Bayesian estimation of rare sensitive attribute

Joon Jin Song, Jong Min Kim

    Research output: Contribution to journalArticlepeer-review

    2 Scopus citations

    Abstract

    Randomized response models have been used to estimate a population proportion of a sensitive attribute. A randomized device is typically employed to protect respondent's privacy in a survey. In addition, an unrelated question is asked to improve the statistical efficiency. In this article, we propose Bayesian estimation of rare sensitive attribute using randomized response technique, which includes a rare unrelated attribute. Two cases are considered, the proportion of a rare unrelated attribute is known and unknown. A simulation study is conducted to assess the performance of the models using mean absolute error and coverage probability. The results show that the performance depends on the parameters and is robust to priors.

    Original languageEnglish (US)
    Pages (from-to)4154-4160
    Number of pages7
    JournalCommunications in Statistics: Simulation and Computation
    Volume46
    Issue number5
    DOIs
    StatePublished - May 28 2017

    Keywords

    • Bayesian estimation
    • Poisson distribution
    • Randomized response model
    • Rare sensitive attribute
    • Rare unrelated attribute

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