Abstract Details
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Activity Number:
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270
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Type:
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Invited
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Date/Time:
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Tuesday, August 5, 2014 : 8:30 AM to 10:20 AM
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Sponsor:
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Business and Economic Statistics Section
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Abstract #310907
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View Presentation
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Title:
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Hierarchical Dynamic Modeling of Count Time Series with Latent Durations
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Author(s):
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Nalini Ravishanker*+ and Jonathan R.M. Hosking and Hongxia Yang
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Companies:
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University of Connecticut and IBM Research and IBM Research
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Keywords:
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Bayesian models ;
count time series ;
gap times ;
MCMC
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Abstract:
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Consider an application in business, economic, or finance, where the ith unit in the cth cluster experiences several events that are irregularly spaced over time. Associated with each event is a random response vector of counts or incidences. The goal is to model the random responses as functions of observed predictors as well as latent durations (or gap times) between the events. We describe a Bayesian framework for the hierarchical dynamic models that we build. We illustrate our approach using simulated data as well as real data from an advertising/marketing scenario.
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Authors who are presenting talks have a * after their name.
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