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Abstract Details
Activity Number:
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298
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Type:
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Contributed
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Date/Time:
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Tuesday, August 2, 2011 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Bayesian Statistical Science
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Abstract - #303153 |
Title:
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A New Prior for the Unconditioned Covariance Matrix
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Author(s):
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Samprit Banerjee*+ and Stefano Monni
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Companies:
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Weill Cornell Medical College and Weill Cornell Medical College
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Address:
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, New York, NY, 10065,
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Keywords:
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covariance matrix ;
reference prior ;
high dimensional ;
hit and run
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Abstract:
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Estimation of the covariance matrix, especially in higher dimensions ("large p small n") is a challenging statistical problem which is of great interest in many applications. It is well known that the sample covariance matrix is a poor estimator even for moderately high p. The currently accepted best estimator for the unconditioned covariance matrix is that based on the reference prior. We propose a new prior (reference-like) and demonstrate the improved estimation for higher dimensional matrices via simulations. We provide a Markov Chain Monte Carlo algorithm to implement the computation and highlight key aspects required to sample efficiently.
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