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Activity Number:
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385
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Type:
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Contributed
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Date/Time:
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Wednesday, August 9, 2006 : 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 - #305410 |
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Title:
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Automatic Estimation of Multivariate Spectra via Smoothing Splines
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Author(s):
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Ori Rosen*+ and David Stoffer
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Companies:
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The University of Texas at El Paso and University of Pittsburgh
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Address:
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Department of Mathematical Sciences, El Paso, TX, 79968,
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Keywords:
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spectral matrix ; Cholesky decomposition ; MCMC
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Abstract:
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The classical method for estimating the spectral density of a multivariate time series is to first calculate the periodogram, then smooth it to obtain a consistent estimator. We suggest a Bayesian approach that uses Markov chain Monte Carlo techniques to fit smoothing splines to each component, real and imaginary, of the Cholesky decomposition of the periodogram matrix. The spectral estimate is then obtained by reconstructing the spectral estimator from the smoothed Cholesky decomposition components. Our technique allows for automatic smoothing of the components of the spectral density matrix. We illustrate our methodology with data on the Southern Oscillation Index, as well as with a DNA sequence.
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