A Bayesian model for multinomial sampling with misclassified data

M. Ruiz, F. J. Giron, C. J. Perez, J. Martin and C. Rojano

Journal of Applied Statistics, 2008, vol. 35, issue 4, 369-382

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.

Keywords: Bayesian inference; Gibbs sampling; misclassified data; noisy multinomial process (search for similar items in EconPapers)
Date: 2008
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Citations: View citations in EconPapers (1)

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DOI: 10.1080/02664760701834832

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