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Abstract Details
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Activity Number:
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517
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Type:
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Contributed
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Date/Time:
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Wednesday, August 1, 2012 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Bayesian Statistical Science
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| Abstract - #305864 |
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Title:
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Functional Time Series Analysis: A Bayesian Framework
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Author(s):
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Giovanni Petris*+
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Companies:
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University of Arkansas
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Address:
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1 University of Arkansas, Fayetteville, AR, 72701,
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Keywords:
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Functional data analysis ;
time series ;
state space models
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Abstract:
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We present a framework for Bayesian functional time series analysis, based on the extension of the classical Dynamic Linear Model to Hilbert space-valued observations and states. After reviewing basic notions needed from probability on function spaces, we define a general Functional Dynamic Linear Model (FDLM) and we show how inference on future observations and hidden states can be performed, conditional on any unknown model parameters. Inferences that include unknown parameters can be performed using standard Markov chain Monte Carlo techniques. Examples and open problems will conclude the presentation.
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Authors who are presenting talks have a * after their name.
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