Semiparametric posterior limits

TL;DR

This paper reviews and extends Bayesian semiparametric inference, particularly the Bernstein-von Mises theorem, to both regular and irregular problems, offering new theoretical insights and applications.

Contribution

It generalizes the semiparametric Bernstein-von Mises theorem without relying on least-favourable submodels, broadening its applicability.

Findings

Extended Bernstein-von Mises theorem to irregular problems

Applied results to partial linear regression and normal location mixtures

Provided new theoretical framework for semiparametric Bayesian inference

Abstract

We review the Bayesian theory of semiparametric inference following Bickel and Kleijn (2012) and Kleijn and Knapik (2013). After an overview of efficiency in parametric and semiparametric estimation problems, we consider the Bernstein-von Mises theorem (see, e.g., Le Cam and Yang (1990)) and generalize it to (LAN) regular and (LAE) irregular semiparametric estimation problems. We formulate a version of the semiparametric Bernstein-von Mises theorem that does not depend on least-favourable submodels, thus bypassing the most restrictive condition in the presentation of Bickel and Kleijn (2012). The results are applied to the (regular) estimation of the linear coefficient in partial linear regression (with a Gaussian nuisance prior) and of the kernel bandwidth in a model of normal location mixtures (with a Dirichlet nuisance prior), as well as the (irregular) estimation of the boundary of the support of a monotone family of densities (with a Gaussian nuisance prior).