Posterior consistency of dirichlet location-scale mixture of normals in density estimation and regression
Journal Article (Journal Article)
We provide sufficient conditions under which a Dirichlet location-scale mixture of normal prior achieves weak and strong posterior consistency at a true density. Our conditions involve both the prior and the true density from which observations are obtained. We consider it to be a significant improvement over the existing results since our conditions cover the case of fat tailed densities like the Cauchy, with a standard choice for the base measure of the Dirichlet process. This provides a wider choice for using these popular mixture priors for nonparametric density estimation and semiparametric regression problems. © 2006, Indian Statistical Institute.
Duke Authors
Cited Authors
- Tokdar, ST
Published Date
- February 1, 2006
Published In
Volume / Issue
- 68 / 1
Start / End Page
- 90 - 110
International Standard Serial Number (ISSN)
- 0972-7671
Citation Source
- Scopus