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Adaptive Spectral Estimation for Nonstationary Time Series

Presented by: 
D Stoffer University of Pittsburgh
Date: 
Friday 17th January 2014 - 11:30 to 12:15
Venue: 
INI Seminar Room 1
Abstract: 
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. The number and lengths of the segments are assumed unknown and may change from one MCMC iteration to another.
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Presentation Material: 
University of Cambridge Research Councils UK
    Clay Mathematics Institute London Mathematical Society NM Rothschild and Sons