Griffin, Jim E. and Steel, Mark F.J. (2006) Order-Based Dependent Dirichlet Processes. Journal of the American Statistical Association, 101 (473). pp. 179-94. ISSN 0162-1459. (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)
In this article we propose a new framework for Bayesian nonparametric modeling with continuous covariates. In particular, we allow the nonparametric distribution to depend on covariates through ordering the random variables building the weights in the stick-breaking representation. We focus mostly on the class of random distributions that induces a Dirichlet process at each covariate value. We derive the correlation between distributions at different covariate values and use a point process to implement a practically useful type of ordering. Two main constructions with analytically known correlation structures are proposed. Practical and efficient computational methods are introduced. We apply our framework, through mixtures of these processes, to regression modeling, the modeling of stochastic volatility in time series data, and spatial geostatistical modeling.
|Subjects:||Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics|
|Divisions:||Faculties > Science Technology and Medical Studies > School of Mathematics Statistics and Actuarial Science > Statistics|
|Depositing User:||Jim Griffin|
|Date Deposited:||16 Mar 2009 10:31|
|Last Modified:||21 May 2014 11:27|
|Resource URI:||https://kar.kent.ac.uk/id/eprint/9410 (The current URI for this page, for reference purposes)|