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Geometric Stick-Breaking Processes for Continuous-Time Nonparametric Modeling

Listed author(s):
  • Ramses H. Mena

    ()

  • Matteo Ruggiero
  • Stephen G. Walker
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    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.

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    File URL: http://www.biblioecon.unito.it/biblioservizi/RePEc/icr/wp2009/ICERwp26-09.pdf
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    Paper provided by ICER - International Centre for Economic Research in its series ICER Working Papers - Applied Mathematics Series with number 26-2009.

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    Length: 13 pages
    Date of creation: Dec 2009
    Handle: RePEc:icr:wpmath:26-2009
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