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Abstract Details
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Activity Number:
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225
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Type:
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Topic Contributed
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Date/Time:
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Monday, July 30, 2012 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistical Learning and Data Mining
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| Abstract - #304432 |
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Title:
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Joint Spectral-Temporal Analysis of High-Energy Astronomical Sources
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Author(s):
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Thomas Lee*+ and Vinay Kashyap and David A. van Dyk and Raymond K. W. Wong
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Companies:
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University of California at Davis and Harvard University and Imperial College London and University of California at Davis
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Address:
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Department of Statistics, 1 Shields Ave., Davis, CA, 95616, United States
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Keywords:
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astro-statistics ;
L1 penalty ;
minimum description length principle ;
Poisson count data ;
semi-parametric modeling ;
structural break detection
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Abstract:
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In this work we apply semi-parametric techniques to the joint spectral-temporal modeling of high-energy astronomical data. This includes the automatic detection of emission lines and structural breaks in the temporal direction. We apply L1 penalties to regularize the model fitting. The "dimension" of the best-fitting model is chosen by a new form of the minimum description length principle that is designed for the "large p small n" scenario.
This is joint work with Vinay Kashyap, David van Dyk and Raymond K. W. Wong.
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Authors who are presenting talks have a * after their name.
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