Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1147668 | Journal of Statistical Planning and Inference | 2011 | 14 Pages |
Abstract
We propose a new class of time dependent random probability measures and show how this can be used for Bayesian nonparametric inference in continuous time. By means of a nonparametric hierarchical model we define a random process with geometric stick-breaking representation and dependence structure induced via a one dimensional diffusion process of Wright-Fisher type. The sequence is shown to be a strongly stationary measure-valued process with continuous sample paths which, despite the simplicity of the weights structure, can be used for inferential purposes on the trajectory of a discretely observed continuous-time phenomenon. A simple estimation procedure is presented and illustrated with simulated and real financial data.
Related Topics
Physical Sciences and Engineering
Mathematics
Applied Mathematics
Authors
Ramsés H. Mena, Matteo Ruggiero, Stephen G. Walker,