efficiency in quantitative RRT models', Journal of Statistical Theory and Practice 12(3), 506-511. DOI: https://doi.org/10.1080/15598608.2017.1415175
Gupta, S., Shabbir, J., Sousa, R. & Corte-Real, P. (2012), `Estimation of the mean of a sensitive variable in the presence of auxiliary information', Communications in Statistics-Theory and Methods 41, 13-14. DOI: https://doi.org/10.1080/03610926.2011.641654
Hansen, M. H. & Hurwitz, W. N. (1946), `The Problem of Non-Response in Sample Surveys', Journal of the American Statistical Association 41(236), 517-529. DOI: https://doi.org/10.1080/01621459.1946.10501894
Khalil, S., Zhang, Q. & Gupta, S. (2021), `Mean estimation of sensitive variables under measurement errors using optional RRT models', Communications in Statistics-Simulation and Computation 50(5), 1417-1426. DOI: https://doi.org/10.1080/03610918.2019.1584298
Kumar, S., Bhougal, S. & Nataraja, N. S. (2015), `Estimation of population mean in the presence of non-response and measurement error', Revista Colombiana de Estadistica 38, 145-164. DOI: https://doi.org/10.15446/rce.v38n1.48807
Kumar, S. & Kour, S. P. (2022), `The joint influence of estimation of sensitive variable under measurement error and non-response using ORRT models', Journal of Statistical Computation and Simulation 92(17), 3583-3604. DOI: https://doi.org/10.1080/00949655.2022.2075362
Kumar, S., Kour, S. P. & Zhang, Q. (2023), `An enhanced ratio-cum-product estimator with non-response and observational error by utilizing ORRT models: a sensitive estimation approach', Journal of Statistical Computation and Simulation 93(5), 818-836. DOI: https://doi.org/10.1080/00949655.2022.2122463
Singh, R. S. & Sharma, P. (2015), `Method of Estimation in the Presence of Non response and Measurement Errors Simultaneously', Journal of Modern Applied Statistical Methods 14(1), 107-121. DOI: https://doi.org/10.22237/jmasm/1430453460
Tiwari, N. & Pandey, T. K. (2022), `An Improved Two-Stage Forced Randomized Response Model for Estimating the Proportion of Sensitive Attribute', Journal of the Indian Society for Probability and Statistics 23(2), 451-464. DOI: https://doi.org/10.1007/s41096-022-00131-8
Warner, S. L. (1965), `Randomized response: a survey technique for eliminating evasive answer bias', Journal of the American Statistical Association 60(309), 63-69. DOI: https://doi.org/10.1080/01621459.1965.10480775
Waseem, Z., Khan, H. & Shabbir, J. (2021), `Generalized exponential type estimator for the mean of sensitive variable in the presence of non-sensitive auxiliary variable', Communications in Statistics-Theory and Methods 50(14), 3477-3488. DOI: https://doi.org/10.1080/03610926.2019.1708399
Zhang, Q., Kalucha, G., Gupta, S. & Khalil, S. (2018), `Ratio Estimation of the Mean under RRT Models', Journal of Statistics and Managements Systems 22(1), 97-113. DOI: https://doi.org/10.1080/09720510.2018.1533513
Zhang, Q., Khalil, S. & Gupta, S. (2021), `Mean Estimation of Sensitive Variables Under Non-response and Measurement Errors Using Optional RRT Models', Journal of Statistical Theory and Practice 15(3), 1-15. DOI: https://doi.org/10.1007/s42519-020-00135-2
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