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Activity Number:
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224
- New Insights from Analyzing Functional Data in Biomedical Research
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Type:
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Invited
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Date/Time:
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Wednesday, August 11, 2021 : 10:00 AM to 11:50 AM
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Sponsor:
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ENAR
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Abstract #315536
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Title:
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A Sequential Monte Carlo Gibbs Coupled with Stochastically Approximated Expectation-Maximization Algorithm for Functional Data
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Author(s):
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Ziyue Liu*
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Companies:
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Indiana University School of Medicine
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Keywords:
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Functional Data;
Gibbs Sampler;
Particle Filter;
Sequential Monte Carlo;
State Space Model;
Stochastically Approximated EM
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Abstract:
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We develop an algorithm to overcome the curse of dimensionality in sequential Monte Carlo (SMC) for functional data. In the inner iterations of the algorithm for given parameter values, the conditional SMC is extended to obtain draws of the underlying state vectors. These draws in turn are used in the outer iterations to update the parameter values in the framework of stochastically approximated expectation-maximization to obtain maximum likelihood estimates of the parameters. Standard errors of the parameters are calculated using a stochastic approximation of Louis formula. Three numeric examples are used for illustration. They show that although the computational burden remains high, the algorithm produces reasonable results without exponentially increasing the particle numbers.
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Authors who are presenting talks have a * after their name.
