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

Sat Gupta, Pidugu Trisandhya () and Frank Coolen
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Sat Gupta: Department of Mathematics and Statistics, University of North Carolina at Greensboro, Greensboro, NC 27412, USA
Pidugu Trisandhya: Department of Applied Sciences, Bharati Vidyapeeth’s College of Engineering, New Delhi 110063, India
Frank Coolen: Department of Mathematical Sciences, Durham University, Durham DH1 3LE, UK

Mathematics, 2025, vol. 13, issue 15, 1-13

Abstract: 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.

Keywords: auxiliary information; calibration estimators; measurement error; randomized response technique models (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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