|
Activity Number:
|
541
|
|
Type:
|
Contributed
|
|
Date/Time:
|
Thursday, August 2, 2007 : 10:30 AM to 12:20 PM
|
|
Sponsor:
|
Section on Bayesian Statistical Science
|
| Abstract - #309875 |
|
Title:
|
Bayesian Inference for a Distribution-Valued Stochastic Process
|
|
Author(s):
|
Zhen Wang*+ and Steven N. MacEachern
|
|
Companies:
|
The Ohio State University and The Ohio State University
|
|
Address:
|
2605 Lorain Ct, Columbus, OH, 43210,
|
|
Keywords:
|
Dirichlet process
|
|
Abstract:
|
We propose two nonparametric Bayesian models that are natural extensions of traditional weighted least squares analysis. One model is appropriate for error distributions in a scale family; the other for data which are totals or averages. As the work extends weighted least squares, we consider a linear mean structure and take a model of normal errors as our starting point. The two models are indistinguishable in the parametric, normal-theory case. They become different when the error distributions are non-normal. For our models, the nonparametric component relies on a smoothed Dirichlet process prior with a normal base measure. Posterior inference is made on the basis of an efficient Gibbs sampler. For the first model, latent variables are introduced to facilitate computation.
|
