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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Main Authors: Sat Gupta, Pidugu Trisandhya, Frank Coolen
Format: Article
Language:English
Published: MDPI AG 2025-08-01
Series:Mathematics
Subjects:
Online Access:https://www.mdpi.com/2227-7390/13/15/2532
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author Sat Gupta
Pidugu Trisandhya
Frank Coolen
author_facet Sat Gupta
Pidugu Trisandhya
Frank Coolen
author_sort Sat Gupta
collection DOAJ
description 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.
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spelling doaj-art-e6049b220025448d8d67ee4a4bb46c8e2025-08-20T03:04:43ZengMDPI AGMathematics2227-73902025-08-011315253210.3390/math13152532Some Calibration Estimators of the Mean of a Sensitive Variable Under Measurement ErrorSat Gupta0Pidugu Trisandhya1Frank Coolen2Department of Mathematics and Statistics, University of North Carolina at Greensboro, Greensboro, NC 27412, USADepartment of Applied Sciences, Bharati Vidyapeeth’s College of Engineering, New Delhi 110063, IndiaDepartment of Mathematical Sciences, Durham University, Durham DH1 3LE, UKThis 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.https://www.mdpi.com/2227-7390/13/15/2532auxiliary informationcalibration estimatorsmeasurement errorrandomized response technique models
spellingShingle Sat Gupta
Pidugu Trisandhya
Frank Coolen
Some Calibration Estimators of the Mean of a Sensitive Variable Under Measurement Error
Mathematics
auxiliary information
calibration estimators
measurement error
randomized response technique models
title Some Calibration Estimators of the Mean of a Sensitive Variable Under Measurement Error
title_full Some Calibration Estimators of the Mean of a Sensitive Variable Under Measurement Error
title_fullStr Some Calibration Estimators of the Mean of a Sensitive Variable Under Measurement Error
title_full_unstemmed Some Calibration Estimators of the Mean of a Sensitive Variable Under Measurement Error
title_short Some Calibration Estimators of the Mean of a Sensitive Variable Under Measurement Error
title_sort some calibration estimators of the mean of a sensitive variable under measurement error
topic auxiliary information
calibration estimators
measurement error
randomized response technique models
url https://www.mdpi.com/2227-7390/13/15/2532
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AT pidugutrisandhya somecalibrationestimatorsofthemeanofasensitivevariableundermeasurementerror
AT frankcoolen somecalibrationestimatorsofthemeanofasensitivevariableundermeasurementerror