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A Bayesian model for multinomial sampling with misclassified data

Author

Listed:
  • M. Ruiz
  • F. J. Giron
  • C. J. Perez
  • J. Martin
  • C. Rojano

Abstract

In this paper the issue of making inferences with misclassified data from a noisy multinomial process is addressed. A Bayesian model for making inferences about the proportions and the noise parameters is developed. The problem is reformulated in a more tractable form by introducing auxiliary or latent random vectors. This allows for an easy-to-implement Gibbs sampling-based algorithm to generate samples from the distributions of interest. An illustrative example related to elections is also presented.

Suggested Citation

  • M. Ruiz & F. J. Giron & C. J. Perez & J. Martin & C. Rojano, 2008. "A Bayesian model for multinomial sampling with misclassified data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 35(4), pages 369-382.
    • Handle: RePEc:taf:japsta:v:35:y:2008:i:4:p:369-382
    DOI: 10.1080/02664760701834832
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    References listed on IDEAS

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    1. Anil Gaba, 1993. "Inferences with an Unknown Noise Level in a Bernoulli Process," Management Science, INFORMS, vol. 39(10), pages 1227-1237, October.
    2. Anil Gaba & Robert L. Winkler, 1992. "Implications of Errors in Survey Data: A Bayesian Model," Management Science, INFORMS, vol. 38(7), pages 913-925, July.
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    Cited by:

    1. Liu, Yaqing & Liu, Juxin & Zhang, Fuxi, 2013. "Bias analysis for misclassification in a multicategorical exposure in a logistic regression model," Statistics & Probability Letters, Elsevier, vol. 83(12), pages 2621-2626.

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