Geometric Stick-Breaking Processes for Continuous-Time Nonparametric Modeling

Ramses H. Mena (), Matteo Ruggiero and Stephen G. Walker

ICER Working Papers - Applied Mathematics Series from ICER - International Centre for Economic Research

Abstract: This paper is concerned with the construction of a continuous parameter sequence of random probability measures and its application for modeling random phenomena evolving in continuous time. At each time point we have a random probability measure which is generated by a Bayesian nonparametric hierarchical model, and the dependence structure is induced through a Wright-Fisher diffusion with mutation. The sequence is shown to be a stationary and reversible diffusion taking values on the space of probability measures. A simple estimation procedure for discretely observed data is presented and illustrated with simulated and real data sets.

Keywords: Bayesian non-parametric inference; continuous time dependent random measure; Markov process; measure-valued process; stationary process; stick-breaking process (search for similar items in EconPapers)
Pages: 13 pages
Date: 2009-12
New Economics Papers: this item is included in nep-ecm and nep-ets
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.bemservizi.unito.it/repec/icr/wp2009/ICERwp26-09.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:icr:wpmath:26-2009

Access Statistics for this paper

More papers in ICER Working Papers - Applied Mathematics Series from ICER - International Centre for Economic Research Corso Unione Sovietica, 218bis - 10134 Torino - Italy. Contact information at EDIRC.
Bibliographic data for series maintained by Daniele Pennesi ().