Some Calibration Estimators of the Mean of a Sensitive Variable Under Measurement Error

Some Calibration Estimators of the Mean of a Sensitive Variable Under Measurement Error

This study explores the estimation of the mean of a sensitive variable using calibration estimators under measurement error. Three randomized response techniques are evaluated: Partial Randomized Response Technique, Compulsory Randomized Response Technique, and Optional Randomized Response Technique...

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Bibliographic Details
Main Authors: Sat Gupta, Pidugu Trisandhya, Frank Coolen
Format: Article
Language:English
Published: MDPI AG 2025-08-01
Series:Mathematics
Subjects:
auxiliary information
calibration estimators
measurement error
randomized response technique models
Online Access:https://www.mdpi.com/2227-7390/13/15/2532
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Summary:This study explores the estimation of the mean of a sensitive variable using calibration estimators under measurement error. Three randomized response techniques are evaluated: Partial Randomized Response Technique, Compulsory Randomized Response Technique, and Optional Randomized Response Technique. Theoretical properties of the proposed estimators are analyzed, and a simulation study using real COVID-19 infection data is conducted. Results indicate that the Optional Randomized Response Technique outperforms Partial Randomized Response Technique and Compulsory Randomized Response Technique in terms of efficiency, underscoring its effectiveness and practical utility for improving data quality in sensitive survey settings.
ISSN:2227-7390

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