Bayesian Adaptive Estimation Using a Sieve Prior

We study the Bayes estimation of an inﬁnite dimensional parameter from a Sobolev smoothness class. We propose a family of sieve priors whose resulting Bayes estimators are adaptive optimal, both in posterior concentration rate and in risk convergence for the inherent distance of the model, denoted dn (e.g. the Hellinger distance). This result is applied to several examples, including white noise model, regression function and density model.

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Year of publication:	2010-06
Authors:	Arbel, Julyan
Institutions:	Université Paris-Dauphine (Paris IX)
Subject:	Sieve prior \| Sobolev

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Series:	Economics Papers from University Paris Dauphine.
Type of publication:	Book / Working Paper
Classification:	C11 - Bayesian Analysis
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10011073385