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A Bayesian Maximum a Posteriori Algorithm for Categorical Data Under Informative General Censoring

Stephen Walker
Journal of the Royal Statistical Society. Series D (The Statistician)
Vol. 45, No. 3 (1996), pp. 293-298
Published by: Wiley for the Royal Statistical Society
DOI: 10.2307/2988467
Stable URL: http://www.jstor.org/stable/2988467
Page Count: 6
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A Bayesian Maximum a Posteriori Algorithm for Categorical Data Under Informative General Censoring
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Abstract

An EM algorithm to obtain maximum a posteriori estimates for incomplete categorical data under informative general censoring is presented. It is an alternative version to the Bayesian approach described by Paulino and Pereira but which allows more general prior specifications.

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