Abstract Details
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Activity Number:
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132
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Type:
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Contributed
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Date/Time:
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Monday, August 5, 2013 : 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 - #309072 |
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Title:
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Reduced-Rank Stochastic Intensity Modelling for Multivariate Point Processes
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Author(s):
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Victor Solo*+ and Ahmed Pasha
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Companies:
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University of New South Wales and University of Sydney
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Keywords:
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point process ;
high frequency data
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Abstract:
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As in many disciplines large data problems have begun to emerge in multivariate point process modelling. Two driving areas are neural coding and high frequency finance. But as yet there are few dimension reduction methods available. Here we introduce a reduced rank model for the multivariate point process stochastic intensity. We develop a maximum-likelihood model fitting method based on a so-called non-negative matrix factorization (NMF) type algorithm which ensures positivity of the stochastic intensity. The method is illustrated with a simulation and some real data.
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