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Asymptotics of Bayesian estimation for nested models under misspecification | IEEE Conference Publication | IEEE Xplore

Asymptotics of Bayesian estimation for nested models under misspecification


Abstract:

We analyze the asymptotic properties of the cumulative logarithmic loss in the decision problem based on the Bayesian principle and explicitly identify the constant terms...Show More

Abstract:

We analyze the asymptotic properties of the cumulative logarithmic loss in the decision problem based on the Bayesian principle and explicitly identify the constant terms of the asymptotic equations as in the case of previous studies by Clarke and Barron and Gotoh et al. We assume that the set of models is given that identify a class of parameterized distributions, it has a nested structure and the source distribution is not contained in all the families of parameterized distributions that are identified by each model. The cumulative logarithmic loss is the sum of the logarithmic loss functions for each time decision-, e.g., the redundancy in the universal noiseless source coding.
Date of Conference: 28-31 October 2012
Date Added to IEEE Xplore: 07 January 2013
ISBN Information:
Conference Location: Honolulu, HI, USA

References

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