On rates of convergence for posterior distributions in infinite–dimensional models.

This paper introduces a new approach to the study of rates of convergence for posterior distributions. It is a natural extension of a recent approach to the study of Bayesian consistency. Crucially, no sieve or entropy measures are required and so rates do not depend on the rate of convergence of the corresponding sieve maximum likelihood estimator. In particular, we improve on current rates for mixture models.

MoreLess

Year of publication:	2004-10
Authors:	Lijoi, Antonio ; Prünster, Igor ; Walker, Stephen G.
Institutions:	International Centre for Economic Research (ICER)
Subject:	Hellinger consistency \| mixture of Dirichlet process \| posterior distribution \| rates of convergence

More details

Extent:	application/pdf
Series:	ICER Working Papers - Applied Mathematics Series.
Type of publication:	Book / Working Paper
Language:	English
Notes:	16 pages
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10005077203