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Activity Number: 554
Type: Contributed
Date/Time: Wednesday, August 12, 2015 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics in Epidemiology
Abstract #314916
Title: Likelihood-Based Inference on Weakly Interacting State-Space Processes
Author(s): Joon Ha Park*
Companies:
Keywords: Epidemiology ; State space model ; Partially observed Markov process ; Spatio-temporal inference ; Statistical computing ; Sequential Monte Carlo
Abstract:

In this presentation, we propose a likelihood based inference method on state space models of moderately high space dimension. We assume that each dimension of the state space processes is weakly interacting. Our method is based on the particle filter method, but is much less afflicted by the well known `curse-of-dimensionality'. This is achieved by making efficient particle proposals, by considering the strong temporal correlation within each dimension but the weak correlation between dimensions. Combined with stochastic optimization methods, our proposed method can be used to estimate parameters. A potential application to spatio-temporal disease processes is discussed.


Authors who are presenting talks have a * after their name.

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