Bayesian nonparametric modelling with the Dirichlet process regression smoother
Griffin, Jim E. and Steel, Mark F. J.. (2010) Bayesian nonparametric modelling with the Dirichlet process regression smoother. Statistica Sinica, Vol.20 (No.4). pp. 1507-1527. ISSN 1017-0405Full text not available from this repository.
Official URL: http://www3.stat.sinica.edu.tw/statistica/
In this paper we discuss implementing Bayesian fully nonparametric regression by defining a process prior on distributions that depend on covariates. We consider the problem of centring our process over a class of regression models, and propose fully nonparametric regression models with flexible location structures. We also introduce an extension of a dependent finite mixture model proposed by Chung and Dunson (2011) to a dependent infinite mixture model and propose a specific prior, the Dirichlet Process Regression Smoother, which allows us to control the smoothness of the process. Computational methods are developed for the models described. Results are presented for simulated and for real data examples.
|Item Type:||Journal Article|
|Divisions:||Faculty of Science > Statistics|
|Journal or Publication Title:||Statistica Sinica|
|Publisher:||Academia Sinica * Institute of Statistical Science|
|Official Date:||October 2010|
|Page Range:||pp. 1507-1527|
|Access rights to Published version:||Restricted or Subscription Access|
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