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A Bayesian method for addressing multinomial misclassification with applications for alcohol epidemiological modeling

Author

Listed:
  • William J. Parish

    (RTI International)

  • Arnie Aldridge

    (RTI International)

  • Martijn van Hasselt

    (University of North Carolina at Greensboro)

Abstract

In this article, we describe a new command, bamm, that implements a Bayesian method for addressing misclassification in multinomial data; see Swartz et al. (2004, Canadian Journal of Statistics 32: 285–302). We also describe a postestimation command, bammdx, that was developed to provide additional esti- mation diagnostics. We describe the method and the new commands and then present results from both a simulation study demonstrating bamm’s performance under a known misclassification data-generating process and an empirical example from alcohol epidemiology modeling.

Suggested Citation

  • William J. Parish & Arnie Aldridge & Martijn van Hasselt, 2024. "A Bayesian method for addressing multinomial misclassification with applications for alcohol epidemiological modeling," Stata Journal, StataCorp LP, vol. 24(1), pages 113-137, March.
    • Handle: RePEc:tsj:stataj:v:24:y:2024:i:1:p:113-137
    DOI: 10.1177/1536867X241233671
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