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Journals / Balıkesir Üniversitesi Fen Bilimleri Enstitüsü Dergisi / 2020 / Cilt: 22 Sayı: 2

A new regression estimator in partial randomized response models

Kısmi rastgeleleştirilmiş yanıt modellerinde yeni bir regresyon tahmin edicisi

Nilgün Özgül(HACETTEPE ÜNİVERSİTESİ)0000-0003-0331-9044
Hülya Çıngı(HACETTEPE ÜNİVERSİTESİ)0000-0002-7329-9037
Pages
581–590
Publication Date
2020-04-10
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The proposed estimator is found to be more efficient than other existing estimators.

📌 3 Key Takeaways

🎯
Objective & Hypothesis: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).
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Key Findings & Data:The proposed estimator is compared with existing estimators based on auxiliary variable in RRM with a simulation study.
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Conclusion & Impact:The proposed estimator is found to be more efficient than other existing estimators.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Hassas değişken · yardımcı değişken · regresyon tahmin edicisi rastgeleleştirilmiş yanıt modelleri

Abstract

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.

Özet

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.

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Cite This Article

Özgül, N., Çıngı, H. (2020). A new regression estimator in partial randomized response models. Balıkesir Üniversitesi Fen Bilimleri Enstitüsü Dergisi, Cilt: 22 Sayı: 2, 581–590. https://doi.org/10.25092/baunfbed.742610

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References (11)

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