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Kernel density estimation of a sensitive variable in the presence of auxiliary information

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  • Wenhao Shou
  • Sat Gupta
  • Sadia Khalil

Abstract

Auxiliary information is widely used in various studies to improve the precision of estimation. In this article, we extend the application of auxiliary information within the context of randomized response techniques (RRT), building upon the prior research on kernel density estimation (KDE) under additive RRT models. Inspired by Mostafa and Ahmad (2019), we proposed a kernel density estimator that incorporates an auxiliary variable to enhance the accuracy of estimating the distribution of a sensitive variable. Extensive simulations are conducted to evaluate the performance of this proposed methodology, highlighting the advantages of utilizing auxiliary information and the impact of factors such as noise levels, sample size, and the correlation between the auxiliary variable and the sensitive variable.

Suggested Citation

  • Wenhao Shou & Sat Gupta & Sadia Khalil, 2025. "Kernel density estimation of a sensitive variable in the presence of auxiliary information," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 54(14), pages 4509-4516, July.
    • Handle: RePEc:taf:lstaxx:v:54:y:2025:i:14:p:4509-4516
    DOI: 10.1080/03610926.2024.2422883
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