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Asymptotic inference for mixture models by using data‐dependent priors
Author(s) -
Wasserman L.
Publication year - 2000
Publication title -
journal of the royal statistical society: series b (statistical methodology)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.523
H-Index - 137
eISSN - 1467-9868
pISSN - 1369-7412
DOI - 10.1111/1467-9868.00226
Subject(s) - prior probability , frequentist inference , inference , computer science , econometrics , bayesian probability , bayesian inference , mathematics , artificial intelligence
For certain mixture models, improper priors are undesirable because they yield improper posteriors. However, proper priors may be undesirable because they require subjective input. We propose the use of specially chosen data‐dependent priors. We show that, in some cases, data‐dependent priors are the only priors that produce intervals with second‐order correct frequentist coverage. The resulting posterior also has another interpretation: it is the product of a fixed prior and a pseudolikelihood.