Misspecification in infinite-dimensional Bayesian statistics

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We consider the asymptotic behavior of posterior distributions if the model is misspecified. Given a prior distribution and a random sample from a distribution $P_0$, which may not be in the support of the prior, we show that the posterior concentrates its mass near the points in the support of the prior that minimize the Kullback--Leibler divergence with respect to $P_0$. An entropy condition and a prior-mass condition determine the rate of convergence. The method is applied to several examples, with special interest for infinite-dimensional models. These include Gaussian mixtures, nonparametric regression and parametric models.
Published at http://dx.doi.org/10.1214/009053606000000029 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)

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Statistics Theory, 62G07, 62G08, 62G20, 62F05, 62F15 (Primary)

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http://salesiana.dossiersoluciones.com/handle/123456789/131112

Consulte el texto completo en el siguiente enlace:

https://arxiv.org/abs/math/0607023
 http://arxiv.org/abs/math/0607023
 Annals of Statistics 2006, Vol. 34, No. 2, 837-877
 doi:10.1214/009053606000000029

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arXiv