Finite Sample Bernstein – von Mises Theorem for Semiparametric Problems

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Maxim Panov, Vladimir Spokoiny


Bayesian Analysis


The classical parametric and semiparametric Bernstein – von Mises (BvM) results are reconsidered in a non-classical setup allowing finite samples and model misspecification. In the case of a finite dimensional nuisance parameter we obtain an upper bound on the error of Gaussian approximation of the posterior distribution for the target parameter which is explicit in the dimension of the nuisance and target parameters. This helps to identify the so called critical dimension pn of the full parameter for which the BvM result is applicable. In the important i.i.d. case, we show that the condition “p3 n/n is small” is sufficient for the BvM result to be valid under general assumptions on the model. We also provide an example of a model with the phase transition effect: the statement of the BvM theorem fails when the dimension pn approaches n1/3 . The results are extended to the case of infinite dimensional parameters with the nuisance parameter from a Sobolev class.

Keywords: Posterior distribution, Bayesian inference, Semiparametric, Critical dimension


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