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Accounting for Measurement Error and Untruthfulness in Binary RRT Models

Author

Listed:
  • Bailey Meche

    (Department of Mathematics, University of Louisiana at Lafayette, Lafayette, LA 70504, USA)

  • Venu Poruri

    (Department of Mathematics, University of California, Berkeley, Berkeley, CA 94720, USA)

  • Sat Gupta

    (Department of Mathematics and Statistics, UNC Greensboro, Greensboro, NC 27412, USA)

  • Sadia Khalil

    (Department of Mathematics and Statistics, UNC Greensboro, Greensboro, NC 27412, USA
    Department of Statistics, Lahore College for Women University, Lahore 44444, Pakistan)

Abstract

This study examines the effect of measurement error on binary Randomized Response Technique models. We discuss a method for estimating and accounting for measurement error and untruthfulness in two basic models and one comprehensive model. Both theoretical and empirical results show that not accounting for measurement error leads to inaccurate estimates. We introduce estimators that account for the effect of measurement error. Furthermore, we introduce a new measure of model privacy using an odds ratio statistic, which offers better interpretability than traditional methods.

Suggested Citation

  • Bailey Meche & Venu Poruri & Sat Gupta & Sadia Khalil, 2024. "Accounting for Measurement Error and Untruthfulness in Binary RRT Models," Mathematics, MDPI, vol. 12(6), pages 1-15, March.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:6:p:875-:d:1358275
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