Abstract Details
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Activity Number:
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482
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Type:
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Contributed
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Date/Time:
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Wednesday, August 7, 2013 : 8:30 AM to 10:20 AM
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Sponsor:
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Biometrics Section
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| Abstract - #307921 |
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Title:
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Matrix Time Series
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Author(s):
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Lynne Billard*+ and Yaser Samadi
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Companies:
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University of Georgia and University of Georgia
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Keywords:
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autocorrelations ;
estimation
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Abstract:
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Descriptions and properties of time series, both univariate and multivariate series, are well developed in the literature. Less developed are properties of matrix time series, though Wang and West (2009) did consider a matrix normal distribution for both observational and evolution errors of the dynamic linear model of a matrix time series to fit and explore dynamic graphical models in a Bayesian context. In this work, we introduce a model for matrix time series and develop some properties for these kinds of models.
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Authors who are presenting talks have a * after their name.
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