Bayesian frequentist hybrid inference

Details Bayesian frequentist hybrid inference Bayesian frequentist hybrid inference

2009-08-24

arxiv.org/abs/0908.3413v1

Annals of Statistics 2009, Vol. 37, No. 5A, 2458-2501

Mathematics

Statistics Theory

Published in at http://dx.doi.org/10.1214/08-AOS649 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of

Scientific paper

10.1214/08-AOS649

Bayesian and frequentist methods differ in many aspects, but share some basic optimality properties. In practice, there are situations in which one of the methods is more preferred by some criteria. We consider the case of inference about a set of multiple parameters, which can be divided into two disjoint subsets. On one set, a frequentist method may be favored and on the other, the Bayesian. This motivates a joint estimation procedure in which some of the parameters are estimated Bayesian, and the rest by the maximum-likelihood estimator in the same parametric model, and thus keep the strengths of both the methods and avoid their weaknesses. Such a hybrid procedure gives us more flexibility in achieving overall inference advantages. We study the consistency and high-order asymptotic behavior of the proposed estimator, and illustrate its application. Also, the results imply a new method for constructing objective prior.

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