Technion, IEM faculty - Statistics Seminar
 
Speaker: Ori Rosen, University of Texas at El Paso
 
Title: AdaptSPEC: Adaptive spectral estimation for nonstationary time series
 
Date: 01/01/2012
 
Time: 14:30
 
Place: Bloomfield-527
 
Abstract:  <http://ie.technion.ac.il/seminar_files/1323162796_Rosen.pdf>
 
Or read it here:
 
We propose a method for analyzing possibly
nonstationary time series by adaptively dividing
the time series into an unknown but finite number
of segments and estimating the corresponding local
spectra by smoothing splines. The model is
formulated in a Bayesian framework, and the
estimation relies on reversible jump Markov chain
Monte Carlo (RJMCMC) methods. For a given
segmentation of the time series, the likelihood
function is approximated via a product of local
Whittle likelihoods. Thus, no parametric
assumption is made about the process underlying
the time series. The number and lengths of the
segments are assumed unknown and may change from
one MCMC iteration to another. The frequentist
properties of the method are investigated by
simulation, and an applications to EEG is
described in detail
 
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