Asymptotic Inference for Mixture Models Using
Data Dependent Priors
For certain mixture models,
improper priors are undesirable because they
yield improper posteriors.
On the other hand, 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
it is the product of a fixed prior
and a pseudo-likelihood.
Keywords: coverage, mixtures, non-informative priors.
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