This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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427
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Type:
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Contributed
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Date/Time:
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Tuesday, August 3, 2010 : 2:00 PM to 3:50 PM
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Sponsor:
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IMS
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Abstract - #308629 |
Title:
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Inference on Discretely Observed Linear Birth-Death-Immigration Processes
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Author(s):
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Charles R. Doss*+ and Vladimir Minin and Ian Holmes and Marc Suchard
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Companies:
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University of Washington and University of Washington and University of California, Berkeley and University of California, Los Angeles
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Address:
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Department of Statistics, Seattle, WA, 98195,
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Keywords:
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Linear Birth-Death Process ;
EM Algorithm ;
Gibbs Sampler ;
generating function ;
data augmentation ;
myobacterium tuberculosis
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Abstract:
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We are interested in estimating parameters of linear birth-death-immigration (BDI) processes, observed discretely at unevenly spaced time points. Viewing this task as a missing data problem, we develop novel implementations of the expectation-maximization (EM) and Bayesian data augmentation algorithms. When the rate of immigration is constrained, we reduce the E-step of the EM algorithm as well as calculation of the Fisher information to one dimensional integration. To tackle the non-constrained immigration rate, we modify a direct sampler for finite state Markov chains; we use this for sampling in a Monte Carlo EM algorithm as well as in a Gibbs sampler in which we augment the BDI model parameters with missing continuous time trajectories. We test our algorithms on simulated data and then estimate birth and death rates of a transposable element in Myobacterium tuberculosis.
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