Incomplete categorical data analysis: a bayesian perspective
作者： Paulo Soares , Carlos Daniel Paulino
DOI: 10.1080/00949650108812088
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摘要: In this paper the Bayesian analysis of incomplete categorical data under informative general censoring proposed by Paulino and Pereira (1995) is revisited. That based on Dirichlet priors can be applied to any missing pattern. However, known properties posterior distributions are scarce therefore severe limitations computations remain. Here shown how a Monte Carlo simulation approach an alternative parameterisation used overcome former computational difficulties. The makes available approximate estimation parametric functions implemented in very straightforward way.
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