A new regression estimator in partial randomized response models

Yıl 2020, , 581 - 590, 10.04.2020

Nilgün Özgül Hülya Çıngı

https://doi.org/10.25092/baunfbed.742610

Öz

In this study, a new regression estimator has been developed with using two non-sensitive auxiliary variables for the population mean estimation of the sensitive study variable in Partial Randomized Response Models (PRRM’s). The proposed estimator is compared with existing estimators based on auxiliary variable in RRM with a simulation study. The proposed estimator is found to be more efficient than other existing estimators.

Anahtar Kelimeler

Sensitive variable, auxiliary variable, regression estimator, randomized response models, efficiency

Kısmi rastgeleleştirilmiş yanıt modellerinde yeni bir regresyon tahmin edicisi

Öz

Bu çalışmada, Kısmi Rastgeleleştirilmiş Yanıt Modellerinde (KRYM), hassas değişkenin kitle ortalamasının tahmini için hassas olmayan iki yardımcı değişken kullanarak yeni bir regresyon tahmin edicisi geliştirilmiştir. Önerilen tahmin edici, simülasyon çalışması yapılarak RYM'de var olan diğer tahmin edicilerle karşılaştırılmış ve önerilen tahmin edicinin var olan diğer tahmin edicilere göre daha etkin olduğu bulunmuştur.

Anahtar Kelimeler

Hassas değişken, yardımcı değişken, regresyon tahmin edicisi rastgeleleştirilmiş yanıt modelleri, etkinlik

Kaynakça

• Özgül, N., Oran ve Ortalama Tahmin Edicileri, Doktora Tezi, Hacettepe Üniversitesi Fen Bilimleri Enstitüsü, Ankara, (2013).
• Warner, S.L., The Linear Randomized Response Model, Journal of the American Statistical Association, 66, 336, 884-888, (1971).
• Greenberg B.G., Kuebler R.R., Abernathy J.R., Horvitz D.G., Application of the randomized response technique in obtaining quantitative data, Journal of American Statistical Association, 66, 243–250, (1971).
• Thornton, B. ve Gupta, S. N., Comparative Validation of a Partial (versus Full) Randomized Response Technique: Attempting to Control for Social Desirability Response Bias to Sensitive Questions, Individual Differences Research, 2, 214-224, (2004).
• Gjesvang, C.R., Singh,S., An improved Randomized Response Model:Estimation of Mean, Journal of Applied Statistics, 36, 12, 1361-1367, (2009).
• Hussain Z., Shabbir, J., Improved Estimation Procedures for the Mean of Sensitive Variable using Randomized Response Model, Pakistan Journal of Statistics, 25, 2, 205-220, (2009).
• Gupta S., Shabbir J., Sousa R., Sehra S., Mean and sensitivity estimation in optional randomized response model, Journal of Statistical Planning and Inference, 140, 10, 2870-2874, (2010).
• Cingi, H., Örnekleme Kuramı, Ankara: 3.Baskı, Bizim Büro Basımevi, (2009).
• 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).
• Diana G., ve Perri P.F., A class of Estimators for Quantitative Sensitive Data, Statistical Papers, 52, 633-650, (2011).
• Gupta, S., Shabbir, J., Sousa, R., Real, P. C., 2012, Estimation of the mean of a sensitive variable in the presence of auxiliary information, Communications in Statistics,Theory and Methods, 41, 1–12, (2012).